Nov

1

 I am always impressed with how speculation is crowd oriented. This is particularly true when one company in an industry is targeted for acquisition and its industry mates rise in sympathy.

OK, that's a given. However at this particular time there are a number of companies in the "footcare retailing" business giving similar signals. What happened? Did Americans wake up and realize that they were shoeless?

Sep

1

The flexion of the day stayed in Germany [8/30/2018]. Note how the Dax is down 110. Apparently they left for beer at 11. And the bunds are up 78.

Anatoly Veltman writes: 

Note the reluctance to discuss or contemplate LEADING indicators that actually present economic sense. For example: everyone knows that EUR currency is associated with economic development and "order". While Swiss currency is associated with defensive posture and "calamity" hedge. The EURCHF pair doesn't move as much as other pairs in FX, because both currencies in the end are European currencies. Yet the pair has reversed since yesterday's SP record, and managed a straight 1% drop since…Now(?) Steve here is raising a possibility of a calamitous announcement over the weekend, but he wasn't raising it "before" the SP moved lower?

Cagdas Tuna writes: 

Average short interest % to floating shares in FAANG is 1.64% and if we exclude Netflix it is 1.07% Does that kind of statistics provide any hint to market tops or bottoms? 

Bill Rafter writes: 

In our shop we have done a lot of work with short interest (SI). First, we noted that THE expert on SI (Erlanger) first identified "stocks to buy" and then screened them for any added benefit that could come from SI. Next, we worked the research from the opposite angle. That is, we first identified stocks with good SI potential, and then went on to screen them.

We were wrong. Apparently half of the stocks with high SI are truly good shorts. Of the remaining a relatively small percentage became good short-squeeze candidates. The others just went nowhere.

However we went further, studying stocks with extremely low SI. The theory is this: If you have a stock that even a damn fool idiot will not short, it probably means something. Assuming that certain fundamentals are unknown, we came to believe that it reflected on the quality of management. Of course we have no way of proving that, but still consider extremely low SI as bullish sentiment. That's intuitive, but at least we have some research to back it.

Aug

13

 "Payroll Tax Receipts Growth"

Reconciling macroeconomics and "job chatter", understand that the data do not support the enthusiasm of the news. Everywhere there are reports that the number of job openings outweigh the numbers of those looking to be employed. That may be the case, but the fact is that they are not being employed. Not yet at least. Maybe it is because the prospective American workers are unqualified (e.g. cannot pass the drug tests).

Whatever the reason, the jobs are not getting filled. That will change, but it may require some technology to assist the new workers. The trick is to make the job simpler for the unqualified, but no so much so that their jobs can be taken over by robots.

There are countries that have significant growth, and it is usually where the education system has provided the students with more than a sense of entitlement. Pardon my pessimism. We are actually quite bullish, but we would appreciate it if the numbers confirmed. Soon.

May

31

This is disappointing. It does not suggest a bad economy, but one in which the growth in jobs is proving quite stubborn.

Apr

11

In the last few days one of the economic talking heads commented on how he has "not seen volatility like this since" sometime in the past. I forget whether the former time was 1998 or 2008, but it doesn't matter, as there are many periods in the past with greater volatility.

My quick look at past volatility consists solely of looking at the height and duration of VIX in earlier periods. I took the standard measure (VIX) because of its relatively universal acceptance. I could use some of my own measures, but not without the risk of being flamed for subjectivity, despite the fact that they compare with VIX on a relative basis.

Question: Is there something I am missing? Is there some measure of vol that I am unaware of? Could the high volatility simply refer to the gentleman's equity balance? Could this simply be an effort to gain a headline, i.e. fake news? Any thoughts?

Gibbons Burke writes: 

The VIX seems skewed to being more sensitive to downside volatility and not so much to upside volatility, and it is based on one instrument: the S&P 500 index calls and puts and their ability to speak to the volatility of the underlying index.

The standard Historical volatility calculation of the same underlying instrument used as the input for option pricing models is somewhat more flexible in that it can be applied to any instrument since all it requires is daily closing prices, and the S&P 500 retroactively before the VIX was created.

The two measures, VIX and SPX historical volatility correlate closely—and most interesting is when they depart from that correlation, which shows that the options market is anticipating something which has not shown up in the movement of the underlying. You know all this of course, and have developed some very interesting work on options and their open interest already as it relates to the underlying, no?

In technical analysis realms, average range, and Wells Wilder's Average True Range (which considers the previous day's close as part of the day's range if it is above or below the high or low of the day, which captures post-close volatility and gap moves) has been used as a volatility measure for input into risk allocation components in trading systems, and as breakout bands for trading systems like one made famous by Larry Williams and others like Steve Notis.

A newer volatility measure which came out of chaos theory ideas when they became popular measures the total range (or true range) over some n-period window of previous market activity, and measures the sum of all the individual period ranges (or true ranges) as a ratio. Two instances of this volatility measure are Adam White's VHF index (vertical-horizontal f-something) and CTA Ed Dreiss' Choppiness Index. Both are solid conceptually, easy to calculate, and are already implemented in many systems.

anonymous writes: 

For the S&P, here is the mean daily High-Low range as a % of the Open, for each year since 1962:

year  /  mean daily H-L as % of Open

2018 -  1.44%
2017 -  0.51%
2016 -  0.95%
2015 -  1.10%
2014 -  0.86%
2013 -  0.85%
2012 -  1.06%
2011 -  1.62%
2010 -  1.36%
2009 -  2.00%
2008 -  2.74%
2007 -  1.17%
2006 -  0.85%
2005 -  0.88%
2004 -  0.95%
2003 -  1.41%
2002 -  2.08%
2001 -  1.75%
2000 -  1.84%
1999 -  1.54%
1998 -  1.58%
1997 -  1.42%
1996 -  1.01%
1995 -  0.72%
1994 -  0.82%
1993 -  0.71%
1992 -  0.82%
1991 -  1.11%
1990 -  1.31%
1989 -  0.95%
1988 -  1.22%
1987 -  1.77%
1986 -  1.12%
1985 -  0.79%
1984 -  1.00%
1983 -  1.01%
1982 -  1.60%
1981 -  2.03%
1980 -  2.21%
1979 -  1.55%
1978 -  1.60%
1977 -  1.37%
1976 -  1.60%
1975 -  2.16%
1974 -  2.58%
1973 -  2.06%
1972 -  1.53%
1971 -  1.54%
1970 -  2.09%
1969 -  1.74%
1968 -  1.78%
1967 -  1.62%
1966 -  1.77%
1965 -  1.26%
1964 -  1.16%
1963 -  1.26%
1962 -  1.73%

Sushil Kedia writes:

​VIX measures the price of volatility all are wagering on. Price is the weighted mean/vector sum of all individual values of volatility the various have for themselves. 
Combining a few well accepted ideas, here & everywhere else: 
Depending on where one is in the market food chain there are different versions of what is noise and what is tradeable information content. 
So a simple and effective & consistent to calculate the value of volatility for oneself is to objectively write down what is the minimum movement size below which you dont act. For a HFT robot it could be every tick & for "markets cannot be timed behemoths collecting only other people's money, a.k.a. long only passive funds" it could be 5%. Whatever it be define your sensitivity and lets call it your sensitivity unit move. 
Then each occurence of a move of a unit size is counted — as in counting by toes or a computer programme over any observed length of data. Count the absolute vaues of the Unit sensitivity. Divide the net change over the same length of data with the sum of absolute values of unit sensitivities observed. 
A straight line move would thus give you zero volatility or noise and a perfectly tradeable information content. If however over the observed length of data, on the other hand, net change is zero then there is only noise. 
I remember, many years ago Bill & few others had discussed here how Point & Figure method from the university of mumbo jumbo is an approach that is very similar to this thinking and a fantastic way to separate signal and noise relevant to each as per their forebearance within the food chain. 

Mar

14

 Interesting article on the cost of a loaf of bread in 19th century inspired by reading of David Copperfield where he bought a loaf of bread at 9 years old for a pence to stave off hunger.

Bill Rafter writes: 

Let me assume that the costs of making bread by hand in 2018 is somewhat equivalent to making bread commercially 200 years ago. Since the bread of Victorian times was "wheaten", I will compare it with today's whole wheat.

I know these things because I make virtually all the bread we eat because it tastes better, looks better and is undoubtedly healthier.

When you make bread by hand (no electric mixers) you always make two loaves because it is more efficient. If the second loaf is more than you need, you will have no trouble giving it away and make a friend by doing so.

You start with 1000 grams (2.2 lbs.) of flour. If that is the supermarket brand it might cost you $1.25. To that you will add say 750 grams of water (free), 22 grams of salt (nominal) and ¾ teaspoons of yeast (~10 cents). You don't need to buy yeast, as you can make your own (that's what they call sourdough), but the latter is only efficient if you make bread daily. So all-in, your raw material cost for two loaves is less than $1.40, or 70 cents per loaf. To that add the cost of the oven, 475 degrees for an hour and you are probably looking at a dollar per loaf.

The result will be great-tasting with a nice crust, a fantastic peasant-type bread that is highly nutritious. The two loaves will weigh about 1040 grams, or 570 grams per loaf. You would think more, but all that water steams off. So for comparison to Victorian times, the two loaves will weigh about ¾ of the mentioned quartern loaf meaning that the quartern loaf today would cost you $3. BTW, The largest loaf I have made myself was 3 kilos (6.5 pounds) and a real pain (pardon the pun) to handle.

I have not included the cost of labor. although making bread requires skill, it is easily mastered. After all, everyone in the third world knows how to make great bread, and there's a company here that uses prisoners to make great bread. In Dickensian times the baker's assistant was probably not paid, but given bread as wages, which is contrary to the article. Note that a lot of the time involved in creating bread is in waiting, during which the breadmaker can be doing other things. For example, I can easily bake bread while trading the markets. Thus the cost of labor is somewhat hard to quantify.

Aside 1:

The above is the basic plan for great homemade bread. But limits can be pushed. For example, my personal favorite is adding 450 grams of Kalamata olives to the kilo of flour and substituting beer for water. My family's favorite adds 400 grams of chocolate bits, 200 grams of walnuts and uses pear cider instead of water. It's not too hard to imagine a loaf of homemade bread costing in the vicinity of $10. But of course, the taste is incomparable.

Aside 2:

The article mentioned "wheaten". In Victorian times the bread in England most likely included a fair amount of barley flour, which was more common and cheaper. Today, barley flour is not as common and more expensive. I like the addition of barley as it gives a sweeter flavor.
 

Mar

4

If you look at the Daily Spec site you first see a calendar. Most people probably just breeze on by. But of interest this month is the correlation between stocks and bonds. In February those markets, which usually oppose one another, have been moving together. That is evidenced on the calendar by either Green days (both moving up) or Red days (both down). This month only 2 days have not been either red or green. Of the many market statistics we watch, the moving correlation of stocks and bonds is our oldest (i.e. time-tested) and a very important input to our basic market algorithm. It is valuable information.

Feb

27

Well, as long as you are here, let's see what the entrails say:

Fundamentally, there is no recession in sight. Here's a look at one of our best indicators on that front, the comparison of Total Loans and Leases against Initial Claims. There were some fundamental data that foretold a problem, but they have dissipated with the recent selloff.

However, the current growth of Payroll Taxes is disappointing, meaning that the stock market should not get a boost from the next Non-Farm Payrolls (i.e. Jobs) Report. Many of the changes enacted by the new Administration have not yet taken effect. You will know that is underway when you see the Payroll Taxes accelerate. It will happen; just not yet. Use any weakness as an opportunity to get long and longer.

N.B. The effective date of the Non-Farm Payroll Report this month is February 16, but this chart follows through another week. We have several ways of showing the Payroll Tax data. The view here is the one we usually display, and it is illustrated for consistency. However other views are considerably more pessimistic. Rather than be alarmed, look at this as opportunity.

What you must watch out for is sentiment, or its partner "exuberance", which had a monumental effect recently. Here's an update on the "Smarts vs. Amateurs" which we had posted before.

As always, please feel free to contact us with questions.

Feb

14

There has been some comment on the timing of the so-called "smart money". Just how good are our betters at trading these exciting markets?

While we have no specific knowledge of who bought when, we have an algorithm that identifies when the average "smart money" goes from bullish to bearish, and vice-versa, while at the same time the amateur money is betting in the opposite direction. This link will give you its recent history.

This is a sentiment indicator and it has its theoretical roots in the Efficient Market Hypothesis. It plots the best fit over successive N days, where N varies from very short term to say more than a year. The best of the best fits are the smarties, and the worst of the best fits are the amateurs. The smarts are attentive and the amateurs tend to be complacent. This model is not perfect but it tells some interesting tales. At the most recent peak, the smart money turned bearish as of the close on January 30th. They have not yet turned bullish as of February 12th.

Feb

3

 Markets can experience contagion. I remember from trading futures (nee commodities) that a crash in one market tended to bleed through to others. We would always remember it as though someone who had a great position in beans would sell it out to meet a margin call in silver that should have been dumped. That is, cutting your profits to let your losses run.

In that vein I wonder how much the recent hit in Bitcoin contributed to the equities decline.

Jim Lackey comments: 

Ben K Green Horse Trading.

Bitcoin the gypsy trade
Currency Rebel Commander 
Nazz Maniac Mule

Jan

27

As an observer/researcher I see that lately there has been an increase in unhedged options transactions. I believe the language would go something like, "Why hedge, the outcome is not in doubt."

I will see if I can put together a graphic over the weekend.

P.S. one bugaboo potentially is North Korea immediately after the Olympics.

Dec

2

Infographic: Visualizing the Journey to $10,000 Bitcoin

How did Bitcoin jump 10X in value in the matter of just 11 months? This timeline visualizes the events in the journey to $10,000 Bitcoin. After dotcom popped, many companies lost 98% market cap - yet an operating concern remained (YHOO comes to mind). What's behind Bitcoin? I have removed 2000-3000 as an area of support following this weekend's madness. Clearly, she'll end below 1 Alas, as I always said, the hi print is likely prior to CME debut.

Andy Aiken writes: 

"Clearly" and yet Anatoly claims to have no position. Evidently his net worth is tied up in airline vouchers.

anonymous writes: 

Actually the "right" trade during the dotcom bubble was to be long and own low delta, far out of the money puts. The same was true during the silver bubble, the nat gas bubble and all exponential moves. What I find astounding is that some people never learn from their past mistakes. If you don't know who the sucker is at the poker table, look in the mirror…. Of more interest than calling the "top" or "bottom" in bitcoin (or anything else) for bragging rights and which are worthless, what do intelligent people expect the opening futures yield curve/implied interest rate for Bitcoin futures to look like? There is no real borrow market; so should futures be in backwardation? Or should it be upward sloping like a regular currency with a positive interest rate? My guess (based on learning from experience) is that speculative flows will swamp arbitrage flows and so it will be in backwardation so long at the market is rising strongly — and once the price has topped and it starts declining, the yield curve can/will go positive. My instinct is that the shape of the futures yield curve will provide a better clue about the status of the bear/bull debate than pulling numbers out of the air — and it's options on futures where the real fun will be had. Does anyone have a better perspective on this?

