There are arguments for and against the log returns in data analysis. Any preferences and why?
Alex Castaldo writes:
The nice thing about log returns is they are additive across time. The log return for the year is the sum of the log returns for Jan, Feb, Mar… Also the log return is to a first approximation normally distributed.
The nice thing about the percentage return is that the % portfolio return is a weighted average of the individual stock percentage returns, weighted by portfolio weights. So for example if you have 1/2 your money in MSFT and 1/2 in GOOG, the portfolio percent return will be the average of the percent returns on these two stocks.
So when I work with portfolios I always prefer the percentage returns. When I work with indexes I work either with percents or with logs (especially when concerned with options, since option theory is all in terms of log returns).
I was looking at Greece's unemployment rates historically last night and found something interesting. The Greek economy seemed to hit a pothole in 1981 from which it never extricated itself. Between 1980 and 1982, unemployment tripled, and has stayed that way as a base since then. (I say 1981 because the rate didn't return to where it was, it increased.) Now, there were recessions in the US in 1980 and 1982, and Greece is a tourism-based economy. So a short-term increase in the rate can be explained in that way. However, that doesn't explain that the rate didn't go down in the 1980s. Why? Any suggestions as to the reason? It seems to me that that reason may provide more insights to the current situation than simply that the Greeks lived beyond their means. Something changed in their means.
Alex Castaldo writes:
According to Greek analyst Nick Tsafos, one reason for the low growth rate that started in 1981 was monetary mismanagement.
From 1953 to 1973 the 'third drachma' like most currencies was tied to the dollar; the exchange rate was 30 GRD per USD. This was the period that Greece experienced its best economic performance.
After the mid 70's the currency floated. It was (in round numbers) 58 in June 1981, 148 at the end of 1985, 157 at the end of 1989, 240 at the end of 1994, 328 at the end of 1999 and 325 in 2002. (In 2002 the Euro was introduced).
In other words from 1981 to 2001 the GRD was a 'soft currency' that allowed the Greek government to finance itself easily at the cost of higher inflation and currency depreciation. It could create government jobs, pay generous retirement benefits and get away with it by issuing more drachma. And the Greek politicians were masters at this kind of thing, buying support with monetarily financed expenditures.
The inflation ended in 2002 with the introduction of an externally managed currency, the Euro. For a time everything seemed wonderful. But old habits die hard and the politicians kept up their old ways of solving problems. Government debt increased but interest rates were very low, so it did not seem to matter. But the debt this time was hard debt, that inflation and devaluation would not erase…
Now for a rhetorical question: if Greece abandons the Euro and introduces the new drachma, how do you think the new currency will be managed? The past history is not encouraging.
See picture enclosed.
Humor from Yellen:
"The economic and financial news has been grim," she told colleagues at a mid-March2009 policy meeting, according to the transcripts. "Things are now so bad that I actually open the Greenbook with greater trepidation than my 401(k)." The Fed's so-called Greenbook is its official summary of economic and financial conditions.
The Fundamental Dilemma of all market manipulation, whether legal (as in this case) or illegal: when you stop pushing up the value of an asset, it falls back, giving you mark to market losses on your inventory. Today the SNB is taking tremendous losses on the Euro assets it has accumulated in its Euro buying binge. I would not want to be a private shareholder of the SNB (or a swiss taxpayer) on a day like this.
I first met Ed sometime in 1997 or 1998. I was in graduate school and trying to earn a few bucks by working as a data cleaner and spreadsheet builder at a hedge fund not far from the university. I and the rest of the staff worked in cubicles in the middle of a big trading room, while the important people had glass enclosed offices around the perimeter. One of these people, a well dressed, tall and large gentleman soon introduced himself as Ed Dunne. In the following few days he would often stop by and chat about what was going on in markets all over the world. Most often I could see him in the office of the head of the firm, the billionaire Mr. Why, talking and gesturing while Mr. Why nodded politely from time to time. I never talked to Mr. Why, but I felt I had a indirect link to him via my conversations with Ed. And he knew or spoke with so many other important people in the investment world.
