Big Surf, from Jim Sogi

January 23, 2009 | 1 Comment

SurfWe've been having some big surf here lately up to 25 feet. The timing of the swell hitting our local breaks is a big issue. Typically the primary wave model is used, but I've found that in fact the swell period is a better predictor than the wave height model for timing the arrival of the swell. The wave size model is distorted by the interaction of the islands in the swell direction and is usually grossly wrong. Since everyone reads these erroneous reports, we often get perfect waves to ourselves.

-wave height model

-period model

Entering the water over the rough rocky shores requires waiting for the end of a large set waves. We always sit a watch the water for a while before going out and count and time the wave sets to see how big and how long the period between the sets and how many waves are in each set. Wait for the water to run up the shore on the last wave of the set. Jump in and let the outgoing retraction of the swell take you out to sea, and paddle out in between sets. If the timing is wrong, you fight the surge, get pushed back on the rocks, and can get pummeled by the next or the remainder of the large set.

The market has also been having some big waves and it seems anecdotally that a periodic model might be a good predictor for the arrival of the market swells. Of course market wave size is very important and the average volatility is up, but combining timing the entries along with noting the wave size is helpful, just like in the ocean, and can avoid drowning or getting washing machined by the market. Also since the public often follows an erroneous model, it is possible to get good market action to yourself.

Phil McDonnell adds:

Several key points about ocean waves:

1. Unlike sound waves or light waves that occur in a medium, ocean waves occur on the boundary between two media (air and water).

2. Because of 1., wind can have an effect on waves. Waves can add turbulence to wind.

3. Larger amplitude waves go deeper than small ones.

4. Waves sets are formed by the interference and cancellation of multiple waves with similar periods. Effectively the complex of these waves creates a wave envelope which itself is somewhat sinusoidal.

5. When a tsunami strikes the curvature of the Earth acts to refocus the wave fronts about 12,000 km away where they can do additional damage.

Some good discussion can be found at seafriends.org. Especially interesting was the factoid about the Chilean tsunami that traveled at about 500 mph to New Zealand.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008

Jeff Watson comments:

Swell height and period have another correlation, which Sogi-San doesn't have to worry about with the monster waves he gets every winter. The longer the period, the more powerful the swell. With the same wave heights, I would look forward to a six foot swell with a period of 15 seconds, whereas I might not get too excited about the same six foot swell with a period of nine seconds. I went surfing yesterday and wrote about it:

While I was out waiting for waves to come, I was thinking of how a surfer positions himself to catch a wave. You see waves on the horizon, and knowledge and experience tells you approximately where to paddle to get into position to catch the wave. A successful speculator needs to do exactly the same thing with the market. Practice, experience and knowledge will tell you when and where to position yourself with entry points to try to ride the market wave. After you catch the wave, experience tells you how to ride it….whether you bail out, wipe out, tear it up, rip, pump for speed, ride it lazily, or take it all the way into shore. There are lots of market lessons in surfing.

Russ Sears writes:

The wave not in the old model:

I recently locked in a refi-rate at the rate of 4 3/8 % (simple interest) for 15 year. And also could have locked in 4 3/4 % on 30 years on that date I moved in 2005, so I had a 5.5% mortgage on 30 year.

A few comments about this mortgage some I learned myself while "shopping" some was told by the mortgage officer.

1. In 2005 the rates where very close from one bank to the next. Like the gasoline station several had same rates. If they didn't, you could often simple tell them about the rate their competitor gave and get it lowered. I went with the local bank last time only because I wanted to close faster. This time the money seems to be at the local banks. Bank of Oklahoma, and MidFirst are both regional banks and a strong balance sheet. Many of the smaller banks didn't get into the trouble the big guys did. Not sure if this was because they didn't have the size to get into a specialty space like sub-prime or if they simply didn't

2. While I didn't buy points, they were cheaper than in 05 to get the same rate reduction of 1/4 it was about 1/2 price.

3. Like the banks, you need a good balance sheet to get these cheap rates FICO scores of 740 or better got the best rates.

4. Jumbos loans are not nearly as low of rates.

5. Rates have since gone up. about 3/8 %. It was getting too hectic at 4 3/8 % or 4 3/4 %. But they are still very busy. I got the impression that they let up on the gas, simply because they were so busy, without the big boys to compete against.

Still at these rates it is obviously the trigger point: A few 1/8th higher and the spigot will close, a points lower and they will get a big big wave.

While clearly much more restrictive underwriting than in 2003, we may see more than 1/2 that kind of turn-over.

You should ask a true Wall Street quant, but in my opinion what causing the log jam in credits, in balance sheets and therefore the mortgage originators and the housing market, is nobody knows where the trash is hidden in the MBS markets. This wave of fresh air could very well separate the wheat from the chaff.



 One of the imagineering tenets is to bring yourself back to your childhood to create spectacular entertainment. They like to use erector sets and play dough and sand and paints to get their ideas. I tried to go back to my childhood to get some ideas while I was driving and allow the non-logical brain to brainstorm as they recommend. I figure that kids like to rhyme and sing music and swim and do independent things and do jobs and find out about the world.

I started with rhymes. The question is whether the market rhymes. I started with the last x minutes before 10 and looked to see whether there was a one-three rhyme or a one-two rhyme. I found no evidence of a one-two rhyme but much evidence of a one-three rhyme as in "Yankee Doodle." For example, the rhymes at 10 repeat at 12 but not at 11, defined with reasonable precision, with a chance likelihood of 1/20. The subject of how rhyming and childhood play in the market deserves exploration.

Jeff Watson writes:

A common element of video games is that a series of different recurring patterns are interspersed throughout the game. The best players are the ones who practice endlessly and learn to identify and predict the patterns. It would be an interesting study to design a trading platform that mimicked a video game, and allow a group of young video game wizards to try their hand at it. With the right software, would the best video game players be the best traders?

Jim Sogi writes:

Etymology of rhyme from Wikipedia:

The word comes from the Old French rime, derived from Old Frankish language *rim, a Germanic term meaning "series, sequence" attested in Old English (Old English rim - "enumeration, series, numeral") and Old High German rim, ultimately cognate to Old Irish rím, Greek ????µ?? arithmos "number".

An essential part of rhyme is meter, as in the essential and compelling use by Shakespeare of  iambic pentameter. There is something in this structure that captures the human function and rhythm. Di dah di dah di dah, di dah, di dah.

The meter of the market might even be broken down from days, di dah, to the actual timing of the spoken and thought phrases, rather than from hour to hour.

As with jokes, ballads, most nursery rhymes come in sets of threes. It's a natural rhythm seen in natural phenomenon as well. Rhymes have application in markets and quantification.

Phil McDonnell adds:

Some years back my son was an accomplished video game player. He was rated number one on the Microsoft Zone in Warcraft. At the time they had about a million players. Our family often played as a team. I, my son, and daughter played against three other opponents. Our motto was 'The family that slays together, stays together.' That won out against 'The family that preys together, stays together.' My guess would be that became the Madoff family motto at some point.

One time my son left my PC logged on as his screen name. So I sat down to play. At the time my son was number one in the world. Immediately I was messaged by a 16 year old kid with the screen name of psycho. He lived in nearby Redmond and was rated about number three or four in the world at the time. At the time I was rated about number 10 in the world.

We played a 2 vs 2 game against some world ranked players. We kicked their butts. However the reality is that psycho won the game. I was merely a major contributor. After the game was over he asked me why I was off my game. It was clear that there was a huge difference between number one in the World and number 10. I had no choice but to fess up to psycho. The fact is that there is a huge difference between number one and number 10 in terms of performance.

To date, my daughter has been to Singapore for the local software company and is now back in Redmond at headquarters working in Treasury. My son went on to work for a Redmond based group of ex-Microsoft video game people. Then he joined the big Redmond behemoth. Subsequently he was stolen by the big G in Mountain View. His purview was as tech lead on the last software to look at your search results. Presently he is off to Zurich to improve the efficiency of the many big G programmers Euro programmers.

It is fair to say that some traders are destined to be great and others not to. It is also fair to say that many people with video game backgrounds are unrecognized for many reasons.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008

From the President of the Old Speculators Club:

I suggest that searching for a rhyme is obvious and that if the market is anything, it is not obvious. A more fruitful idea may be to look for rhyme's forgotten brother: assonance.

"Assonance, (or medial rime) is the agreement in the vowel sounds of two or more words, when the consonant sounds preceding and following these vowels do not agree. Thus, strike and grind, hat and man, 'rime' with each other according to the laws of assonance." (J.W. Bright and R.D. Miller, The Elements of English Versification, Ginn and Company, 1910)

These are harder to identify and when they appear it's difficult to determine whether their existence is by happenstance or design. However, if a sufficient number occur it might be worth examining. But be careful of an overabundance of them:

"Beware of excessive assonance. Any assonance that draws attention to itself is excessive." (John Earle, A Simple Grammar of English Now in Use, Smith, Elder, & Company, 1898)

Since the vowel sound is key, and must be bracketed by non-agreeing consonants, one might look for similar volatility between simultaneously divergent averages (e.g., the S&P and the NAZ).

Russ Sears adds:

This quotation is from an interesting article entitled the "16 Habits of Highly Creative people."

While perhaps the author's ideas need some testing, the quotations alone are worth the read.

"There is no use trying," said Alice. "One can't believe impossible things." "I daresay you haven't had much practice," said the Queen. "When I was your age, I always did it for half an hour a day. Why, sometimes I've believed as many as six impossible things before breakfast." - Lewis Carroll

And my favorite, considering 2008: “If you are not confused, you are not thinking clearly" — Tom Peters, quoted by Shalu Wasu

Scott Brooks comments:

Of course the market rhymes…until the poem changes. And the Mistress loves to change the poem when most are least expecting it.

During a secular bull market, the Mistress says: Up, up and away with very little volatility on any given day.

I'll make you money and you'll think it's great

you'll leverage to the hilt until it's too late

For when I change from bull to bear

you will pull out all your hair

If you confuse brains for a bull

it will be ugly when the mistress starts to cull

Culling the quants, the fundamentalist, the techs

You will feel the noose around your neck

When times are great, we all think bulls will always last

Those who do, ignore the past

During a secular bear market the Mistress says: Up today, gone tomorrow

I hope you didn't make your bull returns with money you had to borrow

The market changes from bull to bear so fast

So that if you were leveraged you lost your a$$

For a bull is very different than a bear

I know this for I've lot most of my hair

When times are bad we all think bears will always last

Those who do, ignore the past

So a trader must remember:
All the gains do not matter when you're leverage just gets fatter and fatter

So don't swing for the fences it's okay to settle for just a hit

then you'll win more than you lose and won't find yourself in deep $#!^

And finally, remember to stay out of the poo

losses hurt more than gains help you



There is a long SP500 monthly series on Shiller's website, which I used to compute the 10Y rolling returns (for the 10 years ending at the present) from 1880-12/2008. I did not attempt to factor in dividends (which were significant once upon a time…), and keep in mind it was hard to "hold the index" until Jack Bogle came along. Nevertheless…

The enclosed chart shows that rolling 10 year returns are negative now as they were for (approximately) 1974-82, 1932-47, 1914-24, 1890-98, and 1884-5. If you advance these dates by ten years and look again at the chart, you would be tempted to conclude that the subsequent 10 year returns were strong.

Also interesting to note an apparent pattern in the wait time between peaks in 10Y return:

1887-1906 (19Y)
1906-1929 (23Y)
1929-1959 (30Y)
1959-2000 (41Y)

The second wait is 4y longer than the first, 3rd 7y longer than 2nd, and 4th 11 yrs longer than 3rd. It is tempting (again) to compare the elongating waits between 10Y peaks with concurrent increasing life-expectancy:

This table compares waits with (midpoint) life expectancy (for white males, of course):

.                  inc wait   LE    inc LE

1887-1906 (19Y)              48

1906-1929 (23Y)  4y          50      2

1929-1959 (30Y)  7y          63     13

1959-2000 (41Y) 11y          71      8

This whole investigation was pursuant to an academic paper suggesting increased risk-aversion for people who lived through bad markets: Do Macroeconomic Experiences Affect Risk Taking? by Malmendier and Nagel. Eventually the risk aversion decreases again through (ahem) natural population replacement.

Phil McDonnell comments:

Although there are only five complete cycles, I note that each speculative cycle is increasing in amplitude as well as increasing in period.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008



 In my local paper the last two mornings is bad local economic news. The other day in my area R&J Trucking out of Boardman, Ohio laid off 36 drivers up the road from Belpre at their local hub. Then International Converter in Belpre shuts down and 45 jobs are lost and in this morning's paper Eramet near Marietta, Ohio lays off 110 workers out of 330. I note oil at $40 today and gas prices dropped a dime today in Belpre.

The news attributes oil decline to less usage. This makes sense with all the lost jobs to truck drivers and individuals who drove their autos to work. I wonder if a formula could be developed to correlate lost jobs nationwide to the decline in oil price?

Phil McDonnell replies:

A trucking company should be more profitable because of lower gas prices, not less. They should be able to lower prices, if necessary because of their own reduced cost and this should stimulate demand and allow them to hire more people.

The fundamental point is that any model of the economy that took into account oil prices would only work for a certain part of the business cycle. At some times the higher price of oil is a tax on the economy which slows it down, so oil prices and the economy move inversely. At other times a drop in oil price serves as a reflection of the fact that the demand is reduced because of reduced economic activity, so oil and the economy move down together. This latter reality is what we are facing today.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008



 NASDAQ is using Amazon's Cloud Computing technology to store massive amounts of tick and quote data for rapid retrieval by pros and non-pros alike. The Market Replay product is described here.

The following article is a write-up on the fast-growing concept of Cloud Computing:

NASDAQ Using Amazon for Cloud Storage January 7th, 2009 : Rich Miller

The NASDAQ Stock Exchange is storing "many terabytes" of stock market data on Amazon's S3 cloud storage system. NASDAQ's use of Amazon Web Services is detailed in a story by Penny Crosman at Wall Street & Technology and will be the subject of a presentation at O'Reilly's Money:Tech conference on Feb. 4-6 in New York.

NASDAQ is using AWS for Market Replay, a product that provides data on historic trades and lets investors analyze pricing in relation to news events and earnings calls to gauge the market response. Claude Courbois, associate VP of product development for NASDAQ OMX, said the use of S3 has helped it control costs for the service. Rather than using a database, the exchange is storing text files at Amazon and using an Adobe AIR client application to analyze the trading data and create trend graphics.

Courbois told WS&T that it stores many terabytes of NASDAQ, NYSE and Amex data in Amazon's storage cloud, and adds 30 gigabytes to 80 gigabytes of data every day to the cloud. The data retrieval time is less than one second, and the system scales instantly.

"If we built this ourselves or used a standard ASP (application service provider), we'd have to ask for more space than we initially need and pay for all these empty terabytes until we fill them up," Courbois said.

See Wall Street & Technology for the full story.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008



 I've always been intrigued by circular definitions, which are described as, the meanings of whatever is to be defined are found in the definition itself. Time is one of those constructs that might exist, but one would be hard pressed to find a definition of time that didn't have "time" included in the description. Many other circular definitions exist in the world, and many fundamental units such as the kilogram are best described by circular definitions. Circular definitions, sometimes paradoxical in nature, extend to other areas of nature and humanity with regularity. One would be hard pressed to define exchange without including some aspect, meaning, of exchange in the definition. I can't think of how one could define the meaning of the word trade, without having an element of the meaning of trade in the definition, Trade and exchange could even be used interchangeably Debt could be another term best described with a circular definition, as I'd be hard pressed to find a meaning that didn't include owing something in the definition. Value, as in monetary terms, is another construct that could best be described by a circular definition. Although it's a stretch, the word money, when stripped to it's essence is best described using a circular definition, as "medium of exchange" is still money. It seems that when you drill down to the essential things in science and nature, the building blocks, the things we take for granted, circular definitions pop up with increasing regularity. If fundamental units like time and mass cannot be described without resorting to circular definitions, then our entire bedrock of human knowledge, from the time of Aristotle, is laid on quicksand.

Art Cooper writes:

The bedrock of human knowledge is in fact based on universal human experience in its broadest sense. Your criticism of circular definitions brings to mind Noam Chomsky's universal grammar, which relates to universal human experience. There is a universal human understanding of such fundamental concepts as time and mass, although there are cultural differences in the way such concepts are perceived.

Vinh Tu comments:

For those who are inclined towards things computer-sciencey, the free MIT online book Structure and Interpretation of Computer Programs is great, and in particular I found the chapter and lectures on the "metacircular evaluator" to be mind-expanding.

Vincent Andres writes:

Among the best things I have read about time are :

from I. Prigogine
1. La Nouvelle alliance - avec Isabelle Stengers, 1986,
ISBN 2-0703-2324-2
2. Les lois du chaos (Le leggi del caos) - 1993, ISBN 2-0821-0220-3

I think 1. is : Prigogine, Ilya; Stengers, Isabelle (1984). Order out of Chaos: Man's new dialogue with nature. Flamingo. ISBN 0006541151. Unfortunately, I don't know if 2/ was translated in English.

Both books are clearly written (but not always easy). It appears I. Prigogine did a great work as a contemporaneous scientist. But in those books he also achieves a truly impressive history of science job. It's this sort of book you just regret to not have read earlier.

I'd like to hear about other good books/texts on the topic of time.

Phil McDonnell adds:

How about this for a non-circular definition of time:

Time is a condition of increased entropy in the universe.

The usual meaning of 'circular definition' is when someone uses the word itself in an attempt to define the word. In order to understand such a definition one must already understand the word.

However this discussion has embraced a much wider interpretation of the word circular. If I understand correctly it is that a definition is equal to the thing itself. That is always true for every definition. A thing is equal to itself and by extension to its definition.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008

Jim Sogi writes:

In Henri Poincare's time, there was great dispute over time, where the meridian would be and exactly what time was it? Two places could not agree on time without adequate communication and accurate clocks, neither of which were available back then.

Now time in data is still an interesting issue. Time in Europe, NY, Chicago, Tel Aviv, Japan… whose time is it and what time is it really? Whose framework will prevail. I think this is still contested daily and weekly now. If we take a holiday, does time stop? Who is moving the markets? There have been many big gaps when our markets are closed. Whose time is it?




In recent months the market has become more volatile. This volatility has led to further evolution in the traditional option to underlying linkage. In many cases we are seeing the option move the opposite to its traditional correlation with the underlying.

For example, the calls on the Russell 2000 index ETF (IWM) sometimes drop when the underlying goes up. Even on days when IWM is up more than 2% the calls will often drop. The reason for this paradoxical behavior is the simultaneous radical change in expected volatility. When the market goes up the VIX will usually fall. The correlation is a powerful -83%. When VIX falls options become less valuable. Specifically we are seeing the VIX effect completely overwhelming the effect of delta.

To trade this successfully one needs both a directional model and an ability to predict volatility. The ability to merely predict direction is no longer sufficient.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008

Greg Calvin writes:

We have also seen call options hold their value on days the market/underlying is headed down fast. A misplaced sense of comfort may arise in these situations. One needs confidence that the underlying will rebound in the future planned trading time frame, as the IV settles back down and time decay marches on. I have found option spread trading to be one way to [sometimes more than] offset losses on long calls or puts due to IV drops. The sold calls or puts will also drop in value when IV declines. In addition to vertical spreads, calendar spreads can provide opportunities when front month options have significantly higher IV. Volatility tends to increase not only when underlyings decline, but also as announcement events approach. Lagging into spreads as volatility increases is particularly attractive, as the higher IV premium tab gets picked up by someone besides you.

Diego Joachin remarks:

I think options' behavior is the underlyings' subconscious. It reveals the fear of players. That's why studying volatility is more important now.



 A random look at S&P web site shows that the 10 year performance of big cap, mid cap and small caps are roughly as follows:

Big (500) 3% Mid (400) 10% Small(600) 10%

The big caps have significantly under performed the other sectors of the market. Even REIT's have had about a 12% return which seems surprising given the real estate market.

However the question that leaps to mind is why is there such a disparity between big caps and the broader market?

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008



 I was just perusing a "Hedge Fund Monitor (27 Oct)" note from Merrill. It cites Trimtabs research reporting record high hedge fund redemptions in September of $43bn, and says:

Such forced selling drive asset prices lower which in turn creates more losses for HFs and lead to more selling- a vicious circle. …We also think that losses to large prime brokers who provide funding to HFs, may have exacerbated some of the forced selling. While HF returns over the past 12 months are negatively correlated to Financials overall they are positively correlated with investment banks, who are also prime brokers to HFs. Just as HFs' cash needs were rising, funding became more difficult.

The note goes on to comment that a popular hedge fund strategy was to invest in equities with cheap yen and points to the strong correlation between USD/JPY and the S&P, giving us yet another variant of the carry trade.

In this narrative, the move in the USD/JPY is less a result of new flows into yen than it is a consequence of severe hedge fund liquidations that have forced an aggressive unwinding of the equity-yen trade. For what it's worth, the ML note looks to the latest COT data (already stale as it reports positions as of last Tuesday's close) and finds crowded net long speculative positions in USD and JPY, suggesting USD/JPY may have further to go on the downside. It's all interesting reading, and almost everyone seems to agree that these violent moves are the result of forced hands, not of a fair reassessment of fundamentals. A good time for the long-term investor to scale in to global equity markets, perhaps. The other thought I had when reading this report is that while I have been expecting a massive rally in USD/JPY when risk appetite subsides, if the USD and other currencies also have super low interest rates by that time, then the yen could have some competition on its hands as the funding currency of choice.

Phil McDonnell writes: 

In recent days the yen has been incredibly strong. The other notable feature of recent trading is that volatility has been historically high. Since 9/11 this year there have been 30 trading days. Only four of those have shown less than a 1% move in either direction. High change days are the norm, not the exception these days. VIX has been rising and made repeated new highs and still resides at high levels.

To see if there is a relationship between these it is often good to look at correlations between coterminous changes. Some of the more notable coincident correlations over the last three months are:

VIX Yen 71%
VIX TBill 68%
Yen TBill 59%

All these relationships are substantial. From these we can conclude that when investors perceive increased risk the money flees to both tbills and yen.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008

Riz Din adds:

A couple of additional thoughts on the carry trade:

1. One can imagine another reason why the market has fed off itself on the downside is that the carry trade is itself entwined with volatility. The carry trade thrives in a low volatility environment and it is not so long ago that we were experiencing what some called the Great Moderation, an apparently new era of low volatility in the real and financial economy. In such a world where investors are confident that fx rates will lie somewhere within a tight range x months hence, the attractiveness of the interest rate component of low rate currencies grew massively, and the yen and other low rate currencies became cheap financing vehicles for other investments. Alas, to benefit from these low financing rates these investments would have had to have been unhedged for fx risk, and when volatility spiked up, the perceived cushion of saving provided by the lower interest rates paled in comparison to the daily swings in the fx prices. Add this factor to margin calls, margin calls, redemptions, etc and you have another powerful reason for the recent aggressive, self-perpetuating, forced selling that took place across the markets. Always thinking from the other side, after such a large reversal and cleaning out of carry traders, I wonder if there will be opportunities to put this trade on as volatility heads lower– history could be a guide for those who have access to the data. I hear Iceland is offering 18%.

2. I must be missing something obvious here, and maybe this thought can be easily skewered, but I wonder if this simple explanation can be used to show why the carry trade seems to defy economic theory (uncovered interest rate parity) over prolonged periods, only to eventually come crashing down: If two similar bonds or similar stocks are trading massively apart for no reason, immediate buying of one and selling of the other closes the price gap. The price corrects back very quickly and the opportunity disappears in the blink of a eye (Porsche/VW aside). However, entering in to the fx carry trade by selling the low yielding currency and buying the high yielding currency surely only pulls prices further apart, making the carry trade even more attractive to those who use history as a guide. Is this a self-perpetuating cycle that simply carries prices to unsustainable levels?

Alston Mabry replies:

Can't help but think that it has been the other way around: The carry trade and other cheap money forces were what kept volatility low. The image that comes to mind is the pressure of air inside a big balloon, or water inside a sprinkler system; when the pressure is constant, the system is smooth and stable, but when the pressure slacks off, the system sputters and collapses.



Institutional investors now have a decade of no return. With some detailed credit work they can get 15-20%+ annualized from more senior securities and meet long term liabilities. Why subject oneself to the vol of equities when all your peers are moving to liability management policies and many are way behind the curve? The word on the street is hedgefund managers ( those still in existence) are blowing out their equity teams under the banner, "debt is the place to be for the next decade." Granted equities are undervalued by many historical measure but can stay so for a lengthy amount of time and the recent moves can be lethal if not careful.

Victor Niederhoffer asks:

Given that it would be possible to make 10% on senior debt, what would the required return on equities be at this level? That's my point about VIX and the required a priori rate of return.

Tim Melvin replies:

I would humbly suggest two times the level of senior debt rates.

Phil McDonnell ventures:

One reasonable and quantifiable approach might be to assume the market demands comparable Sharpe ratios from various asset classes. Consequently the ratio of the observed or estimated standard deviations of stocks to bonds may be the same as the ratio of the required expected returns.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008



philThe volatility of the market has dramatically increased in the last few weeks. It is often said that the correlations between various markets increase at such times due to forced margin selling. Certainly such a suggestion is plausible on its face but like everything else must be tested.

One can look at the same-day correlations between various macro variables and the S&P. For this purpose the relevant ETFs were chosen. The correlations with SPY are as follows:

Oil                81%
Gold            -32
Tbonds        -53
Tbill             -53
Yen             -64
UK stocks     93
Japan stocks    93
VIX               -86

The most striking is the strong positive correlation with oil. One interpretation is that oil is driven by recession fears just as stocks are. Another explanation may be that oil is being liquidated to finance stock margin accounts just as the pundits claim. Clearly holding oil is not now a hedge against a stock portfolio.

But when we look at gold the correlation is negative. This would tend to serve as evidence against a wholesale correlation of assets being sold. To some extent gold is still a hedge against a stock portfolio. The same goes for treasury paper and the yen. But we do see UK and Japanese stocks being strongly positively correlated. So it appears that many world markets are strongly correlated with each other. Again this may be a sign of coordinated margin liquidation. The strong negative VIX correlation can be interpreted as the markets are now being strongly driven by fear. None of this is predictive but is an interesting descriptive look at where we are now.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008



BucketThings have been moving at lightening speed recently and only now at the weekend do market folks get the much needed reprieve, the few days of quiet time to rebuild depleted mental strength, collect thoughts and ponder future moves and plays. I've collected a bunch of research from the Street that I thought may be worth a skim read to some on the list.

James Montier - Mind Matters: The Strangest Feeling Goldman Sachs - Europe: Portfolio Strategy - Recession – now priced as the central scenario Rosenberg - Global economics weekly UBS - The broken lighthouse
UBS - Global Bear
UBS - A Bear Market & Then a Crash Macqaurie Research - Japan strategy weekly - Bear market bottom indicators Morgan Stanley - FX Pulse

Just to cover myself, I'll provide the caveat that I generally look at bank research to broaden my outlook, learn new things etc, but I find it almost meaningless for specific forecasts.

Best wishes to all in the week ahead. It's tough navigating out there. Personally, I'm bullishly optimistic that the UK bank rescue plan will be adopted in various guises internationally and that this will provide the basis for a sharp recovery in the markets. Whether it's a good thing for the world in the long term is another issue, but what it almost does seem to succeed in achieving is eliminating the tail risk of risk of a sustained evaporation of credit leading that would lead to the hell in handbasket scenario. I may be missing something big here, and whether we fall further or not at this particular juncture is unknown, but over the weekend I went to the gym to strengthen body and mind have since been building a confident belief that the way things are developing the situation is increasingly turning in to an asymmetric bet with the world government confirming that they will do whatever it takes to stop the system from seizing up.

Here are a few quotes and notes from the pieces:

David Rosenberg from Merrill Lynch gives an nice overview of the policy response (a spot of optimism from Rosenberg of all people!):

"Policy crescendo: We have now had in very short order some extraordinary moves by policy makers. In no particular order, a UK bailout (the best, most comprehensive one we have had by any country so far, in our view), Fed buying commercial paper, a Spanish TARP, coordinated rate cuts, deposit guarantees in Europe, a banking sector support plan in Russia, Brazil intervening, etc. Unless we are assuming that global policymakers are incompetent, they will sooner or later get it right.

Critical policy measures: guaranteeing term funding and EM CB reserves The UK likely achieved the former the best way, with a 250bn sterling scheme to provide government guarantees of new short- and medium-term debt issuance to assist in refinancing maturing funding. The Fed's move on commercial paper has been nothing short of extraordinary.

CBs and sovereign wealth funds globally control US$9tn in assets. These have been built up for a rainy day. Well, the rainy day has arrived. Watch as EM CBs start utilizing these reserves creatively. Brazil intervened for the first time, joining India, Korea, Russia and others. Russia has been the most creative, using reserves to support its banking system, domestic equity market and currency."

James Montier says, 'Only 2 stocks manage to pass our deep value screen in the US. However, 35 names in Europe pass and 125 in Japan. This emerging value presents me with the strangest feeling, I think it is called incipient bullishness! Obviously not on the overall market, but with respect to a basket of deep value stocks.'

The two US stocks are Ashland (ASH) and Nucor (NUE). Montier provides a full screening list in the note.