Andy Aiken writes: 

Finally an interesting question on this subject. There could be some good spread trade opportunities, since I expect the term structure to move wildly in the initial stage of market development.

I expect it to be mostly in contango at first, but move to a modest backwardation that reflects an implied yield.

Bill Rafter writes: 

From the cheap seats, bubbles tend to coexist with inversions (backwardation). Current uncertainty places a premium on the near month while the distant months play with the expectation of mean-reversion. Isn't that exactly what Bitcoin is all about? So you would expect Bitcoin futures to show backwardation. The only problem is that you cannot build an economically rational model for such a price structure. Thus it seems as though momentum and sentiment will rule the day. Appropriate quote from the Senator: "It is conjecture. When a researcher lacks hard evidence, conjecture is his greatest tool. Some conjecture better than others. Some conclusions are more conclusive than others."

Nov

20

One, from Bill Rafter

November 20, 2017 | 1 Comment

Seeing the news of Mugabe being deposed reminded of the scene in The Count of Monte Cristo in which Caderousse is the first of Edmond Dantes' tormentors to die. On that occasion the Count simply says "One".

Who will be "Two"? Maduro? Kim? A few years ago I would have included Assad, but not now. And what about the de facto coup in Saudi. That was nicely engineered.

These are exciting rather than scary times, IMO.

Nov

17

Today we had four people ask us about the likelihood of a current liquidity problem. Someone out there in Financial Journalist Land remembers the last line of the journalist in The Man Who Shot Liberty Valence: If the legend is more interesting than the truth, print the legend.

Here was our response (it's very short). As pictures and charts often do, these compel belief.

Mr. Theo writes: 

Thanks Bill. I would also add that historically the flattening of steep yield has been the best environment for equities.

Oct

31

IQ, from Scott Brooks

October 31, 2017 | 2 Comments

Normal people can have extraordinary abilities. Prof. Haier writes about a non-savant who used memory techniques to memorize 67,890 digits of π! He also notes that chess grandmasters have an average IQ of 100; they seem to have a highly specialized ability that is different from normal intelligence. Prof. Haier asks whether we will eventually understand the brain well enough to endow anyone with special abilities of that kind.

The Neuroscience of Intelligence also includes a good introduction to the history of intelligence research, beginning with the development of the first IQ tests. Prof. Haier notes that a significant turning point was Arthur Jensen's famous 1969 article in Harvard Educational Review. Jensen wrote that genetic limits on intelligence meant that there were limits to what could be achieved through early education, and that there was a significant genetic contribution to the black/white gap in IQ. This so horrified liberals that for the 1970s, 80s, and part of the 90s, it was impossible to get grant money to study IQ. Even today, most research on the brain ignores intelligence, and instead concentrates on such things as schizophrenia, Alzheimer's, and other mental disorders. The Jensen article set in motion what Prof. Haier calls "a decades-old concerted effort to undercut, deny, and impugn any and all genetic studies of intelligence."

This campaign was a success. Despite the enormous body of evidence to the contrary, many people still think that no person has any inherent limitations, and that with the right role models, cultural sensitivity, and other mumbo jumbo, anyone can become a lawyer or scientist. Prof. Haier writes that one reason for this is that people who make policy are usually fairly smart and don't know anyone who isn't. They have no idea what life is like for stupid people. Prof. Haier adds that the other reason is that denying genetics is an attempt to explain away race differences in IQ.

Bill Rafter writes: 

Did Will Haier suffer the same fate as Jensen?

Another money quote:

"As Prof. Haier notes, there are 51 million people in the United States with IQs of 85 or lower. Their poverty and social failure are not their fault. After 50 years of "programs" that do nothing, we should recognize that a huge part of the problem is stupidity and try to cure it."

Sep

13

We have an algorithm that we value greatly. I have written about it in this space and have produced a white paper on it. It uses macroeconomic data and has a record over the past 25 years of generating a 13+ percent compound annual ror with a 17+ percent maximum drawdown. The SPY's numbers are 9% and 55%, respectively. Clearly the positive returns come from dodging the drawdowns; there is no beta. BTW, the 75 year history is also very good; suffering only in the 1987 selloff.

At this time the algo is very close to going bearish. It has not signaled bearish yet, but there is a definite possibility. I would not exit long equities without that signal.

The problem is that the macroeconomic data (weekly) is reflecting the effects of two hurricanes. It is perfectly understandable that such data would mirror those unfortunate events. The circumstances clearly are different this time; when have we had two disastrous storms back to back? Because the data is macroeconomic, it is not a flexion fakeout. In fact the technical indicators all point higher. Admittedly we would like to have more information, but that's not forthcoming. An interesting and frustrating problem. At least, the signal has not yet been given.

Rocky Humbert writes: 

I believe the market's reaction tomorrow to American Airline's post-close news this evening may be generally predictive. American guided earnings lower because of the hurricane effects and also because of fuel costs. If Mr. Market doesn't blink, then expect a slew of companies to use the hurricanes as a penalty-free way to guide earnings lower. That is, the teflon market just got a fresh coat of teflon….from the hurricanes.
 

Aug

29

 What is the composition of the rainwater dumped by the storm? The eventual source is the ocean, but is the means of getting into rainwater evaporation (in which case it's "fresh water"), or has it simply been sucked up into clouds? If the latter, then it must have significant salt, and therefore be detrimental to crops.

Stefan Jovanovich answers: 

The rain is fresh water; Japan gets half its annual rainfall from typhoons. The salt water comes from storm surges - basically high tides aided by sustained onshore wind; but it is not the source of the flooding. The updrafts in typhoons are so destructive because they push the clouds higher and, when the storm comes against structures, create pressure differentials that can literally blow buildings apart from the inside. That is why, even though it is counter-intuitive, you have to have air vents that can be left open so that the pressures inside and out can equalize. The only "sucking up" of actual sea water is the wave action, but that is caused by the rotational windspeeds, not the updrafts.

As bad as Harvey may seem, Hato's effects will probably be even more damaging.

Aug

27

If you plot daily range versus daily volume for the S&P over a long time interval you get the following graph. I have included straight lines illustrating that 2 distributions (relationships) are apparent.

anonymous writes: 

Bill: Excellent visualization! This double hump result is surprising. Vic's random walk explanation was elegant and intuitive.

How does one intuitively explain the two humps? The most intuitive way would be a regime change of some sort — and primarily affects the measured volume.

Regime changes might be changes in market structure (i.e. HFT, commission-rate changes, plus-tick shorting rule changes, growth of ETF's, the way exchanges calculate volume including dark pools, etc.) The commonality of these regime changes is that there is a before-and-after …. so the second hump may be more/less pronounced after a give date??? If one were to do this scatter plotter for each year and make a moving slide show from the result, the result might look very differently…and give some interesting avenues for further research. 

Jul

12

In our shop we consider ourselves "data monkeys" rather than quants, hoping that the disrespect of the moniker will limit wannabees. But if it looks like a duck and walks like a duck…

The problem of ever changing cycles/ figuring out the current regime/ the Church of What's Working Now is solved by most in a brutal fashion rather than a subtle one. Suppose you drive an old car from sea level to say 12,000 feet and it struggles. You could lift up the hood and tear the engine apart. You could also make an air-intake adjustment. Both methods work.

We data monkeys believe that the only things that count with regard to markets are sentiment and momentum. That is, it's all behavioral, and it's reasonably efficient. Sure we like to comment on fundamentals, but the fundamentals to us are only important because they influence the behavioral. When a market has been moving in a certain regime, sooner or later a market Watcher gets the inkling that a change is afoot. His action or inaction will disseminate exponentially to others, and then the regime really will change. The key to keeping up with this is to watch what the Watchers are watching.

To us this means that if you are monitoring data with human input (e.g. price) you had best be making your inputs adapt to what they are watching (i.e. usually the length of past data) and it should have an exponential component to it, rather than linear because human knowledge moves exponentially. If the in-crowd has switched to watching the last week and you are watching the last two months, a change will occur before you become aware. Non-human influenced data (e.g. most fundamentals) can be fixed and linear.

Rocky Humbert writes: 

Roy Niederhoffer wrote a prescient piece 3 years ago. It's worth re-reading this as I think he makes some excellent observations: "CTAs Could Face Historic Challenges From Rising Rates"

anonymous writes: 

Roy Niederhofffer's piece points out that the structure of futures markets for interest rate futures has favored those that didn't expect rates to rise. A large portion of the earnings of investors around these markets would make money because futures had a bias to be priced with an expectation of higher rates than eventually occurred. Those who took the bet that rates would rise lost, and the reverse. We've had a long run of this bias back to the rate peak in 1980/82. Certain types of investors made better than market returns because of this.

The source of this has been Fed led by their providing excess liquidity, and making pronouncements that they would continue the low rates so carry trades would transmit low Fed funds rates to other instruments. THese low rates provide under pinnings for other business investment, and for increasing stock multiples as the only game in town.

What's next?
 

Jan

19

One of the truest axioms of trading is that the thing you worry about least is the thing that will bite you in the rear. As others have noted, expectations are extremely positive now and few are worried about the downside. But whose expectations?

Something we have written about previously is the length of historical data being watched closely by professional traders, particularly when juxtaposed with that being watched by those who sit in the bleachers. The best bull moves occur when the pros are looking long term and the amateurs are nervous nellies. Right now we have the opposite. With tonight's close we see the amateurs being complacent; they are looking back at what has happened since Election Day. The pros meanwhile are monitoring prices in a 4-day window, a most tenuous stance.

Stefan Jovanovich writes: 

One of my dubious theories is that the internal correlations that we all see in "the market" are largely a product of the development of the New York Banks becoming the clearing house for the nation and their converting that dominance into the "need" for official central banking. The data from the 19th century, which is limited enough to be within my meager mathematical capacity, suggests strongly that the business cycle was much more a matter of the fluctuations of particular businesses than one of the movement of the "economy" as a whole. Weyerhauser's fortunes and Swift's were not on the same cycle. The movements of "Timber" and "Pork" were largely independent.

I wonder if that is becoming the case once again. Optimism may be the general news, but the prices of retail companies, particularly those in the clothing business, very much fit the opposite of Bill's description of the general mood. The general assumption is that everyone will lose their business to Amazon.

Russ Sears writes: 

"One of the truest axioms of trading is that the thing you worry about least is the thing that will bite you in the rear."

I call this the fundamental law of risk management: What risk you ignore or discount incorrectly are the risk you over-load your portfolio with, thinking you have found the "key to Rebecca"/free lunch or at least you have optimized your risk metric such as sharpe ratio. This is what happened to the modeler of RMBS, unknowingly overloading on model risk.

Alston Mabry writes: 

I have often thought (but been unable to effectively implement) that if you could determine what factors the market is not paying attention to, you could place some profitable bets or at least put on some good hedges.

Which leads to a non-quantifiable definition of a bubble as a big move up that continues even after a critical mass of players have become aware of the fatal risks - everybody knows they're playing musical chairs, but it's too profitable to stop.

Jan

2

 The December Jobs data is neither encouraging nor exciting. Admittedly there is considerable hope and some announcements of future hiring, but of course no change is yet visible. I will post a chart based on payroll taxes this week before Friday.

One particular concern for future jobs should be the minimum wage hikes. It is rational to expect that higher minimum wage rates in some locales will stifle employment increases in those locations while neighboring areas experience growth. The good side of these rules is that each changed location will in effect become an economic Petri dish, so we finally get to see unequivocal evidence on the matter. Local experiments that underperform are better than a failed national experiment.

It is also possible that reductions in regulations (among other changes) will create such a successful business climate that demand for workers will render minimum wage laws moot.

Dec

17

An anecdotal observation:

Recently there has been a STDV > 1 rise in the level of the Open Interest of Index Put Options. Historically this seems to be coincident with declining equity prices rather than rising equity prices.

If you know any students looking for a possible project, pass it on.

Dec

13

When the economy takes a turn for the worse, employment declines, right? Well, not all employment. Specifically, part-time employment tends to rise aggressively during economic downturns, somewhat concurrently with full-time employment declining. Because of that, one can play off the two types of employment and get decent broad brush investment timing decisions. The purpose here is to provide a general guideline to the "average Joe investor" (admittedly, a conundrum) to tell him when to be in and out of equities.

Quite simply, when the growth rate (annual rate-of-change) in part-time employment exceeds that of full-time employment, exit equities and only return when those numbers reverse. Doing so will enable an investor to avoid gut-wrenching declines. And of course the most valuable key to increasing wealth is to avoid getting behind.

The following chart illustrates what one's investment posture would have been. Employment differences should model the economy, something the stock market rarely agrees with. However in this case the employment differences seem to do a good job with the market. Again, this is not a trading plan, merely an illustration of what is possible.

Full time employed

Part time employed

Nov

17

 When a virtual tsunami hits, the markets tend to thrash around for a time until they figure it out. If you individually tend to be prescient, go with your instincts. But if your opinions first need some direction from the markets, how long do you wait? Put another way, how many time periods does it take before the detritus left by the tsunami clears? There are several guidelines.

Is the tsunami a one-off event, or is it a rolling event. For example, we all thought Brexit was one-off, but now with court challenges it appears to have legs. One-off events clear faster, obviously, since the rolling ones have the possibility of reversal or modification.

With more "professionally-traded" markets, detritus clears faster. Those who take positions in forex, futures and options (particularly the writers) do not have the luxury of time. Or more to the point, they do not have the margin money. If you look at market-derived information you must be conscious of intra-day movement, particularly that from the two most important times of the day.

The more amateur the market, the more time it takes before you get a clear picture. In this case of course we are talking bonds and stocks, but particularly stocks. But even though those guys tend to act slowly, the markets clear remarkably fast. How long? Typically between 4 and 8 trading days. So we are just now getting there.

Obviously the lesson for the spec is to watch the leveraged markets.

Now, a sidebar question for the "technicians": what do you do with the information (i.e. prices) that occurred during the tsunami? One of our favorite gurus, a CalTech AI expert we call BikerBoy says, "You never, ever throw away information."

Nov

12

 During the Apollo 11 flight, landing and return, the entire planet was absorbed. This election had that same feel. It was that important.

Of course the difference is obvious: In 1969 there were only winners, and of course America was truly great. At this time there are winners and losers. If America does in fact become great again, the only losers will be those whose political bent does not allow them to accept it.