Ed was to me was the quintessential expert on international financial markets. He would explain how hedge funds and re-insurance companies work together, how oil can be stored in idle ships for later delivery, what factors affect the prices of grains, and a hundred other things. All things that I was very curious to learn about. To become as savvy and as wealthy as Ed seemed within easy reach if I could have a few more discussions with him. One time he invited me to attend one of his meetings at Princeton University with several economics and statistics professors (though I did not see Ben Bernanke there), and afterwards to a sumptuous lunch in a nearby restaurant. That was just one example of his generosity.
Later on Ed claimed that he introduced me to Victor and got me the dream job that I now have. I think it was more complicated than that and that there were several people involved but I don't remember the details. But certainly Ed played a role and I am grateful to him for that.
December 23, 2014 | 1 Comment
Here's a pretty kettle of fish. Suppose you have two forecasts that are disparate. One is bullish and the other is bearish. For example it's up 100 over 4 days. That's bearish. But it's up 4 days in a row, that's bullish. How to combine? There's a bayesian approach, a regression approach, and an inverse variance weighted approach, and a practical approach that Zarnowitz found. Just add up the number of bullish and bearish and that's your forecast. But what's your best way of solving same? The answer might provide a meal for a lifetime. I asked Stigler this question 15 years ago, and he thought it was a very good question, and I've not seen a good answer yet.
Alex Castaldo writes:
I would start with Diebold and Pauly: The use of prior information in forecast combination.
Gary Rogan writes:
There has got to be some way of incorporating the rare nature of one of the set of circumstances. Clearly 100 points is more unique than 4 days. Does this carry any special weight? Also there is a very large number of other possible "circumstances", like time of day, month, year, what the future portends if prior history was similar during this time of day, month, year. where are we in the economic cycle? With respect to various moving averages? What's the money supply and it's history? What has the price of oil and any number of other thing doing and where is it? And what matters more: all these other things or the one unique thing?
You're mixing apples and oranges. The premise for regression and related approaches is that there is a fixed law that can be discerned, or at least modeled, in such a way that it does not vary in any dimension. Whatever the model/rule was 50 years ago is still what it is today—unless of course, additional information either disproves the model or allows for its refinement. Either way, it's time invariant. Bayesian analyses are different by definition. Unless the prior is the same, the result will be different. Since priors will change with the passage of time, the analysis is time-dependent. You might try to specify the Bayesian model as fixed at any one point in time and try some form of combination, but since the moment you do that, the prior will shift and the exercise becomes worthless.
I have been thinking about what could be a good set of criteria to measure trading (strategy) performance for individual traders.
The criterion of average return divided by the variance of the returns seems to have its shortcomings. One reason is that some large positive returns can cause the variance to go up resulting in an indication by the criterion that the performance deteriorates. But some large positive returns are good to have.
Other criteria like Sharpe ratio seem more suitable for institutions.
I think using properties of the linear regression line of the cumulative return curve might be a better choice.
Two useful properties are the slope and the "width" of the linear regression line. By "width" I mean the deviation of the cumulative return curve around the linear regression line.
A good performance should have high slope on the one hand. And if we do not consider reinvesting profits, it should have narrow "width" around the linear line.
So then the value of slope/width seems meaningful.
If we take the linear regression line as a risk free benchmark, then this value may be very similar to the definition of Sharpe ratio, but practical for individuals.
Would anyone please comment on the pros and cons of this, or any other better ways to measure performance.
Alexander Good writes:
I think it makes sense to measure linearity of PNL and convexity separately so I agree with you that R sq is a good one to employ. I am curious how width differs from the strategy's std though…
One thing that you can do as a cheap proxy is median return * sqrt(252)/std return and then for skew then have a (rolling max peak to trough draw down)/(rolling max peak to trough draw up).
You can benchmark your strategy vs. bonds, the S&P and a traditional 60-40 mix or your other strategies. It's very hard to beat a vol weighted portfolio of stocks and bonds so it's a good benchmark in my humble opinion assuming you're trading your PA and you don't have large retirement holdings. I assign different weights to skew and median return depending on my portfolio construction.
In portfolio construction you'll often find things with strongly positive skew have good inverse correlation to market PNL series and are typically 'long vol' (idea ripped off AQR's value and momentum everywhere).
Trending strategies frequently have very positive skew (momentum) whereas mean reversion tend to have skew that looks like the S&P (value). So if I'm net long beta my marginal utility of doing trending models is higher whereas if I'm net short I tend to size up mean reversion strategies.