'In the short term we must rely upon the margin of safety concept, which argues that buying stocks that are already heavily beaten up provides us with some protection against the downside. Having cash is a suitable hedge and provides the opportunity to deploy capital at a later stage if we are too early. So a barbell strategy of cash and deep value looks to be the best idea to me.'

The Macquarie Japan report hunts down indicators for a bottom in the Japan bear and provides some insightful charts:

'Japan's P/BV, at 1.05x, is at 20-year lows, having fallen beneath the 1.25x level of September 2002. The latter was a time of intense financial system stress in Japan. Japan's dividend yield is blowing away its 20-year history, reflecting increased payouts on increasingly respectable corporate profitability.'

'With bank deposit rates near zero, the history relative to the yield on 10-year government bonds is shown below. The equity dividend yield is now materially above the bond yield.'

UBS Broken Lighthouse report starts off with:

'we all assess market opportunity and risk in a way that gives us signals about when and how to act. But what happens when those signals lead us astray? We liken the current situation to a ship and a lighthouse. A ship's captain counts on the light from the lighthouse to keep the boat safely away from land. But if the ship runs aground, then what? The next time the captain sees the lighthouse, can it be trusted again? Similar to the ship's captain, there has been a loss of confidence in recent weeks of investors in global equity markets. Stocks everywhere have been under massive pressure, with all-time high readings on volatility and risk aversion. Despite some signs on (desired) policy response the sell-off has been relentless. Indeed, those signals that may have guided optimism in recent weeks, have been false signals.'

I find table 3 in this report quite insightful. It looks at historic bear markets and recoveries, albeit only going back to the 1970s. Simply judging by the duration of previous declines we are much closer to the end than the beginning.

From 'UBS - A Bear Market Then A Crash':

'Fundamentals are irrelevant today, but today won't last forever. To be clear, we expect a recession and every additional day credit markets remain frozen the more challenging it is likely to be.'

'Excluding (these) financial write-downs the S&P is trading at 9.8x trailing EPS. On an interest rate adjusted basis, we believe this could be the least demanding trailing S&P 500 PE ever.'

Phil McDonnell replies:

P McDThanks to Riz Din for this font of current wisdom.

However it is far from clear that the various government geniuses have it right yet. They have no plan to revive real estate values. That is the fundamental underlying factor in this situation. Not only do they not have a plan, they aren't even talking about the need for such a plan. If we don't solve the right problem then any solution, no matter how brilliant, will come to nought.

Even a zero down, zero per cent interest, 30 year mortgage will not convince anyone to buy a home in a declining real estate market.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008



Everyone knows last week's 18% drop was the worst in DJIA history (10/28-present). Here are the stats excluding that week (but including Great Depression weeks):

Descriptive Statistics: week ret

Variable     Mean    SE Mean   StDev     Minimum  Median Maximum
week ret   0.0012  0.00038  0.02442  -0.1554    0.0025  0.1821

So last week's return (return?) of -0.18 was about 7.4 STDEV below the mean.

Not only not normal, but where to look for example periods to model quant strategies? We now see it was a mistake to exclude 00-02. Even 29-40. What about the KT boundary?

In honor of this occasion, one will ditch the clever-as-if-it-was-known tone of this web site, and offer an opinion:

There are periods when markets behave well and are amenable to characterization (quantitative or otherwise). And there are periods when they are not. If you could know this with any precision, you would be extremely wealthy; which by design makes it about impossible to know.

Markets wouldn't trade actively if there weren't opportunities, or if it were easy to tell genius from luck.

There are periods when markets behave well and are amenable to characterization (quantitative or otherwise). And there are periods when they are not. If you could know this with any precision, you would be extremely wealthy; which by design makes it about impossible to know.

Markets wouldn't trade actively if there weren't opportunities, or if it were easy to tell genius from luck.

Rich Bubb responds:

So this "characterization" could be a 3×3 cube plotted representation (albeit this might be a little simplistic for most of the SPEC-Listers), with axes listed, in no particular order:

x aka a bubble exists in sector/commodity, scale might read: "no chance" at far left end; hysteresis happening and/or lobagola should happen or just did happen being in the mid-zone/s; and price/cost up n% in m-time meaning "here there be the bubble monster" and tread lightly or get out as fast as feasible.

y aka the interest rate du jour… scale trending down means economy &/or mkt trending down; scale trending up means economy &/or mkt trending up… but the scale would be visually represented by an upside down bell curve. So, for example, if fed rate is trending down, then the might be converted to a z-scale transformed scale with 0 in the mid of the scale, +3 on high end of scale, and -3 (std devs) at the low end.

z aka axis might be volatility or put-call ratio, or money supply, or OBOS%, or your indicator of choice.

With enough data points one might be able to observe the rate of change in 3D as things (x, y, z) move about.

I think this could be done with the charting tools in Excel…

Phil McDonnell observes:

The analysis Rich Bubb has described is essentially a 3D scatter plot. There are many examples of 2D scatter plots in Education of a Speculator and Practical Speculation as well as any introductory stat book that covers regression. The 2D plots relate to regression of one variable in an attempt to explain or predict another. The 3D case would relate to the case of using 2 variables to explain a third one. The regression would be of the form:

Z = a * X + b * Y

The usual caution is to avoid variables which are serially correlated. Usually price CHANGE variables are not correlated because an efficient market will remove any such correlation. By the same token price LEVELS are always highly correlated. One notes in passing that interest rates are a kind of reciprocal of the price level of the underlying debt instrument. Thus interest rates would be expected to be highly correlated but CHANGES in interest rates or prices would not be correlated.

Volatility is a more interesting animal. Volatility and therefore Vix levels are highly correlated. Again it is probably better to use changes in Vix than levels.

A final note is that one can perform a two variable regression of the above form even in Exc3l. To do that you need to have the Analysis Tools Add-In loaded. The data columns should be right next to each other (vertically). Then the regression analysis can be performed by clicking 'Tools/DataAnalysis/Regression'. 



skullI note with a certain degree of gallows humour that today's villains are highly regulated institutions like commercial banks, insurance companies and broker dealers. Ten years ago, the LTCM debacle had the wolves crying for greater regulation and transparency of fast money. Now the hedgefund community is relatively healthy and will attract huge inflows once the dust settles. The key is that most are not publicly traded (though some are) and have reasonabe lockup periods and few disclosure requirements. In short, they are nimble. The big boys lke GS, MS, JP etc… insist on being global banks and hence require massive amounts of capital accessed via the capital markets. I wonder what Mr. Market will think is the most appropriate market intermediation model 10 years from now?

Philip McDonnell adds:

Regulation is a dirty word to most free market fans. It always entails cost, both to the operating businesses and to the tax payer. After all running a regulator involves an expenditure of public dollars. Having said that some sort of independent oversight is necessary so that the con men and charlatans do not dominate the market place.

However a large part of the responsibility for the current financial crisis can also be attributed to the current regulatory environment. In particular FASB, the Financial Standards Accounting Board changed the rules in the middle of the game. FASB promulgated that the banks had to revalue their sub-prime assets this past summer. Particularly hard hit were the securities which had to active markets. The net result is that banks which were caught 'holding' found huge swaths cut out of their portfolios. This was true whether or not the underlying mortgages were performing or not.

Strictly speaking FASB is not a government entity but it is as least partly government funded. The directors include people from government and the private sector. Mainly they are accountants.

What is needed in the current environment is less restrictive regulation not more. If anything we need to undo the draconian measures which are killing bank asset valuations. To be fair FASB is now quietly revising its earlier directive of only 90 days ago. The original directive was undoubtedly intended to strengthen the banking system. Yet the proximate result was to topple the House of Morgan and WAMU and to bring the entire banking system to the precipice within 90 days. What were they thinking?

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008

Stefan Jovanovich responds:

The House of Morgan" would, by Morgan Sr. and Jr. and Mr. Peabody's calculation, be J.P. Morgan Chase, not Morgan Stanley. The idea of looking elsewhere for the funds to support your positions in the market would have seemed to them incredible; even as a market maker you always had to be in a net cash position. (The reason Ron Chernow's book on Morgan is good only for pulping, in spite of the author's extraordinary industry, is that he can only see the Morgan Bank with modern eyes. Whatever Morgan, Peabody and J.P. Morgan & Co. were, they were not a 19th century Bear Stearns with the added advantage of being Episcopalians.) The Morgans and their original partner would have found the Treasury's current rescue plans to be fundamentally wrong-headed. They would have wanted the Federal Reserve and the solvent member banks to buy the failing and failed banks' non-speculative liabilities - the savings and transaction deposits - and left the shareholders, derivative claimants and creditors to liquidate the assets on their own, with or without the help of the bankruptcy court. M Sr.,M Jr. & P would have scoffed at the idea that governments should, would or could reset asset prices in the midst of a panic by writing checks based on their ability to issue sovereign debt. That fantasy is one that only the 20th and 21st centuries have accepted as wisdom.



Phil McD

During one's lucubrations on the state of the markets perhaps it would be good to consider options as a way to deal with the current volatility. In particular, option spreads can help to manage the risk of a position. Given the current volatility, risk management can be a valuable thing.

One spread to consider is a volatility spread. The mechanics are simple. Buy two out of the money calls for 2. Sell one at the money call for 5. You now have a net cash credit of 1 point ($100). You will also have to maintain margin of $500 for the one call you have sold, assuming the strike prices are 5 points apart.

Assuming some realistic Greeks you might have the following:

Q Option Pr delta vega

b 2 Nov 69c 2 .32 8.5

s 1 Nov 64c 5 -.59 -10

Net 1 .05 7

In the short run you have a slightly bullish spread with an overall delta of about 5%. The spread will benefit from volatility as evidenced by the net vega of 7.

The spread will make a profit of 1 anywhere from 64 down to zero so you are protected from banking Armageddon. Small losses will occur between 64 and 69. The max loss will be at 69 and will be limited to 4 points. Above 69 the two calls will kick in (less the short one call) and the spread will become increasingly profitable with no upside profit limit.

If one believes that the banking system is set for a domino collapse scenario then a similar spread can be done with puts. Sell 1 at the money put and buy 2 out of the money for a net credit. Then the spread will have unfettered profit potential all the way down to zero. It will lose a small amount if the underlying closes out between the strike prices. The spread also will enjoy a small fixed profit in the amount of the net credit anywhere above the upper strike price.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008



Dr PhilAll moving averages have to be based on a backward looking window of time. So a 10 day average is the average of the last 10 days and so on. But the center in time for that average is really about five days ago. To be more precise it is (n+1) / 2 days ago or 5.5 days ago.

So comparing two moving averages of different lengths is really comparing apples and oranges. If we compare a 10 day to a 30 day average, for example, then we are comparing the average of 5.5 days ago to 15.5 days. In other words they are not the same point in time. Mr. Glazier's enlightening 3D representation of moving averages of various lengths shows that the longer windows respond more slowly to ripples in price than do the shorter moving averages because of this lag effect.

Another feature visible in the chart is the apparently cyclical undulations. The problem with that is that it may simply be a manifestation of the Slutzky - Yule effect. Essentially Slutksy-Yule says that any series, when averaged, will show sinusoidal oscillations as a result of the averaging process. This is true even if the original series was composed of random numbers which could not possibly be sinusoidal in nature.

Another common pitfall when using moving averages is to think that all one has to do is to find the magic combination such as a 19, 27 and 79 day triple crossover with a minimum threshold of 1%. The problem with any such system is that there are an infinite number of these combinations. We quickly fall into the data mining trap where we will appear to find something even if it is merely a product of chance.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008

Yishen Kuik adds:

Another interesting point about moving averages is that the daily change in an N period moving average is caused by the difference between the values of the Nth day and the current day:

MA(t) = (1/N) * ( p(t) + p(t-1) + … + p(t-N+1) )

MA(t) - MA(t-1) = (1/N) * ( p(t) - p(t-N) )

So in cases where N is small, and where the p(t-N) value that fell out of the calculation is large, the moving average can experience sudden drops. This causes that cognitive dissonance when one sees a moving average fall even as the values are climbing between yesterday and today.This also provides the intuition to Slutzky Yule - for any given set of observations, there exists a cluster of points that has the highest average of all similar sized clusters, so while that cluster is passing through the calculation period of the moving average, there will be a peakedness, with two troughs surrounding it.

Alice Allen remarks:

While we’re talking about moving averages, a practical caution from my own experience with a popular commercial trading platform: If you are in a fast trading situation, monitoring a price graph with less than a 1-day display unit (e.g., 60-min, 30-min, 15-min), a line labeled “200-Day Moving Average Study” may not be the true 200-Day MA but perhaps the MA of the last 200 ticks. Under these circumstances, you may visually note that the price has crossed your MA line, but it will not necessarily be a true MA crossover as calculated by programs. Maybe this is obvious, but it took me a while to figure out and perhaps is unique to the platform I use.

Anatoly Veltman writes:

The best use of MAs that I know has nothing to do with crossovers. And it happens to be essential to one’s daily/weekly chart perspective. Extremely useful! I first saw it described by Stan Weinstein; then the periods and trading signals were optimized by a few proprietary shops. I believe it to be one of the better tools; if not for all markets, then at least for stocks.



Phil McDSome people are hanging onto every little twitch of the Gallup during this political season. Inevitably this brings up the question of how accurate are polls. To be sure there is the statistical error say plus or minus 2% or whatever. However sometimes this masks a more fundamental uncertainty in the poll. Namely the underlying data may be inherently unstable or highly cyclical. This can render the supposed statistical error irrelevant because the inherent instability of the data dominates.

One of my favorite examples is of the Gallup Poll's Measure of Daily Mood. For example if the poll showed a reading of 60% last Friday and then it fell to 45% this Monday one might be tempted to conclude that the mood of the public was adversely effected by the financial turmoil over the weekend. Nothing could be further from the truth. The reality is that the Daily Mood is totally swamped by what day of the week it is. Every single week the mood swings from 45% to 60% without fail. It is governed by how close we are to the weekend and little more. See a visual of this on Gallup's web site.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008



P McDIt seems every day for the past few months we heard another story about how bad the economy is. The mainstream media have had a monotonously lugubrious message about how bad it is. Against this backdrop we have today the salutary news that GDP rebounded at an adjusted annual rate of more than 3% in the second quarter. Simply put, the widely predicted recession never happened.

This still begs the very real question as to why the mainstream media are so bearish on the economy. There are several reasons for this but one is often overlooked. Part of the blame for the bearishness of the press can be placed on Google.

Clearly the people at Google are not sending out negative messages nor are they inherently pessimistic. In fact the opposite is true. Google is one of the most successful companies in history. Most of its employees are active participants in its success story. So the folks at Google are just short of euphoric on the economy. It is working very well for them. But that is exactly the problem. Google has a had enormous disruptive influence on other media companies. It has adversely impacted newspapers, magazines, radio, TV and pretty much every other advertising medium.

Across the board things are bad for almost all media companies who do not have a significant Internet presence. Sales are falling and consequently earnings have been decimated. No question, the media industry is in a Google-induced depression. So it should be no wonder that the mood in the financial press is depressed. They are losing their jobs wholesale. Over the last year many if not all senior columnists or reporters have been replaced in many companies. Typically they are being replaced by younger, prettier and, most importantly, cheaper talent. Many of these Young Turks have never seen a bear market. The guidance of the financial media has never been very good at its best, but this new generation may represent a new level of ineptitude. They are far too quick to hit the panic button.

However that is no reason that the rest of us need to panic. This time it is not different. Financial crises happen all the time and with a certain cyclical regularity. The GDP number tells us the worst is behind us and that the non-financial non-real-estate part of the economy is just fine. I believe stocks are more undervalued than at any time since 2003.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008



Phil McDThe Shanghai Composite Index (^ssec or 000001.ss) peaked at 6124 in October of last year. Since that time it has fallen to about 2500, a drop of about 58%. Yesterday it dropped about 5%. For perspective this decline is almost twice as great as the roughly one third decline in the US in the 1987 crash. Yahoo! has  a one year chart of the damage.

It is noteworthy that this decline comes at a time of great national pride - the coming of the Olympics to China. But in a sense this may be part of the problem. In a clear display of where Chinese governmental priorities are they shut down many factories and production in order to mitigate their extreme pollution problems. Chinese pollution is thought to be the worst in the world. So it was a classic Chinese act to save face at the expense of making money. It also sends the clear message that China's new found capitalism is expendable if it embarrasses the regime in any way.

The attempt to curb pollution had several effects on world wide markets. China's import of crude oil fell in July. This coincided with the recent all time top in crude and subsequent rapid decline from 145 to the recent 112 area. China is the second largest importer of oil in the world. It is clear that the Chinese factory shut down has had significant worldwide implications. But presumably it is only temporary.

The Olympics end August 24th.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008



J SogiThe use of fixed mechanical resting stops seems to be an admission of inability to trade your way out of a paper bag. It is also an admission you are undercapitalized. It is one thing to realize you were wrong. It is another thing to give up on the bottom tick.

Isn't it better to trade your way out of a bad situation rather than give more of your money to the opposition in defeat? It is a harmful mechanical crutch. It is better to watch for a better opportunity to exit with some grace. It is better to know the market, and know yourself.

Larry Williams objects:

What if you cannot exit with grace — market goes limit down 10 days? No way to trade your way out of that…

Stops prevent failures and allow one to regulate the size of the loss.

I'm talking trading here; not investing… value investors buy and hold until value changes or overall market gives a sell, that seems to be best strategy.

Shui Kage adds:

The old Japanese market proverb: "Mikiri senryō".

"To ditch a small loss is worth a thousand ryō" (In today's language: is worth one million dollars).

Most amateurs are unable to take losses at small size and most amateurs are not very good traders.

Phil McDonnell dissents:

PhilIf the market goes limit down (or up) against you then stops will not help either. The stops will not be executed. In that case only proper position sizing in the beginning or an option hedge will protect your position. There is no guarantee a stop will be executed at your price or anywhere near your price in the event of a gap open.

There is no theoretical basis that stops should work either. I have written about this here on numerous occasions. Thus the best advice is to back test, taking stops into account explicitly. When testing stops one should use great care to increae the assumptions regarding slippage. Invariably stops will be hit during fast markets when slippage is the greatest. Compare that to a back test without the stops. If the test using stops gives a superior overall risk reward profile then it is reasonable to use stops. One should never think of stops as the sole money management technique because of the slippage and gap issues discussed above. Rather stops are more of a trading tool to reshape your risk reward profile.

There is another reason to consider stops and that is psychological. Many of us are simply unable to pull the trigger when we get into a losing situation. Suppose you had a trading model that predicted that tomorrow would be up by the close. The obvious way to trade that would be to get in and get out by the close tomorrow. But if your system was wrong (and they all are sometimes) then you may find yourself holding the position simply unable to admit the loss and freezing on the trigger. It is easy to come up with all sorts of rationalizations for this behavior. "The drift will bail me out" might be one. Suddenly your plan has changed from a one day trade to hold it for ten years until the long term drift bails me out. So if you find yourself doing this too often then having a preset stop may be the psychological crutch you need to be successful. Better than that, of course, might be to simply write your plan down and execute it as planned.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008

Janice Dorn adds:

J DornI would add to this that placement of stops is both art and science. It is among the most difficult concepts for a trader to grasp, and there is more confusion surrounding stops than almost any other aspect of trading. How often do we hear: “They see my stops” or “There is clear stop-running going on” or something similar re: stops. That is why when I trade ( not invest), I use multiple contracts, keep taking profits and trailing stops ( on a good trade) and get out as quickly as possible when the trade is not going right for me. Also, I am prepared to lose on a certain percentage of all trades per my trading plan. I used to hate and could not accept getting “stopped out” but now accept it as part of the cost of doing business.

Also, it is very challenging for most traders to “stop out” and then get back in again. Part of the reason for this is inexperience, and the other part is the way that losses are seen by the brain. Losses are weighed about 2.5 times as heavily as gains. This means that if you are down 10% on one position and up 10% on another position, you are break even on paper, but are down 25% in your brain. There is a complex process that goes on inside the brain of the trader that is looking at losses. But that is another topic and I have already digressed from the “stops” thread.

Dr. Dorn is the author of Personal Responsibility: The Power of You, Gorman, 2008

Jeremy Smith tries for the final word:

Everyone uses stops.

Some put them in immediately.

Some keep them stored in gray matter for later deployment.

Some wait for the margin call.

Kim Zussman exclaims:

Kim Z"Say uncle!"

If you trade less than 100% of your investable capital, that is a stop.

If you trade predominantly the capital of others, that is a stop.

If you let the account blow up without borrowing against your home or retirement accounts, or hitting up   friends/family, that is a stop.

If you decide to trade small enough to preserve your marriage, sanity, or life, that is a stop.

Even the Kamikaze had stops.

Nigel Davies suggests extending the discussion:

What about broadening this discussion still further to include the 'reverse-stop', ie a profit target? I don't see much difference between the two from a conceptual point of view, the issue here being psychological (one represents a loss, the other a win).

Can one be ideologically opposed to stops without also being unable to take a profit? I don't see how we can discuss one without the other and they all come under the category of 'planned exits'.



The ratio of surface area to volume is a concept that is often used to optimize, e.g. the leaf has evolved to maximize this ratio. Are there applications of this to charting that might lead to insights and testable hypotheses?

Phil MPhil McDonnell replies: Hidden variables may be at work. When we see price and volume reported it is actually the result of up to four hidden variables. At any given time there is the book, the collection of all limit orders to sell and limits orders to buy. These two variables interact with two other unobservable variables which are the traders who are about to enter market orders to buy and to sell.

Market orders extinguish limit orders on the other side of the trade. Together the interaction of these results in the two observable numbers which are reported -price and volume. If we then add the third observable dimension of time we actually have a 3D space that can be quantified.

I am struck by the similarity of this line of thought to Kepler's Laws. Recall that Kepler's Laws are all expressed in terms of squares and cubes of the relevant variables.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008



 I found myself lying awake in my bed last night thinking about the Nobel Prize Winner. No! Not like that….but about what he said in Stockholm last week. Expected Utility Optimization. What he said is that the goal of asset allocation should be optimizing the expected utility for the actual investor in question, and that the mean variance model should just be looked upon as a special case. And of course he is right. I mean, by the way he sets it up, he is right by definition. But….I am thinking how it would play out in the real world. In my fantasy, a consultant would sit down with an investor, asking questions to find out his preferences. Of course this is already happening in a general sense but here it would end in a very specific investor utility function). Then the asset allocation would be done based on the utility function.

I am thinking that what will be overlayed on the usual return/risk models, are constraints (e.g cutting off tail risk, smoothing out fluctuations and what have you) and while the model presumably maximises return given a risk level and those added constraints; if we add constraints there must be risk premia transferred to someone else? By definition, since the investor specified his utility function (and given that the formulas and models held up and he got "what he wanted") he is better off than before, but so must someone else be?

I am not sure this new allocation model will start a revolution in the way asset allocation is done. I think however that finding situations where other investors are up against constraints, could help open up possibilities and profits. In the micro realm, many traders prefer to cut off the risk of gaps against them, by not holding overnight. This might open up possibilities for traders well capitalised and with good stomach, to do just that (this must be tested). Other suggestions are welcome.

Adi Schnytzer critiques:

AdiIt never ceases to amaze me that people who know markets and work in them don't realise that we don't know the probability that anything will happen tomorrow unless we are in a fair casino. So the idea that anyone can maximize expected utility is nonesense since you don't know the probabilities. I am currently working on developing a risk index as a follow-up to such an index developed recently by Aumann. He cutely argues that even though we don't often know the probabilities to assign to events, it's important that, in principle at least, we have an index. Well, I've been looking for real life examples of his index (and my follow-up) in stock and derivative markets, and simply cannot find one. As a top bookie once said to me: "If I only knew the winning probabilities of the horses, I wouldn't need to know winners; I'd be making a fortune anyway." Spot on.

Jim Sogi adds:

Martin talked about "…cutting off tail risk".

The thesis that outliers shape the future is intriguing, but also that the risk cannot be eliminated. The idea that one can cut left tail risk is an illusion that in itself creates a greater risk. As Phil says, it also cuts right tail return.

Jeff Watson concurs:

Risk can be quantified, assumed, bought, sold, transferred, created, subordinated, reassigned, split, delayed, diluted,  fragmented, hedged against, and layed off……. Risk can respond to some methods, but it is still risk, and is near impossible to eliminate.

Speaking of planning in general, Stefan Jovanovich adds:

I have quoted this before, but it seems worth repeating, if only to add a mite to Adi's wisdom. Planning in business is all very well, but the trouble is that your plan's assumptions always turn out to be works of fiction. As John Wannamaker said, "I know half the money I spend on advertising is wasted. If someone would tell me which half, I would very much appreciate it."

Vince Fulco concurs:

This quote has always seemed appropriate… 

Moltke's famous statement that "No campaign plan survives first contact with the enemy" is a classic reflection of Clausewitz's insistence on the roles of chance, friction, "fog," and uncertainty in war. The idea that actual war includes "friction" which deranges, to a greater or lesser degree, all prior arrangements, has become common currency in other fields as well (e.g., business strategy, sports). [Wikipedia].

Russ Humbert warns:

One of the hardest things to get people to see is that most people/businesses have a long term utility function but operate as if all risk is short term volatility.  For example, I work for a company that has a niche market and is privately held. The owner wants to pass this business on to his great-grand kids so each will be as well off as he is now.  He has only teen kids now. This niche has very little volatility of earnings and good ROEs. But this just encourages piling on the same long term risk, to minimize the short term risk.  That is: grow the core business, not diversify. We already have the leading player in this niche.  Barriers of entry: a learning curve, requires some marketing  nimbleness, and need for stable size and reputation.   However, long term this has  no good ending. Best case we double our market share and flatline growth. But many worse cases.  Bigger, deeper pocket competitor or many, learns our niche attracted by the ROE and stable vol. We are regulated out of the market. Products slowly go obsolete, replaced by Government safety net. We lose our reputation, etc.  See this in spades throughout the fallen out of favor or failed businesses, due to subprime mess.  Low vol high ROE business, until….  For the speculator this would be like choosing a strategy that 95% time gives "Alpha" in a beta model based on quarterly results of recent history.  But all the "alpha" is hidden because, 5% time it causes you to go broke or close to it.  It just hasn't happen yet, or recently.   Basically volatility as a risk measure can hide long term complacency defeating most utility functions.

Going back to the military aspect Bill Egan adds:

An interesting aspect of the fog of war is the common mistake of not reevaluating the plan often. A major cause of this error is that people confuse perseverence towards a goal (a good thing) with sticking to the particular plan they are using at the moment to achieve that goal. Criticism of the plan and proposing actual changes to deal with new information or uncertainty are considered as defeatism or disloyalty and the operationally fluid are smacked down. The no longer relevant plan is then ridden on to failure to a loud chorus of "yes, sir! yes, sir! three bags full, sir!" A pleasant sight if it is your opponent doing this but awful if it is your leadership. I have fond memories of serving as a company commander under a battalion commander who always asked us to tell him if he wasn't making sense and meant it. Good man. 

Phil McDonnell  enlightens:

PhilThere are many deep questions in Mr. Lindkvist's ruminations on Expected Utility Optimization.

My first comment would be that there are at least two distinct classes of utility function. The first class might be what can be called the Ad Hoc Class. This would include the questionnaire method of approximating one's utility function.

Other methods might be classified as normative, as in what one should ideally want to use for a utility function. As a well known example we have the Sharpe Ratio. This is based upon the normative idea that one should maximize expected return but with a quadratic penalty for increased volatility which is treated as a surrogate for risk.

The idea of using a square root function as a weighting for betting returns actually goes back several centuries to Cramer, a mathematician. His friend and frequent correspondent Daniel Bernoulli countered with the idea of a logarithmic weighting function, which is also what I espouse with extensions. Bernoulli's ideas were not translated into English until the 1950s and thus were lost to Western thinking until very recently.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008



PhilIt is often enlightening to see what moves the market. At various times in the business cycle the market responds differently. To look at this question a simple correlation study was performed using ETFs as surrogates for various macro variables.

Correlations with SPY for the last 95 days:

Oil USO 12%
Gold GLD -10
Bonds TLT -53
TNotes SHY -72
Euros FXE -15
Yen FXY -65
Fincls XLF 88

All of the above relationships are coincident and therefore not predictive. However it is interesting to note that gold is negatively correlated with stocks. On the other hand oil is positively correlated, which is somewhat unusual. The relationship between bonds and TNotes is negative and very strong. It seems that the recent fair weather in the stock market has been punctuated by recurring flight to quality squalls.

The dollar seems to be a significant factor as well. However it is notable that stocks are much more strongly impacted by the yen than the euro. Finally the financials are strongly and positively correlated with the market as one might expect.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008



Victor1) With a good heart I mention, regardless of whether one was long or short, but from the standpoint of the dispassionate observer, the Osbornian man from Mars, or the O'Brianesque or Ferberesque all-seeing eye, it was beautiful the way this holiday week ended. Completely the opposite of the Easter holiday as is natural, and with total fright of a repeat of the French Bank inside trades on Washington's birthday. The memory of the terrible beginning of the year, and predictions of the Palindrome and Sornette, and the weekly old timer, and what happened in the last June to July comes to mind. Who could have the courage over the weekend except those who trade all markets without commissions and make money 95% of all days by marketmaking to the public, and enjoying borrowing costs of less than 2.5%. It's a perfect recap of the year, and a warning that only the strong could possibly withstand giving the public a chance to lose so much more than they have any right to lose. And today's action was so similar to the meaningless Employment number of January 3. With a rise by a gnat's earlash preceded by a run of two grand terribles. Everything is designed to deceive, and prevent the weak among the public from capturing the full differential of 6% earnings return plus 6% growth, compared to 3% on Treasuries. There were so many beautiful touches. The four down opens this week following five up opens last week. the down 50 this week in S&P after up 40 the previous week. the fake decline of the ten year below 115 and then back up to 116, a situation repeated endlessly over the last months, but each time with gusto and real sincerity. And the weak closes on Thursday and Friday followed by down down to surprise, discombobulate and ruin the vacation of all those who like to fade it.