What was really cruel was the fact that the pollsters, Hollywood, etc. (which were tools of the Ds – willingly or inadvertently) conned the Ds that the race was theirs. Most people can accept losses as part of the game. But the Ds were led to believe they could not lose, and they are in shock like the people of Mudville who never believed The Mighty Casey could strike out.

Nov

4

I agree that the BLS number will be bullish tomorrow [2016/11/04]. Why was there recently an article from NYT singing the praises of impartiality of the BLS? Seems suspicious to me, who considers BLS nothing but a bunch of cronies.

The payroll taxes are shown. Not really bullish. However the recent rise covers the exact period that will be sampled in the Jobs Report. The subsequent downturn is not in that sample.

Note: this view of the payroll taxes views the [consequences for the] employment scenario. If we targeted the impact on GDP we would get a more bullish picture.

Oct

31

The idea of systematic trading was not generally accepted 10-15 years ago. Markets were mostly viewed as efficient at that point. Or mostly efficient and any excess returns were just a compensation for risk taking – still efficient in a loose definition of efficiency. Today, trading systems are everywhere. Systems are now called "indexes" or "smart beta". Different strategies are now called "factors". Is 2/20 fee structure going the way CD, Dodo, floor broker, etc. If outperformance can be replicated by some "factors", who needs an expensive trader/manager?

Peter Pinkhaven writes: 

"Strategies that have reasonable sharpe ratios are usually cyclical" - Asness

I believe AQR were one of the pioneers of the recent systematic factor investing.

Bill Rafter writes: 

The automatic trading systems make the markets more efficient and more liquid. They are not predictive but extremely efficient in their reactivity. What the spec should do is view that as an advantage rather than a problem. It would only be a problem if he were a scalper. There is a very good solution to this for the spec (professional or near-pro), and it revolves around knowing which games to play and which to pass.

There will certainly be some professional specs who outperform, and they will be worth the fee. However that fee may continue to be 2&20 on the basis of value, or it may work lower for any number of reasons. Full disclosure: "someone we know" charges 1&10, as they want to keep clients forever rather than have the fee level be an issue in the future.

Oct

12

One of the off-the-radar things we watch is the length of time various subsets of options are held. The flip side of that is the turnover rate of those options. Several years ago I put out a white paper on the concept, and about a year ago there was a small WSJ piece. There is evidence; it's not anecdotal.

The general gist is that those who are more conscious of attuning their options positions (i.e. greater turnover) tend to be correct. Conversely, those who are complacent tend to pay for their complacency. Whoever is longest in a position tends to be "wrongest". As of this evening it is the holders of call equity options who are the more complacent.

One beautiful thing about this indicator is that it appears to measure portfolio shifts rather than mere trading shifts. That is, there isn't much fluttering back and forth.

Disclosure: we have been out of our longs for about 2 months (on the strength of other indicators) and we don't ever short equities.

Oct

5

Can anyone point me to research regarding Futures settlement price vs closing price and subsequent returns in a volatile market environment? I would like to see if settlement is more important (due to margin) during periods of high volatility which I foresee over the next few weeks. I'll try some back of the envelope tests over the weekend.

Bill Rafter writes: 

We have tested many possible prices for importance with regard to generating signals (e.g. momentum, sentiment, etc.). In reality the only price you can guarantee for testing execution in retrospect is the settlement (subject to slippage), followed by the opening (greater slippage). But for signal-generating capability we tested highs, lows, midranges, etc. We also tested subsets, such as the ability of using lows to indicate up/down, vs. highs to indicate up/down. Nothing beats the settlement. Specific to your question, if the settlement differs from the last sale, take the settlement. "There's a reason why it is the settlement."

With regard to stocks we also tried VWAP. Same conclusion.

We also tested to see if the futures settlement influenced cash, or the opposite. In virtually all cases the futures dictated to cash. That conclusion suggests that cash can be manipulated by some clever futures transactions, which of course has happened. Certain markets were famous for it (eggs comes to mind). Anyone who has ever manipulated a market will tell you that you wait until the end of the day and pick your spots (i.e. low liquidity).

If however you are doing some "fuzzy" work, you might explore using something other than the settlement or close. That is, suppose you just needed a qualification as to whether a market was "up" or "down", without regard to actual changes. Consider the following: "The market was up all day, but closed slightly lower." Was it up or down and how do you code for that? This is not esoteric BS; it makes a difference.

The above is the benefit of our own testing. I am not aware of any academic work in this area. It seems too mundane a topic. A cash v. futures settlement thesis might be interesting but the conclusion would be anti-flexion and we know how that would be perceived.

Larry Williams writes: 

Hold on…

In reality data providers have something they call the closing price. That's what we get when the market closes and that stays in our data until about an hour and a half, sometimes two hours, after the markets open in the afternoon when they change the closing price to the settlement price.

You have to be very careful because there can be a wide difference between the closing price and the settlement price. Unfortunately we don't have the settlement price until after the market is open when we have already begun trading. So most trading systems are developed using the official settlement price because that's what is in the historic data but for signal tonight after the market closed we don't get the settlement price until after trading has begun.

Whoever said the life of a trader is an easy one did not look into closing prices.
 

Sep

16

The normal state of affairs is that 1-month expected volatility (i.e. VIX) is lower than 3-month expected volatility. In many ways this is similar to short term interest rates being lower than longer rates. The logic is that a lot more grief (random or otherwise) can happen over the long term and the market prices that in.

Let us suppose you believe that expected volatility is forward looking (the standard belief). Should you happen to find yourself in the (less common) situation where the market has priced 1-month expected volatility higher than the 3-month, the logical conclusion is that the market places a higher risk on the near term. Since higher levels of expected volatility tend to be bearish, your subsequent conclusion is that the market will get its butt handed to it fairly soon.

Hey, that means you could simply take the difference of the two expected volatilities. Sounds great, but the levels of 1-month and 3-month expected volatilities are not directly comparable. To make them comparable the geek/data monkey has to normalize them over the most representative period. To further complicate this, the last item (the representative period) is never static, but variable. However all of the above are minor items that can be dealt with.

Now the question is: Why am I telling you this NOW? Go figure.

N.B. I am deliberately choosing not to show this in chart form.

Aug

30

A cap-weighted or quality-weighted look at U.S. employment

Aug

29

 "Why can’t we see that we’re living in a golden age?: If you look at all the data, it’s clear there’s never been a better time to be alive" by Johan Norberg

Jeff Watson writes: 

There's huge money in doom and gloom.

Ralph Vince muses: 

A person should live each day of his life with the same mindset, the very same attitude of savor and gratitude for every minor thing, as if he got out of jail that morning.

Or, as the Old Frenchman himself would say, "If you have the same address as a thousand other guys, you don't have a lot going on."

Alston Mabry writes: 

Pessimism is a strategy. People who have learned, usually from childhood, that they cannot act on their most important impulses use pessimism as a way to devalue what they deeply believe they are not allowed to want.

Bill Rafter adds: 

Just a minute…

As we all know from trading, if you want to increase your profitability over time the most effective strategy is to limit losses. Possibly related to this is the result of several studies attesting that fear is a greater motivator than greed, buy a factor of 3 to 1. Furthermore, we all look at prices and know both instinctively and historically that those prices will not be constant over time. They may be higher or lower, but not the same. Thus, pessimism is historically justified, profit-saving and possibly life-saving.

But to want to trade these markets for profit, one also has to be optimistic, often excessively so in light of bad experiences. You need both.

Jim Sogi writes: 

Jeff is right. Television causes pessimism. Don't watch TV. I haven't had TV for 47 years. It's not only the content. It does something to the brain. It's harmful. 

Stefanie Harvey writes:

Exactly. Television, especially US news television, is the poster child for confirmation bias. 

anonymous writes: 

Many good reasons for worry exist. If you're not worried, you're not paying attention. All of the worries stem from something completely nobody talks about in polite company: population explosion. In 1804, the world's population was 1 billion. In 2012, it topped 7 billion. It's projected to reach 9 billion in 2042 — within my son's lifetime.

True, Paul Erlich got it wrong when he said we'd all starve by the end of the 1970s– but go back read his book. Then reflect on how much different life is.

All those people are unsettling policymakers, with these results (and they are what's secretly worrying us):

Unspoken Fear #1: War. Today's empire builders are intent on grabbing resources; nuclear weapons are in too many hands.

– China: rich and populous; thanks to the free-trade break we gave them in the 1970s, they've created a war machine and ready to go for our jugular.

– Islam: implacable and populous; we have spent trillions trying to establish a decent government, and the area keeps morphing into an empire that despises us and all we stand for; they want their old empire back, be it from Baghdad or Istanbul.

– North Korea: Our strategy is, "Let's all ignore that man in the corner, and maybe he'll quiet down."

– Russia: ruthless, and intent on restoring the empire of Rus.

Unspoken Fear #2: Dystopia.

– When people don't have honest work, nothing good can come of it. In America alone, 94 million people are out of the work force. We're not being honest about the impact of robots and artificial intelligence. It's this fear that gave Trump the nomination, not that he knows what to do with it.

Unspoken Fear #3: Central government that keeps growing.

– Confronted by the population explosion, the elites have decided that the masses must be controlled and pacified. This political philosophy shows up in the fear of liability for anything fun, in subsidies, in central banking. We see sledgehammer policy-making, from FDR to Obamacare.

– And the educated love it! Calls for authoritarianism are the norm among socialist youth, aging hipsters, authors and "educators" at all levels.

These memes and unspoken but rational fears show up in pop music, with its ugly pounding overamplified brutalist mindlessness; in contemporary academic music, with its screams and jaggedness; in art, with its sneering cynicism; in architecture, with its boxy Stalinist aesthetics.

It shows up in the piggishness of the powerful, with Hillary Clinton the prime example. The rich expect multiple homes in idyllic spots, bodyguards, private jets; the poor suffer in overbuilt, crowded, noisy, polluted cities.

I happen to be an optimist, and always see the glass as half-full. Please note I am not prescribing anything; for one thing, it's gone too far. Nor do I think that going to Mars will help.

Russ Sears writes: 

First, human super-cooperation is built on trust. To evolve as a group, a high percentage of that group must be trustworthy for the compounding effect of the prisoners dilemma to work. As the group grows too big, it becomes too easy for a individual to feign cooperation. Hence the need for creative destruction and for power being placed in the smallest sized group necessary. It has always been easy to look at the big groups and see the corruption and assume that they are in control of the long term future. But the truth is they are dinosaurs and will lose out to the small but wise group/ businesses that still operates at the human individual trust one another level and are quite hidden from the spotlight, because of size. But these time and time again raise the tide for all.

Second, personally, it is too easy to dwell on the jerks that simply can ruin it for everyone but that fall into everyone's life. They can ruin many nights even if as a rule I try to avoid them. A single jerk can derail my perspective and keep me up at nights and easily crush my spirits if I let them. I found the best antidote for me is to turn the tables if I start thinking of the jerks and think instead of those in everyone's life that have blessed them with love, grace and patience. I think of my Dad's second wife, caring for a dementia patient at home for 13 years and weeping tears of love on his passing, the coach that helped me, the friend that's always there, etc. I try not to let the jerks own my mind rather than those loving, lovely (my spouse), good and virtuous people in my life. This also goes with those news makers, politicians and on the dole.

Aug

24

 Should one follow a purely Quant approach, as seems increasingly popular today, or should one on the contrary combine quantitative and qualitative ideas for best results in trading? 

Intuitively mixing qualitative judgment with quantitative signals matches pension funds' desire to blame someone if something goes wrong, so intuitively it should command higher fees and more assets. Less cynically qualitative judgment is harder to replicate. Theoretically. In reality I find most people's qualitative judgment is just a randomly executed quant system.

For similar reasons I can imagine purely quantitative processes performing better, when the sole mandate of the manager was to define methodologies to turn systems on then subsequently turn them off. But it's hard to ignore the effect of AQR on fees and industry events like Cohen plowing into Quantopian, as both worsening pricing and increasing competition in the quant space.

I'm trying to figure out what method is the best to pursue. Should I be reading the earnings transcripts, talking to management, using the software companies make and ad platforms of tech companies, doing my best to make a robust qualitative view? Or should I be improving my use of machine learning models and getting more proprietary data sets?

More simply, does the next 20 years in have asset management have a stronger bid for the qualitative, the quantitative or the hybrid?

I would be most grateful for your wisdom.

Bill Rafter writes: 

Let's say you have a quant "system" that you have tested and it has a positive expected value that is of interest. Adding some qualitative/anecdotal tinkering on top of your tested program has a real risk of lowering your expected value (assuming you have no ability to test your tinkering.) So why tinker? Well, it's human nature to do so, and by tinkering you might find something better. Okay, then put 90 percent of the capital into the program with the tested positive expected value and experiment with 10 percent, or just hold that latter capital back for when you positively test another system.

BTW you might want to read Ralph's thoughts on how much to bet.

The tougher part is coming up with the "system". Obviously test everything, especially your assumptions. From reading your note I see that you might have some untested assumptions. For example do you think earnings are important, something which I myself do not know? I'm not saying they are unimportant, just that I don't know. For example we do a lot of macroeconomic forecasting, but we never trade based on it because we have learned that the market does what it wants to do, and not necessarily what the economic numbers suggest. And also we know that a lot of the macro releases are fudged.

One thing you should give serious consideration to is which time venue you will target. Unless you have the right infrastructure it will not be high frequency trading. So will it be days, weeks, or much longer? That will dictate the type of approach you pursue and your research. If it will be very long term, then you have to get deep into company research.

The people who care about earnings tend to look at the much longer time frame. Meaning that your capital is exposed for a long time during which lots of randomness can work their evil ways. [The factors that we are most capable of dealing with are momentum and sentiment, and consequently our time frame of interest is shorter, say 4 days to 6 months.] So identify your strengths and go with them, particularly if those strengths differ from that of the crowd. If you don't know what your strengths are, be prepared to put in a lot of time on research. Minimize your trading during that period otherwise you will not have seed capital to trade when you acquire the skills. You know that, but it bears repeating.

Be prepared for the counterintuitive. For example, when we first acquired the computer skills to do the research we did "test 1". Test 1 was "if you know the market is going to go up, which stocks do you buy?" We assumed it would be the high beta stocks, as they would go up more. But they didn't. Turns out that beta is backward-looking and going forward the high-beta moniker just means higher volatility, which is a negative. So test everything and assume nothing.

Aug

16

 "Captain" Vic in Vinalhaven Maine, looking over the harbor and thinking about analogies between boats and trading…

Bill Rafter writes: 

Observing boats can be very interesting because of the diversity of the boats. They are constantly being modified to fit circumstances. The phrase "different horses for different courses" holds very true for boats. It is indeed fair to say that the sea designs all boats as the unsuccessful designs wind up at the bottom.