Would be curious to know what other people are using/ how other people think about this/ if they have good papers on the subject.
Leo Jia writes:
Aren't they different?
std of returns has this term: (Ri - mu)^2, where mu is the same for all i's.
The width has this term instead: (CRi - Vi)^2 where Vi is the value on the linear regression line at time i and is all different across all i's.
Alex Castaldo writes:
Personally I just like to look at the equity curve visually, and it is not difficult to store large numbers of graphic files in a folder and quickly "flip" through them by hitting a key on the computer.
But for automated evaluation Leo's two criteria (slope of regression, and "width around the regression" (which is also called the SEE or standard error of estimate.in regression textbooks) make sense to me.
However I know there are many other criteria that have been proposed. There is one with a foreign name that I think starts with "v" but that I can't remember. I am sure some people here know what I am talking about, it was much blogged about 2 or 3 years ago.
In looking for it I accidentally googled another measure of equity quality, the k-ratio , that believe it or not has 3 different versions.
Any other ways to measure equity curve "quality"?
As with many things involving non linear information, my experience suggests that one must mix, blend or combine different 'quantities' to form a unique and proprietary time series.
For example, some form of 3D 'curve' that combined the three quantities return, AUM & volatility that gets thicker as AUM in the strategy grows and changes colour as volatility of returns increases perhaps…
Ralph Vince writes:
percent of 6 month periods underwater
percent of 1 year periods underwater
percent of 2 year periods underwater
percent of time at equity highs
percent of time within 1% of equity highs
percent of time within 5% of equity highs
percent of time within 10% of equity highs
percent of time within 20% of equity highs
February 19, 2014 | Leave a Comment
I believe there are 4 cases, conventionally called:
h(t) > h(t-1) and l(t) > l(t-1) an uptrend day
h(t) < h(t-1) and l(t) > l(t-1) an inside day
h(t) > h(t-1) and l(t) < l(t-1) an outside day
h(t) < h(t-1) and l(t) < l(t-1) a downtrend day
Victor Niederhoffer writes:
Alex Castaldo replies:
OK, OK, let's call then upshift day and downshift day then.
Kim Zussman adds:
What about outie and innie?
Using SPY (2000-present), checked for outside days (today's high > yesterday's high, today's low < yesterday's low). Then checked the return for the next day.
Days after outies were also checked if the market was up-trending (20DMA > 100DMA), and, in addition to up-trending, if the intra-day return was up (C>O). Here are the c-c returns after (vs zero), for all days (c-c ret), all days after outside days outie+1), days after outside days and up-trending (and 20>100), days after outside days and up-trending, and the outside day was up intra-day (and C>O):
One-Sample T: c-c ret, outie+1, and 20>100, and C>O
Test of mu = 0 vs not = 0
Variable N Mean StDev SE Mean 95% CI T
c-c ret 3550 0.00022 0.0131 0.0002 (-0.0002, 0.0006) 1.01
outie+1 353 0.00049 0.0118 0.0006 (-0.0007, 0.0017) 0.77
and 20>100 234 0.00061 0.0090 0.0005 (-0.0005, 0.0017) 1.03
and C>O 89 -0.00047 0.0090 0.0009 (-0.0023, 0.0014) -0.49
Zeroish, with none significant, and days after outside days and up-trending winning the race at 0.06% per day avg.
There was a lot of interest a few years ago in Joel Greenblatt's book The Little Book That Beats the Market . He set up a web site www.magicformulainvesting.com which selects stocks based on this method (using a combination of 2 criteria: earnings yield and return on capital).
Two years ago I saved a list of 30 stocks it recommended and just recently I calculated the performance up to now. The results are below:
MagicFormula Investing Stock total return from 2012/01/29 to 2014/01/29
dell -14.40 acquired 10/30/2013
lps 129.00 acquired 1/3/2014
mrx 37.67 acquired 12/11/2012
prx 37.99 acquired 10/01/2012
ldos 48.21 ticker change from sai to ldos
As you can see the return over 2 years was good, at 44.16%, but indistinguishable from the equal weighted S&P at 44.02% and close to the (ordinary) S&P at 40.30%.
[Strictly speaking you are supposed to hold the portfolio for a year and then rebalance it, which I forgot to do, so it is not a completely accurate replication of the MagicFormula method.].