2) The sight of a commercial space on the southeast corner of Fifty-third and First, long ago the Mayfair restaurant, not rented out for five years on the grounds that rents will go up and they should wait, reminds me of the builder who doesn't work overtime to get the rents, and those who buy the two year but not the 10 year, on the idea that rates could go up. But how much do they have to go up over the subsequent eight years to equal the total return of the 10 year, and, similarly, how much do the 10 years have to go up 10 years hence to equal the 30 year? It's terrible to see.

Alan Millhone adds:

The second part got me to thinking about collecting rent after hours. Years back a renter offered me their rent in cash and I did not have a receipt book at hand. Later I told my Father of this and he told me to never go anywhere without a receipt book in the car. People will pay you at odd hours and always be ready to accept payment! Goes with the territory of having rentals. Nowadays I always have a rent receipt book in my home, car, truck, warehouse office, and never miss the opportunity if presented to take rent when presented.

Phil McDonnell extends:

PhilWould you buy a business to earn 6% a year?

The long term growth in stock prices seems to be a fairly consistent 6% per year over the long term. Of course it can be quite variable in the short run. However this is only part of the story. The earnings of a company with a PE of 16 represent about 6% return per year. Part of this is is kept as retained earnings and thus is already a primary constituent of the growth in share prices. But part is distributed as dividends. Historically this has averaged about 3% per year.

As Dimson, Marsh and Staunton and the earlier Fisher Lorie study demonstrated about half of the return to investors came from dividend reinvestment and half from simple price appreciation. On the face of it the math does not seem to work out. 6% from growth and 3% from dividends seems to be only a third from dividends not half. But the real story is that the growing dividend stream and the very significant benefits of dollar cost averaging work out to half of the return.

Dollar cost averaging is simply the idea that if we have a steady income stream from dividends, salary or bonds then we buy more shares at the market lows and fewer at the highs. Thus our overall share cost is below the average price of the market. The Dollar cost averaging effect really is a very effective timing tool that works. The only thing requires is a source of income.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008



PhilIn our garden we grow about two dozen different varieties of vegetables as well as strawberries and blueberries. Naturally all of this bounty makes for a tasty treat for the local critters. In particular the local rabbit population has clearly been eying our garden as a source of gourmet delights.

Recently I was watching a young spring rabbit running back and forth along a fence. He ran to one end ducked under and then ducked back and ran to the other end and ducked under and ducked back. Each of the holes under the fence would have been unnoticeable to my eye were I not watching him use them. Occasionally the rabbit would make slight improvements to the holes by digging them out a tiny bit more. The fascinating thing to me was that the rabbit was not just going back and forth to these holes but he was actually sprinting at full speed. Finally I realized he was practicing his escape routes. And at the same time he was fine tining his holes if he could not get through fast enough.

Traders, like rabbits, must be quick and nimble. Perhaps the metaphor extends beyond just speed. The rabbit can teach us that the smart trader must also have multiple entry holes and multiple exits to escape the many predators and deceptions in the market. When we decide on a trade perhaps the better one is one that allows entry over several periods and can be exited over several periods in the future.

In the same manner the rabbit can teach us the importance of practicing our escapes at speed. For the novice trader this can take the form of paper trading real time prior to the first trade. This might be good advice for a new system regardless of one's trading experience. No one ever lost money by paper trading. Even for experienced traders the idea of practice translates, at a minimum, into back testing our trades before we take them. The rabbit can teach us much.

Dr. McDonnell is the author of Optimal Portfolio Modeling, Wiley, 2008

Scott Brooks adds:

This is the "prey" side of the equation. The predator side of the equation is also worth looking at. It involves learning the habits and escape routes of its prey. My good friend Phil mentions how he wouldn't have noticed the escape routes had he not chanced upon the rabbit using them. Noticing things like this is the mark of a predator.

Even though Phil may not have noticed the escape routes if it weren't for the visual observation, predators in the wild rely on more than their eyes to spot the routes of their prey. Probably their most important sense is that of smell. We as humans can't really understand an animals sense of smell and just how acute it really is. But having spent thousands of hours in the woods, I've witnessed first hand just how incredible a wild animals sense of smell really is.

These escape routes that this rabbit is so meticulously putting together will very likely be his undoing. Coyotes and dogs often hunt in packs or small groups (I know my two dogs, Rex and Layla, hunt as a team and have ambushed many a squirrel, rabbit and chipmunk). These canines can smell the path that the rabbits have been using and know where they're likely to travel. All they have to do is set an ambush. One coyote chases the rabbit through hole A while the other coyote waits at hole B.

I've witnessed, in the wild, coyotes hunting as a pack, almost corralling a deer into an ambush. Once, I was up in a tree stand and saw some coyotes chasing a deer. I then heard a noise behind me and noticed that some coyotes were coming thru the woodlot in the opposite direction. The deer was running from the coyotes across a harvested corn field toward the apparent safety of my woodlot, not realizing that an ambush was waiting.

The coyotes thought they had the ultimate trap set up and were about to go "long" a deer. I was also long deer that day too. But the deer coming towards me was too small for my tastes, so I decided to let that deer live another day. I changed to a short position on the deer (I sold his life back to him and will buy it back in a few years), and went "long" coyotes.

I drew my bow back, took careful aim and "placed my trade" on the nearest coyote. The ultimate arbitrage…..the predator became the prey! This quickly displayed just how weak the coyotes position really was, as I was able to break apart their entire portfolio for the day with that one shot (er….ummmm……I mean trade). I profited well on that coyote trade. I'm still short that deer, but hopefully in a few years, I'll have the chance to "cover my short" on him!

I wonder how many examples of this there are of predators in the market……and how many examples of predators becoming prey there are? As Professor McDonnell points out people learn that they need to set escape points, to protect themselves. But in many cases, they only end up bringing about their own demise. For instance, so many people set stops at the round, rather than a few pennies above and then wonder why they get a fill so far below their stop, not even realizing that the masses were trying to use that same escape route. The smart predators sniffed it out in advance and capitalized on it.



Stock market indices have the same problem that chain calculations of the cost of living have, but the difficulty of assaying what is added, left in and taken out should not deter us from accepting the common sense observation that wealth increases in open and competitive societies. It does so even though governments are always clipping the coins we use each day. The positive drift of the market seems to me the inescapable consequence of the increase in savings that comes from people having the liberty to trade. Part of that liberty is the ability to pick up and move to another jurisdiction where the local authorities are lying and cheating less. That freedom to light out for the territory with one's money has been the genius of our Federal political system. Every time wiser heads decide that open capital markets for money are a bad thing (Nixon, Ford and Carter's successive attempts at exchange controls being the most recent example) the drift stops being positive. If one needed proof that otherwise very smart people can refuse to learn this central lesson of American history, I offer Paul Volcker's recent proposal that the solution to the world's banking problems is to have a single currency. Fortunately, Americans and others throughout the world still have the ability to let Gresham's law work. When the competition among sovereign monies is eliminated, there will soon be no real drift at all no matter what index we use.

Laurence Glazier notes:

At an options seminar I attended last week, it was pointed out that there is an artificial aspect to the upward drift, as when indexes are rebalanced, dogs are dropped and stars added. The implication was that in fact there is no directional drift, as the ever growing index does not remain the same index. I don't believe it, but I had never considered this point before…

Phil McDonnell responds:

In some sense this kind of study has already been done. There is a fairly popular strategy that tries to predict when a stock will join the S&P 500 index and which ones will be dropped. For the most part it is a mechanical exercise based on market cap. Just before stocks join they tend to rise. Before they are dropped they tend to fall. But as an afterthought the dropped stocks rebound after being dropped. In other words this effect does not account for the drift because it tends to cancel out.

Another way to look at it is from the Dimson, Marsh & Staunton study of all major countries and all listed stocks for the last 100 years or so. This study did not look at existing indices where a pre-joining bump might be in effect. Rather it compiled its own comprehensive index of all stocks.

For another perspective consider the cap weighted vs. equal weighted indices. Over the last 3 years the Wilshire 5000 cap weighted has underperformed the Wilshire 500 equal weighted by about 15%.

Vince Fulco adds:

As for the quant desks on the Street, traditionally they start pumping out research as early as Feb-March trying to game the upcoming Russell rebalances in June. 



 I am exploring the concept of circumnutation and tendril movements as a model of universal spiral movement in all parts of the plant world and markets, and found an article that is a good jumping-off point.  I would be interested in readers' ideas on the horizontal and vertical aspects to which markets cling and go around in clockwise and counterclockwise direction.

James Sogi replies:

After a tendril winds up high and breaks and falls on its own weight more than some percentage of its height, it might take a day or so of random waving around before it finds some support towards the end of the day and can try climbing back up.

One thing these tendrils that fall down to the ground in Hawaii do is if they touch the ground is they start to send out roots and morph into a new plant. A gardener can to take that new start and grow some new plants and reap some fruit. After January's big fall, the markets fallen tendril was able to grow some roots and some fruit into the spring.

Asparagus roots grow foliage, gather energy, and produce edible shoots, but after some production, run out of energy and need to recharge. Seems to be a common natural cycle.

Phil McDonnell adds:

PhilWe grow a vegetable garden with many diverse varieties. I am always amazed at the strategies different plants seem to use to survive. In particular the legumes seem to particularly favor circumnutation and tendrils. Most peas and beans are grown on some sort of support like a pole or trellis. For the really tall pole beans that grow to six feet I have learned to use their natural circumnutation to advantage.

One form of this is the tendency of the tendrils to wrap themselves around some convenient support. But there is another form of circumnutation this gardener has learned to use to advantage. It is well known that many plants turn themselves toward the sun: heliotropism. Clearly this is an adaptation to maximize their light gathering ability but it also allows them to compete with neighboring plants and potentially block them out. One aspect of this is that the stems bend toward the sun in the morning and tend to track it as the day proceeds finally bending to the west at night. Curiously at night the stem proceeds to bend back through what is nearly a full circle so as to face the rising sun in the morning. One can use this type of circumnutation to train the plant to wrap itself around a pole. Each day another wind on the pole will be added.

The smaller bush varieties of legumes tend to rely more on their tendrils. Effectively when they are planted densely the tendrils connect to the other plants and form a complex structure of multiple stems with cross connected supports from the tendrils. Together the complex is stronger than the individual parts.

When the market is in an uptrend it seems to spiral around its basic trend channel. Clearly this resembles the helix like structure of circumnutation. One is struck by the similarity to other similar patterns. For example in a fluid flow in a cylinder there is a natural tendency to spiral inside the fluid channel. This behavior is predicted by the differential equations which describe this process. In a similar analog the Earth Moon system causes the Moon to describe a helical structure as the system orbits the Sun. One wonders if there is a common model which underlies all of these processes.

Scott Brooks expands on this theme:

ScottThis applies to what we do on my farm as well. Every year, I plant food plots for the deer, turkey and other wild game. In our warm season plots, I want a variety of plants to grow that complement and "assist" each other. For instance, I like to mix together creeping soybeans, cow peas, lablab and other creeping growth plants that create tons (literally tons) of forage on their own per acre. But if I sow into the mix a moderate amount of corn, milo, sudan grass, or other such "stalky" plants, it greatly increases the amount creeping forage that grows!

These stalks greatly increase the ability of the creeping plants to circumnutate around the stalks. If you put enough of the stalk type plants in the mix and they are close enough, you can actually see where the vines jump from stalk to stalk creating bridges. Other plants then "hitch hike" across these bridges, growing the diameter and strength of the bridge. Vines then grow up from the ground into these bridges.

After a rain storm, especially one with wind, some of these tendrils will break away from the bridges or stalks and fall to the ground. Fear not, for other plants will use those fallen tendrils to climb to the sun. One tendrils misfortune is 10 other tendrils opportunity!

As weeds move in, the weak tendrils are killed off. But the strong ones push ever higher to fight for ever elusive sun light. This growth has an interesting effect on the ecosystem. The new growth is tender and succulent with a lignin content (lignin is the woody/stalky back bone inside plants that is not easily digestible). These tendrils are tasty morsels for the local wildlife.

Deer especially like to move in and eat these succulent tendrils. Conversely, deer also love to eat new growth on the weeds that the tendrils are competing with (actually weeds are one of the main food sources for deer). Deer also the thick cover that the tendrils, weeds and stalk plants provide. Being genetically programmed to conserve energy, deer will eat their fill and likely just bed down in the thick mess.

This bedding down and walking around thru the food plot causes the weeds to get smashed down and the tendrils to be broken and driven to the ground. This allows new tendrils to hitch hike up the old broken tendrils and allows the the tendrils that weren't broken to grow even more.

As the summer progress, the ligin content increases in the plants as they near the end of their lives. This is when they really start to bear seeds. Many of these seeds fall to the ground to lay dormant until the conditions are right for them to bloom. Some of these seeds are eaten by birds. Many times these seeds pass thru the digestive tracts of these birds still intact to be "deposited" elsewhere, laying dormant until the time is right for them to make an attempt to grow.

Then, just when the creeping plants are nearing the end of their useful life, the stalk plants begin to bear fruit. The "seeds" of these plants are full of life giving energy in the form of carbohydrates. The corn or milo seed or the seed at the top of sudan grass (which looks like a really tall version of milo…..fyi: sudan grass is also referred to as grain sorghum) is available for the wildlife during the hardest part of the winter when other main food sources are no longer available. Throughout the spring, summer and fall, the deer have built up their fat reserves by eating a lot of food high in protein, but to survive through the winter, they need lots of energy, especially to build up their depleted reserves from the fall rutting/breeding season.

As you can clearly see, the tendrils and their tendency to circumnuate around the stalk plants play a very important role in the overall synergy of the ecosystem.

As I've watched my food plots grow, it's hard not to notice the connections between them and the markets and economy. I see staunch stalky plants and they remind me of the big blue chip companies. They provide the back bone upon which the economy is built. But they are now more important than the smaller companies which account for the majority of the economy employment. These smaller companies grow around and circumnuate around the stalks of the big companies.

Recessions and market corrections come in and damage or destroy some of those companies and push them downward. But their misfortune or stumble is the gain of 10 other companies as they come in snatch up the lost employees, assets, infrastructure or ideas.

The strong companies grow in the midst of this dangerous highly competitive environment. Some are beaten by weeds. Weeds especially become a problem once a plot has established itself and is successful. Bad businessmen, dishonest businessmen, and less competent businessmen spring up on this fertile ground and environment, trying to hitch hike on the backs of the stalks and gain prominence/market share on the bridges that were created by the honest smaller companies.

But ultimately, these smaller creeping companies and larger stalky companies really only serve one purpose: To feed the consumer. Like the deer, consumers move in do business with (eat) the products of the small and larger companies, including the weeds. When a consumer finds a comfortable food plot they usually bed down there (i.e. use their products regularly and become a frequent shopper of the company).

But ultimately, things change. As companies grow, they become more rigid and less flexible in their ability to adapt changing environmental conditions. Their lignin content (rigidity) becomes such that they are no longer growing.

However, in business and the markets there really is no season stagnation. Sure, sectors and whole asset classes may lay dormant for a decade or more, but something somewhere is always flourishing and growing.

Out there, at all times, there is no "winter" in the markets and economy. Sure, we can have periods of time like 1968 - 1982 or 1929 - 1950 which sure seemed like winter in the markets and economy. But the reality is that somewhere, at all times, there is a tendril forming and growing. It is circumnutating it's way up some stalk of some established company or idea. But it doesn't even have to have a blue chip company to grow around! That's the beauty of capitalism!



VN.pngIn considering the phases of the moon I found the following two passages partially illuminating:

"Considering the moon as a circular disk, the ratio of the area illuminated by direct sunlight to its total area is the fraction of the moon's surface illuminated; multipled by 100 it is the percentage illuminated. At New Moon the percent illuminated is 0; at First and Last Quarters it is 50%; and at Full Moon it is 100%." Source

"When a sphere is illuminated on one hemisphere and viewed from a different angle, the portion of the illuminated area that is visible will have a two dimensional shape defined by the intersection of an ellipse and circle where the major axis of the ellipse coincides with a diameter of the circle; if the half ellipse is convex with respect to the half circle then the shape will be gibbous, bulging outwards, whereas if the half ellipse is concave with respect to the half circle then the shape will be a crescent." Source

The explanations made me start thinking of the angle of incidence and the angle of reflection, and the % of the time that a market is above zero and below zero, and other concepts engendered by the phases of the moon.

I wonder what other ideas about markets are generated by considerations such as the above. Also, a layman's proof of the ellipse/circle statement might be helpful to speculation.

Jim Sogi writes:

Speaking of moon effects, there is one of Vic and Laurel's classic penumbras forming off the 1400 round in S&P off this recent high.

Also, the other image I got is the kids playing jump rope with a kid at each end spinning the line around. There is a definite drift to the spin in one direction, but one can play the spin until tapping out or whatever they call it. Its definitely tradeable though may or may not show on radar of a fixed wavelength. The game is different at the bottoms than at the tops for sure. Do kids play this game anymore?

Andrew Moe adds:

Reminds me of earnings season. First Alcoa, then a handful of others. The first sliver of the waxing moon. As the days pass, the number of companies reporting earnings steadily increases until a full globe of information illuminates the markets. Hunting/gathering levels peak just as the flow of earnings begins to dissipate. Next comes the struggle for survival on diminishing resources as the moon wanes into oblivion. Only the strong will survive the dark days until the cycle begins anew.

Phil McDonnell enlightens:

Phil.pngThe side of the Moon facing the Sun always looks like a circle from the perspective of an observer on the Sun. The simplest way to understand how the shape of the visible lighted portion of the Moon changes is to view the circle of light as a rotating circle. It is well known in mathematics that a circle rotating on a North South axis will appear as an ellipse in general. It will only be a true circle when viewed full on.

From the perspective of a viewer on Earth the circle of light on the Moon is rotating once every 29.5 days. So from the Earth perspective the line between day and night on the Moon will generally be visible as an ellipse because it represents the edge of our rotating circle of light. However the lighted edge of the Moon is still circular at all times so the edge of the lighted portion will always be described by a circle. Thus the combination of one edge bounded by a true circle and the day-night line of demarcation by an ellipse will always be true.

To understand the convex concave claim we need only consider the ellipse formed by our North South rotating circle. When the Moon is exactly half full corresponds to when the circle is rotated by 90 degrees and faces us edge on. At this time the ellipse appears to collapse to a straight line because the circle is edge on. This is what happens at first quarter and last quarter Moons. During the Gibbous phase the ellipse will appear convex from Earth because what we are seeing is the convex portion of the lighted ellipse. Similarly during the crescent phase (either waxing or waining) the visible ellipse will be the concave portion of the lighted circle.



How many things are there in baseball, the swings, the runs, the cycles, the signals, the deception, the consistencies, the standings, the All Stars, et. al., that are directly relevant to trading and could make us better?

Allen Gillespie replies:

I grew up a Braves fan and unfortunately after watching them win many pennants but only one World Series over a decade of dominance I can say this: there is a significant difference between championship teams and good regular season teams.

This is andedotal, but their one champion team I think won close to 30 games in the 9th innings. Spec lesson: never give up, keep it tight, and focus in the clutch. 2) The Braves have always had deep pitching — which helps in the regular season, but in the playoffs things change as teams shorten rotations, and so the Braves were always a bat or two short in the playoffs. Spec lesson: play the late months (Nov, Dec) — more offensively than the long season months. 3) Champions, even when they loose during the season, rarely get blown out because there is too much pride. While I have never been a Yankees fan, Derek Jeater did earn my appreciation when I saw him in some meaningless game during the season hustle to catach a fly while crashing into the stands on the 3rd base line and busting up his face a little to make an out.

Tim Melvin expatiates:

First, it is a long season. Although you have to play to win every day, no team ever has. Winning 100 out of 162 is considered a mark of greatness. A trader who wins 60% of the time day in and day out will probably also reach greatness. There will be losing days in the market as well. Shrug them off and learn from them. There is another game tomorrow.

Swing for the hits. the home runs will happen on their own. Sluggers who routinely swing for the fences every at bat may hot a lot of home runs. they will strike out a lot as well. Good hitters look to make solid contact knowing that the home runs will come when the conditions are right. a fastball inside or a curve hanging out over the plate. The major concern is to put the ball in play and advance the runners. In trading the objective should be to make good trades. the home runs will happen on their own when the conditions are right.

Focus when the play starts.
Baseball players seem to stand idly around between pitches. But watch how they focus once the pitcher steps on the rubber. Once you hit they key to enter the order, it is time to pay attention.

Situation matters. It is okay to steal second in the third with no outs and no score. In the 8th with the game tied and two outs it is usually not such a great idea to waste the potential winning run. A bunt early in the game with the bases empty and a three run lead does not make a lot of sense either. But in the ninth with a runner on first, no out, a tie and the top of the order coming up, its time to lay one down. If the markets is making new lows several days in a row, it might make sense to buy big on the long side. if it has been making new highs, maybe not so much.

Sometimes you just don't want to pitch to the guy. If a power hitter is up, a base is open and the game is on the line, it might make sense to just walk home and face a less powerful hitter. Sometimes, the small loss is the best one if it appears powerful forces could cause your trade to go strongly against you.

The game is not over until the last out.
Keep playing. baseball is riff with stories of 5 run comebacks in the ninth. So is trading. Stay focused and look for the chance to rally.

Defense matters. Ask the Texas rangers. You can play powerful offense but if your pitching and defense callow the opponents cheap runs, it is hard to be a winner. If you have large winners combined with large losses all the time, it is tough to win over time.Not every team will win the World Series. Only one will. But a winning record and playoff appearances fill the seats with fans. Not everyone can be the best trader at every time, but you can be a winning trader.

If you can steal the other teams signals, or just figure them out, you have an advantage. In the market you can gain one by being aware of what large successful traders and investors are doing. Thanks to COT reports and sec filings, it is easier for investors than ballplayers!

What position are you playing and what is your role? Pitchers and catchers are involved on each and every play. Fielders have to watch every play but are only involved when the ball is hit their way. The designated Hitter is only involved three to five times a game at most. Short term day traders are in every minute of every day. Macro oriented traders only when the markets move towards their entry points. Longer term investors only when conditions are exactly correct for entry. Knowing what you are trying to achieve and what style fits your strengths can be critical to your success.

It takes more than one person. Ask Barry Bonds or Nolan Ryan. you can be the best ever at your position but if the team around stinks it will be hard to succeed. in trading I think this goes beyond just the coworkers and analysts you might work with and take advice form. our team is those people we surround us with, bounce ideas off of, celebrate wins and suffer losses with at the end of the day. Our team is our family, friends and confidants. I do not think anyone can be successful without having the strong network of friends with them along the way. It is like being a pitcher with no team. you cannot just be good, you have to perfect as any ball put in play is a run. Pretty damn lonely even if such a perfect person were to exist.

Be ready when called on. Recently jay payton of the baltimore orioles went 5 for 5 as a late inning pinch hitter. it is a big reason the Birds are winning right now. Same with the bullpen. Even when market conditions are not right for your type of trading, stay sharp and focused. You never know when a late inning rally puts you in a position to come off the bench and drive in the game winner. Markets and games can change in the blink of a surprise fed announcement or a three run homer. be ready.

There is more to life than baseball. You must practice your skills, study your opponents and work hard. But it helps to be able to relax away from the game and enjoy other endeavors as well. Same with the markets. Study learn, anticipate, but take the timeout for books, music, friends, family and all the other things that actually make life so damn good. Maybe even take in a baseball game once in awhile…

Dean David adds:

In hitting it is important to let the pitch thrown determine what type of swing you offer. As an example, Rudy Jaramillo teaches hitters to try to hit outside pitches to the "opposite" field. Frequently this approach results in a firmly struck single, where attempting to "pull" an outside pitch will result in a weak grounder the opposite way or a "pop up". It appears that this is lost art early in the season as many hitters look to bolster power numbers by pulling every pitch without regard to its location. Strategy for the pitching coach would be to pitch away to hitters that have yet to demonstrate a willingness to "go with the pitch". This is an addendum to Mr. Melvin's comments about the importance of singles.

Jeff Watson comments:

In baseball, one must always be on the lookout for a pitcher who throws a spitball, a 3rd base coach who steals signals, and a batter who uses a corked bat. In trading, one must look for the same type of behavior.

Alston Mabry notices:

Some similarities between baseball and markets:

Random events are interpreted as meaningful: "Have you noticed how many times you see the guy who made the last out on defense be the next guy up at the plate?"

Talking heads use meaningless stats to produce commentary: "Rodriguez is hitting .243 for the season, but on the road against left-handed pitching, he's only .218."

Lots of fresh data produced every day.

Quants taking innovative approaches see some success, e.g., Boston.

People will, in fact, cheat to get ahead.

The public gets tapped to support the infrastructure.

In the long run, the better teams steadily increase their lead over the poorer teams. In the short run, e.g., the playoffs, "anything can happen."

Phil McDonnell writes:

Some years ago I coached my son and daughter' teams in baseball and softball respectively. In particular one phenomenon noted was that there was a king of the hill effect. We recall that king of the hill is the game where one kid stands on the top of the hill and all the others gang up to bring him down. Then a new king emerges and the gang has a new target. Needless to say no one ever remains king for long.

In my Little League days the effect was the same. Aware of the king effect our team somehow managed to lose every single pre-season game in every year I coached. Naturally one took the opportunity to mention it to every other coach in the League.

On opening day we made slight adjustments to the line-up. Somehow we managed to win 7 out of the first 8 games - yes, every single year. We played about 16 to 18 games each year so that was about the mid point of the season. Usually about then people started to try to figure out the standings. At that point the season got a lot tougher. Coaches would know that we were on top and invariably we would only see the best pitcher on each team. The only advantage that our team had in the latter part of the season was that my batting order simulation model was getting smarter because it had more statistics on our players as the season went on. My estimate was that the model gave us about a 1 to 2 run edge in every game and the average number of runs was about 6 so this was considerable. Still somehow we managed to come in first or second every year, but the headwind from the king of the hill effect made it much more difficult.

The parallels with the top trader or money manager each year are profound. When a manger is on top two things happen. First his style or technique becomes reverse engineered and his trading space become more crowded. Secondly the king of the hill effect is at least as strong in trading as in baseball. If one was number one last year then literally everyone else is out to get you. Literally the other managers and traders cannot afford to let anyone stay at number one too long. They would lose all of their accounts to the top trade. So they have no choice but to gang up to survive.



PhilMost of us are aware of the benefits of portfolio diversification. The simple fact is that it pays to have diversified positions in different industries, countries and even diverse markets. The key to it all is to look at the correlation between the various components of the portfolio.

However there is another kind of risk that many investors are exposed to. It can be fairly assumed that the vast majority of investors are exposed to this single risk in all of their positions. Simply put it is the risk of default if your broker goes under.

There are two ways to defend against this risk. One is to assess the broker's financial position personally. In particular look at how leveraged the broker is. As a rough check one can simply look at the stock chart of the broker if they are publicly traded. If the stock has been tanking faster than the industry it is a clear red flag.

Secondly the investor can identify multiple brokers who appear sound. But even then it makes sense to diversify using multiple accounts with two or more brokers. Remember if a broker shuts down losing half your money is a whole lot better than losing it all. You can still come back.

None of this discussion is meant to assert that the SIPC, FDIC and the many other protective agencies cannot perform on their guarantees for investor safety. Probably they can. But in the eventuality that a decent sized firm goes down, the process to sort the mess will undoubtedly takes months or years. After all it is the government at work. At best you might get all your money back but a very long time from now. Certainly you will miss any buying opportunity which develops from this crisis.

George Parkanyi remarks:

That’s why I pay a little extra commission to deal with a Canadian big five bank’s discount brokerage and not, say, E-Trade (at least not in this environment).

Another point to add is that certain types of accounts are segregated. Registered accounts for example are held in trust, so if your broker goes under, the assets in those accounts are yours. They can’t be touched. It’s margin, short, and option accounts where you have the risk — because the assets are commingled with the firm’s. I believed cash accounts are also segregated.

But if your boutique broker does go bust, it may take a while to sort out the mess and be able to access your segregated accounts, so it’s still a good idea to either steer clear or diversify brokers, as Dr. McDonnell recommends.

J.T. Holley writes:

With FDIC the thing no one realizes is that you are only getting your principle back!  They don't care that you bought a 5 yr CD in 2005 that was yielding 5.5%!  Now get to the back of the line and wait for your $100k!