The diversity of design is evident in ugly commercial vessels, but also true for sailing vessels. Observe the different positions of the masts. The Swiss mathematician Euler won several prizes related to naval architecture, after finishing second in the first contest about mast positioning. If you are lucky you will get to see a ship with the masts raked (tilted) sternwards, common with clipper ships and also a Chinese junk with the mast raked forward.

Interesting also is the trade-off between speed and stability evidenced by the ratio of length to beam (width). The tipping point between the two seems to be a ratio of 6 to 1.

There's a lot to see.

Aug

2

 Book Review: "Who Needs the Fed?" by John Tamny 2016

What really attracted me to this book was the title, something I am in agreement with. I had not been aware of this author before reading a positive review in Forbes and the WSJ. Among other notables is a review from Andy Kessler, whom I have previously found to be objective, and of course a markets person.

First, in favor of the book: the author makes a very good case. Indeed it is safe to say that he finds nothing of value in the Fed's existence. Although a supply-sider, he criticizes them also. He is an adamant free-market advocate who favors no reserve requirements for banks and no FDIC. The Fed was originally created to provide liquidity to solvent banks, and has morphed into providing liquidity to insolvent institutions and even forcing solvent ones to take its money. The author favors creative destruction, whereas the Fed is a major player in central planning and the redistribution of assets to the "weak". "Why keep around that which intervenes in the natural workings of the markets? Didn't we learn in the twentieth century (often through mass murder and starvation) just how dangerous it is to empower central planners?"

The flip side: The tome is 180 pages whose points could have been successfully made in 45. There is so much repetition that it occurred to me the book could be an anthology of previous articles. Why else would the author repeat the exact same text over and over? Does he assume the reader to have Alzheimer's? In each of the 21 chapters he defines his meaning of "credit". He even repeats the exact quotes from Hazlett. Some text is occasionally difficult to read in that some sentences are too long to follow if only read once. He also frequently drops articles (e.g. "the"), probably because he thinks it sounds cool. It doesn't.

The book has no charts, graphs, tables or formulae. Undoubtedly someone told him that those things discourage readers. It is quite the opposite, as they can be used to illustrate a point. One chapter is devoted to how the price of oil responds solely to the price of the dollar with respect to gold. Being a "data monkey" I have the ability to check that out, and when I did I learned why there was no such chart. Yes, there is a sometimes relationship, but nothing to be relied upon.

His concept of real estate is that it solely constitutes consumption by households, not investment. Interestingly my best investment ever was when I acquired and improved a vacant lot 15 years ago for X dollars. Without any subsequent improvement that property currently produces 1.25 X each year in profits. If I were to characterize that as something other than an investment I would possibly call it a winning lottery ticket. I wish I had more of those.

My real reason for acquiring the book is that with a title like that, the author must have some idea as to what non-Fed variables might be of interest. That is, I agree that the Fed is detrimental, so if I had previously been a "Fedwatcher", what do I watch now? Fortunately I found one (just one) that might prove to be valuable.

If you need a guidebook on being skeptical of the Fed, get the book. His examples are great: Taylor Swift, Jim Harbaugh, Uber, etc.

Jul

22

A personal observation:

When a market has had a successful run and is ready to roll a seven there are several scenarios in which the turnaround occurs. A very interesting one is where the market in question does not initially falter and give a sell signal. Rather, what happens is that competitors or alternatives to that market start to look interesting first. It is almost as though those in control of portfolios start to move their cash into the alternatives before selling the primary market.

For those of you who play these markets by the numbers I suggest you check your signals for bonds, gold and equities. Observe if you are getting buy signals in bonds and gold, but not yet sell signals in equities.

This does not have to be a big move, just a portfolio adjustment.

Jul

11

Say that you have a yearly goal of 40% and you achieved in 7 months, or that you have a monthly goal of 10% and you achieved it in 11 days. Do you stop trading at this point? Or do you continue trading thinking the luck is on your side at the moment? Or do you adjust your goal and continue trading with the new goal?

Cheers, Leo

Victor Niederhoffer writes: 

The market will sometimes go much below your goal and to even things out you have to make as much as you can above your goal. Furthermore, the market doesn't care whether you've achieved your goal or not, it will always go its own way, and if you can make a profit on an expected future value basis, you should go for it. Luck is random, but the skill will persist. Apparently you or a colleague has it. Don't throw it out.

Andrew Goodwin writes: 

Your answer may rest in the structure of your money management operation. If it is a hedge fund structure, then heed the following points made in a post on the hedgefundlawblog.com. If you get behind you must know how you will deal with the moral hazard. Since you are ahead greatly, then your incentive is to take the money unless you know with some certainty that you cannot fall below a high watermark and will likely increase your gains.

1) The management fee, over time, usually does not generate enough income to operate and the profitable traders expect bonuses even when the overall fund loses.

2) The winning traders will leave to other firms or will start their own if there is no performance fee gathered to pay them.

3) If fund performance goes negative then high watermark provisions normally go into action. This can lead the manager to swing for the fences or simply close shop.

4) The wind down of the fund can deplete the investor assets and lead to general price markdowns of holdings especially if others had similar strategies and exposure.

5) The fleeing investors will enter into a new fund with a new high watermark and start the process over again.

Here is where the game gets interesting. The author suggests creating exotic option outcome provisions that he calls "Modified High Watermark."

These include A) Reset to zero under certain circumstances. B) Amortize the losses over a period so that the manager can still earn some incentive fee. C) Create a rolling period for the high watermark so that after a time the mark level drops.

His modified high watermark solutions might keep the manager from swinging when the performance fee looks too distant and might keep genuinely unlucky managers around until their skill manifests itself in due course.

Nigel Davies writes: 

There's a case for reducing leverage as one's account size increases so as to reduce the 'risk of ruin', and for some this might be done in a very systematic way. Another question is if there's a point at which one's financial goals have been achieved, especially if one's dreams lie elsewhere. 

Bill Rafter writes: 

You did not specify if your annual goal of 40 percent is based on analysis that suggests a 40 percent return is the mean or maximum. Let me assume that the 40 percent is the maximum annual gain you have ever achieved, if only as an academic exercise. Thus the 40 percent is your quitting point based on perfect knowledge of a particular system.

How frequently have you been calculating your forecasts (or inherently, your position choices?) As was learned from the Cassandra Scenario, "that more-frequent forecasting is inherently profitable, even more so than some forms of perfect knowledge." So:

(1) If 40 percent is your mean annual gain, then continue to trade at the higher level. That is, if you started at 1000 and now have 1400, continue to trade the 1400. Obviously it would also be good to shorten your forecasting period. (2) If 40 percent is your maximum expected gain, then pocket the 400 and start over trading with 1000. Shortening the forecasting period is not a given in this case.

Phil McDonnell adds: 

Let us assume the market has a normal distribution of returns and that the probability of making a 40% return or better, at random is 15%. Then if you decide to take all profits at the 40% level then your probability of a 40% gain will double to 30%. This result follows directly from the Reflection Principle.

The above assumes that your returns are random and implicitly assumes that you have no ability to predict the market. To the extent that you can predict then you should make your decision on your current outlook and not on any arbitrary price point like 40%.

Gibbons Burke comments: 

It seems to me that one should be disposed to let the markets give you as much as it wants to give you without putting artificial limits on that phenomenon, but that practical limits should be enforced on how much lucre it can remove from your wallet. Is more return ever a bad thing, assuming that the distribution of returns is not serially correlated? As our gracious host has noted, the markets have no idea how much money you have made or lost, so the idea of reversion to the mean on an equity curve makes no sense in the same way that it makes sense for market prices which are making repeated excursions up and down seeking the implicit underlying value of the thing (the ever-changing "mean" to which the market is always reverting.)

So, setting a goal to achieve a 40% return seems a reasonable thing to do, but I submit that this goal should be accompanied by the qualifier "or more" and be willing to let a good thing continue.

Regarding the 'limiting losses' idea, in the Market Wizards interview with Jack Schwager, Paul Tudor Jones admitted to having risk control circuit breakers in place so that if he ever lost more than x% in a month he would shut down trading for the remainder of that month. Limiting and rationing losses in ways such as this seem like a reasonable discipline if one is going to set limits on how the market will affect your stake.

An old floor trader's trick I learned while reporting on the futures pits is that if a trader enjoys a windfall gain on a trade, and reaches a pre-figured goal (or more), he takes half the position off the table as a positive reward for being right and taking action on that conviction. Leave the rest of the position on to collect any further gain which the market might want to provide, but he raises the stop to break-even for the remaining position (not counting the profits already taken off the table) in order that a winner would not then turn into a loss. If he stop get hit, he still has half of a windfall gain return in the bank. If the market continues in a favorable move and another windfall gain is realized, the process can be repeated.

This tactic has an anti-martingale character which some more bold traders might object to.

All these thoughts are mostly elaborations on the first two fundamental rules of trading: 1) let your winners ride, 2) cut losses.

Stefan Martinek comments: 

This loss avoiding behavior was well researched by Paul Willman and others. It is observed within traders of all levels approaching a bonus target; cutting off is generally viewed as irrational and Willman discusses how to adjust incentives to get a trader back to risk neutrality. Which reminds me more general but relevant quote from W. Eckhardt: "Since most small to moderate profits tend to vanish, the market teaches you to cash them in before they get away.

Since the market spends more time in consolidations than in trends, it teaches you to buy dips andsell rallies. Since the market trades through the same prices again and again and seems, if only you wait long enough, to return to prices it has visited before, it teaches you to hold on to bad trades. The market likes to lull you into the false security of high success rate techniques, which often lose disastrously in the long run.

The general idea is that what works most of the time is nearly the opposite of what works in the long run.

Jun

13

 Forgive the length, but I thought this was too good not to share:

Let's take their model, their parable, their most extreme case, and walk through it for a moment. It takes Frank Ramsey's basic model, in which savings equals investment equals capital growth, and extends it to a world in which capital can flow freely around the globe to wherever it earns the most interest.

If savings can flow across countries to wherever the interest rate is highest, and if people can borrow across countries without trouble (say, by mortgaging their home to a bank that borrows money from investors in Japan), then in the long run there's only one possible outcome: the most patient country owns everything. The most patient country owns all of the capital equipment in the world, all of the shares of stock, all of the government bonds, all of the mortgages, everything. What happens in all of the other countries? [the "Impatients"] Eventually they spend essentially all of their national income repaying debt to the most patient country. They literally mortgage their future through decades of high living, decades during which they borrow cheap money that is gladly lent by more patient countries.

…After years of enjoying a grand life of consumption, the average Impatient [country] eventually ends up spending its whole income on interest payments, forever.

Well then, who are the Patient countries? Those who lend and export. Who are the Impatient countries? Those who borrow to spend in the short term. Okay, that's definitional. But is there another way to define the Patients/Impatients? It turns out that national average IQ defines them well. And here's the shocker: The U.S. has an average IQ of 98. The U.K's. is 100. East Asia (i.e. China, Japan, South Korea, Singapore) have average IQs of 106. If we look say 25 years into the future, it's likely China's average IQ will have increased. What do you think will happen to the average IQ in America?

This is from "Hive Mind" an excellent book by economist Garett Jones of George Mason University.

anonymous writes: 

Mr. Jones ignores a few minor problems. The first is default; the second is that Ramsey's equation only works in a world where Marx and monetarists are the only people who keep the tally sticks. The patient people may think they own everything but only until they discover that their debt claims are not going to be paid, that neither principal nor interest will be forthcoming. then there is all that investment in apartment blocks and bullet trains. they certainly cost a great deal; by labor theories of value they should be an enormous accumulation of wealth, except there are no actual tenants who can afford rents for the apartments and no travelers who want tickets for the trains. the last and worst fallacy of aggregation is the ranking of average IQs. the world tuns on the machinery and thought that the very smart people produce and the grunt labor that the rest of us do. we depend on the really smart people's discoveries and enterprise and the scut work done by people who stack the grocery shelves and vacuum the think tank carpets. Whether on average people score C+ or B on what is a school exam called an IQ test makes no difference, except, of course, to the people whose livelihoods depend on the rest of us paying ever increasing tithes to the priestly class of schoolies.

Jun

9

When we research strategies, there is a need to measure performance. Some techniques like volatility targeting tend to improve more the equity based measures (e.g. Sharpe, Sortino) but damage or not improve the trade based measures (e.g. Profit Factor, Expectancy). Some techniques like term structure used in asymmetric sizing tend to improve more the trade based measures. Is there any clear argument for or against equity vs. trade based performance statistics?

Rocky Humbert writes: 

Ed Seykota was fond of saying "Everyone gets what they want out of the markets."

That's an elegant way of saying that every investor has their own utility curve.

So an answer to your question is it depends on what portfolio/trade parameters that you are trying to maximize and minimize. Each of the approaches that you describe involves some sort of a trade-off. Academics will talk about optimally efficient frontiers, but for practitioners who are in the markets for the long run, I believe it's a function of what you and your investors want to achieve and most importantly, maintaining the discipline to consistently apply the tools that you mention.

There are many paths to heaven. There is no free lunch.

Bill Rafter writes: 

We prefer equity stats. Our primary metric for longer term research is (Compound Annual ROR)/(Max Drawdown). For example, the equities markets depending on the period chosen tend to have a CAROR in the single digits, while having max drawdowns of ~55 percent. With work and diversification you can invert those numbers such that the ratio is greater than 1. Most of your success will come as a result of reducing losses.

In theory one might argue that if you take care of the trade stats, the equity stats will take care of themselves. As in, fight the battles and the war will take care of itself. This is most exemplified by HFT. If that is the trading time frame of your choice, then by all means go with that. However it is hard for the individual to compete in the HFT framework, meaning that you will probably have to lengthen your trading, gleaning greater gains, but also larger losses. Eventually I think you will come around to preferring the equity stats. But your choice is going to be subjective or trading-plan-specific, which agrees with Rocky's every investor having their own utility curve.

anonymous writes: 

The conception of Seykota's quote as a utility curve is Rocky's. Seykota might have been making a point about market psychology more akin to a Deepak Chopra quote. That's not to say that Seykota did not make money trading. My sense was that his idea about everyone getting what they want from markets applied to those who might have hidden motivations in things other than in optimized financial gain according to a risk adjusted measure.

Jun

2

It is interesting to consider whether certain month's employment announcements tend to be consistently bullish or bearish. A former employee,  writes to me that the May employment numbers have been quite bearish for stocks.

Bill Rafter writes:

The NFP report is always murky to me. It always needs "interpretation" which is why it looks different several days after its release. The big interests (from the media, at least) are the unemployment rate and the number of new jobs. Both are the result of rather obtuse calculations. I prefer the growth of payroll tax receipts which require no interpretation. The source is the Daily Treasury Statement, effectively the bank account of the government. Attached is the data from last week; no change in appearance since. It may not agree with the early or late interpretation of the NFP report, but it speaks truth about the actual job situation.