Bill Rafter writes:
Before Greenblatt there was Haugen who ranked stocks by 60+ variables. He published them in his book, the Inefficient Stock Market. BTW all of his books are interesting. Haugen's material looked really good, but seemed to fail to deliver outstanding returns.
Then Greenblatt's book came out and showed good results from only using two variables: ROE and ROA. One of those was a Haugen variable (I forget which). Then Haugen produced a second edition in which he bumped up the number of variables to ~70, and included both ROE and ROA.
We did a lot^2 of work with both H and G and found that neither are particularly good at picking winners, but both are very good at finding clunkers. And of course if you eliminate the clunkers, the remaining portfolio will certainly outperform the market indices. But of course that only really works if you essentially invest in ALL of the non-clunkers. Thus they are good strategies for large mutual funds.
FWIW we found that you can cull out most of Haugen's variables (but include ROA and ROE) and do fairly well with 16 variables. But again, they are best used as ranking tools to eliminate the underperformers. Our opinion is that both H and G methods are the financial markets equivalent to comfort food. If comfort food makes you feel better, then use it.
I agree that Haugen (1942-2013) was an interesting character who did some innovative work. I have read many of his books/papers; how well it holds up remains to be seen. He used the kitchen sink approach which Rocky so detests, but he found things (such as the low vol phenomenon) that were new and interesting (now low vol has maybe become too popular). BTW the two year test period I used is far too short, it could be argued. You need more like 10 years.
Gordon Haave writes:
"Our opinion is that both H and G methods are the financial markets equivalent to comfort food."
This by the way is very important for non-professionals. Most not-professionals lose money because of over-trading, moving in and out on a whim, chasing winners, etc. (actually, most professionals do as well).
A system like Greenblatt's that tells a good story, tells you to only re-balance one per year, and does "as good" as the market will tend to outperform what the individual otherwise would have done.
Richard Owen writes:
Bill, when you say clunkers, do you mean it was great at selecting clunkers by the lowest ranking? Or it was great at selecting clunkers by false positives - i.e, many of the best stocks were very bad despite their attractive ROE etc. and this is what hurt performance? And so they are good for 'cleanup'? Perhaps it's a bit unfair to pick '12-14 data, but very interesting nonetheless. I suspect the best outperformance of such strategies would come in the early bull market and then converge with the market as it matures, then lagging at the end / collapse of the bull?
Bill Rafter writes:
Sorry, I did not mean to be cryptic. By clunkers I meant stocks that you should not own, because they are and will be poor performers.
We used both Greenblatt and Haugen variables to rank the Russell 3000 for 20 years and put the stocks into 10 bins (i.e. deciles) and observed the performance of those bins over the following 6 months (with no overlapping periods). Considering the deceased stocks the total number of securities exceeded 8,000. You have to include the dead stocks or you will have survivor bias. The lowest ranked bin performed really bad, such that they could have been shorted profitably (we are long-only and had no interest). Importantly the performance of each higher decile was monotone increasing. That last bit means the strategy has merit. However, the top three deciles did not have the performance that would encourage us to pursue the strategy. That is, they outperformed the market, but they were not an improvement over other strategies.
It makes perfect sense to find those clunkers and eliminate them from your subsequent ranking routines. But there are many ways to find them. It turns out that the underperforming stocks have the mark of the beast written all over them, and you don't need to look at 10+ variables. If data mining for fundamentals is too onerous for the researcher, ranking by volatility will find clunkers. Deselecting for volatility will also keep you out of some high-flyers, but that's a good price to pay. High short interest coupled with declining prices is another.
We did the research 2-3 years ago and I apologize that I don't have the time to dig it out and clean it up for public consumption, as it was only done for our shop. Realize that we are statistical traders who typically hold either ~20 or ~50 positions because we cannot forecast individual security performance, but only bin performance.
Every business has its peculiarities. In the article
"The Tell All Diary of a Sundance Producer" by Galt Niederhoffer we get a window into the world of independent film production from this week's New York magazine.
July 21, 2013 | Leave a Comment
I am only now taking a look at "credit suisse global investment returns sourcebook 2013" which was published in February 2013.