I had a wonderful lady who happened to be my client back in '01-'03 who passed at the age of 98.  She was risky as heck w/ her "discretionary" money, but her fixed income side of the portfolio was rock-solid.  I once was assisting her with her 1099s for the tax season and noticed that she had 15 $100k CDs at 15 different banks in the area.  I asked her why.  She said that was the limit at each and she didn't want to go through "it again". I asked her to explain and she said she had her money taken in the Great Depression before FDIC at the age of 22. According to her, the only way to properly have your money diversified is as Dr. McDonnell explained! 



BearsHedgefund monitoring service Greenwich Alternative Investments reports 58% bears on the S&P, 58% bears on the dollar and 67% bears on the 10 yr T-Notes. Sentiment is overwhelmingly negative.

Nigel Davies replies:

Seems odd that these learned gentlemen would be so bearish on both the dollar and the S&P. I would have thought there'd come a point at which a weak dollar would start to get good for exports.

Jim Joyce writes:

Sentiment stats must be tested. One can't just glibly assume they are contrarian indicators.

Victor Niederhoffer remarks:

The key to this market was when Abbey Cohen refrained from making any more bullish forecasts and it was accepted that we were in bear market by Goldman itself.

Stefan Jovanovich explains:

Measures of the current cycle need to include adjustments for the change in the value of the dollar. If those changes are included, the S&P 500 at 1374.9 is still down roughly 25% from its 12-month high on May 29th of last year and down 7% from its 3-month high at year-end. One could argue that the "bear" market is still intact — given that the S&P 500 adjusted from the value of the dollar is down 60% from its high on August 30, 2000 and up only 17.3% from its low on March 3, 2003. Comparisons with 1938 seem appropriate when looked at with this particular historical lens.

Nigel Davies agrees:

It is helpful to consider the value of assets relative to other assets rather than just the dollar. The dollar is by no means a fixed entity, though when one talks about 'bottoms' or 'tops' in assets like stocks or gold, there's an implicit assumption that it is.

J.T. Holley replies:

The dark clouds cover only the Big Apple. The dark and dirty forecasts are associated with NYC. My assumption is cutbacks, losses, write-offs, and a slowing beat of the heart of the financial world. Outside NYC, in beautiful Brentwood, TN where the buds are blooming, daffodils sprinkle the green fields, and opportunity is much appreciated, I'm as bullish as ever. It seems that far and few are remembering the drift, that bear markets exist only by looking at the rearview mirror, while one is driving forward utilizing the windshield to block the bugs and grit. 

Kim Zussman reports:

Yesterday was third highest first day of month in 14 years (SPY c-c). Those >3% gain were, on average, followed by gains the rest of that month:FDOM



PhilWhat's a good working definition of standard error?

Suppose you had a regression model:

SPY = .30 * FXY - .0015

where SPY is the percent change in SPY and FXY is percent change yesterday in FXY (yen ETF). Then you backtest your model on the last 100 days of data. Each day you get a prediction and you already know the actual. The difference between the predicted and actual is called the residual or error. Take the standard deviation of all the errors — that is your standard error.

The interpretation of standard error is straightforward as well. If we make all the usual assumptions about normal distribution etc., then the standard error is a plus or minus confidence band around our prediction. Today FXY is up 1.20%, so our prediction from the model would be for SPY to be up tomorrow by .21%. This is an actual fitted model with a standard error of 1.4%.

So we can think of the prediction as the center or mean of our distribution of prices tomorrow. The standard error is the variability around that mean. Assuming normally distributed errors we would expect two thirds of our observations to fall within one standard deviation of the prediction. One sixth would be greater than prediction plus one standard error and one sixth would be less than predicted minus one standard error. In the same manner 95% should lie within two standard errors of the prediction.



PhilMost of us spend our whole lives trying to predict the direction of the market. Usually the results are, at best, only moderately successful. Market direction is the most difficult to predict, but as has been discussed here volatility is sometimes easier to predict because there is demonstrably more serial correlation.

Assuming we had both a direction and change in volatility model how would it be used? One way is to look at the cases. They are:

direction volatility

up        up

up        down

down     up

down     down

We could use simple option strategies depending on our outlook for volatility. When vol is expected to rise we would want to be long options. When vol is expected to fall we would want to sell options.

The choice of the option then is controlled by our expected directional outlook. The following table covers the cases:

direction volatility strategy

up         up         Buy call

up        down      Sell Put

down     up         Buy Put

down    down     Sell Call

It is worth noting, in passing, that direction and vol are generally negatively correlated. This implies that the cases up/up and down/down are relatively rare. The other two cases will occur more often.

Perhaps others can suggest different strategies within this framework.

Steve Bal responds:

I would suggest that volatility and direction are both a matter of time. One is obviously a matter of timing the market (as there may not be another time for a top/bottom) and volatility is a matter of time - time that it takes to revert to the mean.

A different strategy may be playing the time frame of volatility. In this exercise on would try to time the daily volatility within the context of weekly/monthly volatility of time.

In this scenario if your daily timing of volatility is wrong there is the possibility that time may be on your side over the longer term. I would never suggest relying on hope (of the longer term) but to reduce/hedge positions if volatility is not on your side.



 Another way to look at changes in volatility (increase/decrease in price swings per unit time) is to view it as having a typical overall move in price but with the time scale expanding and contracting. This might offer some additional insights, for example point and figure charts by ignoring time also ignore volatility. So if you test these they'll produce a quite different set of patterns which are essentially 'volatility blind.'

Sushil Kedia replies:

A filtration process such as Point & Figure is essentially based on defining one's tolerance for noise in the price series. The box size achieves that. Point & Figure is the only method in Technical Analysis to not plot the time axis. It is perhaps the only method of looking at prices which has a nature similar to the time/distance equivalence in Einsteinian physics. There perhaps is a case for finding a congruence in defining a unit of time for a particular security based on a certain standard movement in its price.

Bill Rafter explains:

Point & Figure purports to separate "signal" from "noise" and discard the latter. Every time you run data through a filter, you eliminate both some signal and some noise.  The short-term data that you assume to be all noise contain substantial signal.  Thus, it is illogical to assume that you will improve results by discarding information.  Further, and most-importantly, we applied Point & Figure filtering to all of our trading methodologies and the degradation of results was universal.

Phil McDonnell expands:

PhilThe Grand Master poses an interesting question and Mr. Kedia wisely suggested an analogy to Einstein's relativity theory. There is much to be learned from these ideas.

Suppose we have two assets which substitute for each other in investment portfolios. For example they might be stocks (s) and bonds (b). Given that there is only a constant supply of funds (m) available for these two investments we can posit that their combined value would be equal to:

m ~= ( s^2 + b^2 )^.5

The above is essentially the equation of a circle of radius m. One possible flaw is that the market for s may be much smaller than the market for b. Thus a given fixed disinvestment from b might move s by considerably more. Our model would therefore no longer be a circle. Without loss of generality we can assume the fixed quantity m to be 1 (100% of all the money). Then we have:

1 = ( s^2 / a^2 + b^2 / d^2 )^.5

where a and d are two constants of proportionality which relate to how quickly the two markets move. Thus each market now has its own ease of movement parameter in this new elliptical model.

One of the key properties of the theory of relativity is that as one approaches the speed of light both time and space are distorted. In particular the Lorentz transformation governs this process and is given by:

gamma = 1 / ( 1 - v^2 / c^2 ) ^ .5

where v is the velocity of the spaceship and c is the speed of light. This represents the transformation in the x direction which we shall assume is the direction of acceleration. Referring back to the elliptical model formula above we see that the one dimensional form looks remarkably similar to the denominator of the Lorentz transform (gamma).

Qualitatively such a model would be consistent with the Davies/Kedia conjectures. Time would slow down as the market moved faster. Magnitude in the price direction would dilate as well as a function of velocity.

Having a theoretical model of the market is all very nice but unless the market follows it then it is useless. To test this a study was done of the above stock to bond relationship using SPY and TLT ETFs. Fitting the parameters a and d to past data one finds that the constants were 200 and 100 respectively. Then the fitted model was compared to the actual past history of SPY and TLT and found to provide very good agreement. Perhaps we may look at these constants as the speed of financial information (light) in the stock and bond medium respectively.



PhilWhen taking a bath one of the better ways to circulate newly added hot water is to swirl the water in the tub. One hand paddles backwards, the other goes forward and a giant wave soon circles the tub. At any given moment there is a limited volume of water in the tub. The goal is to circulate the water around to each point in the tub equally.

Our friend Sushil Kedia has posited that the Indian market leads the US market. Perhaps this is an application of the bathtub theory. To test this we might wish to look at simple correlations between INP, the Indian ETF, and SPY, the S&P 500 ETF. If in fact the Indian market leads we would expect that there wold be significant correlations at some lag. Following are the recent correlations of the INP with SPY at various lags:


Notably, five of six are strongly negative and the sixth is insignificant at 1.2%. So it would seem that the globe is a giant bathtub and that water does slosh around it in a non random manner. But if there is correlation then we know that a predictive regression model can be developed.

Using only lags 1, 3, 4, 5 & 6, our regression model has an overall correlation of 24.9%. All of the coefficients are negative, mostly in the neighborhood of -.04. The regression constant is -.001, reflecting the recent weak market behavior. Effectively what this says is that when money flows out of the Indian market it tends to go into the US market over the next six days or so. The converse is valid as well, in that if money goes into the Indian market it will come out of the US in the next six days. In any event the bathtub theory works reasonably well for the Indian - US markets, with India leading.



The Bath Tub Theory is based on the premise that there is a fixed amount of water (money) in the tub at any given time. In the earlier piece we saw that money flowed from Indian stocks to the US with about a 5 to 6 day lead time. However at the core of any reasonable such theory the money must be returned. Just as the wave travels to one side of the tub it must also swirl back to the first side as well because the amount of water is relatively constant for any short period of time. So does the theory hold up and allow the money to return from the US to India?

To answer this question we only need to look at correlations between SPY and INP but, this time, with the SPY etf leading. Following are the correlations between the two with a lag of 5 and 6 days.

lag correlation
5 -10%
6 -18

A regression of the two lagged variables shows an overall correlation of 22%. We also note that the correlations and their respective regression coefficients are negative. This indicates that money flows out of the US and into India as expected by the Bath Tub Theory. For what it is worth the prediction for INP on Monday is a loss of about 1%. But be careful. The standard error for the model is about 3.9% so the prediction is well within one standard error. Over the last 95 days the chances of a successful prediction have been about 58% so its a little bit better than a coin flip.



KimIn checking historical US stock returns, the probability of loss declines as the holding period increases. Twenty years is commonly touted as safe, but there have only been 4 such (non-overlapping) periods since the Depression so it's hard to feel secure.

(There is also the problem of whether this came by "luck": e.g., look what happened to German and Japanese markets when they lost WWII)

There were four non-overlapping 238 month (2 short of 20 years) periods in DJIA monthly returns 1928-2008. The compounded return of these (w/o div) shows only one which was down (with ending dates):

Date 20Y cpfactor
2/1/2008    5.994
4/4/1988    2.264
6/3/1968    4.941
8/2/1948    0.721

(Dividends formerly a bigger part of total return, so exclusion under-estimates final compounded return)

Randomly re-ordering the same empirical monthly returns into 100 simulated 80 year series, I calculated compounded 238 month returns and checked for up and down periods. Of the 400 simulated 238 month periods, 47/400 were declines (12%). This is about half as often as actually occurred, suggesting that the negative market momentum around the Depression may not have occurred as result of random ordering of monthly returns.

Kevin Bryant counters:

In the grand span of economic history, 100 years of stock market data is barely a drop in the bucket. This is why I derive little comfort from this kind of analysis particularly during the current period which is quickly proving to be well outside normative experience.

Kim Zussman replies:

Just because long-term stock returns are positive, it doesn't mean they continue into the future, but begs the question whether there are better indicators than history. And a related but very different question is the feasibility of deploying insights/leverage to beat buy-and-hold without increased risk of ruin.

That 3 out of 4 twenty year periods in stocks since 1928 were up should make young people with 401K's feel better, but seems dangerously irrelevant for day traders using leverage.

Riz Din adds:

'In checking historical US stock returns, the probability of loss declines as the holding period increases.' - Kim

My favourite chart to illustrate this important point is Figure 76 in Chapter 7 of the Barclay's Equity-Gilt Study. Limited observations, international examples, and changing times provide good reason to be cautious, but it is all to easy to get lost in the month-to-month or year-to-year volatility and lose track of the extent to which downside risk (negative real returns) have rapidly disappeared over time. Indeed, when looking at the UK data (1899-2005) the study finds that 'For holding periods of five years or longer, the incidence of losses greater than 5% or 10% is the same for equities and gilts.'
Over the long haul, the real returns to UK assets have been 5.3% for equities, 1.1% for gilts and 1.0% for cash. For the US since 1925, the numbers are 7.1%, 2.3% and 0.7% respectively.

To quote Christopher Walken in Wedding Crashers: "We have no way of knowing what lays ahead for us in the future. All we can do is use the information at hand to make the best decision possible."

Phil McDonnell writes:

PhilThere can be no guarantee that history will repeat.

Those words, in one form or another, are found in virtually every prospectus ever offered by the financial industry. The main reason is that they are true. There really is no guarantee. But to the speculator the real question is how should one bet?

The converse of the history repeats proposition is that it does not repeat. Should one bet on something that has never happened before? Clearly betting on something which has happened frequently in the past is the better choice than something which has not happened. The best of all worlds is to combine a frequentist approach based on counting, tempered with a modicum of judgment and reason based on any changes in the contemporaneous financial landscape.

J.T. Holley comments:

I couldn't agree more, the art w/ the science. I've often thought in reference to Monsieur Le Cygne Noir why one would bet with such conviction on Sisyphus not to roll the rock up the hill, but furthermore that the rock wouldn't come right back down for ole' Sis to push it back up again? It wouldn't take me too long watchin' that rock n roll to place a bet, I'd be there taken the scrapes from those that thought otherwise as well, but not denying them their fair attempt.

Jim Sogi concludes:

The proper questions to ask are: How are things changing, and how does the trading strategy need to evolve to adapt. A dogmatic approach will not lead to good analysis and will lead to mistakes. Things are changing from the 2003-06 regime.

1. Volatility is up.
2. Global influences are greater
3. Governmental influences are increasing.
4. The industry is consolidating and shifting to electronic.

Time series sample selection in data becomes more important since last year. The idea of regimes being helpful in cycle analysis.



PhilDespite the much publicized efforts of the Fed to make the world safe for banking again there are still signs of a lack of liquidity in the system. In particular the net free reserves in the banking system are about $1 trillion. This sounds like a lot of money but it is not. Despite all the efforts of the Fed the net free reserves have fallen at a rate of -113% over the last 13 weeks. How a positive number can fall 113% and still remain positive is beyond my ken, but perhaps it is some sort of annualized rate.

The other notable thing is that M1 has been fidgeting around the zero growth rate line for something like 18 to 24 months. It is now joined by M2. Ultimately the cause of the current debacle lies with the Fed which has been snugging the money supply for too long. Details are at Wsj.com .

We are all bottom fishers. We are all waiting for the markets to put in a bottom. If we are investors in real estate funded by a 30 year mortgage we are looking for the bottom in the interest rate cycle before we lock in our 30 year commitment. We all play that game. As the Fed continues to ease and lower rates we are all on the sidelines waiting for that bottom. But few are borrowing and it is the act of borrowing that creates money.

The Fed needs to make its moves quickly and boldly and then make it clear that they are done. Only then will people believe that the bottom is in and it is safe to get off the pot and lock in a 30 year loan.




"…Left off the balance sheet is the value of the asset that Gary Becker, Nobel Laureate in Economics, calls human capital. Professor Becker says that the skills and experience of our people are worth more than half a million dollars per person. By this calculation, traditional assets comprise less than 25 percent of the national balance sheet, which means that true U.S. assets exceed $180 trillion…" Mike Milken

I haven't had my morning coffee yet but here's an attempt at arithmetic along Beckerian lines:

A recent FT article put Japanese assets at 75% of its GDP. Pulling a totally random number out of nowhere, if the return on assets is 5%,

GDP = 5%*(total assets)

GDP = 5%*75%*GDP+5%*humancapital

19.25*GDP = humancapital

Looking at World Bank statistics

World GDP 2006 = 48.2 trillion

World financial assets = 170 trillion

Return on assets = 5% (can someone give me a better number?)

Return on financial assets = 8.5 trillion

Return on human capital = 39.7 trillion

Human capital = 794 trillion

population = 6.6 billion

Average human capital per capita (hheh..) = 120303.3

Anyway, very rough calculations with numbers plucked from the ether, but the order of magnitude at least is in line with Prof. Becker.

(Also, I left out something important - GDP is not just a return on capital (even human capital) because human ingenuity produces excess profits and increases the value of the capital. So you'd have to adjust the calculation of human capital to account for growing return. And risk adjustments. Etc. etc.)

Any real macro economists care to point me in the direction of more accuracy?

Phil McDonnell replies:

Rather than pulling an imaginary growth rate out of our … armpit, perhaps a better approach can be found. GDP is the goods and services produced by a society. However much of that is consumed as well. The number we are really seeking is the net 'profit' figure that can be carried forward into the next year. It is good to remember that any 'profit' carried over must be held in the form of an asset. Thus a reasonable measure of the rate of return would be the net increase in total assets year over year.

Yishen Kuik counters:

Maybe I'm too classical, but I've always thought that GDP is a flow measure of all the goods and services we produce, of which one portion is consumed to give us present utility and the remaining portion is invested to enhance our ability to increase GDP in the next period.

Presumably all goods have some aspect of both utility and investment ("school is fun and you learn something" or "bridges are beautiful and enable transportation"), but we can think of the investment portion of GDP as flowing into a stock of accumulated capital.

The stock of capital deteriorates over time, so some of that flow is just running to keep still. Part of the stock is human, part of it is physical plant, and part of it is institutional arrangement of society (courts, laws etc). The dollar figure we attach to capital stock is just a very rough attempt at measurement, and doesn't take into account the importance of having the right arrangement of the 3 kinds of capital stock. The right arrangement catalyzes a $100mm investment to return 15%, while the wrong arrangement will have no such catalyzing effect. That is why $100mm produces such different results when invested in America versus Africa.

I think it is this accumulated capital stock (human/physical/institutional) which is the right place to discuss big picture returns on investment. Unfortunately much of it is unquantified and unquantifiable.

Adi Schnytzer brings up the stock market aspect:

Surely the real issue here is that, however, we define GDP, it's notoriously unpredictable? After all, why has the market been shooting up and down so furiously lately? In part it's because every one has been wondering whether or not the US economy is moving into a recession. Well, if we could agree on a way to to measure and predict GDP, we'd have solved that issue for the market pretty quickly, wouldn't we? 

Derek Gard dissents:

This assumes the market moves based on GDP at all.

From 1950 to 1960 GDP went from 1696.765 to 2517.365 (48%) and the DJIA went from 198.89 to 679.06 (241%)

From 1960 to 1970 GDP went from 2517.365 to 3759.997 (49%) and the DJIA went from 679.06 to 809.2 (19%)

From 1970 to 1980 GDP went from 3759.997 to 5221.253 (39%) and the DJIA went from 809.2 to 824.57 (2%)

From 1980 to 1990 GDP went from 5221.253 to 7112.100 (36%) and the DJIA went from 824.57 to 2810.15 (240%)

From 1990 to 2000 GDP went from 7112.100 to 9695.631 (36%) and the DJIA went from 2810.15 to 11357.01 (304%)

GDP during the 60s was higher than the 50s and yet the market barely budged. And GDP during the 60s and 70s surpassed the growth rates of the 80s and 90s, yet what decades saw the greatest gains in stocks? Stock market moves do not correlate well with actual GDP data over decades or even years, let alone the daily thoughts and musings of financial pundits.

To say stocks move on GDP data, or confusion thereof, is not supported by raw data. This is the same logic that says, "Stocks rose on a drop in oil prices" one day and then the very next day says, "Stocks fall despite a decline in oil prices." It is a fallacy promulgated by the same people who earned 3.5% per year in stocks during the 80s and 90s when the market was earning more than triple that.

Adi Schnytzer replies:

My argument was not that the market moves in line with GDP, rather that lately the market has been reacting to news suggesting either an imminent recession or not. To measure the relevance of this assertion you need to check whether or not the market falls some months before a recession (thus anticipating it) and not whether over a long period the market tracks GDP.

Nigel Davies opines:

As a simple chess player I must admit to being confused by the apparent
implication (seen everywhere right now) that positive GDP is good and
negative GDP is bad. In my own admitedly primitive pursuit one rarely
gets the opportunity to play expansive moves on a continuous basis,
there are periods when one must regroup in order to increase the
potential energy of a position.

So if I were an economist I would not be looking for answers in simple
linear relationships. Instead I'd try to study the interplay between
'potential energy' (one might try to define this in many ways, for
example by defining debt in 'real' terms) and GDP. And I'd hypothesise
that one of the most bullish economic times would be during a recession
in which personal debt was being reduced.

Vinh Tu tries to sum up and conclude:

Whether more GDP is "good" or "bad" is a normative judgment. To an
economist, however, since GDP by definition refers to the production of
"goods", it has to be good. (It is generally assumed that utility is
monotonically increasing with goods.) Whether the increase in goods
produced corresponds to an increase in share prices is an entirely
different matter. A share represents a claim on assets which, in turn,
yield a stream of goods (or money which can be exchanged for goods.)
Whether an increase in GDP is beneficial for share prices has
everything to do with where that increase comes from. An increase in
efficiency, whereby the return on existing assets increases, would
probably increase share prices, all else being equal. On the other
hand, the creation of new capital assets would not increase the value
of pre-existing assets if it resulted in the assets being less



LemurBurton Fabricand wrote two interesting books: The Science of Winning and Non-Brownian Movement in the Stock Market. One of the major principles of the books, highly recommended as a supplement to Bacon, is that when a horse goes off at odds that are unusually unappealing, it's good to bet on it. He applies the method to a small sample of horse races, and finds that for specific applications of the principle, a slightly winning system can be developed.

I was reminded of this principle by the very unusual action of the stock market the last two overnights. Thursday evening and Friday morning, New York time, the market moved up about 1% overnight after yet another 40 day low on Monday. The optimism was broken by the Merrill announcement and the disappointing Fed testimony, as well as the credit downgrades. One of the worst declines in history occurred, 47 points from the open to close, exceeded only by the 66 point decline on 4/17/2000.

You would think that after such a decline, especially after an up opening, with fear in the air as never before, there would be a terrible fear about opening the market up again overnight. But no, it's up 2/3% overnight and Japan during the last two days, when the US market has been down 4%, is up some 1% from 13505 at Wednesday's close to 13650 as I write at 11:00 pm EST.

The insight of Fabricand is relevant, that this seeming underlay, this amazing courage in the light of the pessimism is not quite as amateurish , "boy, don't try too hard in the stretch unless you really are going to take it because I want the odds to be up next time" as it might seem.


I have been studying the intake of clay by lemurs and parrots so as to neutralize the alkaloids and other poisons in seeds that they eat and disperse. What are the comparable foods that the market must eat to neutralize bad events? What does the speculator have to do to neutralize the many uses of specialized information and unlimited capital that the trading houses can apply when they are not acting over and above the various Chinese Walls that they can climb whenever there is a merger or downgrade?

I found 38,000 articles on "underestimation of change" on Google and have not read them all yet. Victor Zarnowitz found that underestimation of change was a persistent aspect of his data on GNP forecasts although the rarity of predictions of declines made his data consistent with algebraic underestimates as well. I thought a realistic way to test this was to look at all the moves from close to 2:00 am EST to see if the big ones are underestimates. I found there were 18 big ups of more than 10 points as of 2:00 am, and 18 big declines of more than 10 points. Of these 36 big moves, 18 had reversed by 10:00 am and 18 had continued. Thus, there was no evidence in a real data set without revisions or biases or contrivances, that there was an underestimate of change. 

Martin Lindkvist adds:

Fabricand also wrote the books "Horse Sense" and "Beating the Street".
In both he explains the principle behind his systems: The principle of
maximum confusion. Writes Fabricand in "Horse Sense":

"The betting public is most likely to err in determining the winning
probability of the favorite in those races where the past performance
record of the favorite is very similar to that of one or more horses
in the race."

in "Beating the Street" he continues on the topic:

"For the races, the intuitive idea behind the principle is that
although the favorite appears very much like the other horses in
ability, there must be some reason or reasons not immediately obvious
for the betting public to make that horse favored. Yet, because the
two horses seem alike on the surface, the public may be confused
enough to bet too heavily on the other horse, making the favorite

In "Beating the Street" Fabricand also lays out a stock trading system
based on the principle. 

Yishen Kuik reports:

A new word for today, Geophagy.  Wikipedia says:

Geophagy is the human practice of eating earthy or soil-like substances such as clay, and chalk, in order to obtain essential nutrients such as sulfur and phosphorus from the soil. It is closely related to pica, a classified eating disorder in the DSM-IV characterized by abnormal cravings for nonfood items.

Geophagy is most often seen in rural or preindustrial societies among pregnant women and children. However, it is practiced by members of all races, social classes, ages, and sexes. In other parts of the world the practice is less stigmatized, and geophagy is not studied as a pathology but rather as an "adaptive behavior" that supplements the diet with essential nutrients or treats a disorder such as diarrhea.

In some parts of the world, geophagia is a culturally sanctioned practice. In many parts of the developing world, earth intended for consumption is available for purchase.

Bill Craft relates:

In the rural Southeastern US there exist deposits of Kaolin along the Oconee Group (formerly called the Tuscaloosa Formation). The locals and miners call it 'chalk' because of the white look and ability to stick when wet and permeate when dry any supposedly closed space.

Some of the residents have consumed the 'chalk' for centuries as it was a cure-all. Even the Creek Indians used it with Yaupon Holly (Ilex Vomitoria) for ritual 'cleansing.'

Mix some Washington State Apples with it and you get:


Ahh! Ritual Cleansing! Just what the mistress ordered!

Phil McDonnell explains:

Ketchup/MustardMost toxins are alkaloids, which in turn are bases. These toxins can usually be neutralized by ingesting acids. This is a practice which is not unique to backward civilizations. Check common condiment ingredient lists for vinegar. It is in ketchup, mustard and many other items. Chemically it is an acid. Oil and vinegar salad dressing is another example. When an acid and a base combine they neutralize each other and form a salt. Many salts are water soluble and can be readily flushed from the body.Even in modern society we have many minerals that are important to nutrition and are routinely used as remedies. Antacids such as Tums and Rolaids are simply calcium carbonate — chalk. Products such as Milk of Magnesia and Pepto Bismol are long-time mineral based remedies as well. Most so-called vitamin pills also contain a long list of minerals that are essential to our daily well being.

In the markets the toxins are the bad stocks at any given time. Recently the toxic stocks have been the big banks, most are down something like 50% over the last year. They continue to feel the worst effects of the current financial meltdown. Money usually goes somewhere. So when the banks are sold the good stocks are the beneficiaries. Google is a prime example. The high growth of earnings continue on track. So for a while GOOG continued to surge ahead. But toward the end of a panic the market acts more like the police when they raid a house of ill repute. They take the good girls with the bad. So even the formerly strong GOOG has seen a come down from well above 700 to a touch below 600. But there is nothing wrong with Google as a company. It is only that the big G has to act as an acid to neutralize the toxic base which is the subprime dependent stocks. So that salty taste in your mouth may not be just blood. It may be the act of a market neutralizing its toxins in order to return to good health.

George Parkanyi writes: 

To answer Victor's question about ingesting antidotes to poisonous markets, I eat 2x leveraged short ETFs, and I'll tell you why.

I live in Canada. Our family assets are tied up in tax-deferred registered retirement savings plans and registered education plans denominated in Canadian dollars. Although we can buy U.S. equities (and have to convert currency back and forth every trade), we can't buy options (we can write covered calls), we can't use margin, and we can't use futures. So there was a time when you had two choices in these conditions, sell or hold.

One morning last year I'm making my kids' school lunches, watching the Business News Network, when a commercial comes on proudly trumpeting three new pairs of long/short 2x leveraged ETFs. I remember thinking "Finally, something useful!". Later that day I researched those same Canadian ones, and found the U.S. ones. There was a wide range of available pairings, not only by indices, but by industry sectors as well. Some even paid dividends.

I use a specific strategy that requires full investment. By embedding (OK, eating) just a few of these ETFs on Jan 2, I was able to maintain my holdings, and keep my drawdown to only 3% as of today's close (despite all those NASDAQ stocks — ahem). It didn't take many; at most, 1/6 of the portfolio was in short ETFs, and I even scaled these back as the down-leg progressed.

Bottom line — your garage mechanic or plumber now has the ability to turn his retirement savings into a hedge fund.

If you can now so easily buy what is essentially market-catastrophe or profit-protection insurance, could this be changing the fearscape when markets fall? It makes being a contrarian more complicated. Reminds me of the Monty Python sketch where the people's bandit Dennis Moore is so successful stealing from the rich and giving to the poor that the poor become rich and lazy, leaving him confused and conflicted. Eventually he ends up holding up stage coaches and just re-distributing the wealth amongst the passengers.



Optimal Portfolio ModelingWhen working with spreadsheets and random numbers there are some techniques which can be helpful. For example suppose we used the real data of daily market changes but wished to randomly reorder them to see if the original order had different properties than the random order. We could perform 999 reorderings and compute some stat of interest for each reordering. Then sorting the 999 stats in order smallest to largest would give us a very nice table in which we could look up the p value of our stat of interest.