Stef Estebiza writes: 

Employment data are smoke and mirrors, are more a political need to do to accept further cuts/taxes and justify these policies. The new jobs are precarious and at reduced wages.

anonymous writes: 

I suspect that I read about the Chair's views on the unemployment rate in years past, but is it safe to presume that the numerator smoke/mirror terms cancel out the denominator smoke/mirror terms?

Or does the science of people counting treat the employeds different than the idleds at the tabulation level?

I've generally treated the unemployment rate as a good bit more reliable than the overall jobs number.

May

31

Those of us who love speculators but rarely trade wonder what the counters think of this comment from a market historian who is a complete hermit but (I think) a very smart guy:

1) DJIA has gone more than a month without setting a 20 day high or low
2) DJIA is confined to a range of less than 6%
3) DJIA is within 10% of a 2 year high
4) Shiller P/E is 18+

There are seven years in recorded history that fit these parameters:

1929
1937
1965
1973
2000
2008

Victor Niederhoffer writes: 

The counters would say that depending on where you prospectively date such events, the expectation going forward is the same as the past. However, there are a number of special numbers used like 6%,18, 2, shiller p/e, that give so many degrees of freedom that it is amazing the hermit couldn't come up with a more bearish scenario. The hermit is an ignoramous. 

Math Investors writes: 

One of the first studies of the market that one does in one's career is to examine the immediate history of major moves, particularly up moves. What happened just before it took off? We found the usual precursor to an up move tends to be rather boring. For an example just look at one of John Bollinger's "Squeeze Plays". There is certainly not a V-shaped bottom or anything definitive; just a slow sideways drift, typically with narrowing volatility. But knowing that doesn't get you to first base. The fact that the market has been boring does not mean it is going to get exciting. You must have some other input.

But what should be your other input? From years of studying this, we have our favorites*. Although a superior input is indeed better than most, the mediocre inputs aren't that bad. Because when a market is really setting up for a move, the signals tend to be writ wide across the landscape.

For example, first-year nursing students tend to get erratic results when measuring patient blood pressures. But if you had five novices take the BP, and then took the average, it would be pretty close to what an experienced nurse would get. That is, combining multiple imperfect measures is more likely to provide a good estimate than none at all. **

*Our favorites can be seen (and played with) by going to www.mstwizard.com or www.mathsoftek.com.

** This example from a book I am currently reading, "Hive Mind" by Garett Jones an Associate Professor at George Mason University. I heartily recommend it.

May

18

The Dow Theory, Big Cap, little cap, SPY/Russell, 2 factor theories are well tested on a variety of divergences. I think they work somewhat with interest curves as well.  I'm wondering about currencies, and countries. Would global/US, or small/big two factor model be predictive at all?

Bill Rafter writes: 

Two factor models work best when the two variables/inputs exhibit at least some negative correlation (obviously with changes, rather than levels). Equities v. Debt is a good example.

Also, we have noticed that in a competitive 2-horse race the overtaker is usually the first to move. That is, the buy signal in A is given before the sell signal in B. We have surmised this is because the smarter players start to acquire A while the complacent participants are reluctant to dump B until late in the game. Impossible to prove, but it makes some sense. This coincides with the experience that assets move up slower than they decline. As Matt Ridley puts it (Evolution of Everything), "Good things are gradual; bad things are sudden."

May

2

 This reminds me of the Sherlock Holmes short story called "The Silver Blaze" in which the mystery was the dog that did not bark.

Why would a practitioner have success with one stock (AAPL) and failure with another (FB)? How are they different (or is there something else) and what are the implications for price forecasting? For example, our tactical algorithms have most recently "nailed it" (AAPL) and "gotten nailed" (FB). Technical analyses sensed something in AAPL, but were 180 degrees off in FB. Why one and not the other? Could Apple's earnings (or at least an inkling of them) been in the market, whereas Facebook's were a total surprise? The market reactions suggest both were a surprise, but yet there were clues with one and not the other.

Here's a link to what we saw or didn't see.

There are many factors which can be used to explain price activity. Among them are price momentum and sentiment, both of which can be modeled by a practitioner or his computer. Somehow someone gets the inkling, real or imagined, that the wind is about to change direction, and either acts accordingly or just declines to follow the well-trod path. Then change happens. It is inexorable, almost evolutionary.

Freely traded markets are very efficient, but not perfectly efficient. That's why "technical analysis" or "counting" works, at least some of the time. Information leaks out and it shows up as a marginal change in the price. Could some companies better enforce a no-leaks policy than others? Maybe. But information can get out in other ways. For example, Apple has stores that are usually crowded.

Suppose all of a sudden they aren't crowded; that's a tell that can be modeled. The people who watch the stores will know before the earnings are released. Okay, then how do you do that for Facebook?

Facebook's revenues and earnings (i.e. fundamentals) are hard to model from the outside. We don't know of any tells. And they may have a rigorous no-leak policy. Which other companies have those same characteristics?

If you look in your program, both companies have similar profiles with regard to share statistics. That is, they have similar relative percents held by institutions and insiders. Their shorts as a percentage of float are similar. However their old school analysis characteristics are different; no one buys FB for the dividends.

Great quote from Robert Schiller: "We should not expect market efficiency to be so egregiously wrong that immediate profits should be continually available." That is both true and comforting when we are licking our wounds. If you have an edge, it's a small one, so diversify or watch the size of your bets.

But no matter how good you are at modelling momentum and sentiment, random things can screw up the forecast. Suppose that all of your algorithms identify a stock that is headed upwards. Then the company's corporate jet falls out of the sky with the executive team on board. That stock is going down, damn the forecast.

To us this is both a practical issue (our bank account) and a philosophical one (our minds). We would appreciate any and all ideas.

BTW, if you want to play with the algorithms yourself, send me an email and I will send you a link.

Apr

13

A few years ago there was a discussion on the site about an esteemed Dailyspecer's paper:
"Modeling the Active versus Passive Debate
"

That article generated a considerable amount of hate mail from investment "professionals" who felt the piece threatened their buy-and-hold livelihood. I consoled myself with some rather unkind thoughts.

Roger Arnold writes:

This reminds me of the discussion we had here 15 years or so ago when Triumph of the Optimists was published.

When I discussed the subject of the outsized returns of equities versus other asset classes with the principal author, Elroy Dimson, he said that in his opinion the 20th century returns were unique and not likely to be repeated over the next century. I won't go into his reasoning here as we discussed it then and I'm not sure if It's been discussed during my absence from the list.

The gist of the conversation though was that everything that provided the positive drift to publicly traded equities has been exhausted.

The positive drift is what made passive management a plausible money management scenario.

Mar

30

The numbers on Payroll Taxes are quite bullish. However if the Jobs Report shows similar, the stock market response could be negative, anticipating hawkish Fed moves.

The big difference in the data is that the BLS Jobs Report indicates jobs without any discrimination as to actual earnings. That is, a $10 per hour job counts as much as a $1000 per hour job. Payroll taxes intrinsically reflect the quality of the job.

Victor Niederhoffer writes: 

And yet Erica Groshen is still Commissioner of Labor Statistics and she's a very good friend of the Chair and they frequently speak together at testimonials and I believe coauthored an article on inequality together. However, unlike Erica, I have not been able to find evidence that the Chair sent her kids to Camp Kinder the way Erica did.

Bill Rafter writes: 

Today's comments by the Fed Chair give us an interesting observational platform.

If the Jobs Report on Friday is bearish on the economy, then it would appear that the Fed Chair was informed and stepped in before the release to keep the party going. (Whether such response is good is debatable.) Note that the survey period for this month ended on Saturday March 12th, so there has been plenty of time to inform someone who has a need to know.

However if the Payroll Taxes are correct and the jobs numbers are bullish on the economy, then the Fed Chair must be either poorly informed or illogical. Neither is comforting. In such a case one might question the need for such a Fed.

Mar

19

In two weeks the March Jobs Report will be out (Friday April 1st at 8:30am). The data to be reflected will be that collected thru this past week (March 12th). The Payroll Tax Receipts (distributed by the U.S. Dept. of the Treasury) thru March 16th already presage a Jobs Report considerably stronger than the prior one.

Mar

14

In the last four weeks U.S. equities have risen nicely. Some were lucky or good enough to forecast what happened (check their records). And there are some who are apprehensive about where the market is now. I cannot guess everyone's motive, but I believe more than a few of the hesitant are so because they fear a further bursting of the Chinese Bubble. However I present to you a brief phantasmagorical tour showing that the Chinese Bubble has already deflated.

In terms of three usable commodities (copper, wheat and cotton) the Shanghai Stock Exchange has mean-reverted to its price in mid-2014. If you are betting on a further Chinese decline, be cautious.

Mar

13

"Many scientific “truths” are, in fact, false"

In 2005, John Ioannidis, a professor of medicine at Stanford University, published a paper, "Why most published research findings are false," mathematically showing that a huge number of published papers must be incorrect. He also looked at a number of well-regarded medical research findings, and found that, of 34 that had been retested, 41% had been contradicted or found to be significantly exaggerated.

Since then, researchers in several scientific areas have consistently struggled to reproduce major results of prominent studies. By some estimates, at least 51%—and as much as 89%—of published papers are based on studies and experiments showing results that cannot be reproduced.

Bill Rafter writes: 

In academia the currency is published articles. It should therefore not be a surprise that many published articles are useless or worse, flat-out-wrong to the point of being fraudulent. Consider that in the United States the typical number of scientific-based papers published in a peer-reviewed journal by a doctoral candidate is ONE. In certain other countries that number could easily exceed a dozen. Consequently the avid reader of scientific papers learns to discriminate in his reading habits against certain universities and certain countries of origin.

Would you do business with a bank that had a reputation for handing our counterfeit currency? And the fact that counterfeit banknotes exist casts suspicion over all transactions.

Mar

3

A very reliable model of mine is the sign “CLOSED” on a store’s door.  It invariably means the store is closed.  But I was just given an example that a slight change in circumstances can render it totally off the mark. 

There’s this corner candy store near me that sells graham crackers smothered in dark chocolate.  I allow myself one a day at the end of lunch and thoroughly enjoy the event. 

So I drive up to the store at 1 PM on Monday and the CLOSED sign is hanging on the front door.  It’s one of those simple ones that says WE’RE OPEN on the obverse.  Elsewhere the hours are posted as 12 – 8 Monday thru Saturday.  But I move on.  Same thing happens on Tuesday. 

Today (Wednesday) finds the CLOSED sign still in place.  Despite what my model tells me, I try the door and find it unlocked and ask loudly if they are open.  A guy substituting for the owner Carol welcomes me and handles my weekly purchase.  And I learn that he had no idea about the simple sign on the door that had been chasing away all customers for the last three days. The owner is recuperating from surgery and the guy never noticed the simple sign.  Another O-Ring example in which a small item has disastrous consequences. 

Again we find that no model is perfect.

Feb

27

Forgive me for posting two items, but I believe them to be related.  In the first instance we have our oldest algorithm (from 1988), nicknamed “Thermos”. This plots a moving correlation between stock and bond levels. As of Friday (2/26) it has gone bullish for stocks.

 
 

Secondly, a major Teutonic bank just announced a buy recommendation in gold. Coincidentally we notice that our measure of professional sentiment just went bearish on gold.

 
 

A week ago we had a similar signal to sell bonds. We have long noticed that whenever bonds and gold are in agreement, equities make a move in the opposite direction. Either way, long or short.  

Feb

26

Gut feelings matter, but not the way you think. An individual’s gut feeling is anecdotal. Chances are that even he cannot statistically study his sympathies. However many of us model the gut feelings of investors at large, and those can be statistically studied. Here are a few examples:
 
Commitments of Traders of futures. Many researchers ply a theory and then try to find data to support it. And their theory typically revolves around following the large (reporting) traders and mimicking them. The trouble is that not even the big guys are right all the time. A better approach is to examine the data without a preconceived theory. In doing so you will find that the small (non-reporting) traders are more consistently wrong than the big guys are right. That is, winners rotate, but losers are consistent. Further analysis reveals that the little guys tend to be even more wrong when they are short. And the best combination is when the little guys are short and the big specs are long. Following the hedgers should be avoided as the hedgers speculate, but on the basis, not the actual price. If you don’t know what that means, don’t play in that venue. 
 
Options data. This usually takes the form of the putcall volume ratio. Excessive levels tend to occur at market turning points. And by the way, the smart money bets against the excessive level. One problem to be mindful of is that most researchers look at CBOE data, which typically only constitutes a third of all option data. If you want to get it all, get the Options Clearing Corp data, which is free just as CBOE data and more reliable.
 
While you are looking at option data, go a step further and look at the open interest levels.  I assure you that if you like putcall volume data, you will value the open interest data more.  The latter also tends to give less ephemeral signals. 
 
Is there any way to combine the two?  You betcha!  In any given period the number of New Positions (NP) equals the volume plus the change in open interest.  Further, the total open interest divided by the backward cumulative NPs identifies a number of trading days which can be described as either the age or average holding time of those positions.  On a very broad scale that data gives a view significantly different from putcall volume, and one that is quite reliable. 
 
Polls?  There used to be a newsletter which purported to measure contrary opinion for futures. What the publishers (Mr. James Sibbet and Earl Hadady) did was rank the bullishness of various newsletters and take a percentage. The theory was that if every publication was bullish, the market was overbought. The trouble was (paraphrasing Keynes) opinions could stay bullish for longer than you had margin money for picking the top. However if a market was up in the high 90s percent bullish for several weeks, the first downturn in opinion to even mid-80s presaged a price selloff.  It wasn’t the same people each time, but when the collection of gut feelings changed its momentum, the price tended to go along. 
 
While on the topic of polls, VIX and its offshoots are surveys that are very reliable. 
 
Price alone. What do you do about a market without telltale derivatives or surveys of newsletters? If you run a regression fit of the price data and extend it, you have a forecast. The deviation of the actual price from the forecast provides a measure of the combined opinions of professionals regarding that price. Small deviations go hand in hand with low volatility which is bullish on prices of assets that go into portfolios. Large deviations are scary which manifest themselves in price discounts. 
 
So all in all, Virginia, gut feelings matter. 

Feb

17

The options viewpoint.

Point 1: Virtually any macro investment strategy can be replicated with options. (Previously stated by one brighter than I.)

Point 2: The use of options can enable the strategist to hide his moves.

Point 3: Options transactions tend to be the milieu of the professional.

Possible conclusion: The broad analysis of options transactions can reveal some interesting truths about the current investment environment.

We have studied the broad pattern of equity options transactions this century and have found whichever side creates more options positions is correct. That is, the condition where new positions consistently exceed liquidations. This is equivalent to a shorter age or holding time (open interest divided by new positions). "Whomever holds longer is wronger" to coin a cheeky phrase. Specifically, if the turnover rate is higher for calls than puts, it's generally safe to be long.