Cumulative returns on US asset classes in nominal terms 1900-2012
Equities 1 to 25507 (9.4% per year)
Bonds 1 to 253 (5.0% per year)
Bills 1 to 74 (3.9% per year)
Inflation 1 to 27 (3.0% per year)
Cumulative returns on US asset classes in real terms 1900-2012
Equities 1 to 952 (6.3% per year)
Bonds 1 to 9.4 (2.0% per year)
Bills 1 to 2.7 (0.9% per year)
Table 1 shows real equity returns for various countries 1900-2012:
USA 6.3% per year (see above)
UK 5.2% per year
World 5.0% per year
The authors are exceedingly cautious about the future, making various downward adjustments to these figures for prediction purposes.
N.B. The Sourcebook is not easily found online, an abbreviated free version called the Yearbook is available here: Credit Suisse Yearbook 2013 [69 page PDF] . The range of data is less and it is not organized in the same manner, however.
In the last 4 days we have seen every colour on our calendar (see above).
Tueday June 4 Red - Stocks and Bonds down
June 5 Blue - Stocks down and Bonds up
June 6 Green day - Both Stocks and Bonds up
June 7 (today) Yellow - Stocks up and Bonds down
Interesting, but what does it portend?
I learned today there is an apparently relatively simple and practical option pricing formula for the case when the stock follows a (slightly extended) GARCH(1,1) process.
"Fast Analytic Option Valuation with GARCH" by Thomas Mazzoni, September 2008
I thought it might interest the option theorists on this site.
I attended a presentation yesterday on "Optimal Order Placement in Limit Order Markets" given by Arseniy Kukanov.
It was an interesting presentation but was perhaps best summarized by a well dressed white haired gentleman in the audience who asked at the end: "given all the simplifications and assumptions you had to make, of what use is this 'solution' other than to enable you to get a PhD".
In choosing between limit orders and market orders you have to trade off cost savings provided by limit orders against "non-execution risk" that accompanies limit orders (risk that the limit order will not execute).
The problem is: you must buy S shares of stock within a time horizon T.
There are N markets (exchanges) available, for each market you know: the bid ask spread the bid queue lengths at time zero the maker/taker fees that each market charges.
In each market you can at time zero place a market order (which has a 100% probability immediate of execution) and/or or a limit order at the best bid price (other price choices are not included in this version of the model).
There are two parameters lo and lu which are the penalties (in dollars per share) that you charge yourself for buying fewer than S or more that S shares in the available time.
The model minimizes the sum of the trading costs plus the penalty.
The simplest solution is in the case of only one exchange (ex: the minis are only traded on the CME).
In this case there is an algebraic expression for the optimal strategy, and what to do depends on the value of lo:
If lo is below a certain value lbar1, it is optimal to enter a limit order.
If lo is above a certain value lbar2, meaning there is a high urgency to buying the stock, it is optimal to enter a market order.
For lo intermediate between these two values it is optimal to enter both a limit order and a market order at time zero that add up to the desired quantity.
Certain theorems about the solution can be derived, for example as the amount of stock S to be bought increases there is an increased reliance on market orders.
From my point of view one of the most unrealistic assumptions is that the probability distribution of future order flows and cancellations does not depend on the size of the order you enter. In my experience on the contrary if you enter a big limit order it can 'scare' the market and move it away from you.
All in all it was very good for an academic presentation.
Thanks to Anatoly for the invitation.
Written in honour of all splitters, rookies and lookbackers:
A common pursuit in decision making is to compare data of many kinds and find the best way of separating the observations into different classes.
For example you mite be considering what are the factors that contribute to long life. Weight, height, mid range of parents age , % of meat consumed , exercise mite be the variables. You mite find that when parents mid range was above 140, and meat eaten less than 2 times week, and exercise more than 5 hours a day, the longevity is 10 years higher than the average.
Trees are usually used to separate the groups. An example picture of such a tree is here: [pic ] Lots of good pictures of such trees and a discussion of a typical tree sort is in An Introduction to Classification and Regression Tree (cart) by Roger Lewis. And programs like CART and AID and many modern variants are widely used in medicine and markets. In fact all market people systematically or implicitly consider such problems. When does the market go up? When gold is down more than 10, when bonds are up more than 100, and the previous 5 day sp move is between -1% and 0 %? The problem is that there are many variables to consider, and many levels at which one would reasonably split the data.