For example, suppose we wanted to test whether the recent trading ranges were wider than the should have been at random. We would take 999 reorderings and calculate the range for each. We then compare the range from the original empirical distribution to see where it falls in the sorted table of random ranges. If it is in the 50 it is too small at the 5% level (one tailed test). If it is in the top 50 it is too large for a 5% one tailed test. For a two tailed test we look for the bottom and top 25.

On an Excel spreadsheet if we use the RANDBETWEEN() function as an INDEX into the original table of numbers it will allow us to randomly sample from the original distribution to create our new random distribution. The VLOOKUP() function is often useful here as well. In R one can use the boot() function, from package boot, to do all the heavy lifting of randomization.  Easier than trying to do it in a spreadsheet.

In my book Optimal Portfolio Modeling I recommend a slight variation on this technique. The usual technique is to randomize each day. The thinking behind this is that the low autocorrelation usually present in markets implies that there is little or no linear relationship between successive days in a time series. But this says nothing about any non-linear relationships — patterns, cycles, mean reversion, conditional heteroskedasticity and many others. To capture these (if they exist) we can take random blocks of time, such as 20 days. We would randomize the start time of the block. So if our random number picked day 31 as the random time we would use the block from day 31 to day 50 as the block. The idea is to splice together these random blocks to see if they behave differently from the original.  Making the block length long relative to the granularity of the data (say 20:1) helps to preserve most of any putative non-linear behavior.

It is also instructive to compare the randomized daily design to the randomized 20 day block design. If there is a significant difference then something non-linear may be going on.



CBOTI probably have enough info gleaned from old-time pit traders to write a large book. I loved to hear the stories and teachings those old guys had to share, and sought out as much of it as they were willing to tell me. However, much of that information is anecdotal and it would be hard to apply the scientific method to most of it. I did learn a whole bag of tricks for extracting extra cash while trading in the pit, but most of the tricks are either mechanical in nature, or educated guesses (such as estimating how much wheat is for sale in the pit at any given time).

I did learn one slam-dunk way of pulling out money out of the pit. I worked hard at identifying new, inexperienced traders who were certain losers, and fading all their trades. This technique worked out very well for me. However, identifying certain losers is a skill in itself and takes time to develop. Pit traders can have a special insight/feel for the market, but only the net winners have that feel. A majority of those who step up to the plate to trade don't make money, and fade into oblivion.

Steve Leslie responds:

In poker, the professionals are sharks; they prey on the weak, in poker vernacular, the dead money. That is why they are called fishes or pigeons. Professionals avoid tangling with each other — it is far easier to exploit the weakness of the youthful or inexperienced rather than the wizened veteran. Therefore the professional uses time to his advantage by patiently waiting for the amateur to venture out into the waters and make a mistake. It is similar to the Highlander television show from some years back. For the Highlander to gain more power, he must kill his adversary by taking off his head. Same in poker, by destroying your opponent you assume his chips and as a result, his power.

Larry Williams extends:

While the gummint guys say, and rightfully so, "Past performance is no assurance of future success" there is one exception to this I have found and isolated: Advisors, funds, newsletters, etc., that have not done well in the past will not do well in the future. Jeff's pit wisdom does spill over to outside the pits as well.

Phil McDonnell adds:

I performed an analysis a year ago that showed that among mutual funds the worst performers were predictably among the worst in the next time period as well. The results were statistically significant for the worst group. However among the best performing funds there was no correlation. Superior performance did not persist probably because many people  mimic the trading styles of the most successful traders of the last time period. It is a bit like the generals who are always fighting the last war.

Steve Leslie writes:

Ken HeebnerI hope this complements Dr. McDonnell's work since I am sure he did some deep research on this. With respect to his comments a few points can be made.

First, I assume that he is talking about open-ended mutual funds. There are significant differences between open-ended funds and close-ended funds. And even open-ended funds who no longer accept new accounts but only money from existing shareholders. This is a very complex field, evaluating performance of mutual funds because there are so many variables that exist in the arena. Fund managers change, inflow of capital, hot markets such as large cap growth, value, international, etc. With respect to performance my first question would be how performance was measured. Was it against an index or against a peer group? For example if a fund were a midcap growth fund, was the evaluation against other midcap funds or against the Russell 1000, S&P, etc. In short, were these absolute performance or relative performance comparisons?

When I was a broker, I know that if a fund had exceptional performance the prior year, the sales rep for the company would come in and push performance. Then the brokers would take the literature and push it to the clients. Money would flow into the fund, making it more difficult for the manager to manage. Therefore the fund was a victim of its own success and performance would suffer. The most startling examples of this were in the late 1990s and 2000 when tech funds had great absolute numbers. Every sales rep who came into the office was pushing these funds. Dramatic amounts of money would flow into the funds thus putting tremendous pressure on the managers. All the clients wanted to buy were the hot funds. Nobody would buy value funds, which over the next several years would have been the proper investment.

Next is, what style does the manager employ, growth vs value, largecap vs midcap vs smallcap vs international? One year may be too short a time to evaluate superior performance of mutual funds — 3, 5, 10 year numbers are much better barometers of perfomance. Trends in the market can last longer than just one year. For example over the last 3-4 years international funds have had their day in the sun. I am confident the worm will turn and they will begin to tire. The next hot sector may be largecap U.S. growth, or other sectors. The jury is out on this. In fact, the Morningstar five-star ratings system is based on 3 year past performance. I believe the highest-rated Morningstar funds for the past three years tend to be worse absolute performers the next three. Conversely, the worst performers the last three years, the one stars, can be the best performance group. Once again the dynamics are in place for things to change.

Finally there are some fund managers who have withstood the test of time. Ralph Wanger, Kenneth Heebner, Bob Olstein, Bill Miller, to name a few, all have had stellar long term results but even they have had bad years.

Ken Smith remarks:

Performance in youth does not predict performance in aged. Performance in pre-marital bed does not predict marriage results. Performance in school does not predict performance at work. And so on.

Dr. McDonnell worked on performance of top mutual funds, found we can't predict future from present results.  I have looked at charts for many years, in fact I began at age 22.  Now just short of age 79.  Of course there was a hiatus when charts were not available.  Overall my experience determined a squiggle on a chart from five years ago will not correlate with a squiggle I will find when the market opens next year.

Marion Dreyfus agrees:

What Ken says in regard to former performance not being valid for future success should be received doctrine, and yet seems not to be. In terms of financial investments, people extrapolate out as if the law is concrete: If it returned 8% in the past 10 years, it will continue to run that way in the future. We take a lifetime to unlearn easy mistakes.



NervousBack in my apprentice days in the wheat pit, a wizened old guy took me under his wing. Since he had been trading successfully since the late 1930s, I was all ears. One of the anecdotal statements he made to me was that "Nervous markets always close lower." I've remembered that sage bit of wisdom for all of these years, and have followed that advice with reasonable success. Lately, I've been wondering if there was a way to quantify that statement, or if anyone in this group can lead me in the right direction. I'd like to see what the numbers and percentages of nervous markets really are.
One might ask, "What is a nervous market?" The only answer is that I can't define a nervous market, but that I know one when I see it.

Bill Rafter replies:

There has been some work done relating volatility to subsequent price behavior. Volatility (depending on how it is defined) may agree with your description of nervousness. Generally the premise is that volatility is bearish, which would be in agreement with what the wizened old guy told you.

There are many definitions of volatility. My suggestion is to look at measures other than 1-day rates of change. Earlier this year I asked around for other measures of volatility, and got approximately 20 variations. Additionally, there are “handmaidens” of volatility, such as institutional holdings.

Jim Sogi adds:

Vic and Laurel recently hypothesized that afternoons closer to the low of the day in S&P could be thought of as you say "nervous." I've also been playing around with NYSE declining volume. Today 857k. Fairly nervous. Over 1m down before the end of the day is very nervous.

Phil McDonnell remarks:

"Volatility is bearish" requires considerable qualification. In my ruminations the results have generally shown that rising volatility is bearish on a contemporaneous basis and over the short run. However high levels of volatility can be quite bullish. It is important to define what volatility we are talking about.

Victor Niederhoffer investigates:

VictorMost definitions of nervousness refer to trembling, quivering, and agitation. I thought I would look at some qualitites of markets that seem to be of that nature. I started with markets that were up at the open, down at 11 am, up at 1pm and down at 3 pm. I found 17 cases since 1999 in the S&P futures, and nine of them went down from 3 pm to the close, with an expected move of three points. Such moves occur about twice a year. I found that a similar gyration, but down at 2 pm rather than 3 pm, which happened eight times since 1999, to be visited with four up from 2 pm as of the close and four down. I found eight cases of the first pattern in bonds, and the next day was 50-50 up with an expectation of four ticks up. In general, I would say that there is not much evidence that a quivering market with numerous crossovers to the down side is bearish. In another study, I found 22 cases since 1999 where the market was up at open, down at 10 am, up at 11 am, and down at noon. Eight of these occured in 2007. Thus, the market appears to be gyrating more frequently in a way most people would get nervous about if it were their limbs or sinews. Seven of the 12 cases showed a rise from noon to the close. Thus, nervous stock markets, defined in this way, did not show any non-random predictive tendencies, although the jury is out as to whether the market shows an inordinate tendency to tremble between hours.

Vincent Andres adds:

One classical way to proceed would be to have someone provide : 100 examples of nervous/non-nervous/"don't know", status markets. (Maybe more than those three classes). Each example being the price series and the graph. Of course, it's possible to ask several people to do the classification.

Some counting would probably teach things, hopefully reveal possible clusters in the appropriate measure space. Well used, tools like neural nets (many NNs are nothing else than stat learners) may be of use here. However, there is a preprocessing to be done on the price series before feeding the counter … and this will often be the clue. (Some ideas have been provided in this thread).

It is however quite a painful job, and requires a rigorous methodology. And even if classification succeeds, it's only a piece of the job. As  P.McDonnell said, nervous => bearish is not a 100% sure implication. And markets are dynamic, not well-defined, etc.



A friend from the other coast writes:  "There is a complete collapse in demand. We here in California are almost certainly in the midst of another property slump." 

Haven't we seen this movie before? California and nearby boom states see moonshots of value and bull-market property geniuses, followed by cyclical shakeouts, despair, lather, rinse, repeat.

In places where property itself was a central business — Miami condoland, the Inland Empire of SoCal — the ugliness can be protracted. Add in the visibility of the problem, and you have the storyline being promoted by The Thundering Herd and other recession-callers. 

Let us look at some national figures.  From today's GDP report: Residential Fixed investment dropped 20.5% (annualized Q/Q change), its contribution to the change in GDP was -1.08%. This data shows how much damage has been done by the contraction in housing. A 20% decline in the sector lopped one percent off GDP. But residential real estate is still "only" ~5% of the economy, and it makes sense to keep that in perspective.

Stefan Jovanovich dissents:

Guys, I am not saying that this is the end of the world; but what used to be a real estate problem has become a banking problem. The Financial Times says that real estate loans are now 40% of bank assets.

I was all-in only a few months ago and took money out of the market only because I wanted to buy a new business. What convinced me that this was not just another slow-down was the spread between our bank's normal jive talk and what they were actually willing to do. We have had the same business in the same location long enough to have seen our "local" (sic) bank morph from Security Pacific to the B of A, and we have seen literally half a dozen people come and go as the branch managers. This is the first time, however, that no amount of collateral was sufficient to get them to say "yes" to a loan even though they are now dealing with a potential borrower who has no debt! They didn't even bother with the usual soft pass — "Have you considered an SBA Loan?" The silver lining that, in this environment, people are going to be able to "trade up" houses is comforting; but it is pure fantasy if the suppliers of actual credit have frozen up - as I think they have. Of course, it could be my bad breath and my charming manners. We shall see. 

Phil McDonnell says:

"News follows price." After a decline bad news will come out to explain it; after a price rise good news stories will come out to explain it.

To some extent the markets are pretty good predictors of events. That partially explains the relationship. However there is a deeper truth in the News Follows Price saw. Simply put the media needs something to write about. They sell fear, they provide information. It is all in pursuit of market share which ultimately leads to advertising revenue.

An author writes a book if and when  he has something to say. The situation is quite different for a media writer. He HAS to say something everyday. It doesn't matter if he actually has something important to say today. His job is to write something anyway. Ideally it will be something so compelling that you, dear reader or viewer, will stop your busy life and check it out.

One of the classic ways to make up a story every day is to look at the market action and try to explain it rationally. After the fact you can always write keen insights like 'The market went up on higher interest rate fears'. The next day might be, 'The market went down on fears of higher interest rates'. Invariably this leads to reinforcement of the meme of the day. In the 90's it was the dot com meme. Recently it has been the declining real estate meme.

But memes evolve. They evolve because the media is under pressure to make the story new. A new twist or angle keeps the meme fresh and compelling. First it was the real estate bubble has burst. Then it was the sub prime mortgage market collapse. The latter evolved into the liquidity crisis. Then the Fed eased. Oops, better not talk about that too much that could be good news. It is better to milk this meme for all it is worth. Then it was the dollar is crashing. But when a meme has been around for a while it gets stale. The media needs to turn to the 'how bad could this get' angle. Perhaps you have heard the old Johnny Carson jokes. Carson could tell a new 'How cold was it' joke every night for decades.

That is why we are seeing stories about recession. More media is piling onto the meme. This could lead to recession, global warming, nuclear winter and a falling sky! Today's article featured the new angle that real estate prices are inaccurate. It is really worse than they are telling us! The article cited one estimate that 1.5% of houses in Denver might be reported as 10% higher in price than they really are. Do the math. That would result in a .15% overvaluation in the Denver market stats - a fraction of 1%.

Quick! Check the sky! Is it still there? I live in the Seattle area so I can't check the sky, as usual. But I trust it is still there.

Bruno Ombreux agrees:

News coming after price has often very low (0) information-content. Easily/rapidly written/understood.

- Easily written: one must be amazed how newswriters are able to produce/pick descriptive "models" in often less than 24 hours ! (should they have thought of it some hours before, they would be millionaires !). Or maybe those models are just fake ?

- Easily understood, at least for people confusing understanding and memorization.

- It's either tautological "markets up because they didn't range nor gone down",

- Or completely incantatory.

Just some words from the liturgy, put together. No predictive power, even not explanatory power. But it may _look like_ something. That's enough. A majority of people will be happy with it. Thanks for this good stuff for self-deception.

- The most elaborate form may be a linear model: "things will continue". Tautological, incantatory, linear … what are others forms ?

After a market move there is always an open question : why ? Few people have time/skills/tools/data to count/answer. Even few people have time/knowledge to read a true, but a bit long and complex, explanation. (A frank explanation being most of time: "we don't know"). Though, we need to fill the question's volume, to reassure ourself, keep up appearances.

Better a void/fake explanation than no explanation at all, At least, better than an true explanation beyond ourselves.

Vincent Andres asks:

After a market move there is always an open question : why ?

The post-mortem explanations of market moves show the huge random element combined with human weakness.

Humans don't want to believe that things happen without a (knowable) reason. Ego, insecurity, uncertainty about death after life. So we ascribe explanations irregardless causation.

This is well exemplified by many of the SP500 moves in response to FED rate announcements this year. On Sept 18, they dropped 50 BP and the market jumped 50 points, because "The FED put is still there" (they will counter market declines). OK, but it is also possible the market could have dropped 50 on the same news, because "The FED sees the economy as sliding into recession", and that they cannot stop it.

Then on Dec 11, they dropped 25 BP and the market tanked (biggest drop on a FED day in recent history), "Because traders were looking for a cut of 50 BP". Yes, but it could also have gone up because the FED determined that recession risk was abating and the original crisis overblown.

Some non-human animal experiments are relevant. Recall the pigeons who were fed after pecking a lever: When the feedings came at random intervals, they began to repeat movements and rotations they thought caused the food to appear - not realizing their dance accomplished nothing. Or the rats with electrodes attached to their tails: One group had levers which stopped the painful shocks, the other's levers worked only intermittently. The rats who couldn't control their stress lost weight, shed fur, and became unhealthy, whereas the ones with control remained normal.

The terrible pain and joy generated by markets and other mostly random gambling is more than enough to bring out the animals, as well as herd them to the chapel on Sundays to ask for explanation.

Save my seat!




 Whenever I reread Paul Dickson's The Hidden Language of Baseball, I am struck by the many layers of the game of baseball, the hidden, inner, and scientific aspects of the game, that most fans are completely unaware of, that are comparable to the hidden, and inner aspects of markets that most of us sense but take account of not nearly enough in our play. I'll try to remedy that in part with some numbers relating to these hidden signals.

Let's start with the fact (all factoids in the hidden language of baseball are taken from Dickson) that in a typical baseball game thousands of signals are exchanged, from catcher to pitcher, catcher to fielder, coaches to baserunners, managers to coaches, and umpire to umpire (in the event that they need backup from an unruly player). Like signals in other fields, the repertoire of signals must be informative enough to cover all situations, but simple enough to understand. Also, they must be hidden from the opposing team that is attempting to steal your signals.

Signaling in baseball emerged from Civil War origins, from the use of signaling in war to describe the enemy positions and movements. Signaling in war reached a temporary plateau in 1855 when the British Board of Trade developed a set of 18 flags that could display 70,000 visual signals. In the Patrick O'Brian series, Jack Aubrey is a master of using these flags to deceive the enemy as to his nationality, his destination, and his intentions. He is also adept at stealing enemy signal books so that he can engage the enemy without forewarning.

Deception in the use of signs is key. The typical third base coach uses a set of decoys to hide his signs, an indicator sign to tell the recipient when the signs begin for real, and hot signs like the movement of a cap that are actually the ones to follow. At the same time, many other players and coaches are pretending to signal so that the opposing side can't tell who's signaling. The signals often change with the positions of the opposing team on base, the score, the player who is receiving them, as well as the stage of the game. "Sometimes is looks like five guys trying to bring a jet onto an aircraft carrier. Some are signs. Some are decoys, and its fascinating to sit there and watch the stuff flying all over the place."

A constant problem in signaling in baseball and markets is the case of the traded player. The Phillies and the Pirates in the 1890s had developed telegraphs with hidden wires under the ground to communicate, as well as telescopes and hidden slots in the scoreboard to signal against opposing teams. But when one of their own was traded, they both agreed that at least against the other team, they would use the signals, and the players involved in the transmission were prominently seated on the bench as hostages.

The analogy to what happens when one of the major brokerages develops an elaborate system of signaling what their recommendations or positioning or latest word from the Federals or the Centrals is, and how they can keep this set of signals secret when one of their own retires or leaves the firm, is clear.

I have always felt that the idea that there is a plunge protection team is invalid because the traded retired officials when they enter the revolving door into Wall Street or write their book would spill the beans. The baseball players have so many signals and they are changing them so often that that they are able to overcome this problem or as above, the quid prop quo, not to steal from each other, but how is this handled on Wall Street? One protection of course is the ethical and legal storm that would be realized when the information was divulged.

I have previously tried to shed some light on this subject by looking at the hidden signals of markets. Is there a movement in one market in one time, that is the key to what's going to happen. Is it volume that tells the story, or the lead of some Central Bank like New Zealand or Malaysia that flashes the hot signal. I have always believe that the movements of Israel have predetermined what's going to happen in the US. However, the time differential, and the necessities of taking account of the Fischer effect, (delayed pricing, etc.), would make a study of that network, path, or feedback system, a complex undertaking. The best that I was able to get after a preliminary study of that subject was a response from an erudite reader as to the likely explanation ("They read our mail").

Similarly I have felt that the open to close in the Nikkei predicts the movements in the US open to close, but a careful regression analysis of the subject over the past several years, finds that the amount of variation explained is very small.

All this signaling must be differentiated from the forward looking purpose of all markets, where they are trying to move prices over time . Also, the function of markets, the bond and stock vigilantes in foretelling to the authorities what the likely outcome of any attempts to get off the straight and narrow, or neglect the unintended consequences of their actions. Witness for example, the recent action of the stock market after FOMC meetings where it was unhappy with the vigilance of the Fed's awareness of the housing crisis, or the reaction of the bond market in the last two days, down a few points, to the illusory price controls on mortgage rates.

I decided to study the subject directly. Why not take the major economic announcements and see to what extent the news was leaked in the preceding half-hour. Of course, most announcements have a mere disruptive effect on the market participants, unleashing the weak from their positions so that the strong can take them over, as well they should since there are so many seasonal adjustments, and ephemeral, already discounted, and unique factors in each announcement that it couldn't possibly have anything to do with the price of eggs. But there generally is a reaction, and if one knew one or two of these reactions for sure, it would be fat city.

With the many avenues for "leaks" of these signals, I thought it would be worthwhile to see how many of the signals were not captured by interested others in advance

As a start I took the four major announcements in the market since 1999 and looked at the moves in the preceding half hour versus the moves subsequently as follows,

.                       Market moves before and after major announcements.

Announcement         up moves     average move       down moves  average move

Type                 half hour      next hour         half hour        next hour

.                      before         after             before            after

Unemployment         n=59           0.7              n=33                0.6

PPI                  n=49         - 0.6              n=41               -0.5

CPI                  n=49           0.4              n=45                0.0

FOMC                 n=39           0.7              n=28               -0.9

Since the standard deviation of the moves in the subsequent hour is of the order of 7 points, about 1/2 %, theses moves of less than 1 point, hardly the bid asked spread, in all cases less than 1 standard error away from expectation indicate that there is no systematic tendency for the signals of major economic announcements to be acted upon in a systematic and market altering impact before and after the announcement.

This is a preliminary study with many gaps, but it captures the gist of a fruitful line of inquiry, I believe, and I'd be interested in your augmentative ideas.

Phil McDonnell adds:

Baseball has sometimes been described as an individual sport and not a team sport. Certainly the batter stands at the plate all alone. Sometimes he is the hero with a single swing of the bat. Sometimes he is the goat, bested by a final deceptive pitch. However the idea that baseball is an individual sport is a misperception that ignores the large number of subtle signals that are communicated back and forth to allow the team to act in a coordinated fashion.

For example the batter looks down the line to the third base coach before every pitch. If there is no one on the third base coach usually will only give a 'take' signal or 'swing away' sign. Often a team will employ a conscious strategy to take the first few pitches until one of them is a strike. Sometimes this is motivated by an effort to tire out the starting pitcher. This strategy is more often employed if the starter is a particularly strong pitcher or if the bull pen is known to be weak.

Statistically speaking it is well known that a batter has a higher probability of getting a hit on later pitches than if he swings at the first one. Thus a take strategy forces the batter to see more pitches. He can get a better idea of the pitcher's release point, fastball speed and ball movement. It all starts with a simple take sign.

Most people are aware that the catcher and pitcher have signs. But few realize that at higher levels of baseball these signs are passed on to the fielders as well. Depending on the situation the pitcher may want to throw a pitch that increases the chances of a double play or a fly ball. If a runner is on first base with less than 2 outs, the pitcher would very much like a hard hit ground ball to the left side of the infield. He needs the grounder to be hard hit because it will get to his fielder faster saving valuable split seconds. The fractions of a second saved can make the difference between a double play and only one out. One of the best pitches for this is a sinker or two seam fastball.

The science behind this is that if the pitcher throws the ball along the axis where the ball has two seams it will create less rotational drag. The other way to throw a fastball is along the four seam axis where four seams are rotated at the face of the ball. One can see this by looking at the seams on a baseball or even a tennis ball which has the same seam pattern. The four seam ball will tend to rise (relatively) because of the added rotational 'lift' from the increased seam surface. In contrast the two seam fastball will seem to sink. If thrown low in the strike zone the two seam fastball is an excellent double play ball. This information is usually communicated to the fielders so they can position themselves for the hoped for double play ball.

To be sure the pitcher is throwing at a significant downward angle from a mound which is one to two feet high. Most pitchers release the ball from a raised arm. If the ball is targeted for low in the strike zone it must drop about 6 or 7 feet over the distance of about 60 feet. During that time gravity is also at work. Therefore we can safely say that pitches arrive at the plate at a significant downward angle. But the rising four seam fastball does get some lift from the spin. This lift causes it to veer upward from its projected downward path. But in the end a rising fastball is still traveling at a downward angle.

In particular the second baseman and short stop will have their own secondary communication regarding who will cover second base in the event of a steal. If they anticipate a ball to be hit to the left side of the infield (toward the shortstop) then the second baseman will cover and the short stop will back up the play. Factors they consider are whether the batter is a lefty or righty, whether the pitch is fast or slow and whether the pitch will be on the inside or outside of the strike zone.

Another use of signals is when the pitcher is throwing a 'soft' pitch. This could be a curve or a change up. In either case the batter would be expected to swing a bit early on the pitch. If he is right handed he will tend to hit more toward left field than usual. The fielders are signaled in advance that this pitch is coming. However they do not physically move in that direction before the pitch, so as not to tip off the opposing team. Instead outfielders use the information to decide which foot to use on their initial drop step as the batter swings.

In markets there are often deceptive rallies and declines just before a pending news announcement. One pattern is that there may be a sudden disruptive move just before the release of a Fed announcement. The idea is to create the impression that someone leaked the decision just at the last minute before the announcement. Usually this can only be effected by a large trading desk that can act in concert within a few minutes. When they start the move by say, buying in unison, a few minutes later naive traders pile on thinking they have picked off another trading team's signals. Meanwhile the original trading desk dumps their stock for a quick profit at the expense of the traders who have fallen for the decoy signals.

David Whitesel wonders:

When you state there is no systematic tendency for the signals of major economic announcements to be acted upon in a market-altering, impactful way before and after the announcement, I thought: why should there be? You chose the specific indicators, so I ask: why would the market show a monolithic response to this particular set? The market is the sum of its parts. Why wouldn't any signal from this class chosen apply to only a class of trade specialists or range of products which are dependent upon the gross participation of speculators? Whereas, stock specific news is routinely faded, which should be characterized as entirely systemic.



 Victor and Laurel have suggested that a fruitful area for market research may lie in replicating the methods of Brahe and Kepler. Brahe scrupulously gathered very precise data though years of observations. It was left to Kepler, his student, to develop the first model. Kepler first identified planetary orbits as elliptical.

Suppose we have two planetary bodies with periods P1 and P2 respectively. A quick review of Kepler's Laws reminds us that his third law is as follows:

P1^2 / P2^2 = R1^3 / R2^3

where R1 and R2 are the semi major axes of the two bodies. It is interesting to note that there are no linear terms in the above relationship. It can be read as the ratio of the squares of the periods are equal to the ratio of the cubes of the axes.

In the markets we know that the Efficient Market Hypothesis tells us that the market price change today should have no linear correlation with the price change tomorrow. Empirically this seems to be true most of the time for most markets. However a strict interpretation of EMH says nothing about the existence of non-linear relationships.

In particular when we evaluate the squares of changes we find they are significantly correlated. The same holds for the cubes at similar lags. It is left as an exercise for the reader to calculate the magnitude and direction of such correlations. So at first blush there may be an application for Kepler's third law in the markets.

In order to see if there is any similar Keplerian relationship in daily price series the data from the table on page 121 of Education of a Speculator hardback was studied. Using the midpoints of the classes in the table the model used only the squares and cubes of change to predict the next days performance. It turns out that the fit is statistically significant. Notably there is no linear term in the model. Checking whether a linear term would help, the data showed that it would not be helpful. Although the regression model was statistically significant it was based on out of date data and would have to be redone with current data.

Michael Cook remarks:

Kepler was not a student of Brahe; he came to Brahe's observatory because Brahe had good data, continuous night by night observations of the planets. Kepler was desperate to prove that the orbits of the planets were circles, because the circle is the perfect shape, consistent with the beauty of the divine Mind. He decided to work on Mars because it seemed to be closest. After much work he realized the ellipse was a better fit. His comment: "I set out to show that the universe was based on the eternal harmony of the spheres. Instead I showed that it rests on a carthill of dung [the ellipse]."

The other beautiful law of Kepler's is that the planets sweep out equal areas in equal times.
It is also significant that all of his laws can be deduced mathematically from the inverse square law of gravitation.

Adam Robinson replies:

As I'm sure Dr. Cook realizes, the point was that the law of gravitation can be deduced from Kepler's laws, as indeed Robert Hooke (whose insights into force and inverse square relationships were at least contemporaneous with Newton's) was able to do.  Newton's genius (in that regard, there were many instances of course) was in showing the equivalence of the acceleration of a falling object with the acceleration of an object in orbit.

Newton, in other words, gave the relationships a theoretic underpinning (until then Hooke's insights, along with Kepler's, were mere "curve fitting," in the literal sense of the phrase!), just as Einstein did, since numerous scientists at the time (Poincare for one) had come to similar conclusions (e.g., the Lorentz contraction), but lacked any overarching theory to explain why such phenomena had to occur.

Dr. Cook's quotation of Kepler also reveals the extent to which aesthetics can hinder the progress of theory as much as promote it.

Michael Cook responds:

Actually, I am not aware of any derivation of the inverse square law from Kepler's laws. I believe Hooke claimed to derive Kepler's laws from an inverse square law, which resulted in Newton's publishing his proof of the result. Hooke never published an actual proof — it's hard to do without calculus. Feynmann has a paper in which he does so, which I don't think he would have published it if it were already in the literature. 