Being long equities when the bullish patterns existed (since 2000) yielded a compound annual rate of return of 11.5 percent. Being short equities during the bear patterns yielded 3.5 percent (CAROR), such that the combined compound annual ROR was 15 percent. Not bad. The trouble for statisticians is that there aren't many switches (less than 40), making statistical reliability problematic. But the minimized "signal flutter" is comforting to longer term investors.

Although this metric called the 2009 turnaround on the money, it should be used to check the climate rather than the weather. Where are we now? Very close to going long. I would be reluctant to give a heads-up in advance of the actual signal, except the chart I show is a smoothed version. The unsmoothed version is already positive.

Here are two charts (2009 and current).

Jan

25

Every now and then it is advisable to check out what the Fed is doing. There have been upticks recently in the aggregates (since New Years, and concurrently since the drop started), although in my opinion the upticks do not alarm or impress.

monetary base

M2

Dec

30

 I think the group will find many useful lessons for both life and trading in these machiavellian maxims and I'm sure that the list will find plenty of fodder for debate contained herein.

Bill Rafter writes:

Those are not Niccolò's thoughts, but the author's wish as to how Machiavelli would think. The two are not the same. Also, many believe they know Machiavelli because they have read The Prince, a very short work hastily put together in three months with an expected readership of only one person, for the expressed purpose of getting a job. Because Machiavelli has become the Progressives' poster child of evil, some anti-Progressives have taken to championing him. But unfortunately they do so poorly read and for the wrong reasons.

The Discourses on the First Ten Books of Livy are Machiavelli's best work, written over three years (concurrently with The Prince) for a universal audience. The Founding Fathers of the United States all read "The Discourses" as a prelude to creating our government. It would be well worth your time.

Gary Rogan writes: 

The Prince was written for, well, a Prince. One problem with applying both the original Maciavellianisms from The Prince as well as these new improved maxims is that they don't seem to concern themselves with basic competency in one's line of business outside of manipulating people. For a Prince it fits: his job is essentially to manipulate his subjects, enemies, and any threat or potential resource provider into benefiting the Prince. He doesn't personally build bridges or grow food, etc. On the other hand, imagine a plumber who is also the world's greatest student of Machiavelli but is a really bad plumber. It's doubtful he can overcome his major deficiency by simply manipulating his customers.
 

Nov

30

What kind of moving average of the last x days is the best predictor of current and future happiness, and how does this relate to markets?

Anatoly Veltman writes: 

The widespread misuse of MAs concept is what gives it bad name. 90% of testers and users look at crossovers, and the remaining 10% look at break of MA from above or below. All wrong

The only proven way to apply MAs from trend-follower stand point is to look at nothing else but SLOPE. (Trading Days) Is 14-day MA sloping upward? If so, then is 30-day sloping upward? If so, then is 50-day sloping upward? If so: then Shorting is forbidden! Mirror test may save you from disastrous bottom-picking.

Bill Rafter writes: 

I beg to differ. There is no way the "average of the last x days is best predictor…" It by definition is at least a coincident indicator and more likely a lagging indicator. BTW the same can be said of the SLOPE of the last x days.

However, you can construct a leading indicator by comparison (difference or ratio) of the coincident to lagging indicators. For this newly created leading indicator, there tends to be a lot of false signals, due to random market action. To guard against that you need to have very smooth coincident and lagging inputs. Making them smooth also makes them more lagged, but that will not hurt you as you are not going to look at them outside of a difference or ratio, which will be quite forward-looking.

The real problem is that investors want to identify a static x. In doing so they are insisting that the market be modeled by x periods. Well, the market doesn't always feel like cooperating. At times the market may be properly modeled by x periods, and at other times by x+N, in which N can assume a wide range of positive and negative values. The solution is to first identify the exact period over which the market should be modeled for the coincident valuation. And then go on from there. Rinse, repeat.

Russ Sears writes: 

This would be a good question to ask the trading expert psychologist Dr. Brett.

It seems that with the same brain imagery he uses is being used in the study of the science of happiness.

While I am no expert I have read Rick Hanson, PhD book "Hardwiring Happiness"/ It has been awhile since I enjoyed this book, my summary of it is "focus on the life/good in the present. Placing things in context to how it has brought you to this moment, then enjoy the moment is enjoying life."

Presence seems to be the buzz-word in studies of contentment and psychology of success. Being aware of all your inputs, your feelings and recognizing them as part of life, then celebrate living. Presence gives you the fulfillment in your life needed to be loyal and disciplined enough for what is working well in your life. Thanksgiving is a day built on this idea, But presence also gives you the courage to turn things around, admit things are not as you want, and gives you Hope for the future. Happiness is more about living your life, being in control, then it is circumstances. Some of my happiest times have been after running hard for over 2 hours exhausted after 26.2 miles, cold and totally and dangerously spent but knowing I gave it my all.

So I would suggest that MA, trend following, momentum, acceleration, nor death spirals nor reversion to the mean, value investing should not ever be the "key to Rebecca", rather judge them in the context of everything else. Some days "the trend your friend" other days "the sun will come out tomorrow". 

Brett Steenbarger writes: 

It's a really interesting area of recent research. It turns out that happiness is only one component of overall well-being. What brings us positive feelings is not necessarily what leads to the greatest life satisfaction, fulfillment, and meaning. I suspect the market strategies that maximize short-term positive emotion have negative expected return, as in the case of those who jump aboard trends to reduce the fear of missing a market move.

Ralph Vince writes: 

Too many times in life I've found myself in darkened parking lots with a small gang of characters who intend me harm, and saw how the pieces would play out enough in advance enough to get out of it, or at least to realize there was only one, very unpalatable way out of it.

Shields up.

Too many times in life, I've had an angel whisper in my ear with only a few hours or seconds to spare to keep from being robbed blind by people I made the mistake of trusting.

Too many times in life I've paced in some anonymous hotel room, wondering "How the hell am I going to do this once the day comes?"

Too many margin calls have had to be met.

Far more times than I would care to, I've found myself confronted with the proposition of having to throw boxcars to survive, and I find myself, yet again, with that very proposition in a life and death context.

Only someone who really loves the rush of the markets, could enjoy wanting a given market to move in a specific direction. I've come to the conclusion it's far better for me to set up to profit from whatever direction things move in on a given day. Those that dont move in a manner so as to profit from this day, will tomorrow, or the next day, or the day after that… I need to just show up on time with my shoes on, collect on that which comes in today, sow the seeds today for taking profits on something at some future date. It's not difficult, and a lot more satisfying.

There's enough episodes in life we need boxcars to show up, and yeah, "Baby needs a new pair o'shoes."

Victor Niederhoffer writes: 

I like all these untested ideas about moving averages but my query was of a more general nature. What kind of moving average, perhaps its top onion skin an exponential average, is the best predictor of human happiness. I.e. if you are happy yesterday and unhappy the day before, are you happier or sadder. I mean vis a vis the pursuit of happiness, not markets, although the two are related I think.

Alexander Good writes: 

My answer would be a medium term moving average works best - about 6 months. We're naturally geared to notice acceleration not speed. After accelerating happiness, it's virtually certain to decelerate which we would have a heightened awareness of. Thus a 5 day moving average would have too much embedded acceleration and deceleration to yield a good outcome.

I would also say 6 months is a good number because there's a fear of 'topping out'. I.e. if you're at the peak happiness of the past 5 years you might get afraid of a larger mean reverting move. 6 months is short term enough not to be victim to noticeable accel/decel, but not too long to be subject to such existential thoughts that lead to unhappiness. 2 quarters is also a good timeframe for evaluation of back to back 3 month periods which seems like a relevant timeframe to most people professionally.

My meta question would be: does measuring one's happiness with a moving average make one more or less happy? 

Theo Brossard writes: 

I would pose that happiness would exhibit similar behavior with market volatility. Short-term clustering (which makes exponential average a good choice, if you are happy today chances are you will be happy tomorrow) and longer-term mean reversion (there must be some thresholds defined by values and time–you can't be very happy or unhappy for prolonged periods of time).

Jim Sogi writes: 

A good way to study this is to rate and record your happiness each day. Also record your acts: exercise, diet, work, family, vacation, tv, meditation, etc. Over time you can correlate the things you do that make you happy. You could correlate day to day swings as Chair queries in a univariate time series.

Nov

9

"The stock market leads the economy, not the other way round"

Are we sure of this old bromide?

anonymous writes: 

Yes, the data support the conclusion. Even more so because we know the results of the stock market immediately, and we get the GDP number only each quarter, and then after a delay of months that is then revised three times.

Andrew Goodwin writes:

A statistical method for testing this theory with precise equations is given here for those who would care to update the work:

"The Stock Market as a Leading Indicator: An Application of Granger Causality"

To summarize the conclusion reached using this "Granger causality" method:

Our results indicated a "causal" relationship between the stock market and the economy. We found that while stock prices Granger-caused economic activity, no reverse causality was observed. Furthermore, we found that statistically significant lag lengths between fluctuations in the stock market and changes in the real economy are relatively short. The longest significant lag length observed from the results was three quarters.

Stefan Jovanovich writes: 

"Is the causality relationship more consistent with the wealth effect or with the forward-looking nature of the stock market? The results from this project are consistent with both the wealth effect and the forward-looking nature of the stock market, but do not prove either. Another possibility for future research is to further evaluate where expectations about the future economy are coming from. Our results reveal that expectations for future economic activity are not simply formed by looking at the past trend in the economy as the adaptive expectations model would suggest. Expectations are being formed in other ways, but how?"

The argument for the "wealth effect": rich people's spending is the Keynesian pump that gets its money flows from the drift towards higher stock prices. The argument for the forward-looking nature of the stock market: the same one that applies to all asset and credit pricing, even those for "true" bills. The argument for "adaptive expectations" models: straight lines are easier to draw.

Stock prices go down because enough rich people think they will go down. God only know what makes them decide to think that, even though they have all the lessons of the past to tell them otherwise.

As Eddy and her Mom and others remind me, my sarcasm can be a bit heavy-handed, obscure and unfunny.

Let me try again, now that Big Al (who has saved me from gold standard oops moments and other follies) has come to my rescue.

The Chair's drift is a fact of enterprise itself; people get richer because they figure out how to do things better, faster and cheaper, and the price for that know-how rises steadily because it is the means of producing more wealth.  (Marx was not wrong to focus on the means of production; he just left our distribution and exchange as the other necessary parts of the deal.)

The people the Chair left behind at Harvard, Berkeley and elsewhere share their own kind of Marxist illusion; they think that people can manipulate the way we all keep track of wealth - the unit of account, the interest rate on government debt - and have the manipulations produce further drift which will, in turn, somehow produce greater wealth.

This all reminds me of what a WW II veteran once told me about sharing a bivouac with the Russians while Truman, Churchill and Stalin carved up the world at Potsdam.  The Americans, with their wonderful energy, had set up tents and installed GI showers and faucets after running lines to the nearest pond with clean water.  After seeing the GI walk over to a faucet and turn it on to fill a pail of water to feed the radiator in his Deuce and a Half, a Russian soldier yanked off the faucet, walked over to the Russian side and defiantly banged it into a post.  He was enraged when he turned the tap and nothing came out.

Fat thumb correction:  stock prices go up and down because enough rich people take one side of the trade or the other that they change the price of wealth expectations for that particular company. There is no way of knowing what their particular "reasons" are; markets are part of Heisenberg's universe.

Bill Rafter writes: 

Allow me to come into this party late and probably tick everybody off. What drives markets most of the time (i.e. 90+ pct.) are two things: momentum and sentiment. If you have a handle on those you can make money. Probably the same two things drive the economy, but you cannot make money trading the economy, as the data coming out of the economy is more lagged than the data coming out of the markets. Hone your skills where they can count.
 

Aug

13

The Monthly Treasury Statement for July has just been published. Of particular concern is the Hospital Insurance (Medicare tax) payments for self-employed enterprises. They continue to languish.

Historically there are no direct causal relationships between this data and equity prices. That is, no one is going to see this data and draw any connection to equities. Most people have no idea that the data exists, and following it is problematic for most (especially financial journalists). The safest thing one can say is that the data does not support any rumors of a renaissance in ultra-small (self-employed) businesses. But you knew that, didn't you.

Jul

6

 By the way, I believe it might be a subject of speculation whether  Mr. Simons and his colleagues have found anomalies that they can still exploit as they might be much too big, and there is much too much competition from other humble anomaly seekers.  Yes, as Mr. Harry Browne would say, as described by  the true believer below, their pantheon of geniuses soars on a much higher level of cognition than myself or any of my colleagues or hundreds of followers - but then again superior intelligence isn't everything. And aside from the profitability of market making, as first enumerated by MFM Osborne, it might be difficult to capture anomalies on a systematic basis that the competitors in St. Louis and other small venues might have missed, no matter their profundity.

Anatoly Veltman writes: 

Does this also answer the query as to WHY would Virtu decide to go public?

A true believer writes: 

If there is anything whatsoever to the legion of gambling analogies to markets, market ecology and human endeavor then most of the chips will end up in very few hands.

The Medallion Fund represents the very apogee of human brilliance so applied to financial markets.

What is more likely, that there is something rotten in Denmark? Or that the combined work of pure genius including:

James Simons

Elwyn Berlekamp

Robert Frey

Henry Laufer

Sean Pattison

James Ax

The whole 'European Contingent' - I will not list those names here.

Plus a host of mere 'worker ants' cleaning data, programming testing machines and keeping the lights on.

Might just have come up with the single best group of high capacity strategies ever known.

We should all celebrate this achievement. It represents everything this list is about, surely?

Trying to pick holes in something like this is the equivalent of the Barron's columnist bearing bearish for 30 years on U.S. stocks.

My belief and optimism is based on facts, not some idol worship groupie phenomenon.

anonymous writes:

Is one allowed to agree with both the True Believer and the Chair? What Simons and the others did was pure genius–they used mathematics to identify the consistent anomalies that occur when people buy and sell securities. Those of us who lack their pure brains and mathematical chops marvel at what they have accomplished and have done our best to create a glacially slow mimicry using employment data and their correlation to the business cycle. (They are playing Scarlatti the way Michelangeli did; I am playing chopsticks hitting one key a month.)

But, as Vic notes, the question is whether or not there remain any arbitrage opportunities left now that those anomalies have been examined in such detail for decades by the far greater number of smart people who have come after the folks at Medallion.

Bill Rafter adds: 

Like others, I agree with both the Chair and Shane. The question then is "how much juice is left in the fruit?" As Stefan says, he gets one a month.