A good starting point in considering such problems is to note that when there are two mutually exclusive groups X and Y the variance of the difference between means is the sum of the variances of the means. The variance of a mean is the variance of an individual observation divided by the number of observations. For example, consider the average move in sp during a day has a sd of 10 and variance of 100. If you take two independent samples of 10 from such a distribution , the variance of one mean will be 100/10= 10 and the variance of the difference between means is 20. The standard dev of the difference is 4.4 What does this mean in practice? One will round. A 50% confidence interval in which 50% of the observation range is 0.65 x 4.4 = 3 points on either side of the difference between means which we'll assume to be 0. That means that by chance half of all the observations will show a difference in means ranging from -3 to +3. To find a region where 95% of the observations lie, ie. A 95% confidence interval we'd multiply 2 x 4.4 =8.8. In other words to find a difference that has less than a 5% chance of occurring thru randomness we would have to find a difference between means of 8.8 on any binary split.
But of course that's not all. Normally we look at 2, or 3 or 4 different splits to find the one that gives the greatest difference. It turns out that if we look at the highest of two differences, the average difference is 1.5 times as high as for one difference. In other words by randomness , the standard deviation for the highest of 2 difference between means is 1.5 x 4.4 = 6 a 50% confidence interval assuming randomness for the highest of 2 differences is 0.65 x 6 or from -4 to + 4. A 95% confidence interval would be between 2×6 and -2 x 6 i.e. between -12 and +12.
Now there were several assumptions made in this analysis. We assumed normality. And we didn't take account that there is sampling without replacement for one. And there are two cases: you look at the algebraic difference (2 tailed test, as above) or you try to maximize the *magnitude* of the difference (the 1 tailed approach). To get a good handle on it, Doc and I simulated what would happen if we divided up 20 random observations from a distribution that had a mean of 0 and a standard deviation of 10. This corresponds to the most frequent and typical thing one does when dividing up stock market changes in points (S&P rises or falls by about 10 full points a day). We then took two random subsamples of 10 each and calculated the mean of each of the two groups of 10. We then looked at the absolute value of the average difference between means when we repeated the process 1000 times. It turns out that the average absolute difference between means is 3.5 for a single split (this 3.5 absolute difference is in line with the std dev of 4.4 previously estimated)), 5 for the highest of two splits, and about 6 for the highest of 3 splits. This leads to incredibly high differences that you must be aware of when you split data to have anything more than a 50% chance that the difference occurred thru luck alone. To be 95% sure that you have something departing from luck for the highest of 3 splits you'd need a difference between the groups of 10. As the numbers in the group get less than 10 ,i.e. for a second or third split, the numbers would get increasingly large. This is a warning to all who search for regularities in data using methods that are implicitly or explicitly like tree sorts. By vic and doc.
Here is a more extensive table of simulation results
Col A: number of observations being split
Col B: number of splits considered, best one is chosen
Col C: mean absolute difference generated by best split (in S&P points)
Col D: standard deviation of absolute difference
Col E: lower 5% confidence interval
Col F: upper 95% confidence interval
20 1 | 3.56 2.66 0.27 8.65
20 2 | 4.97 2.67 1.24 9.81
20 3 | 5.84 2.64 2.07 10.55
20 4 | 6.45 2.55 2.84 11.05
48 1 | 2.26 1.72 0.18 5.63
48 2 | 3.25 1.71 0.90 6.40
48 3 | 3.75 1.66 1.36 6.78
48 4 | 4.20 1.68 1.79 7.24
Table 2 same as above but
Col C: average (algebraic) difference between means (in S&P points)
Col D: standard deviation of difference between means
20 1 | -0.06 4.61
20 2 | -0.13 5.62
20 3 | 0.01 6.42
20 4 | -0.07 6.88
Let's try to apply this to a typical example.
The 30 year bonds each day have a move of about 80 points.i.e a move from 142 exactly to 142 and 26 /31. Call that the stand dev . Let's say you have 100 observations of bonds in your sample. And you split it into two equal halves based on whether gold is up the previous day or down. The variance of any mean difference would be 6400 x2 /50 =256 . That gives a stand dev of 16.