It is incorrect to say the law of gravitation can be deduced from Kepler's laws — Kepler's laws are descriptive, and don't by themselves imply any causal mechanism.

Adam Robinson replies:

I refer Dr. Cook to the letters between Hooke and Newton; there was much controversy between the two about who had which insights, when. Hooke's insight was more of a conjecture, not a formal "derivation" as such. Not surprisingly, of course, since Hooke's inverse square law with springs contains a surprising analogue with gravitation.

Kim Zussman writes:

Jim SimonsA recent Bloomberg article on Jim Simons of RenTech mentions sunspots and markets, so along with Kepler's dung [see Dr. Cook's remarks above] this must explain the beauty of markets.

Recall that sunspots (which have been observed and recorded since well before Galileo) are magnetic storms on the sun, which appear dark in contrast to the photosphere because (though they are hot) they are relatively cooler.  And to the extent that there may be related effects on solar wind (solar ions flowing past the earth), radiation levels, and earth's ionosphere, and radio/satellite communications, here is a study.

Monthly average sunspot count (American, of course) 1944-2007 is available from the National Geophysical Data Center:

Regression of SP500 monthly index return vs. monthly avg sunspot count (1950-Oct 07) shows almost significant negative correlation (P=0.07):

Regression Analysis: SP CHG versus SPOT AV

The regression equation is
SP CHG = 0.0110 - 0.000052 SPOT AV

Predictor         Coef     SE Coef      T      P
Constant      0.010961    0.002534   4.33  0.000
SPOT AV      -0.0000518   0.000029  -1.79  0.074

S = 0.0405916   R-Sq = 0.5%   R-Sq(adj) = 0.3%

Analysis of Variance

Source           DF        SS        MS     F      P
Regression        1  0.005288  0.005288  3.21  0.074
Residual Error  691  1.138548  0.001648
Total           692  1.143836

Here is a plot of monthly avg sunspots vs date, which clearly shows the 11 year solar cycle. Note that we now near a minimum (good for stocks), and regardless of Fed actions relative to the housing market, explains the recent 5 year bull market (OK the last sunspot maximum was Sept 2001, so the prediction was off by about 1.5 yr).

Eric Falkenstein remarks:

One of the keys of finance is the implication that arbitrage implies that pricing is linear in 'risk', or whatever is priced. Otherwise, you could generate arbitrage by buying bulk and selling little bits, or vice versa. It is intriguing to think that there are nonlinear relations in markets, but these necessarily imply profits, so, to the degree they exist, they must not be too obvious (please email me the exceptions!).



TaurusIt's good to remember that stocks are valued based on an infinite stream of cash flows. And any balance sheet losses on assets held just affect the book value temporarily and are lost in the fullness of the sweep of payments for risk and innovations and entrepreneurial ability . Same for whether earnings growth is going to be 1 % or 5% next year. The earnings yield versus bond yield is now at close to an all time high. The yield on risky loans has risen and the cost of debt capital has eased. Presumably if the default rate on subprimes is 10% , it is more than compensated by the increase in yield that such loans would now carry . All this comes to a head with the disruptive move at the close on Wed, down 1.5% in 30 minutes. This reminds one of the breaking in of horses featured in such novels as Monte Walsh where the unbroken horse gives a final leap into the corral fence before shuffling off with the owner paying the debt to Monte.

Phil McDonnell runs some numbers:

Recently the rate on 30 year Treasuries has fallen from about 5.3% to about 4.45%. This is a decline of about 17%. So if the long term earnings are discounted at the long term rate then a very simplistic back of the envelope calculation shows that the value of stocks should rise by something like 17%. However the reality is that stocks have fallen about 8% from when the rate was 5.3%. Together the 17% increase in value plus the 8% should combine for something like a 25% increase in stock values. Some might argue that a more sophisticated model would use the one year rate to discount expected one year out earnings and a two year rate for two year earnings and so on. That is true. But it is worth noting that all the shorter term rates have fallen even more percentage wise than the long term rate.

Bruno Ombreux extends:

Another exercise is to look at what happens when earnings are changing over time. In the discount formula, the denominator is a power. As a result, early years are heavily weighted and later years much less so.

Let's value the stream of discounting earnings as a perpetuity, because it is easy. It is earnings/interets rate. Let's use 8% which is reasonnable for a risky asset and in line with drift.

Assuming constant $10 earnings, the stock is worth 10/0.08 = 125.

Now let's assume that earnings are going to take a hit for the next 5 years.

If earnings are 0 for the next five years and then 10 in perpetuity, the company is now worth 125/(1.08)^5 = 85

This a a 85/125 = 32% drop in the value of the company.

The next few years are very important in valuing a company. It is not surprising that stocks drop on the slightest hint that they could experience troubled times ahead, even if in the long term they are profitable.

George Zachar cautions:

Notional interest rates are only one factor to consider in calculating the appropriate discount. In the current era of (relatively) low and stable rates, perhaps other variables play a increased role.

What tax rate will those future earnings bear? What is the forward trajectory of the regulatory ratchet? Are currency preferences an issue? Finally, should one use real or nominal rates to discount? That would imply the need to forecast inflation too.

While notional rates remain important, the growing/shifting burdens imposed by Washington, and the increased role of international capital pools, means yields are now one discounting factor among many.

Alston Mabry concurs:

To extend George's argument: What about projections for forex rates? Liquid capital flows across borders, and many investment equations now must contain a forex conversion factor. Must not non-domestic investors evaluate future cash flow discounted by both rates and currency fluctuations?

Gregory Van Kipnis raises an interesting point:

There has been always been a dichotomy in market valuation between the earnings discount model approach and the book value approach. If we reduce the current discussion to a P/BV versus a P=PV(E,g,i) model for assessing the market outlook, the following additional point may be important to consider. Is there a relationship between BV, on the one hand, and E and g, on the other? If BV (book value) losses were simply a drop in the net value of bricks and mortar there might not be much of a connection to future reductions in E (earnings) and g (growth in earnings). If on the other hand, much of the loss of BV is the destruction of income earning assets (mortgages and their related derivatives) then E and g are proportionately reduced as well. Since such a large proportion of the S&P earnings is related to the financial services industry the current 'neutron bombing' of the housing sector, and the associated loss of financial BVs, it is likely to translate into a more protracted bear market, I fear.



The beauty of the Fibonacci mathematical sequence and its cousin the golden ratios are indisputable. Mr. Glazier poses a very interesting question. First let examine one of his premises - that the market is self similar at all time scales.

Empirically speaking this is not quite true but appears to be approximately true. The market does somewhat resemble a log normal distribution but with a bit more peakedness and fat tails. It does seem to converge to a more normal distribution as time is increased. Perhaps this is caused by the fact that all distributions with finite variance eventually converge to the normal or log normal.

One interesting property of the normal distribution is that it is self-similar at different time scales. So if the market is normal at say time scales of a week then periods longer than that will be normal as well. They will simply be the sum of normal variables which is known to be normal as well. So we get the result that the market is both self similar and scales to longer time frames. So perhaps there is a grain of truth to that part of Mr. Glazier's assertions..

Let us consider the question of the magical 1.618 and its reciprocal .618. In fact these numbers really result from an underlying logarithmic growth pattern of the Fibonacci series. Check the logs on your scientific calculator. The natural log of 1.618 is .48 and the log of .618 is -.48. The reason for this is that it is a ratio relationship.

So too with music. All musical harmonies are based on ratios of the notes. Simple integer ratios sound pleasant to the ear. So if the market is really growing with a long term compounded drift then it is really nothing more than a process based upon equal ratios just as music is.

Therefore if the log normal model adequately explains the self similarity and the scale invariance of the market distribution then does that necessarily imply that Fibonacci levels will offer better than random turning points. Unfortunately the answer is emphatically no! If the model is a random log normal one then the turning points are also quite random. It is as simple as that.

There is no theoretical basis to believe that Fibonacci support and resistance levels hold any validity for traders. So the only possible rationale might be that one finds that they work empirically. To date no such credible evidence has ever been seen.



LeverageAfter recent discussions on the site about the levered index ETFs, I became curious as to how well these products are tracking their targets. So, using daily data for 9 Nov 2006 through 9 Nov 2007, each 1-day, 2-day, 3- , 4- , 5- , 10- , 15- and 20-day % change was calculated for both the relevant index (either S&P 500 or Nasdaq 100) and the positive and inverse ETFs.

Then the ratio of "ETF % move / index % move" was calculated. For the positive ETFs, the ratio should be ideally 2, and -2 for the inverse products. (The only tricky part is that if the index move is close to zero, the ratio can go to infinity.  So, included were only x-day periods where the absolute value of the index move was at least 0.5%.)

Means and sd's were calculated for all the ratios in each x-day period. Below are the results for each of four ETFs:

length in days | mean ETF/index ratio | sd of ratios

Ticker SSO
1d   1.96   0.40
2d   1.96   0.38
3d   1.97   0.35
4d   1.99   0.28
5d   1.95   0.33
10d  1.94   0.31
15d  1.98   0.29
20d  1.97   0.30

Ticker SDS
1d   -1.98   0.42
2d   -1.95   0.45
3d   -1.92   0.39
4d   -1.97   0.35
5d   -1.91   0.34
10d  -1.87   0.43
15d  -1.84   0.40
20d  -1.81   0.42

Ticker QLD
1d   1.89   0.59
2d   1.91   0.45
3d   1.94   0.42
4d   1.98   0.36
5d   1.95   0.36
10d  1.96   0.38
15d  1.98   0.37
20d  1.99   0.53

Ticker QID
1d   -1.90   0.55
2d   -1.91   0.45
3d   -1.89   0.47
4d   -1.93   0.38
5d   -1.89   0.40
10d  -1.94   0.41
15d  -1.95   0.32
20d  -1.93   0.36    

Adi Schnytzer suggests:

Looks fairly good, but a more revealing test might be to regress daily % change in the relevant index (Y) on change in the relevant ETF (X). So we have Y=a+bX and the test would be not only b=0.5 (which is what you have done) but also the joint F test, a=0 and b=1. Why? Because if a is not zero, then there is a bias in the tracking, i.e. either there is an over/under-reaction to large changes in the index or to small changes in the index depending upon the sign of a.

Kim Zussman writes:

While waiting for this week's bombs to start flying, here is regression of (daily return = [c2/c1]-1 )SSO vs SPY since inception of SSO June 2006 (including dividends):

Regression Analysis: SSO versus SPY

The regression equation is
SSO = - 0.000217 + 2.00 SPY

Predictor        Coef    SE Coef       T      P
Constant   -0.00022  0.00013   -1.64  0.101
SPY          1.99573   0.01630  122.44  0.000

S = 0.00245947   R-Sq = 97.7%   R-Sq(adj) = 97.7%

Analysis of Variance

Source              DF        SS        MS         F           P
Regression        1  0.090682  0.090682  14991.23  0.000
Residual Error  348  0.002105  0.000006
Total                349  0.092787

Obviously a significant slope coefficient, with beta of 2. Notice however that the intercept is almost significantly negative (alpha), suggesting the ETF manufacturer is skimming something every day (probably in the prospectus). Recall that SPY is the SP500 ETF which levies its own (tiny) fee, so you are paying more for the leveraged ETF and might rather consider futures (unless you treasure your sanity).

Adi Schnytzer explains:

Adi SchnytzerWhat matters (in the way you have run it) is the joint F test a=0 and b=2, and I have no doubt you will be unable to reject it at any reasonable level of significance. Note also that 0.00022 is a teensy number. So it would seem that these are a good buy if one is bullish medium term and doesn't mind staying in the market. Mind you, there are those of us who got into the market just before the latest crash and so, mind or no mind, are in there till the recovery. That's the trouble with futures, unless you can pick your closing date far enough down the track.

Gordon Haave remarks:

Theoretically speaking, levered ETFs work in directional markets. That is, the constant leverage results in buying on up days and selling on down days. So, in certain market periods they work out just fine and are good short term trading vehicles.

Phil McDonnell summarizes:

There are three ways investors in leveraged ETFs incur costs. First, management fees, which are usually lower in non-leveraged ETFs, presumably because there is less juggling to do. Second, the leveraged half of the fund must pay interest at the going margin rate. Even if the fund uses futures or options the interest is implicitly built into the price of the derivative. Third, he constant leverage trap. The 2x funds are designed to give returns which are twice the daily return of the underlying. They rebalance daily, which means they sell low and buy high. In choppy markets and over multiple days this leads to slight under performance relative to the 2x benchmark. Mr. Mabry's study looked at multiple days and found this slight underperformance. In contrast, Dr. Zussman's study found a perfect 2.00 multiplier on a daily basis. That is exactly what they promise, 2x returns for the day. The negative alpha is due to the sum of all three costs above. Not to quibble with Prof. Schnytzer, but .00022 is about 5.5% per year in costs. Most of this is because of the leverage. It is either real or implied interest which must be paid.



Let's say that I work really hard and come up with a long-only trading system of largecap stocks that over the last 10 years had a compound annual rate of return of 20% with a maximum drawdown of 15%. The first thing everyone says is my universe was biased — survivor bias or look-ahead bias. I know there is some bias because I test my universe and find the universe I used had a 14% return with a 25% drawdown. So although there is some bias, I still beat the universe. But I am also happy because I know the S&P500 and Russell 3000 each had a 10% rate of return and a 45% drawdown. But the bias charge still nags me. I go back to the computer and come up with a short side to complement my long-only version. So my new system is long-short. Using the same stock universe over the same period, my long-short combined program produces a 10% return with a 3% drawdown. By going to a long-short program, did I eliminate the previously existing bias?

Phil McDonnell replies:

You cannot tell if the bias has been eliminated. Let me give a simple example. The S&P and most indexes are cap weighted. Effectively this means there is a lower bound a company must reach to be included in the S&P. Assume the sample is the current constituents of the index. Then in an historical study the sample includes knowledge of the future because it includes stocks which were added and excludes stacks that were deleted.In the bottom portion of the biggest 500 stocks there is a group of companies which grew their way into the elite index. These stocks probably outperformed. Over the last few years an equal number of stocks dropped out to make way for the new ones. These grew backwards and presumably underperformed.

In this example one would expect bias to arise if the data are filtered on market cap, sales or earnings growth and stock price growth (relative strength). When those factors are implicitly included with future knowledge that the stock will cross the threshold of index inclusion it can lead to a strong bias. For example, relative strength is related to market cap by a simple multiplication by the number of shares.

The only way to really determine what the bias might be is to identify the stocks which were added or deleted from the index but would have met the filtering criteria. Only then can we truly know the bias. But if you are going to do that you might as well simply start with the original stock list which existed at the time and do the study right. 

Rob Steele remarks:

If you were data snooping you'd probably see better performance. Survivorship bias is certainly an issue; if you can, expand your universe to include everything that would ever have come into it over the test period. The big issue, however, is the "I work really hard and come up with …" part. How do you know you aren't data mining? The harder you look the more likely you are to find spurious correlations that aren't predictive. You can never be totally positive you've found something real but you can guard against chimeras to some extent. One way is to not look too hard. That is, limit free parameters and the size of your search space. Another is rolling backtests where you repeated introduce previously unseen data. Aronson's Evidence Based Technical Analysis is good on this.

Gregory van Kipnis replies:

What bias? There was still residual information despite survivor/peek-ahead, or are you saying Dr. Rafter did not use a hold-out sample either? Information decays, but if it doesn't decay too quickly you can exploit it. If (big if) there was bias, then going short part of the remaining universe adds to the bias. It doesn't subtract. Systems that learn from the past are not ipso facto completely biased.

A little bias is not such a bad thing (I stay away from all growling dogs for that reason even though most won't bite me). Learning from the past is great. Adding common sense and questioning if anything is different from the past is what creates an edge. I seek that.



Sen. Carter GlassHistory says deregulation created instability. Glass-Steagall was implemented to correct market excesses in speculation with gimmicks. In recent times this Act was rescinded. We will have to live with the consequences.

Phil McDonnell counters:

Rarely is increased regulation good for the economy or the markets. Ordinarily we would be tempted to ask if our friend Ken has any source or statistics to put on the table to support the statement that deregulation causes instability. The converse of that proposition is that more regulation decreases stability.

The two Glass-Steagall acts were passed in the first half of 1932. So that year is the focal point. Here are the weekly standard deviations for the Dow industrials for 1931 and 1932.

year std
1931 5.6%
1932 7.1%

It looks to me as though the Dow volatility increased during passage of the bills and immediately thereafter. Certainly 1931 enjoyed lower volatility.

Charles Pennington replies:

I'm anti-regulation, in general, but I'm surprised Dr. McDonnell would put these stats up to make the case that regulation increases market volatility. Isn't it plausible that Glass-Steagall was passed in response to the higher market volatility, rather than the other way around, that the market volatility resulted from Glass-Steagall?
Anyway, here are root-mean-square monthly moves for 1931, 1932, and 1933:

1930 8.1
1931 14.1
1932 16.8
1933 14.1
1934 4.8

Ken would surely say that after volatility soared from 8.1 to 16.8 from 1930-32, the benevolent government officials took action, and their efforts resulted in the steep decline in volatility from 16.8 to 4.8 that occurred from 1932-34.



Face of FearA talk at the November 1 Junto by Robert Higgs on the importance of fear elicits many thoughts of relevance to markets. Higgs is best knows for his theory of the ratchet effect of crises on government activities. He shows in Crisis and Leviathan that during times of crisis, government powers are increased and that these powers are never reduced.

He started his talk by saying that he wished he had realized many years ago that fear is the foundation for all such increases and that fears are manufactured according to normal production curves subject to the laws of diminishing marginal productivity and depreciation.
He views fear as the key emotion. And believes that fears are invented to create an opportunity for the Leviathan to expand . He groups fears into categories: fear from government itself, fear of real dangers from which government protects us, and spurious fears which are invented so that power can be increased. Planks in his theory deal with the origin of governments in conquest, the alliance between church and state, the tactics of stationary bandits who exert power from a fixed position, the creation of an ideology of fear, the economics of fear, the growth of fear during wartime. He ends with the hope that we can conquer our fears and thus go about our normal humdrum activities in a more productive way.

Against LeviathanI was quite critical of his theories believing for example that many other motivations of human behavior are more important than fear, including the five levels of Maslow's motivations, starting with physiological, safety, love, esteem, and self actualization. Of these hierarchical levels, only the safety level could in any sense be related in part to fear. I felt that much of the support for his theory was based on anecdotal and isolated events such as King Canute's assassination for collecting high taxes. I also questioned whether there was any predictive value in his classification, whether his theories could ever be refuted, the absence of cost benefit calculations in his condemnation of any and all government actions, including its function of providing for internal and external defense, and how it could be differentiated from other theories of power and behavior. I also disagreed with his wholesale condemnation of the use of fear including his condemnation of the United States entering the First and Second World War, after what he decries as false propaganda concerning the evil intentions of our enemies.

Neither Liberty Nor SafetyHiggs' current book Neither Liberty Nor Safety details many of these theories. And needless to say, he believes that the acts that followed 9-11 served mainly to legitimize a wish list of bureaucratic interventions that had been sitting on desks for 15 years, but never were able to see the ligth of day until crisis hit. He believes they did not increase our safety but took away our liberties, and never will vanish even when the need for extra patriotism recedes.

And yet, I found many parallels to the market's fears. There are 1.5 million conjunctions of fear and stock market on the search engines and many of them relate to maintaining the stock market citizen in a state of subjugation, and contribution to the upkeep even greater than that described by Higgs in his many anecdotes, and revision of his crisis and leviathan theory.

I would be interested in your ideas on the influence of fear on markets, the most recent being the fears of recession, the fear of no further rate cuts, the fear of the subprime crisis spreading, the fear of brokerage house bankruptcies and financial liquidations, the decline of the dollar, the spread of epidemics, the comparison to the crises of 1987 and 1998, the increase in volatility and what that portends, the declining earnings growth, et al., the role in fanning fear by former officials recently retired, as well as those who have long predicted Dow 5000 et al.

High on this list would be the typology of fears that have existed each year since the beginning of stock markets, and how this has engendered the 1 million % a century growth which Mr. Ellison has kindly updated here before.

Alston Mabry adds:

Happiness HypothesisIn The Happiness Hypothesis, author John Haidt uses an interesting image our human brain which has developed over vast amounts of time to handle so many tasks: he likens the mind to a rider on an elephant. The rider is our conscious, rational, aware mind - our neocortex. The elephant is everything else and is trained to be pessimistic, defensive, status-conscious, and many other things that might contribute to survival and success, but not necessarily be conducive to happiness. The rider can see farther and is smarter than the elephant, but the elephant often decides where both will go. Haidt then argues that many ancient traditions understand this dichotomy and know that the brain must be disciplined and trained for happiness.

In trading, I find there is a basic division: analysis versus execution. Analysis can seem so sure and easy, when the market is closed and one is simply crunching numbers - the elephant is asleep, as it were, and the rider is alone with his thoughts. But as soon as the market is popping, and one must put real money on the table - as soon as there is *risk* - the elephant awakens. The mind actually changes, perceives and processes the same data differently from the night before.

Perhaps the discipline of the ancients is the answer. Would Lao Tzu, or Bodhidharma, or the Desert Fathers have been successful in the pits?

Phil McDonnell writes:

MaslowAn alternative approach to Maslow's Hierarchy might be to consider the hormonal make-up of human beings. The two powerful hormones adrenaline and nor-adrenaline control our fight or flight response to potentially dangerous situations.

However they are much more than that. They directly or indirectly influence our heart rate, breathing, blood pressure, serum glucose levels and even our memory. They are a significant factor in motivating us to action. For example researchers have found that after receiving adrenaline human test subjects were more likely to take action in contrived circumstances which potentially involved even physical violence. Humans cannot easily distinguish between true emotions and those induced by adrenaline.

Other research has shown that rats will develop stronger memories when adrenalin is administered. There appears to be a simple physiological basis for this in the neurons. In particular rats that lacked the particular receptor did not develop the stronger memories in the presence of adrenaline. The important point is that memories which are formed or reinforced in the presence of higher adrenaline levels are much stronger that those which are not.

Charles Darwin was the first to study the evolution of emotions in: The Expression of the Emotions in Man and Animals with Photographic and other Illustrations (J. Murray, London, 1872).

Emotions originally developed as a way of avoiding dangerous situations as well as signaling to others the state of a particular individual. For example when an individual is in an emotional state such as extreme anger others may be warned away and learn to avoid confrontation. But when the irrationality of anger is not present others may attempt to reason with the individual. In effect the perception of an emotional state signals to others how an individual might respond in a given situation.

As traders we can turn this knowledge around. Large movements in the markets can induce an emotional reaction in other traders. In particular these movements induce an adrenaline response associated with the fight or flight syndrome. In turn the adrenaline reinforces the memory of the particular gyration in the market. So the memory is stronger and has more immediacy and in a sense more recency. So when a similar event happens again the memory is stronger, generates more adrenaline and is reinforced again.

Each time the effect of the adrenaline is to predispose the trader to action. Traders are more likely to act and act irrationally. Trading volume tends to increase. In effect the market begins to control the emotional actions of traders causing them to think with the more primitive portions of their mind. In the logic of the primitive mind losing money is equated to loss of food and ultimately loss of life. Every drop in the market is met by selling at the worst possible time. Market rises are greeted with herd like buying after the rise has occurred. It is all an emotional dance orchestrated by our own chemistry. orchestrated by our own chemistry.

Micheal Cook remarks:

Yin & YangIt is commonplace to say that two principal drivers of the market are fear and greed. I agree that there are many other higher level motivators, such as Maslow's hierarchy of needs, but the market exhibits crowd behavior, and the crowd is the lowest common denominator of human emotions. The "masses" don't seem to have a hierarchy of needs.

In the context of the market there seem to be two basic fears: fear of loss, and fear of missing out. This latter fear is a form of greed, so maybe fear and greed are two sides of the same coin, the yin and yang of markets.  

I heard a talk recently in which it was said that the market is driven by the irrational emotions of fear and greed, and that rationality consisted in finding the right balance. I found that amusing and ironic, the idea that rationality was finding the optimal mixture of two irrational emotions.
I also find that people seem to spend a lot of time worrying about things that in no way impact any current decision they might make. Things like "will there be a recession," "will the subprime crisis spread?" This strikes me as an expression of free floating anxiety, a channel, a displacement, a sublimation… 

Russ Sears augments:

Perhaps the widest and the true foundation to build on is not "fear" but "pride". Pride that is turned into "us vs them".

Granted that this "patriotism" is often used to create fears to expand powers. When used with fear this can be the most evil and complete expansion.

However, pride or "we are smarter than them" also creates a very stable base to expand love/family to create a counterfeit charitable hand.

That is: the Maslow hierarchy is built upside down to expand government.

Self actualization: what separates "We" from "Them" is "we" are the only ones with a true "need to know" the truth. We here is defined broad, to include all but "them"

Esteem: "We are smart enough to rule everybody's life". "We" here is defined narrowly as those of "us" that are in the government. Everybody else should follow the yellow brick road to see the wizard.

Love/family: "It takes a village" government will replace the dysfunctional family, which is all of "them"

Finally, the expansion into safety: government will protect "us" from "them".

From this view fear is the roof, or exterior, not the foundation. The expansion of government occurs with each level, not simply fear.

This can of course can be seen as a clear pattern in doomsdayist prophecies. "We" are the only ones smart enough to seek the truth, at all cost. Tomorrow is bleak without "us" to warn you and turn bad on its head and into good.

It is a good exercise for the reader to read many of the recent credit crunch articles with this view,  as current propaganda. This of course can be expanded beyond the markets and political readings, even into such areas as religion and popular pseudo science such as Dawkins for instance.

Tom Ryan enumerates:

The influence of fear:

1. The reliance on social proof rather than logic (looking to the herd for confirmation)

2. The tendency to extrapolate past events out into the future

3. The tendency to respond to contrasts more than absolutes

4. The tendency to non-linear weighting of probability (Kahneman & Tversky)

6. Emotional reaction to loss tends to exceed that of gain (Prospect Theory)

7. The tendency towards being consistent in one's behavior despite the financial pain in order to avoid the mental pain (fear of regret)

8. Overreaction to scarcity (scarcity programming - as one who has gone bust before I have this in full measure)

9. Strategic conventionality ("no one ever got fired for buying IBM")

Larry Williams contributes:

Fear is the greatest enemy of long term investors as it kicks them off track, off their game plan… that, coupled with short term 'gain-greed' seems to be why there are few truly long term investors.

Where does the fear come from?

Deep within our hearts and minds I postulate there is a fear mechanism—for our survival—but those fires are fanned, now, twenty four hours a day by media. Media= Negativity (fear)= Subscribers/Viewers.

Solution? Best I've heard comes from John Prine, "Blow up your TV, eat a lot of peaches, ya gotta find Jesus on your own"



Today we were treated to yet another Fed Day dipsie doodle move. What is remarkable is that this event is so predictable on Fed day. It happens almost every time they make an announcement after their meeting.

Instantly, at the moment of the announcement, the market moves dramatically in one direction. This disruptive behavior causes everyone to second-guess his positions. Thoughts of "maybe a drop in interest rates is really bad" crossed many minds. Today's drop of about 12 points in the S&P cash index in just a few minutes was sufficient to free up lots of short term stock.

Just as swiftly the market zoomed upward. All the lost valuation was restored within just a few minutes. The market even added on some ten more points to break into new high territory for the day. "Hooray, it was really good news after all!" Or was it? As this is written it is about an hour after the event and the market is slowly settling back to its level before the announcement came out.

Bruno Ombreux recalls:

Ten years ago I visited a bank trading room on a Fed day. They probably have been replaced by robots now, but back then they had human traders waiting for the Fed headline to print on the screen, with one finger on the sell hotkey and one on the buy, and they sent orders within one second of the headline's printing.

This contrasted with my then-employer's practice. Currency and fixed-income traders were forbidden to trade around headlines. After a Fed communique, the trading manager and senior traders left the trading room to meet in a quiet office. They analysed the Fed action to reach a consensus decision within 15 minutes, after which they walked back to the trading room, gave instructions to execution traders and then the whole room went rock and roll.



Now that the Red Sox are finished kicking the Rockies around we can go back to studying markets. There has been a fair amount of talk about how a few large cap stocks are carrying the market higher. The so called Hindenburg Omen is one such measure. The concern is that faltering breadth is a bad sign for the market.

To test this we can look at how the big cap weighted SPY ETF performs with respect to the equally weighted RSP ETF. Note that both funds are based on the same 500 stocks in the SnP average. So if the rally is broad based then the RSP should perform better. If it is concentrated in big caps then the SPY should be the better horse.

A simple ratio of SPY price divided by RSP price was the metric chosen. To establish what is a large variation in the ratio the min and max levels of the ratio for the last 20 days was chosen. Following are the returns for the subsequent 10 day period:

.       After  After  After

.       Max     Min    All

Count    80     162    1087

Avg.   .26%    .31%    .44%

Std.  1.96%   1.90%   1.87%

Based on the results above we see that when the ratio was at one extreme or another the market actually slightly underperformed the average of all time periods.

Alston Mabry takes it one step further:

Inspired by Dr McDonnell's use of RSP and SPY to measure the small/large-cap relationship, I did a study using the Russell 1000 (RUI) [larger cap] and the Russell 2000 (RUT) [smaller cap].