I would posit that it is a question of time frame. Certainly the HFT opportunities are gone for us simple folk, and maybe much of the day trading. But there are still anomalies if we are willing to accept less certainty and leave our bets on the table a little longer. After all, realize the prop shops do not want their worker bees to have an overnight position. Which means those of us willing to have such a position will have an automatic edge. As an example, compare the Open to Close returns to the Close to Open returns of certain derivatives. There's an edge, less than it used to be, but still there, and the edge favors the overnight holders.

Also, we simple folk cannot expect to outperform by trading only SPY (or perhaps its overleveraged sisters), the most competitive and liquid of assets. The greatest returns have always been in the least liquid of assets. 

Shane James replies: 

I see no disagreement with the Chair on this thread. As with the Chair, myself, Medallion, DE Shaw, Citadel and all such people interested in trading from all walks of life - we shall continue to look at new angles, different ways of splicing the available information amongst much else. Medallion too will do this. The outcome? Only the shadow knows.

On this next point, the Chair, myself and anyone with half a clue will be in violent agreement - it is always best to be the bookie . The RenTech entity, at the last count when the info was still public, collected 8% management fee and 45% performance fee (I may be off by just a little here).

To use a collection of letters used by my children to describe this: OMG.

It's good the be the king. 

Jim Sogi writes: 

Much of what they have done is computer science not just math. It also has to do with understanding and moving or changing and understanding and exploiting regulations at the exchanges. In a competitive environment, there will always be an edge available somewhere. They change and move, but there is always opportunity in change, the change in others, the rate of change, the unforeseen effects of changes. I think there is opportunity for the slow and small as well. Computers are stuck with their algos. They leave tracks, patterns, singly and as a group. The markets are complex, and no person or computer knows exactly how it works, though they may find opportunities in complexity. There are always effects of effects of effects, unknown to the actor. Waves spread out from every action.

Jun

5

I once asked of the Chair, is it really worth it to trade markets not based in the United States? We decided that it was an 'interesting' question.

Taking this further it is of much interest to calculate the relative stability of markets. 'Stability' can be measured in many ways and I leave it to the reader (if there are any) to think about this point further.

For example:

1. Are US T - notes more stable than their international peers?

2. Is the S&P 500 more stable than its international peers?

3. Does relative stability explain why the regularities extant in U.S. markets are often massively more persistent than those for similar markets 'overseas'?

There are some interesting things to look at if one believes that the U.S. markets are at the beginning of the chain that moves other markets.

Clearly the more 'stable' market and the market at the beginning of the chain changes from time to time but my supposition is that it takes some great measure of 'statistical crisis'– for lack of a better term– to upset the U.S. market's hegemony even temporarily.

Bill Rafter writes: 

Presumably stability is the opposite of volatility, but there are a lot of ways to count volatility. And of course there is the question of "over which period?" I'm only guessing of course, but I'll bet that John B would define stability as staying within N standard deviations of a moving mean. And that also begs the question as to the period considered. Should the period be static or floating?

Ideally markets that are more stable would attract more portfolio holdings. That is, there would be a stability premium, or alternatively a cost of volatility. If there were two assets priced at $10 and you knew (don't ask how) they would be priced a $20 at a given point in the future, which do you buy for the portfolio? Obviously the more stable of the two since you may have the need to liquidate before the end of the period. In theory the more volatile one would be discounted vis-à-vis the more stable one. With stocks the end certainty is less defined than with bonds.

The original question implied that the investor/trader was looking to be long country markets that were more stable.

Let's suppose that you believe the country ETFs represent their respective markets. Then you could rank those ETFs by inverted volatility. We have done that after first ranking them by other means. We then would have say 10 ETFs that we would like to own, and make a final selection of a few according to inverted volatility. Alternatively it also makes good sense to buy the entire 10, but with different percentages of your equity.

Does that work? Yes, it is more profitable than holding SPY, but not exciting, such that we don't charge for it. We always include SPY in such rankings, as a tracer bullet. The really interesting thing is that SPY never rises to the top of the daily rankings.

We also have the problem of "over which period". One consideration would be to rank all the country ETFs according to the same period, as though China and the U.S. should be compared by the same time standard. That would seem correct if the account owner had a specific time need. Another consideration would be to let each country ETF dictate the period for comparison. But then you might have the input time for Australia being ranked over two years, with SPY only ranked over two months. That would seem correct if the investor was more of a speculator.

Feb

13

I plan to research few trading strategies based on Commitments of Traders data. Any beliefs (positive or negative) about these concepts? Did anyone try to systematize it?

Bill Rafter writes:

Many have researched the Commitments of Traders Reports. If you really want to pursue this I suggest you go into B-school libraries and review titles of unpublished theses for tips. There is little of value to be found in the "popular" literature.

When researching be mindful that you relate the positions both to the market tradedate-wise to test for significance, as well as relating them to the market releasedate-wise for your profitability. One guy who sells CoT data gets this distinction horribly wrong. Collect your own data.

Most researchers tend to focus on identifying the winners by group, and following them. I would posit that the winners vary by group and are less consistent than you would like. Instead, I suggest that you identify losers by group. You will find much greater consistency with regard to losers.

Anecdote: I used to study the CoT for non-obvious trading opportunities. Once I found a situation where the Large Specs had gone from short to long over one reporting period, while the non-reporters (i.e. small traders) had gone from long to short at the same time. [N.B. little guys tend to do poorly on the short side.] This was in the Oats market, which I generally ignored. The Large Hedgers had not changed significantly. Also, from the reporting date to the release date there had been no market movement. I then called everyone I knew with grain knowledge but learned nothing. (It's important to look for orthogonal information.) Sadly I did not know Jeff at the time. What the hell, I bought a lot of Oats and put on even more Oat spreads (long the near). Within the next month Oats and their spreads moved significantly, giving me a great year, new car, etc. And I never learned the reason for the market's move.

Feb

11

Consider, say, 5 related macro markets, one of which is the dominant market in terms of influence upon the other four.

Further assume that your own individual Rosetta Stone tells you to buy the 4 less dominant assets first but the same methodology doesn't get long the main market until later in the microsecond, second, minute, hour, day, week, month, year. (my we are inclusive of all on this site, aren't we!)

Anyway, the issue to consider is this:

Is it more efficient to buy all 5 assets only when the 'influential' asset signals? The qualitative argument being that if the influential asset keeps declining then one should wait on the other four.

After an enumeration here, and considering the relatively short holding periods concerned, it makes more sense to just do all the trades as they occur, 'influential' market be damned.

In terms of percentage attribution of profit or loss amounts there appears to be no persistent profit from waiting. An interesting question might be, is it a good idea to add to the other four when the main market signals….

In the context of relatively short term trading, there appears to be a plethora of cross market vicissitudes– more than enough to compensate for not having the support of the 'main' market.

Bill Rafter comments: 

If "the Four" always lead "the Main", then the Main as a signal is irrelevant for the Four. The Main then should always be bought ahead of its signal (which is a foregone conclusion). This is aside of any portfolio/diversification/size considerations. If you waited for the Main you would seem to be missing some profit on the Four. As you stated, there seems to be no profit in waiting. You should therefore treat the Main as an independent signal on its own.

Be cautious that you have not stacked the deck against the Main. A silly example (but one practiced by many) is to have one signal determined by looking back over say 20 periods, and another looking back over 40 periods (or 5-minute bars vs. 30-minute bars). In this example you will have stacked the deck in speed against the 40-period/30-minute lookback. The novice then claims he needs to wait "for confirmation". All he has done is to nullify the earlier signal. If the earlier one is always/mostly right, his process is inefficient.

Two other considerations:

The use of signals in some markets to trade other markets. The common example here is to use the inverse of bonds to generate an equities signal. Be aware that signals of "opposite" markets rarely occur simultaneously. Some traders would benefit from knowing which comes first, the exit or the new entry. Think about it: it should be obvious.

Our experience is that some signal always leads, but the leader changes. And of course there are false positives. One solution is to have them vote, but in doing so you will always be after the leader. Considering that the greatest improvements in track records come from the reduction of losses rather than outright gains, it seems prudent to trade a little of the upside for less downside. But that is for each to decide, hopefully after testing.

Dec

15

Is this really true in general?

"The most important thing you need to know about commodities" :

If you have traded stocks for a while, you probably have a sense of when a move has gone far enough to be due for reversal, and you're probably used to seeing longer term positions more or less alternately green and red on the day over any reasonable stretch of time. Be careful, because these (correct) instincts will work against you in commodities, which can trend and trend and trend and end in blowoff moves that go far beyond what anyone expected. Simply put, if you come to commodities from a stock trading background, temper your urge to fade moves…

There was a time in market history when S&P 500 traders (experienced, professional traders) flocked to the soybean pits to daytrade, thinking they could apply their ability from one market to another. That incident ended badly for the S&P traders (but very well for the locals in beans!).

Bill Rafter comments: 

Futures are mean-reverting in the shorter run, and that also applies to equity indices. Much less so with individual equities. That being said, that statement does not apply to squeezes in either. Futures moves tend to be linear, whereas stocks and their indices tend to be parabolic. There are logical reasons for these, but not enough room here to write them.
 

Dec

12

 There is some nice WSJ commentary about Patrick O'Brian today.

"A Centenary Salute to Patrick O’Brian":

Aubrey is an apostle of duty, an advocate of order, and yet he knows that leading his men depends less on his power to punish them than on his power to inspire. Maturin has a far greater appreciation of freedom, rebelliousness, even anarchy, and yet possesses a fierce sense of right and wrong. Together they embody the values of freedom and democracy that allowed Britain to lead the world.

First section, back with the editorials.

Dec

12

 "GCHQ Launches Cryptography App for Budding Codebreakers"

I have not yet seen the Cumberbatch flick Imitation Game and was wondering if it gave any credit to the Poles, who had cracked the first generation of the Enigma. Prior to 1938 there was a disgruntled German turncoat who provided intel to the French (who shared it with the Brits). Both the French and Brits were stymied, and passed what they considered useless intel to the Poles, who then cracked Enigma. For years the Poles managed to read everything put out by the Germans, and even had created a mechanical device to do the work. Then the Germans increased the number of rotators from three to five, and the plug-connections from six to 20, requiring huge additional work. [See Technical Details of the Enigma Machine]. Two weeks before Poland was invaded the Poles gave the Allies what they had on Enigma, shocking them. Without that head-start the Bletchley Park effort would have failed.The market parallel to this is that someone else's research castoffs may be useful to you. Just because someone else has failed to find significance, does not mean you cannot gain utility. Our own most useful tool was a castoff from someone else who failed to make it work.

Dec

3

The normal pattern for INDEX options open interest is for the OI of puts to exceed that of calls. It happens more than 90 percent of the time. It's a bit easier to see if you smooth the data, recognizing that it has a 21-day periodicity. But from approximately January 2013 to September 2014 call index OI exceeded put index OI (or was close enough to be indecisive). Since late September the pattern has reverted to historical.

N.B. the OI pattern for individual equities is that calls outnumber puts, all the time.

A return to normalcy?

Dec

2

A disturbing chart: "This is Probably the Second Worst Time in History to Own Stocks"

Bill Rafter writes: 

The trouble with the chart is that the regression fit was done cumulatively, resulting in older data being subject to look-ahead bias. Thus only the current values are useful, and one wonders exactly how useful. As Steve has commented, the way to foil that is to use a moving regression fit in which the values are static over time, always taking the last point in the fit. Thus all data, past and current are relevant and can then be used in statistical studies.

The question that then comes up is which lookback period do you use. Wherever possible all lookback periods should be adaptive, the question then being to what input. In shorter term price data the market will tell you the relevant lookback period. I have never tried determining lookbacks for longer term data because (a) I don't expect to live long enough to take advantage of it, and (b) too many things can happen in the short run to screw up a good plan. Most people don't marry someone in their 20s based on the supposition that (s)he will look good in their 70s.

I also question the use of any equity or debt data prior to 1972. If you don't know why, ask Stefan. **That's one of the great things about the list; there are sources for just about everything.

Several moving functions you should consider:

Moving linear (i.e., regression) fits and their slopes.

Moving parabolic fits and their slopes. Since most economic and price data are parabolic, this is the better of the two. There is also something to be gained in the difference between a parabolic fit and a linear fit. Fitting parabolas is quite tricky, and it took us a while to code it. If you try to do so and want a check on your efforts, try fitting a parabola to a straight line. If the result is ludicrous, try a different method.

Moving correlations are particularly interesting between markets that might be alternatives to one another. Moving correlations between stocks and bonds (levels to levels) are something we have used for years and continue to do so. I thank Gibbons for his comment that Colby & Myers recommended them, as I had not been aware of that. (I'm not a fan of C&M.)

Gyve Bones responds: 

Colby and Myers didn't recommend the linear regression study per se… the empirical analysis simply showed that study to perform best with a fixed loopback parameter over NYSE index returns data over a long period of time compared to other trend following signal generators. This book was an early attempt to quantify different approaches to see how they performed trying as best as can be done to compare apples to apples. In the mid-to-late 80s, it was the best thing that had been done like that since Dunn & Hargitt's study using punch card futures data in the late 1960s (which found that the Donchian Four Week system was best, the system which launched a thousand CTA, including the Dennis Turtles and their spawn.) Another similar study was done in the 90s by Jack Schwager and another fellow whose name escapes me at the moment which was well done.

Larry Williams adds: 

A question: when was the regression line fit? Today? 20 years ago? 50 years ago? The slope will change based on your starting and end points. How overbought or sold is a function of this. A more careful analysis would either apply this same "method" every year with a set of rules (i.e sell above x% overbought) or would do the same thing on a rolling window basis. It's an interesting chart nonetheless and gives one pause, but I would suggest it lacks a certain amount of rigor. 

Gibbons Burke writes: 

It seems to me that this is a flawed chart to look at historically to make rules from because the trend line drawn into the past contains information about the future. The line is drawn using the linear regression of the entire data set so, for example, the line segment covering 1998-1999 "knows" about what happened in 2014. Very deceptive and misleading to make a rule based on the relationship of the data to the trend line.

Victor Niederhoffer comments: 

The disturbing chart is a case study of why charting is so misleading because of the regression bias and also at the variance of a sum is the sum of the variances. 

Steve Ellison says:

Here is the way to solve the problem of the regression line incorporating future data. Attached is a graph of a "moving regression", as Dr. Rafter calls it. For each date, the red point is the last point of a 30-year regression of the S&P 500 as of that date (the graph is from 2010).

Oct

31

Hefty relative changes in the Monetary Base and hefty relative changes (i.e. "corrections") in the S&P seem to be related. Sometimes the former leads, and sometimes it lags. Unfortunately (for the statistical researcher, as opposed to the Optimists) there are not that many examples. The question: is the current relative decline in Monbase related to the admittedly small SPX correction we have already experienced, or is there more to come? Is there anyone here skilled at looking around corners?

Oct

17

Bill McBride published this interesting piece on wage growth in the US.