Data: March '93 to present. For each day, calculated the previous 60-day % moves in both the RUI and RUT and the simply subtracted the RUT% from RUI% to get a net difference: positive means the large-cap RUI has been outperforming; negative, the small-cal RUT has been outperforming. Also calculated the forward 60-day % move in the S&P. Then pulled out each 60th day so as to have non-overlapping data points.

count: 60

mean RUI%-RUT%: -0.21%

sd: 6.70%

mean 60-day forward S&P: +2.16%

sd: 6.29%

And here are the means for the quintiles, with the data sorted by RUI%-RUT%, as well as the mean forward 60-day % change in the S&P, and the z of this S&P change:

RUI%-RUT% / S&P / z

+8.28% +6.34% +2.30

+2.45% +3.54% +0.76

+0.64% +3.33% +0.64

-2.90% -1.76% -2.16

-9.54% -0.64% -1.54



No statistical technique can prove causation without a controlled experiment, which is not possible with the markets.

Stefan Jovanovich recounts:

David HumeMy dear wife and I once spent the better part of an hour searching the graveyard in Edinburgh for David Hume's final resting place. Finally, Susan found it. Over his agnostic bones his sister-in-law had erected a truly monstrous white marble statue of an angel. I doubt Hume would have minded. However, I think he would have objected to the notion that "controlled experiments" prove causation. This is a crude paraphrase, but Hume was rather insistent that our capacity to associate differing perceptions and to define those associations symbolically does not "prove" anything. All it grants us is the capacity to formulate hypotheses and to observe which hypotheses seem more useful than others. As Hume put it, "… that the sun rose today does not assure us that it will rise again tomorrow." Even the most common sense hypotheses can -logically - be treated only as conjectures, not certainties.

Tyler Mcclellan writes:

Causation is ironically exactly equal in logical form to the foundational principles of mathematics. They are both a priori, synthetic formulations that are both deniable and non definitional.

Ironically, Hume failed to understand that arithmetic and causation belong in the same category of thought, but Kant's brilliant insight into Non-Euclidean geometry and Gödel's work in arithmetic theory advanced that thinking to its current general acceptance. One believes Hume would likely have accepted causation if he knew he would be forced to reject along the same lines the foundation of his empiricism.

I believe the above has relevant corollary to the market because the now accepted method of scientific inquiry is that which is also repeatedly offered by Vic and Laurel as the foundation for sound investing: falsification. And why I believe The Logic of Scientific Discovery is the single most important book for applied scientists/thinkers in the modern time.

Stefan Jovanovich replies:

Immanuel KantI think Hume would have applauded Tyler's description of causation as an "a priori, synthetic formulation that (is) both deniable and non definitional." I am not certain that I fully agree that Hume would have been comfortable being described as an "empiricist." He seems to me someone who thought that the human understanding of the universe would, with luck, grow and flourish; but it would not — contrary to Kant — ever become more than our own speculations about what might or might not be. Thought, whether pure or unpure, would not let us see G-d. Like many genial people who are deep skeptics, Hume was comfortable with the notion that human thought itself was a form of revelation. (I think that is the main reason he was an irksome outlier to all celebrants of the Enlightenment - both past and present.) He would certainly have agreed that falsification was a better working hypothesis for handling money and stuff than any other, and he would undoubtedly have wanted Vic and Laurel to handle his money; but I suspect he would have sided more with Wittgenstein than the non-Euclidean positivists who are the wizards of the markets. My hypothesis is that his answer to Tyler would have been this: Even when humans discover where the square root of an imaginary number lives, a full understanding of what that mathematical tiger of the universe is saying will still elude us.



SeedlingAre you having a bad week? Judging by the Dow and the S&P averages the market was really bad this week. Both are down more than 3% over the last five days. But that is only part of the picture.

The QQQQ is nearly unchanged over the last five days. The cubes (or quads) consist of the 100 big cap Nasdaq stocks and are tech-laden. The Nasdaq index overall is following suit and is down only a little for the same period.

We appear to be in a phase transition. Risky loans are out. Much of the value stock and dividend stock performance of the last few years has been fueled by the ability to borrow cheaply and reinvest in somewhat higher yielding equity value and asset plays. As long as the dividend covered the borrowing costs, it was a safe form of the carry trade. This sort of trade was epitomized by the private equity firms.

Those days are gone. Now it is no longer possible to generate growth through leveraged magic. The market is now favoring companies which have real businesses that are growing. Growth is back.

Samuel Eisenstadt writes:

Amen. Since the beginning of 2007, growth has been in the driver's seat, as evidenced by the superb performance of Value Line's Timeliness Ranking System. The System is having its best performance in years. As Value Line readers are aware, the system is largely driven by earnings growth, momentum and earnings surprise, attributes largely ignored in recent years when "value" was the best game in town.

You might be interested in Mark Hulbert's article Value Line Profits from Patience in MarketWatch.com for October 23.

James Tar suggests:

A simpler expression: Nasdaq stocks have balance sheets that are virtually debt-free. No credit risk equals safety.

We can expand the divergence concept. Now could be an ideal time to consider going long Emerging countries that possess current account surpluses while shorting Emerging countries that have current account deficits.

Emerging countries with either a surplus or deficit have all been scorching higher on the inflationary/liquidity/carry trade going on all over the place the last several years. I suspect this is going to change.



There have been comments from analysts recently about changing correlations, for example, this from Morgan Stanley:

US Equity Derivative Strategy
Getting the Best and Worst of Correlation
October 09, 2007
By Peter Polanskyj, Christopher Metli

Correlation between sectors remains high: Correlation among the various sectors of the S&P 500 remains elevated on average, although off the peaks of late August/early September. Among sectors, recent short-term relationships have in some cases differed meaningfully from longer-term relationships.

Correlations among single stocks within specific sectors are a mixed bag: Several sectors have seen correlations among their constituents drop to relatively low levels, including Healthcare, Food/Beverage/Tobacco, Technology, Media, Software, Consumer Services and Food/Staples Retailing. Several sectors have continued to be highly correlated on an absolute and relative basis. Financials are prominent in that landscape.. Full text

Three years ago, Dr. Castaldo commented that changes in correlation may relate to changes in volatility, due to technical reasons and not to changes in the underlying stochastic process. More recently, an article by Harry Kat at City College of London referred to some of the same research around changes in correlation.

In the spirit of "know your tools" (and correlation is an important tool), these three papers seem most often cited:

Pitfalls In Tests For Changes In Correlations

Brian H. Boyer, Michael S. Gibson and Mico Loretan

Evaluating "correlation breakdowns" during periods of market volatility [pdf]

Mico Loretan and William B English

No Contagion, Only Interdependence: Measuring Stock Market Co-Movements [pdf]

Kristin Forbes, Roberto Rigobon

Boyer, Gibson, Loretan (BGL) make algebraic and empirical arguments. They create two randomly-generated series, x and y, with a correlation coefficient p : 0<p<1, and show that the correlation coefficient of a subsample of the two series is proportional to the overall p, as the variance of the subsample is proportional to the overall variance of x. That is, as the volatility of x increases, so does correlation between x and y. Here is a graph that is one take on the overall argument. The graph shows how both volatility (measured as sd of x) and correlation vary together over two randomly-generated and positively-correlated series (x and y).

Bruno Ombreux adds:

I have been reading generalist books on statistics written by biostatisticians and social scientists. As a rule, they don't like correlation coefficients. Since these coefficients are symmetric/non-causal, they are useless in advancing scientific knowledge.

In finance too, some people don't like correlation coefficients. See for instance these two articles by Embrechts and Alexander [pdf] . They are making points that are in addition to the ones in the links you kindly provided. Some issues are very ivory-towerish: elliptical distributions or joint covariance stationnarity. Others are more down to Earth: extremes creating "ghost effects" in coefficient estimation.

Anyway, the consensus is that correlation coefficients are not a panacea. Actually, it is better not to use them. If one absolutely wants to use them, rank correlation is not as bad as linear correlation. I feel the first article is dismissing rank correlation a bit too fast on the grounds that analytical complexity hinders further mathematical derivations.

What to use instead of correlation? Both articles are promoting their modern alternative pet method for measuring dependencies (copulas and cointegration, respectively). I prefer instead to follow the social scientists' suggestion and build regression models. The nice thing with regression is that assumptions are clear and easy to check. When assumptions are violated, there is a whole slew of more complicated regressions that can be applied.

Phil McDonnell suggests:

To use regression instead of correlation is misguided. They are the same! After all the square of rho is the same as R^2 from the regression.

Bruno Ombreux counters:

Yes, but isn't there more information in regression than in correlation? R-squared only gives the proportion of Y explained by X. The regression coefficients together with their standard errors add more information.

In addition, correlation is symmetric: cor(X,Y) = cor (Y,X). X and Y are playing the same role in regard to any possible explanation or causality. Whereas the regression of Y against X is not the same as the regression of X against Y. These are two regressions lines with different slopes. These create a difference between X and Y, there is an explained variable and an explaining variable. Then adding a time dimension one can introduce causality, like Granger causality .

I think that regression contains correlation but it is not the same concept.  Regression is a procedure that examines a number of different statistics, checks residuals, reformulates the equation if necessary.

Sam Humbert comments:

Another correlation quirk, from Rene Carmona, "Statistical Analysis of Financial Data in S-Plus" Springer-Verlag 2004, pg 99:

"Problem 2.4 This elementary exercise is intended to give an example showing that lack of correlation does not necessarily mean independence!"

Carmona defines X as N(0,1) and shows that Y, a simple function of abs(X) (thus entirely determined by X) with mean 0, variance 1, is uncorrelated with X.

A did a quick R-script to demonstrate; every run will have a slightly different result, but X and Y are always ~0 correlated -

X<- rnorm(100000)

Y<- (abs(X)-sqrt(2/pi))/(sqrt(1-(2/pi)))


mean(X); mean(Y)

var(X); var(Y)


Sample run -

> X<- rnorm(100000)

> Y<- (abs(X)-sqrt(2/pi))/(sqrt(1-(2/pi)))

> cbind(X,Y)[1:10,]

X           Y

[1,] -0.7878436 -0.01665691

[2,] -0.4779746 -0.53069754

[3,]  1.3390446  0.89772859

[4,]  0.3362482 -0.76580698

[5,]  1.3081312  0.84644648

[6,]  1.1859110  0.64369580

[7,] -1.6717642  1.44967611

[8,] -0.3082874 -0.81219113

[9,]  0.5582608 -0.39751106

[10,] -0.2235637 -0.95273902

> mean(X); mean(Y)

[1] 0.001646453

[1] -0.00657469

> var(X); var(Y)

[1] 0.9952368

[1] 1.004244

> cor(X,Y)

[1] -0.001873391


Also, Carmona does a good job of introducing the copula (mentioned in Dr Ombreux's post) as a generalized correlation, and, earlier in the book, nicely motivates kernel density estimation as a generalized histogram, a tool for exploratory data analysis.

At an S-Plus seminar I attended 7ish years ago, Carmona, one of the instructors, spent much time on the copula. Soon afterward, the concept became "famous" via the work of Dr Li  and others.



Suppose you bought any Friday where the stochastic indicator was oversold at the close. What is the percentage of winning trades, placing a sell limit order of c+x points for Monday? I checked in the past 10 years all the situations. If the order is not filled, you exit at Monday's close.

 3 points 96%
 5 points 86%
 7 points 80%
 9 points 70%
11 points 65%
15 points 63%

Larry Williams explains:

the problem is  such an approach has massive equity drawdowns and small average profits per trade. The losses, when they come, are much bigger than the gains. Accuracy alone does not make for a good system or trader. Risk/reward trumps accuracy every time. Eventually large losses devour strings of wining trades.
To evaluate such an approach, look at the equity curve; not just the numbers.

Jim Sogi adds:

The equity curve Larry talks about is a thing of beauty. We all know what happened after 1987 as well. The survivors prospered. If you want to argue sample, only time will tell. History unfolds in mysterious ways and you can never know the future. If you always look at 1987, you'll never trade. One way to avoid annihilation in addition to money management is to stay nimble in addition to having deep pockets. Wall Street has deeper pockets than you.

Phil McDonnell writes:

Phil McDonnellAs an augmentation, the following discussion of the features of a normally distribued random walk with absorbing upside barriers should prove helpful.  Naturally as traders this simply means using the theoretical distribution with an upside profit target.

Using a profit target will:
1.  Double the probability of being at or above that target at the end of a fixed period of time.
2.  Have no impact on your expected gain or loss.
3.  Reduce your variance and standard deviation
4.  Result in larger losses than gains

This result derives from the fact that the normal distribution is symmetric and self-similar.  Thus it obeys a property called the Reflection Principle. Each price path has an equal and opposite mirror image.  Each price point reached has a distribution of points past it and an equal and opposite distribution of points which were 'reflected back'.   Elementery proofs for the analogous case of stops, using nothing more than high school algebra, are given in my book Optimal Portfolio Modeling.

It should be emphasized that this is the theoretical model.  To the extent that one can find empirical evidence that the market does not conform to this, there may be something tradeable.  But just because you can manipulate your distribution to double the probability of a winning trade does not mean that the average winnings will be any better My Motto: You need an edge — never let your money leave home without it.



I've recently switched to an index trading strategy using QQQQ options which I now want to broaden, spreading my risk into similarly priced very liquid option-based instruments which are not very correlated with the main market indices, or each other, and am looking to maintain four such positions. However I'm having trouble finding low-correlation instruments with liquid options that I can use in the same way as I treat the QQQQ. Do Daily Spec readers know of ETFs with highly liquid options, or some other similar instruments?

George Zachar mentions:

The Select Sector SPDR correlation tool might be of value to you.

Phil McDonnell warns:

Careful! Before you rely on it, you want to make sure their correlations are based on net changes — weekly, daily or whatever.  Correlations based on price levels are spurious.  I did not see any mention of their methodology on their page so it would be good to check.



Shape of LifeRecent study of the work of Rudolf A. Raff, including his book The Shape of Life, inspired by the supposition of Galton that there are only a small number of forms that are consistent with life based on biological and physical limitations, has led me to consider the specific fixed forms that a species and a market can take. Many of the fixed forms at the basis of the phyla seem to start with a pipe: a mouth, a gut, and an excretory organ. I find that many times the market displays such pipes. Another line of inquiry are the architectural forms that the market displays. Today, the market action in S&P looks like a cathedral. The study of the shape of life raises many fascinating questions as does the architecture of the market. How they be classified and predicted, is a good starting point.

James Sogi augments:

Both Weyl and Wolfram consider the basic forms of bilateral symmetry as being intrinsic to natural processes in art and nature. (See Wolfram's artificial leaves). Weyl attributes symmetry to even deeper metaphysical processes. The market's basic bilateral process of bid and ask with two opposing forces of buying and selling tends toward the creation of bilaterally symmetrical forms. This lends itself to many predictive applications and the formation of generally negative correlations within lower time frames. The general rule seems to be negative correlation with bouts of correlation breaking out for limited durations. What is not so regular is the durations of said regimes. Study of endings and durations are more robust than study of new beginnings. In other words it is hard to recognize the new regime when it begins, but one can tell when an existing cycle is long in the tooth. On the counting point, Weyl studied the alternating symmetrical patterns prevalent in ancient art friezes. With a typical pattern coming in 3's or other odd or prime numbers, the bilateral symmetry of the market would tend towards an alternating pattern as well. This has predictive application.

Bruno Ombreux adds:

Sand DollarBilateral symmetry is prevalent in nature and the markets (for instance Lobagola, as Vic and Laurel coined it). But it is not the only form of symmetry. Sea urchins display pentagonal symmetry. Could one find higher forms of symmetry in the markets too?

One obvious market is the oil market. There is a fundamental source of of triangular symmetry in the interplay of heating oil, gasoline and crude oil, tradeable in various crack spreads. Going up one further level, oil arb relationships, geographical, time-based and qualitative, are creating a web of multilateral symmetries that are there for the taking.

Changing subjects but keeping with the symmetry theme, I am wondering about the Magic T theory, which is mentioned on pg. 72 of Vic and Laurel's book. Marty Schwartz was a successful S&P trader. He allegedly was a big fan of this so-called theory, though he didn't invent it. The name Magic T is ridiculous, evoking the worst of technical analysis. But it is some kind of Lobagola/mean-reversion theory. There could be something in it. Yet it is not easy to test.

Russ Sears ponders a related question:

A question I have asked myself, but have never studied in life forms is "why is it that the hierarchical ancestral classification of families of animals done many years ago (Linnaeus, etc.) was proven uncannily correct by modern genetics DNA research?"

The basic classification system was based on the outstanding/noticeable physical differences in life forms. This was well before the complex understanding of the chemistry of life existed.

Obviously, the divergence from normal of the life form filled a niche and created a branch. But why would the visually noticeable difference matter, as much if not more than the hidden chemical differences. Especially when the hidden differences are often the more fundamental or theoretically obvious difference of successful adaptation.

I suspect that once the more fundamental difference occurs, the visually obvious adaptations and physical evolution occur quickly.

A clear case of death to the unfit would be lack of immunity to disease for example. For a converse example the difference between herbivores and carnivores. Fundamentally is a difference in stomach chemistry, not a outward appearance. However, a well known adaption is that herbivores have eyes on their sides to see more of everything, whereas carnivores have eyes in front to see specific targets.

In other words once the subtle difference occurred did the physical difference form rather quickly. Or did large physical obvious differences come first and the subtle difference taking more time follow.

For a speculator, I propose a analogy for carnivore/herbivores eyes. The optimist seeing a vast sea of potential food, must be alert for the sudden unexpected attack. The starving pessimist must focus on the targeted prey. However, both should understand how the other view differs from theirs. The optimist to learn how to shake the predators when he is in their sight. The pessimist, should understand that the optimist has a more rounded view, to see where the opportunity truly is when it appears to come from out of nowhere.

Phil McDonnell adds:

The two key driving forces of evolution are survival and reproduction. Sometimes these are characterized by the phrases:

1. Survival of the fittest
2. Survival of the s-xiest.

In order for an animal to reproduce it must first identify a mate. For most animals the primary identification sense is eyesight. This is not to exclude other senses. Certainly hearing comes into play in the form of mating calls and territorial calls. Smell, touch and even complex courtship behaviors are all used to identify and woo potential mates. So to answer the question why is there such a strong correlation between the outward appearance of a species and its DNA we need only to realize that to reproduce the animals must first recognize each other!



SartreA big part of trading is determining ahead of time where prices will be, for profit. You can use what has transpired as a rudder to achieve this goal, or you can go on the assumption that future events are independent of current events. In the first thought, there is a hint of determinism and fatalism. Every event yet to occur in the future is more or less scripted. In the second case, there is the strictest acceptance of free will, or whatever is yet to happen acts independently of what has happened.

Which is correct? I feel these perspectives coexist in that things at times are predicted with great accuracy but at others times it seems futile. Is time the bridge between fate and free will? Luck either bad/good the conduit? Last night I sat back to take a look at the big picture. Deeply appreciating the tools of counting and the law of ever-changing, these questions popped out.

Phil McDonnell explains:

The ultimate question. Is our fate (and trading success) predetermined or do we have some control over it?

Perhaps a better way to express the problem is through the paradigm of statistical thinking. In statistics the central concept is randomness. Randomness is actually a very deep philosophical issue. It is not the same for all people. Rather randomness depends greatly upon what you know, and different people know different things.

Suppose a company has a great quarter. During the quarter many employees will have a pretty good idea that the quarter is going well. Those at the top such as the CEO and CFO will have a very clear picture. After the end of the quarter the outside auditors may get a good idea as well. Then some time later the earnings report is released to the public and the stock moves unexpectedly. To the outside investor the event seemed random and unpredictable. But clearly someone knew.

The central point is that from the perspective of those who knew of the coming announcement in advance the event was not completely random. From the perspective of those who knew nothing the event was unexpected and seemingly random. Randomness and non-randomness can coexist in different people with different information. So then the best definition of randomness must ultimately be egocentric. What is random to me is that which I do not know and cannot predict.

This concept can be quantified very nicely by various statistical ideas. For example when one performs a regression analysis of something like the Fed Model there is a statistic called the R-squared which embodies the percent of the variance explained by the model. So if the R squared was 30% the model explains 30% of the variance leaving 70% unexplained. If we only use the Fed Model as our predictor then the world is 30% less random than before but 70% is still random to us because our knowledge is limited to that model. A little counting can greatly reduce the randomness in our trading.

David Lamb extends:

Through experiences in my life I have come to understand that when I brainstorm with someone upon an idea or topic it seems as though the sum of our thoughts exceed that of only two persons, as if 1+1=3.

If this is true, is it possible that nothing is really random, given a number of participants that are knowledgeable in a given arena? For instance, if we took the topic of market direction and asked each Daily Spec contributor to give his thoughts on the subject along with providing his reasons why, then produce quantifications with qualifications, could each of our random market movements that we experience be sufficiently squashed?



John BollingerJohn Bollinger and I recently had a very enlightening and far-ranging breakfast discussion in Seattle. He was in town to deliver a talk at the Charles Schwab presentation during lunch that day. If you ever get a chance to hear him speak I would encourage it. He is very good.

During our conversation John said he had been trying to convince Vic and Laurel that what they did was really Technical Analysis. As John defines it, TA is using prices (and other market data) to predict future price moves. That definition is data domain oriented. Vic and Laurel do use prices in their work, so by definition they fall under the TA rubric.

However, their definition of what they call counting is really more process oriented. It is more a question of how one analyzes data and tests one's hypotheses that drives their counting definition. It is the application of the scientific method to finance. Counting is a methodology which applies to anything which can be quantified or classified.

Perhaps most importantly, counting looks to repeatable observations and analyses. What one observer sees and analyzes looks the same to another counter. Observations are objectively repeatable. By contrast, TA allows chart interpretations. A pattern which one trained analyst sees on a chart may not be interpreted the same way by another. TA allows subjective non-repeatable interpretation whereas counting does not.

Counting requires some sort of significance testing. To my knowledge there is no TA testing software which includes any sort of significance testing. In fact the only time a standard deviation is normally used in TA is in the calculation of John's own Bollinger Bands.

Another difference between John's TA definition and counting is that counting does not restrict itself to the price, volume and open interest domain. It could include data on corporate fundamentals, politics, volcanos, earthquakes and anything else. Thus a data domain definition of counting does not apply. Only a methodology driven specification accurately defines what counting is all about.

John Bollinger is correct in many ways. There is much overlap between his definition and what many counters practice. Most counters do look at price data because it is high-frequency and thus offers more profit potential. But the essential difference remains. TA is defined by its data domain and counting by its methodology. That is the quintessential distinction.

Steve Ellison responds:

John Bollinger's book has many concepts that are countable and testable, including an innovative point and figure method and a taxonomy of price patterns. Bollinger Bands themselves are relative definitions of high and low at points in time based on a defined lookback period. They rigorously use only data that would have been known at the time, which is very important in counting correctly.

The concept of a relative definition is very powerful. I have used it to develop other prospective relative measures. For example, I calculate percentiles of price changes using defined lookback periods.  Yesterday's 2% increase in gold was in the 97th percentile of the past year's daily changes.

The adaptive box sizing of the Bollinger Box point and figure method is another potent concept that opens many avenues of analysis. I have experimented with variations on box sizing. For example, one might, using logarithms, define a series of price points such that each successive point is 1.01 times the previous point. A tabulation of the moves between price points appears as a series of logarithmically equal jumps, which allows one to use the binomial distribution to look for non-randomness.

Steve Leslie extends:

After 29 years of investing in stocks, bonds, futures, commodities, real estate and collectibles I have come to this distinction between Technical Analysis and counting: counting is more science than art, and TA is more art than science.

I only trade stocks now and I use TA to confirm before establishing a position in a stock. I find it far easier to have an accomodative Federal Reserve with respect to interest rates, and invest during a bull market than to try and outwit the market and swim against the stream like a salmon returning to its breeding ground. People forget that most salmon never reach their hallowed spawning grounds, becoming victims to fishermen, bears and heart attacks from exhaustion.

I choose to have the wind at my back rather than in my face when I sail. Therefore the first thing I do when considering investing in a stock is to perform due diligence and conduct basic fundamental analysis.
I consider elements such as EPS growth, sales growth, and rely upon the excellent services of IBD and Zacks filters for such information.

Then when I look at a chart I am looking for an entry point for a stock. I use a variety of methods such as moving averages, relative strength volume breakouts, island formations gap ups, flags and pennants, Elliott Wave, point and figure.

The third leg of the stool is money management. Building a position in a stock, often called pyramiding, is a wonderful technique to use and also to use trailing stops to shave off a position to insure profits along the way.

The likelihood for success increases dramatically when one finds a company that has good financial strength, good EPS growth, is in the right industry group and is in the midst of a bullish stock market.

The techniques I mentioned are entirely useless in trading commodities and futures. Counting is the weapon of choice here and one which I have zero knowledge. Vic and Laurel are world class experts in this arena, and in my mind most excellent mentors, with a magnificent wealth of wisdom and insight.

To make money, one does not need to know everything about the markets to be successful but rather the goal is to become proficient in one area and focus on this. To be the Master and Commander. Look at it this way, physicians are very highly compensated professionals, and the highest are the specialists such as cardiologists, thoracic surgeons, and neurosurgeons. And in law, specialists reign supreme, from tax law, M&A, civil and criminal litigators and the like. The general practitioners are the lowest on this food chain. 



I have noticed that my trading systems suffer whenever there is a so-called "flight to quality". By this I mean not simply that the stock market is down, but that all "risky" assets are suffering uniformly as funds are redeployed toward safer instruments. When everything starts to trend is when I get in trouble.

What is a good list of indications that we are in such a regime? I would like to recognize this within the day, not days after it has begun. I can think of a few possibilities for what I would call an FTQ indicator:

  1. Simultaneous rise in strong currencies, such as CHF and perhaps a couple of EUR, GBP, JPY.
  2. Change in the spread between government bills, notes, and bonds, and the equivalent duration commercial paper.
  3. Large jump in intraday correlation between interest rate instruments and currencies.
  4. Dramatic sector rotation in equities.

FTQ happens when some market participants start to panic. I guess what I am looking for is an early-warning panic indicator.

Philip J. McDonnell writes:

To Mr. Cooper's list I would add:

1. The VIX
2. The VIC (increased range in the SnP)
3. Large positive correlation between diverse assets possibly due to margin calls even if the swoon starts with alt a debt it spreads to S&P, silver and soybeans because of cross market liquidations
4. The ratio between the S&P (quality) and the Russell 2000 (lesser quality)
5. The SPY (stocks) / TLT (bonds) ratio 



 For the past week I've been running Microsoft's Live Search and Google side-by-side and I found the comparative search results fascinating — Peter Norvig appears to have been a very good hire.

I've switched and am now comparing Yahoo Search and Google. At first glance the results are much more equal although the Yahoo results are taking about twice as long to be returned.

As the relationship between the value of the search results for Microsoft, Yahoo and Google change, comparing the relative value of the search results should make for some nice systematic trading opportunities.

Here's the pseudo code for a search engine trading system:

  1. Each week, download the top search terms from the web.
  2. Run each search term through each search engine, grab the results and compare identical items on the first page.
  3. Score each engine on its closeness to the Google results.
  4. As (if?) the gap with Google narrows, adjust the relative weighting of the two/three stocks, pairs trade, etc.

Philip McDonnell extends:

PoindexterGoogle has many advantages over its competitors. They are not standing still, but are expending more resources on their core search engine technology than the competition is. Google is getting better all the time. The competition is shooting at a rapidly moving target.

One of Big G's advantages is its large number of personal customized users. They can tailor your search query to your personal information if you have given them permission. The benefit is greatly enhanced search results. If you queried for 'movie schedules' you won't have to sift through the movie theater listings from Zimbabwe and Outer Mongolia to see what's playing. The top listings will all be in your area - no additional user input is required.

About a year ago they hired an intern who, within 90 days, was able to show the core engineering group a technique to improve search engine performance ten-fold. Initially there had been no interest in the proposal but when it was demonstrated by an actual prototype implementation, the new technology immediately became the top development priority in the company. It is impressive to see a big company that can turn on a dime when a good idea comes along.

Let me give another imaginative example where the company has an edge. Suppose Cramer comes on the air and recommends his latest Turkey Inc. (symbol: TURK) stock. Millions race to their favorite search engine to check it out. Because of their large sample and rapid search ability they can actually alter their search rankings based upon recent interest as correlated with user personal information. The investing parent will get the stock page, the cook will see a turkey recipe, and the school child will get a page full of information on the country Turkey. It is truly impressive technology that is advancing rapidly, perhaps faster than the competition can imagine.

It is often said that imitation is the sincerest form of flattery. It certainly applies in this case as there is a growing trend for competing search engines to simply scrape off Google's top results as their own. 

Alston Mabry adds:

Interesting essay on Norvig's site: Teach Yourself Programming in Ten Years

Researchers have shown it takes about ten years to develop expertise in any of a wide variety of areas, including chess playing, music composition, painting, piano playing, swimming, tennis, and research in neuropsychology and topology. There appear to be no real shortcuts: even Mozart, who was a musical prodigy at age 4, took 13 more years before he began to produce world-class music.



 There is a kind of trap that is implicit in maintaining a constant leverage ratio. Note that when the market falls one must sell to rebalance the ratio. After the market has risen one must buy more to rebalance. In a negative daily autocorrelation environment, this is exactly the wrong thing to do from a trading perspective.

An example: Suppose a stock is $100 and we buy 2x for $200. The stock drops to $90 we have lost $10 x 2 = $20. We now have equity of only $80. So we rebalance to own only $160 worth of stock or 1.778 shares. The stock recovers to $100. We profit by 17.78 so our equity is now $177.78. We have lost $22.22.

In contrast, if we had bought for cash and the stock recovered to $100 we would break even. If we do not rebalance we also break even. But by not rebalancing we run the added risk that during a decline we are implicitly taking on a higher leverage ratio than the 2x originally intended.

Rebalancing frequency at higher leverage ratios such as 5x is fraught with danger. The best way to calculate the ratio at any given time is to find the leverage ratio and rebalance frequency which will optimize the log of the expected relative portfolio returns. Ideally one should use the empirical distribution of returns as the basis for this calculation.

Charles Pennington writes:

I've tried to answer this question using daily total return data for SPY since 2/1/1993. I tried a series of leverage ratios from 100% to 1400%.

I assume that you pay margin interest of 6% annually (0.024% per day) on your borrowings. Of course one could do a little better on the calculation by including the time dependence of the margin interest rate.

Column labels (in order):
leverage ratio
$1 grew to
compound annual % return

100%, $4.45, 10.8%
150%, $5.20, 12.0%
200%, $5.50, 12.4%
250%, $5.24, 12.0%
300%, $4.51, 10.9%
400%, $2.44,  6.3%
500%, $0.87, -1.0%
600%, $0.20,-10.5%
700%, $0.03,-21.5%
800%, $0.003, -33.3%
900%, $0.00015, -45.3%
1000%, $(5*10^-6), -56%
1400%, wipeout (lose more than everything)


– 200% leverage had the highest total return, but it was not much higher than the return for 100% or 150% leverage, and of course the risk and volatility was much higher.

– Wipeout occurred at 1400% leverage. However, this assumes that one rebalanced daily. If you only rebalanced once per year, then 500% leverage would have more than wiped you out during the 2002 S&P decline of 22.2%.

These are sobering findings that suggest you should not have steady-state S&P exposure exceeding about 150%. Possibly higher leverage ratios can be occasionally useful if you have some reason to think that the expectation is higher than average in the near future, but it doesn't look good to go above, say, 400% unless you have a real live crystal ball. 



 At a certain age, one gets used to being dumb. But it's still annoying to be dumb over and over again in the same way.

A few weeks back I posted on a very high correlation between the volume of index options, on the one hand, and the volume of the leveraged long and short index ETFs. I used a moving average to smooth out the data, and, of course, that was a mistake.

The two moving average series have a correlation of +0.959, but each also has autocorrelation in the +0.6 area. R has a nice function for checking autocorrelation, (acf), and here is a graph of the autocorrelation of one of the series. Lots of non-random structure in that graph.

To eliminate the autocorrelation problem, the volume data for the ETFs and for the index options was summed for each three days, and then the percent changes from one non-overlapping three-day period to the next were used to create two series of what should be independent data points. These series each showed about -0.2 autocorrelation at the 1-lag.

These two series have a correlation of +0.348. Which looks good, but then the next issue is: What if some underlying factor is causing the correlation?

The volume data for the NYSE was taken and converted into non-overlapping 3-day totals to match the index option and ETF series. Interestingly enough, this NYSE series also had a 1-lag autocorrelation of about -0.2.

More important, its correlation with the ETF series was +0.451, and with the index option series, +0.539. So both the ETF and index options volume series are more highly correlated with overall market volume than with each other.

Philip J. McDonnell writes:

The Slutsky-Yule Theorem is quite old and says that taking a moving average of a time series induces periodic motion even though none existed in the data itself. This topic arises periodically in this forum as well. One need not feel to bad after falling into this trap.

There is the famous story of Holbrook Working, a big name economist and arguably the best statistician in the government's employ. His paper, published in the 1960s compendium Random Character of Stock Market Prices seemed to show positive serial correlation in monthly prices. Thus it was a counterpoint to the argument for the random walk. The trend following crowd was born and looked to papers by Working, Alexander and Levy as proof that trend following was the Holy Grail.

Eventually the Working paper was debunked because he had used monthly average prices as his data set. This technique created the Slutzky-Yule effect and artificially induced the apparent serial correlation. When the defect was corrected the correlations were negative. The Alexander and Levy papers were also discredited but for different technical flaws.

Apparently some trend following hedge funds and best selling investment authors still have not received the word. 



 The basic premise of the book The Only Three Questions That Count by Ken Fisher is that only knowledge that is yours exclusively will help you make money in the markets. In his attempt to carry his point, Fisher gives anecdotes of occasions when what investors were taught was wrong, and contemporaneous charts that attempt to indicate the weakness or inadequacy of many of the things that investors believe in, (including, that yield curves are predictive, that presidential cycles matter, that you should buy when the media is bullish, that you should sell when there is fear in the air, that high price to earnings ratios are bad, that deficits are bad, that seasonal analysis works, that high gold prices are bad for inflation, that value stocks are better than growth, that a sinking dollar is bad for stocks, that the VIX is predictive, etc.).

Fisher believes that the reason we believe so many false things is that we are hard wired to fear heights, we tend to believe that what has happened in the past will continue, and we place undue reliance on authority. These reasons are offered up along with a dozen other shibboleths and unproven assertions of the behavioral/finance experts, who scientists like to call the promiscuous asseverators, because of their tendency to pull out one ad hoc hypothesis after another based on experiments in laboratory or college settings that have some explanatory power for some or other anomaly.

Fisher has a host of his own beliefs that he feels are true, and he gives many anecdotes, reports of performance analysis, and examples of columns that he wrote for Forbes, as well as a report of a ten year performance that beat the S&P by two percent a year after fees. In this way he feels like almost every other book that I have read in the last 20 years on this subject, including those on point and figure analysis. He shows that all movements are due to supply and demand, that capitalism is good, that the market has a tendency to go up, that an earnings yields greater than a bond yield causes merger activity to increase and thus buoys up stocks, that the French are bad and government spending is bad, that some sectors of the economy (like health care and consumer staples) are less volatile than the market as a whole.

All of Fisher's assertions are based on similar chart analysis, and selected anecdotes, to the beliefs that he decries. Some of his ideas are rather startling and original, including his view that an ensemble of forecasts as to how much a market like bonds or stocks will move during the year has much predictive value. He believes from a few self reported successes and anecdotal references that if there is a 'space' within which there is no forecast, then that is highly predictive of where the market is going to end up.

Another original idea of Fisher's is that he can predict bear markets by the extent, enthusiasm and pricing of IPOs — Fisher pulls no punches in this book, and he tells you what he thinks about almost everything! He is down on mutual funds, because of their high costs and high frictional costs, he hates the French and thinks we can do anything better than they can. The same goes for government, and academics he mainly has contempt for, as to their practical abilities. He feels that Buffet is highly overrated, and Hank Greenberg is much abused and underrated. He loves everything capitalist, and is a big fan of international diversification. 

The book is replete with endless direct and indirect plugs for his management services, and Fisher reports an excellent track record over the last ten years, for both his picks on Forbes, and the after fees record for his clients. In by far the most valuable part of the book, which has nothing to do with the three questions about what you know that others don't, and what behavioral finance biases you suffer from, he has a nice discussion of how a person should balance the need for cash flow at various stages of his life against a terminal value to leave to his younger loved ones. He also has some sound advice about the flexibility of selling losing stocks to offset gainers when you manage your own portfolio.

The book, and Fisher's approach, suffer from several grave defects. He asks at the end of the book for readers not to criticize him unless they have done some pencil and paper work, yet since almost half of the book seems to be a recap of what Laurel and I have written about in our two books and 700 columns, and documented with proper statistical analysis, I don't believe I would be remiss if I critiqued it. The main defect of the book is that there are a host of assertions in the about what works and what doesn't work, but they are not tested, thus, it is impossible to ferret out the one or two grains of valuable information from the totally worthless.

Fisher's main thread is that everything depends on supply demand, but this is what every book on investment says. The problem is to predict supply and demand, how it will change, and what is already anticipated in the price. Another main thread is that investors are subject to regrets and pride, and that these two tendencies are hard wired into our brains, along with our fear of heights. This apparently makes us buy when it's high and sell when it's low, but the evidence that this causes mistakes in stock market judgment and that it isn't accounted for by tens of thousands of psychologists and economists and their clients, is flimsy at best.

Fisher is one of those authors who is awed by high-school mathematics. He gives an example of how to compute the profits from an 'up two years and down one year' scenario, and wallows in how the formula, which every high school student is supposed to know, is of the higher mathematics. Fisher's discussion of the difference between a geometric mean and an arithmetic mean, and how the former must always be lower than the latter, is woefully misleading and inadequate. Also inadequate are his frequent uses of perfect information to show that if you knew when a market was higher than it's low, it's likely to have shown quite a rise.

 One of the major messages of the book — that despite the tendency of the market to go up by 10% a year, the average investor by computing correlation coefficients can somehow predict when to get out of the market or reduce his exposure — is very fuzzy indeed.

The foreword to the book has one of the strongest recommendations that I have ever seen for a book, by Fisher's friend, Jim Cramer. Cramer himself says that this is not only the best book that an investor can read this year, but that it's much more valuable to read this book than to listen to his shows at all. He admits that the book changed everything he's always believed about the market and contains many a critique of his methodology.

It is regrettable that the book has so many lapses in its analysis, so many unsupported assertions, so much anecdotal evidence without any statistical analysis or awareness of uncertainty, variability, retrospection and multiple comparisons. Because of this the average reader is, unfortunately, likely to come away from this book with as much bad information as good.

As a final positive note, one thing that Ken Fisher claims in his book that sounds true to me, is that any fear that is widely broadcasted in advance will have no impact on the market. Or perhaps in more predictive terms, to the extent it's influencing the market, it creates a bullish situation because it's already encapped by the price, then price will increase when the fear subsides. Fisher gives many examples to carry his point, and if it weren't so hard to quantify this, and there weren't so many highways and byways to tie down, this would be a very good thing to study.

I feel that this point is highly relevant to sub-prime, and also to the asset seizure on the brokerage house that is famous for seizing the assets of its own customers, (how ironic).

Phil McDonnell reviews:

 In his recent book, The Only Three Questions That Count, Ken Fisher provides a readable account of his brand of contrary thinking. His broad theme is to think against the crowd and the media. For example his three questions are:

1. What do you believe that is actually false?
2. What can you fathom that others find unfathomable?
3. What the heck is my brain doing to blindside me now?

The essence of the first query is that the media continually spews forth a kind of pop economics much of which is exactly wrong. If one avoids listening to the meme du jour and does the research personally then the much of the media silliness can be avoided. Simply eliminating the wrong and hurtful memes can improve one's investment performance.

In considering question two the author points out that there are many data sources available on the Internet which even relatively unsophisticated people can access. He describes how to calculate a correlation coefficient, a job which is done by a spreadsheet or stat program. Unfortunately he fails to mention the spurious correlations which arise from calculations based on levels. This is the most serious flaw in the book. Most of the evidence produced is based on correlations calculated on levels.

In his discussion of question three the author resorts to teachings of behavioral finance. This field is one I find suspect. Much of the empirical work is subjective. The analysis and explanation of the subjective results requires a Byzantine set of rules and rubrics to explain the evidence. Fisher also quotes some obscure papers in behavioral finance which he co-authored. Having said this, there is still much to be said for studying human psychology as it is reflected in the behavior of markets. The book offers a relatively pop account and explanation of the vocabulary of behavioral finance for the uninitiated.

As the Chair has recounted, the book is a thinly disguised pitch for the author's investment advisory services. His service is essentially oriented toward tracking the MS world index. The philosophy is to track the index as closely as possible and only to deviate when his three questions indicated that he should deviate by over weighting or by under weighting a sector or country. He calls these deviations "side bets." Generally his track record has followed the index in recent years but notably not underperformed overall.

This book is an irreverent, opinionated discourse on investing and researching market ideas. There are many pearls of wisdom and some notable flaws. Overall it is a fast and fun read and offers many macro economic hypotheses. Because of the technical flaws, testing the hypotheses should be viewed as an exercise for the reader.

Andrew West remarks:

I suspect much of Fisher's book was ghostwritten, probably with an eye to marketing. His Forbes column is occasionally intelligent. However, I lost most of my respect for him when I gave my name and address to his company, and was swamped by primitive and hucksterish marketing pieces in the mail for some time afterwards.

Makes me think of the Little Book of Value Investing, allegedly written by Christopher Browne. A true value investor would never pay for it, given its small pages, large type, and lack of new ideas. Sounds like marketing committees at investment managers are demanding that their figureheads publish books on investing. 



 It should be noted that investment grade bond domestic bond issuance set a monthly record as of Friday, with borrowers selling $105.92 billion in securities (and the month isn't over, with three full days remaining.)

Among the commentary supporting the debt binge, equity strength is highlighted, as well as the perception that despite the aggressive pace of LBO/PE deals. The thinking is that it will be a quite some time until a high-profile deal melts down. Moody's agrees, apparently, with a report issued last week forecasting a decline in default frequency to a record low.

And the proceeds? S&P 500 components have spent more than $440 billion on share repurchases over the past 12 months, according to J. P. Morgan.

Alston Mabry writes:

Having spent a while recently as a spectator at a PE buyout, from the acquirer's point of view, I can offer this observation from the cheap seats. The PE craze appears to be fueled, just like the hedge fund industry, by cheap leverage. The buyout firms are using leverage at 5% to buy cash flow of 10% and pocketing the difference. On a $5B deal, that's $250M/year. And if a few years later somebody comes along and offers you a price you can't refuse, like Riverdeep did for Houghton, then so much the better.

So where's the weak point? Or is it a free lunch? If there were to be a downturn that pushed too many of those cash streams negative, but still the interest payments on the leverage keep coming due, then could some buyout groups get hurt?

Stefan Jovanovich comments:

I doubt that where we sit qualifies as "seats". Even calling the location a knothole in the fence is probably an exaggeration for our odd-lot venue.

Over the last 12 months the return on common stock investments net of commissions before taxes was 19.65%. The pre-tax, post-expense return on our private investments during the same period was twice that - 38.52%. Alas, no one is eager to buy that private cash flow with or without leverage because the world of finance capital for small private businesses here in California no longer exists.

The roll-up boys are long gone and so is small business lending unless you include your Capital One credit card in that definition. The returns on small business equity here in the Golden State will continue to be outsized because there is no way for going concern values to be monetized. No one in their right mind wants to acquire legal employees unless they have already amortized that risk with a full-blown HR department.

If our situation is at all typical, then the flow of capital from small business owners into securities may have a great deal longer to run. We make a great deal of money from our private business, but we know that every new investment in it is truly sunk. We can only get a return from operations, not from selling.

Philip J. McDonnell writes:

In a way the return on private equity is higher than the publicly available equity. With the advent of Sarbanes-Oxley the costs to comply went up for public companies. It simply added costs to their operations with no compensating income gain for the companies or the investors. In addition to that there always was some kind of added cost borne by public companies. As usual the regulatory costs only ratchet upward never down.

With the example of a public company with a 10% return (PE=10) which is purchased for money that was borrowed at 5% giving a net 5% after interest cost return, the real situation may be better. In fact given the regulatory savings the newly private company may be able to yield 11% thus boosting the net return to 6% which is a 20% better ROI.

The benefits do not end there. There are no margin calls in private equity. Contrast that situation with a typical highly leveraged hedge fund. The hedge fund can borrow too. But if it is trading marketable securities there will be margin calls. Typically the portfolio will be marked to market daily and immediate liquidations will ensue if the value falls below minimum margin requirements.

For a private equity firm there is no daily quoted valuation. There is only the book value shown which is typically a high and inflated number set at the time of the buyout. The lender is actually looking to the cash flow more than any vague concept of market or portfolio value. Lenders want performing loans. To them performing means the borrower is repaying as agreed. It is capitalism as it was designed to work. When the government finds ways to regulate and to restrict credit the markets find ways around it. Ultimately the markets will rule.



 The Traveler's Dilemma and Prisoner's Dilemma are two examples of cooperative-competitive games. They contain aspects of reward for cooperative behavior and rewards for competitive behavior. In the Traveler's Dilemma game picking a higher number is cooperative play. The player is maximizing the reward to the two-player community. Picking the low Nash Equilibrium is competitive play. The player is maximizing the minimum reward. Naturally as the reward for competitive play increases the number of actual players using competitive strategies increases as well.

There is a strong parallel to the market. If we all buy stocks with all of our money they will go up. The community of investors will all gain. But human nature being what it is we will always be at least somewhat fearful that someone else will sell first and we will be the last to get out. Thus based on a news event or even non-news some will choose the competitive choice to get out early. They seek to avoid the maximum risk of a putative future decline by getting out before the other guy. However the long-term drift strongly indicates that such anti-cooperative behavior is self-defeating and leads to opportunity loss.



I notice all three major stock indices begin with a "1", as Benford's Law says they might tend to:

I N D U     1 3 3 2 6 . 2 2
S P X         1 5 0 5 . 8 5
N D X         1 8 9 8 . 7 9

Philip J. McDonnell notes:

A quick glance at a slide rule shows that the difference between the number 1 and the number 2 takes up about a third of the scale. The slide rule scale is logarithmic and thus enables adding the lengths of the logs to facilitate multiplication. If the underlying process of any stochastic process is multiplicative as opposed to additive, then the process will follow a lognormal distribution. So if the process is lognormal then one should not be surprised that the proportion of 1-digits is one third.

George Zachar adds:

The Wolfram Demonstrations Project is a new, cool, free math visualization tool. I've run it on both Windows and OS X. 

Gibbons Burke adds:

Wolfram seems to have borrowed some features (especially the parameter sliders) from Graphing Calculator, which was distributed free with every Mac since January 1994 (though no longer — Apple has its own now). Version 3 is available for free and it is enjoyable to play with. 



 A seeker of knowledge inquires:

Q: Can you give me a good layman's term "working" definition of R squared? Questions that pop up are why is it important? What does it reflect? Is it predictive?

A: R Squared is simply the square of the correlation coefficient. As most will recall the correlation coefficient can range from +1 for a strong positive correlation to -1 for a strongly negative relationship between two variables. Two variables which are unrelated will usually have a correlation around zero.

So when we square the correlation coefficient we get a number between 0 and +1. Remember that the negative correlations become positive so there are no more negative numbers. We also almost always get a smaller absolute number because multiplying two numbers less than 1 always gives a small number (except for zero and 1).

There is another interpretation. The R^2 also generally associated with a regression model (but need not be). The R^2 can be thought of as representing the percent of variance which is explained by the model. Mathematically things are linear in the variance but not in the square roots such as the standard deviation and correlation. So we can decompose the total variance of a regression into the part that is explained by the model and the part that is unexplained (the error or residual variance). The three relationships are:

Total variance  =  Explained Variance  +  Unexplained variance
    100%       =        R^2           +       (1 - R^2)
Total SSq       =  Explained SSq       +  Unexplained SSq

In the last line the term SSq means the Sum of Squares. The sum of squares relationship simply comes about because the variance is simply the average sum of squares. The bottom line is that if you have an R^2 of 25% you know that it explains 25% of the variance in the variable you wish to predict. You also know that the correlation coefficient is +/-50% because .5 * .5 = .25. Conversely if you know that a correlation coefficient is 90% then you know the R Squared will be 81%.

From just the R Squared you do not know if the correlation is positive or negative however. For that you have to look at the beta coefficient of the regression which tells you which sign to choose for the correlation coefficient. 

Yishen Kuik adds:

I've found an intuitive interpretation of correlation coefficient (R) to be a measure of how in phase two datastreams are.

For two dataseries X and Y:

R = (sum of Zx * Zy)/(N-1), where Zx is the z-normalized series X and Zy is same for Y

Hence, the more the below-mean datapoints in X and Y coincide, the greater the value in the numerator, since the product of two negative Z scores is positive. The corollary is that above-mean datapoints will also coincide, and since the sumproduct of two positive numbers is also positive, also contributes to a larger numerator.

Hence I think of this coincidence of above/below mean datapoints as in phase.



Decades ago I had heard the quote, "There are three kinds of lies: lies, damned lies, and statistics," attributed to Mark Twain. Then a few years ago I read that Twain got it from Benjamin Disraeli. However just the other day I read it attributed to Thomas Carlyle. The three were contemporaries. Does anyone know the true originator?

Philip J. McDonnell replies:

Phrases.org says Twain attributes it to Disraeli in Twain's autobiography. But Phrases.org claims the earliest known written reference is by Lord Leonard H. Courtney. The observation is made that Courtney's quote attributed the phrase to the "Wise Statesman" who may or may not be a specific person. 



This price movement of June bond futures on the morning of Apr. 23 taught me a lesson.

From 7:52 AM to 8:55 AM (slightly over an hour) the market went from 111-10 to 111-01. During this period it traded 19,459 contracts. There were no announcements during this time frame.

Then from 8:56 AM to 9:57 AM the market completely reversed its direction and went from 111-10 to 111-01, exactly where it began two hours previously. The additional contracts traded in that time frame: 20,469. Almost the exact amount as on the way down.

Why would market participants all of a sudden change sentiment, when there were no announcements? What makes participant bias change so abruptly without news?

Robert Ray replies:

A nine tick Lobagola? Take that same move from the perspective of someone that wasn't watching every tick and it would appear that not much at all went on as the price was the same two hours later. There is a meal here in how one perceives things.

Edward Talisse remarks:

This behavior happens all the time, not only in US but in Bunds and JGBs. It's the hedging of new issue deal flow. As corporate bonds are priced, dealers (read: swap desks) trade the order flow but usually end up flat. It has nothing to do with availability of new information.

George Zachar adds:

Deal flow is information, and gaming the hedges and their lifting is a major part of the debt market's micro-process.

Phil McDonnell explains:

A price quote is for a completed transaction. It is always between a buyer and a seller. So the number of buyers always equals the number of sellers — no exceptions. Only the price adjusts. So logically the number of long contracts equals the number of shorts always, all futures markets — no exceptions.

You can infer something about the initiator of the trade. He is often a market order coming from off floor. The market order will cross on the floor (or in a computer) with the current bid or ask. So a down tick usually means an off floor trade crossed with the bid. An uptick often indicates an off floor market order crossed with the current best ask. This is only the commonest case and must be tempered with the realization that limit orders will appear as bid/ask quotes as well and may be confused with market maker activity. Also you cannot know if open interest is increased or decreased by a single trade, but you can track it on a net basis over longer time periods.

Victor told the tale of the elephants always returning by the same path in his book EdSpec. It was a story originally told by Lobagola. The story holds true for markets as well. Markets tend to retrace the same ground — often many times.

Is there statistical evidence for this? One need look no farther than Doc Castaldo's recent post on the Pythagorean scale and markets. His data showed that markets exhibit small changes far too often for it to be chance. This is the salient feature of speculative markets. It happens all the time. Huge amounts of money are made and lost on the very numerous small change days.

Consider the idealized model of a market with a single market maker. He quotes 100 to 101. Someone sells to him at 100. So he drops his quote to 99 to 100. The very act of dropping the quote inspires more selling. He drops his bids to 98, 97, 96 then 95 in succession as more sales come in. His average cost is about 97.50. Now at 95 a funny thing happens. He hits somebody's threshold of pain, whose stop is executed at 95, and our market maker winds up too long. Now he holds the price firm or even raises it.

On the perception of firming or even rising prices speculators start to nibble. Our market maker now slowly raises prices back up to where they were before. Only this time he is supplying at the ask price. So he makes his spread which he tries to maintain at one point. By the end of the day the quote returns to where it was before. Our market maker has sold his inventory at an average of 98.50. The market has done a Lobagola down then up. The news reports the market was unchanged today and everyone yawns. But our intrepid market maker made his spread going down and then back up. He can afford to eat at Delmonico's yet another night.

Sam Marx adds:

As a former market maker on the floor, I can say that this description is a good approximation of what happens. That's why the distribution of prices is higher at the middle than the normal distribution. The market maker is more confident within the existing range.

Also, when there is an large influx of sell orders, the market maker steps aside, buying smaller quantities, a minimum number of lots at each lower price to perform his function, and lets the price really drop. When buy orders start coming in, or when the sell orders stop, he starts buying. That's why the price distribution is lower than the theoretical normal distribution a short distance from the middle of the curve. A leptokurtic distribution.

J.T. Holley extends:

I'm looking at long bonds today. The UK 50 year yields 4.1% and the French Euro 50 year around 4.0% Does this mean that the US long bond is going to 4%? Has anyone with a scientific bent studied/counted the ratios/differences of these instruments' yields?

Faisal Essa responds:

The UK and EUR long bonds are said to trade at those levels because of the local pension and insurance company law changes that have forced pension funds to match their long duration liabilities with long duration, high quality bonds. This has led to reduction in allocation to equities and a shortage of long bond supply relative to demand. To make matters worse, restrictions on currency exposure and derivatives overlays force the funds to stay in their own market rather than buying other countries' long bonds. This legal framework is quite unfortunate for Dimsonian pensioners.

This situation (along with changes in US pension fund law) does have some influence on US long bonds and long TIPS.

Charles Sorkin suggests:

If the media decide to exploit the notion that the American homeowner needs to be bailed out, bonds could fly. An interesting hedge (although extremely difficult to model and get the ratio correct) would be a short bond position hedged with long support-class POs. Difficult for the small investor to find, but some pieces have been floating around lately in the upper $30s to upper $40s on long paper. Such securities could return your principal within two years on a bond rally of 100-200 bps. 



 When we do a study based on historical data and find a statistically significant result at the 5% level, we really are saying that there is less than a 5% chance that this study is completely attributable to chance. But if we observe some pattern in recent market action and then study it, that can be a problem: the multiple hypotheses problem.

One might think that if only one test is done that only one hypothesis was tested. Sometimes this is true. Other times traders will be intense students of the markets and notice a recurrent pattern. The trader then forms a hypothesis based on this pattern. It is properly tested on the most recent data and shows itself to be statistically significant.

There are two problems with this approach. First, if "the most recent data" include the same patterns that were observed and used to form the hypothesis then we are subject to the multiple hypothesis issue. This is true because that exquisite pattern-matching machine called the human mind continually looks for non-randomness and meaning in everything it sees. The mind tries out incredibly many hypotheses all the time. Most of us cannot even guess how many hypotheses our mind tries out before we identify one as interesting. So including the data, which formed the hypothesis, implicitly includes an element of multiple hypothesis testing.

The other problem is that we already know that the data will validate our study because it was used to help form the hypothesis. So it is not independent data but inherently biased. Thus our significance tests will be biased toward acceptance.

The best way to do these kinds of studies is to form the hypothesis on one data set and to test it on another completely different data set from another period.

Bruno Ombreux adds:

Or consider the same period but another market. For instance, if some phenomenon shows up in US stocks, test it on French and German stocks, too. There must be a reason for the putative phenomenon, either microstructural, behavioral, or economic. If so, it should show up in several markets. This extends the amount of testable data. One must be cautious with microstructure however, because it can differ. 

Philip J. McDonnell responds:

I do not agree with the idea of testing on data from different markets during the same time period, because many markets are highly correlated on a coterminal basis, sometimes as much as 90%. So it is really not an independent test on independent data.

But when one uses different time periods the correlations drop to near zero. So we can conclude that the data are truly out of sample.

Bruno Ombreux replies:

Dr. McDonnell is 100% right, but I still think it is not completely worthless to extend the sample to other markets. If you test a hypothesis on the US market, you'll be interested in the cases when you reject the null. Now, you test the German market and you still reject the null. You're right — not very useful. But if you fail to reject it on the German market, you need to come up with a very good explanation why it would work in the USA and not in Germany.

This is not nearly as good as different time periods, but it can be useful and increase understanding. 

Yishen Kuik adds:

I like to take an idea that has demonstrated its worthiness in actual trading in the US, then port it to other countries to see whether it works or not. If one has a group of countries for which the idea works and another for which it does not, it becomes interesting to try to figure out what members of each group have in common.

Nigel Davies remarks:

Presumably you're also taking account of time zones here. I've noticed that other markets tend to be led by the US during the day session (and even a couple of hours before its open) and have their measure of independence at other times. China is probably leading the overnight action now and Europe dominates during its morning. So perhaps it's not so much cultural as different time snapshots showing a certain similarity.

Martin Lindkvist extends:

Like the human flus that originate in Asia, many market ones seem to come from there too. Now, last night's Chinese flu seems to be of the same strain as that of late February. And as such, the market's immune system should be better prepared now. Perhaps a bit of coughing, and some sneezing for a little while, but not much of a fever this time? 

Henry Carstens adduces:

From a book recently recommended to me: "Routine design involves solving familiar problems, reusing large portions of prior solutions. Innovative design, on the other hand, involves finding novel solutions to unfamiliar problems." To borrow a quote from a friend, "Better necessarily means different." 



 Ostensibly Japanese traders swear by their candlestick charts. There is an entire genre of single day and multi day patterns that supposedly will light the trader's way in the market.

A recent paper by Marshall, Young and Rose of Massey University looked at these candlestick patterns and found them wanting. For the most part, they do not work as expected.

A scant three patterns do show some promise, but given the multiple hypotheses this is probably just be a spurious result.

« go backkeep looking »


Resources & Links