On the one hand, one might argue that this is a surefire harbinger of inflation. On the other, some wage growth might carry with it some opportunity for increased spending (save? in this country??). Some top line growth would, I'm sure, be appreciated by one and all.

And that assumes that there really is wage growth going on. At best, the jury's still out on that one.

Bill Rafter writes: 

Wage growth has not been underestimated. Payroll tax receipts suggest otherwise. The latter do so some signs of coming back from the grave, but absolutely nothing to get excited about.

Regarding inflation, there are two forms of money growth that have to be monitored: that originated by the Fed known as the Monetary Aggregates, and that originated by the banking system known as fractional reserve lending. The aggregates are the Monetary Base, M2 and MZM. The lending data are commercial and industrial loans. The planned growth of the aggregates is designed to limit deflation. Inflation will not proceed apace until you get a growth in loans. So if you are worried about inflation, at this time all you have to watch is the loan data.

Aggregates and loan data are available on the FRED site. Payroll taxes are on the Treasury site.

Oct

17

 The Riddle of the Labyrinth by Margalit Fox is a great book describing the decipherment of Linear B, a Bronze Age pre-Homeric script found originally on tablets in the Palace of Minos on Crete. If that is of interest to you, this book will reward you. For me it was a quick and exciting read. If you are a Sherlock Holmes fan, chances are you will enjoy it.

The decipherment of Egyptian Hieroglyphics was solvable once the Rosetta Stone was found, which contained a translation into Greek. However Linear B looking like stick figures or the runic alphabet, had no comparable Cliff Notes.

But I also found the book an excellent guide for anyone interested in doing research on market behavior. The parallels between the two were uncanny. To decipher Linear B required pattern analysis, counting and frequency analysis before there were computers to make those tasks easier. We have computers to aid our decipherment of the markets, but the process of creating a framework to do the research is the same. A lot of setup and then lots and lots of actual work.

Sep

22

 "Nobel winner Fama: Active management 'never' good":

Eugene Fama, the University of Chicago investing researcher who won the Nobel Prize in economics last year, once again warned investors against the lure of active management.

"The question is when is active management good? The answer is never," Fama said to laughs Thursday at the Morningstar ETF Conference in Chicago .

"If active managers win, it has to be at the expense of other active managers. And when you add them all up, the returns of active managers have to be literally zero, before costs. Then after costs, it's a big negative sign," Fama added.

He's known as the father of the efficient-markets theory, which says that asset prices reflect all available information; investment managers can never truly get an edge.

Fama dismissed the idea that it was possible to pick the best managers.

"The good ones might be good or they might be lucky. The bad ones might be bad or they might be unlucky. We can't really tell the difference," he said. "I don't know if it would ever make sense, even if the fees were zero, I don't think you'd be better off because you'd be investing in an undiversified way."

Read More Economy weak because of 'stupid' policies: JPMorgan pro

Asked about Warren Buffett's long-term record of picking good companies, Fama said the Berkshire Hathaway (BRK-A) chief actually agreed with his index-based thesis. Buffett said recently he actually has directed much of his fortune to be placed in passive index funds after he dies.

"He's, like, my hero," Fama said. "What he says is, 'I can pick a company every couple years, but if you have to form a portfolio, you're better off going passive.'"

"All the behavioral people say the same thing," Fama added. "In the end, they realize that the game of doing something active is fraught with problems."

Fama was also asked about hedging against big crashes, like what happened to the markets in 2008. Attempting to protect against them, he said, was the unwinnable game of market-timing.

"If you sold when the market crashed, you made a big mistake, and if you saw it coming you're a genius," Fama said.

Gary Rogan writes:

Everything that The Sage deems right and proper will happen after he dies, the charities, index investing, who knows what else. I guess it's no longer politically correct to say "Après nous, le déluge".

The statement "If active managers win, it has to be at the expense of other active managers. And when you add them all up, the returns of active managers have to be literally zero, before costs." is probably mostly correct but given that some active managers are also activist managers it's not completely correct. Also imagine that every single person in the world was an index investor, that would be an absurd situation where nothing in particular but the inflow of new money would determine the price of all stocks. And still, if the average of all managers, aren't some managers better than indexing? At the very least Fama could say that no person is capable of either being or choosing a better-than-average active manager, but he isn't actually saying this.

Bill Rafter writes: 

That's a poor logical argument by the good professor. While Dr. Fama may be right that before costs the average return of all active managers must be zero, clearly it is possible (if not likely) that there will be serial winners and losers. Speaking only of the latter, several years ago we were asked to propose solutions to a shop that had managed to underperform the S&P for every one of the prior 15 years. They did not like our proposals and also rejected proposals from other research providers, continuing with their own methods. They are now 0-18 versus the S&P. Since it is possible for some to get this investment "thing" totally wrong, it is perfectly logical to assume that some others have better than average performance with consistency.

anonymous writes: 

In the case of Buffett you might ask: cui bono? His non Berkshire index assets could fill an Omaha thimble. Is it not the same press release as Betfair put out about their fixed odds versus exchange book on the Scots referendum?

Jul

22

Would anyone advise on how to determine backtesting periods?

I presume one should choose the most recent period because it may better correlate with the present situation. But is that really true? If it is, then how far back should one include, and how far in the future can it correlate? My experience seems to say that a short backtest period can lead to a very short future prediction or even a very poor prediction. On the other hand, a longer period often leads to poor performances during the present situation.

Shane James replies: 

At the Spec Party I had the privilege to spend a reasonable period of time one to one with the remarkable Sam Eisenstadt.

His work is likely one of the best examples of creative thought in the history of financial markets. He explained to me that there wasn't much backtesting to what he/they did. He came up with some principles that made sense to him and started applying them in real time.

Now, in our so called modern world, things may have moved on (Sam graciously stated as much to the room when he was giving his views on the modern markets). HOWEVER, maybe not so much…..

Try this:

1. If your trading idea has an average holding period of a few days (preferably less) then start from today and run it in real time for the next 90 days or so. By definition, the prices upon which you are testing your ideas did not exist when you had the idea so you have already eliminated most bias if you do this.

2. If you are happy with the structure of the returns (win, lose or draw) then consider if the results were biased by any factor during your live test phase and if related to long only stock index trading then make the requisite adjustments for drift.

3. Perhaps now consider a backtest.

The point being that I think it makes sense to test on data that did not exist BEFORE you perform the backtest.
Some like to 'exclude' certain data and 'pretend' it didn't exist so they can assume that the excluded data is 'out of sample'. For instance they may take 10 years of data and use the odd number years as test data and the even number years as 'out of sample'. This might be a reasonable idea to make yourself feel more comfortable but there is an intangible and very difficult to explain benefit to performing the kind of 'spontaneous' testing set out above on data that did not exist at the genesis of your idea before one starts seeing how well a set of heuristics performed in 1971!

Leo Jia responds: 

Hi Shane!

Thanks very much for the valuable advice.

Wow, Mr Eisenstadt! I would really love to thank him for my early success stories with referencing the Value Line. But I guess it wouldn't matter to him as he might have heard from too many!

Talking about my early experience (back in the 90's), I actually had been using your suggestion all along. There was never backtesting for me — I got an idea and went to buy the stock the next day. It actually worked well overall.

Should I go back doing the "novice" way? That becomes a question worth thinking now that you mentioned it. Perhaps this goes with the valuable lessons where having had enough struggles using complex ways, one discovered the neglected simple way being far superior. In Chinese culture, Tai Chi can be considered as that type of "simple ways".

Now, a couple questions about your suggestion.

1. By putting a new idea directly live, what problem is one trying to solve? Is it the concern that poor backtesting result may make one throw out potentially a good strategy? And is this concern because of the belief that past data are already different from the present situation?

2. In what ways can this idea that seemed to come from nowhere be better than the many ideas one gets by studying historical data? I know inspirations are invaluable, but one doesn't often get those inspirations that are not the results of study. So beyond the mistrust of the correlations between past data and present situation, are there any other reasons?

Thanks again for your thoughts.

Bill Rafter writes:

I am sorry to jump into this discussion late, but think there are a few points that can still be brought.  Looking for beta over a constant period of time (say 6 months) is somewhat meaningless and useless.  It’s a bit like describing a man with one foot in a fire and another in ice as at a tolerable temperature.  You have got fat tails with market volatility and a static window might be good for a journalist, but of limited value for a trader.

At a given time there is a time period over which the study of a market’s behavior will be significant.  And let’s say that at this time it really is 6 months, or 126 trading days.  Assuming no real changes, tomorrow that time window will be 127 trading days, and so on until you get a market change.

When the sea does change, bad things can happen in a hurry and beta value for the preceding 6+ months will be of little value.  Within the last week this happened with biotech:  it had been happily chugging along with good but not extraordinary outperformance of the indices.  Then it got clobbered with huge excessive relative volatility to the downside.  Had you been adapting your monitoring of volatility you would have been prepared, whereas if you stuck with your 6-month window you would have been clobbered along with the group.

My advice to you is to learn how to deal with the market adaptively.  I assure you that if you have a monitoring mechanism which you like, if you make it adaptive you will improve results dramatically. And it doesn’t matter which signal type (momentum, volatility, sentiment) or time frame (intra-day to weekly) you favor.

Jun

24

 A hundred years ago Milutin Milankovich, a Serbian scientist/engineer, didn't have much to do as he was a POW held by the Austrians. So he calculated the pre-historical temperatures of the Earth, based entirely on planetary distances to the sun. Several other scientists persuaded him to go back quite far in time and eventually he calculated the temperatures back a million years. Of course at that time there was no way to prove his work, until in the 1970s data from Antarctic ice cores became available. It turns out his calculations were very accurate, as were similar calculations for Mars and Venus.

If someone a century ago could calculate Earth's temperature a million years ago, the global warming claims of one camp seem to lack significant credibility.

Stefan Jovanovich writes: 

Milankovic's theory is this: "variations in eccentricity, axial tilt, and precession of the Earth's orbit determine climatic patterns on Earth"

The theory of the warmist researchers is that "the addition of combustion gases - most importantly, CO2 - from man-made uses of energy to the earth's atmosphere determine climatic patterns on Earth".

The reason for the falsifications of data by warmist researchers– I assume here that no one denies that these have occurred– is that the theory of man-made global warming requires a dramatic increase both in temperature and CO2 levels during the period when people have been burning stuff. If that cannot be found, then the theory has to contend with the very data that Al Gore found so persuasive– the Vostok ice core samples– and explain why CO2 level increases seem to be a result rather than a cause of the rise in the earth's surface temperature. That non-modeled data (i.e. the ice cores were actually dug out of the earth, not created in a computer model) is inconvenient and true. The Vostok data shows that changes in temperature always precede the changes in atmospheric CO2 by about 500-1500 years.

The usual rebuttal to this evidence and the fact that its data is entirely consistent with the Milankovic theory is something like this: "yes, it's true there is a delayed correlation; but that ignores the more important fact. Once the rise in CO2 levels start, they take over as the most important climate force."

But here, too, the actual non-modeled data presents a problem; the declines in earth surface temperatures that begin the "ice ages" occur precisely when CO2 levels are at their highest. If the Hansen theory's forces are so strong and can overwhelm the mere changes in the Earth's orbit, then how can the 'weak' signal can start an Ice Age when the strong Hansen signal says the opposite should be occurring?

The answer to that, of course, is the usual ad hominems that are the ever available rhetoric of the progressive mind: (1) you don't understand, (2) you haven't read our secret data and (3) you are too stupid to understand these things.

I think we have another definitional problem here, HA. "Complete(ly) unbiased description(s) of meteorology-climatology science practices" do not get written by people who write: "as a historical science, the study of climate change will always involve revisiting old data, correcting, modeling, and revising our picture of the climatic past. This does not mean we don't know anything. (We do.) And it also does not mean that climate data or climate models might turn out to be wildly wrong. (They won't.)"

Jun

5

 Yesterday while driving I heard a report of strong auto sales of both domestic vehicles (particularly trucks) and BMW and Audi. These would show up in the Daily Treasury Report as revenues in categories such as customs duties and excise taxes. Today I went looking for them, and sure enough the recent data is positive.

I tend to think of those categories as a good upstream surrogate for discretionary purchases. There are excise taxes on auto sales, gasoline sales and even on tanning salon sales.


In the linked chart
, SPX is shown as an historical reference. In my opinion there is not a definitive causal relationship. Historically this had been distorted by the "Cash for Clunkers Program", for example.

But maybe there is a retail recovery.

Jun

4

Does anyone know if there is a Predictive Value to a stock's short interest ratio?

Bill Rafter writes:

Short Interest (SI) is a good area to research. We do a lot of work with it in our shop, and use it in our trading. However, the question you posted was specifically about the SI Ratio, something we consider unworthy of attention with a very few exceptions. If that ratio is all you are going to focus on, we suggest watching a good movie instead.

Many people simply look at the SI Ratio because it is available, say on Yahoo, Google or the Nasdaq websites. The problem is that ratio is more dependent upon changes in volume than changes in SI. Volume is also an area worth your attention, but not in that ratio. We maintain that there are better SI ratios to look at rather than that one. But to do that you are going to have to spend some time getting the data, which means not only SI and volume, but outstanding shares, insider ownership and institutional ownership. Then you will find the profitable relationships, but anticipate considerable work.

We have only found the volume contributor to the SI Ratio useful when in a price explosion the volume exceeds the number of shorts. That circumstance suggests that the price explosion (of a high-SI stock) is a result of short covering, which has now been exhausted. Obviously don't buy that stock!

Phil Erlanger is the regarded expert with SI data. His approach was to find stocks that one liked (say on the basis of momentum or whatever) and then look for SI patterns that would enable a greater run-up. We took the opposite approach, looking to first find good short interest patterns, and go from there. What we found was that Erlanger's approach is the better of the two if one is taking a cursory look at SI. That's because fully half of the stocks with high SI deserve it – they are headed south. Of the remaining percentage, about half of those mill around going nowhere. That leaves about a quarter of high-SI stocks overall that benefit positively, a few of which really take off.

Despite the above warnings, we would not purchase a stock without at least making ourselves aware of the SI.

May

26

For historical reasons I manually downloaded the Daily Treasury Statement files and dumped them in a folder. Once there we go through our data mining process and extract what we want automatically. Our process could be made completely automatic, but it has not been a big enough inconvenience for us to code it. For virtually all other data our downloading and extraction is completely automatic.

Several weeks ago I noticed a change in the Treasury's website that irregularly makes me click once or twice more each time I download (which is only once daily). It has puzzled me why Treasury would take something that worked perfectly and change it such that it no longer worked perfectly. It has just occurred to me that the new little two-step process would certainly screw up an automated download and extraction procedure. Also of late the data is less and less favorable to a government that may wish to claim everything is rosy.

Am I being paranoid in thinking that there might be a connection?

keep looking »

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