Turn to the prob that a standard normal deviate will be between -z and + z. the prob is 50% that a normal deviate will be etween -0.65 and + 0.65, the prob is 90% that a stand norm dev will be betweeen -1.6 andd +1.6. Thus if you find a difference that's less than 0.6 x 16 = 10 its 50% chance, and its 90% chance to be 1.6 x 16
It's now clear to me that everything Rogan said recently is right. There is a force, probably without memorialization wishing to keep the world state going. That force will do everything it can to keep the market up before the election. Shortly after or in conjunction with the wish fulfillment of the incumbent win, a revulsion will occur.
Please help me with the timeline. It starts with the Roberts decision to allow medicare. Then the German decision to allow redistribution from Germany to Europe, then the GE 4 the cooling of Arab Spring protests so as not to embarrass their fellow traveler in the "circular" office, then the 7.8 % from a temporary head who lunches as agrarian party bashes with her kid.
Let us hope that the adversary does very well in cardinal events such as debates and polls in the interim so that massive European and central bank activities unlimited to support the stock market can be implemented in the remaining few weeks.
Alex Castaldo writes:
The next logical step would be a Nobel prize for Bernanke or one of his ilk.
April 26, 2012 | Leave a Comment
The VIX futures never made much sense to me (among other things there is no real deliverable — it is just a bet on what a highly volatile number will be on a specific day in the future) , but maybe the Stock Index Variance Futures to be introduced by the CBOE later this year will have more applications (and certainly could make more economic sense):
April 23, 2012 | 1 Comment
Some interesting stats from Easley, D., M. Lopez de Prado and M. O. Hara (2012b): Bulk Volume Classification, Working paper about E-mini trade sizes:
Most of the[..] trades are small, averaging 4.50 contracts per reported fill. Figure 1 plots the frequency of trades per trade size. […not shown…] The frequency line quickly decays as a function of the trade size, with the exception of round trade sizes (5, 10, 20, 25, 50, 100, 200, etc.).
That round trade sizes are much more common than their neighbors may be attributed to so-called ‘mouse’ or ‘GUI’ traders, i.e. human traders that send orders by clicking buttons on a GUI (Graphical User Interface). As an interesting aside, this footprint of ‘GUI traders’ could be used by machines to learn the patterns of their human competitors, and eventually anticipate them to the advantage of the ‘silicon traders’. For example, size 10 is 2.9 times more frequent than size 9. Size 50 is 10.86 times more likely than size 49. Size 100 is 16.78 times more frequent than size 99. Size 200 is 27.18 times more likely than size 199. Size 250 is 32.5 times more frequent than size 249, and size 500 is 57.06 times more frequent than size 499. Such patterns are not typical of ‘silicon traders’ who usually randomize trades to disguise their footprint in markets.
February 7, 2012 | Leave a Comment
Do markets learn from each other? For example, is the S&P market this year, following a similar path to bonds last year, with every trepidatious move down being requited with a rise? Are such "learnings" graduated to the point of regularities. And is it a domino effect or a path of least resistance or consilience or convergent evolution or what have you? What do you think? Can it be quantified? Should it be quantified?
Gary Phillips writes:
Price discovery has become as anachronistic a term as capital formation. The Fed is supposed to be listening to the market to give it guidance in it's policy decisions, not dictating to the market, what the market should do. If investors feel there are no surprises left, as the Fed is concerned, they will once again lever up, and inflate asset prices…QE1 - QE whatever. Rinse and repeat.
Bill Rafter adds:
Here’s a related (perhaps derivative) question: Do stocks learn from each other?
Let’s say you take a list of ~6,000 stocks and look at them over a 10+ year period encompassing both up and down markets. And you come up with a trading plan that buys a stock if it exhibits pattern ‘A’ and sell it if it exhibits pattern ‘B’. It is not unreasonable to then have a universe of perhaps 150,000 transactions.
On the first pattern you pick you will find positive average results for certain stocks, while other stocks on average will be negative. Some of these winning stocks will knock the cover off the ball by having say 35 of 50 trades positive, and vice-versa.
Now let’s say you pick different patterns, and again you find a collection of stocks that outperform. You think this is going to be somewhat of a random process where some of the winning stocks from pattern A/B become losers when you try patterns C/D or Y/Z. And that does occur. But you also find that some stocks are consistent winners throughout the various patterns. And of course, some are repeat losers (perhaps hoo-do stocks).
That leads to further inquiry to find what constitutes winning qualities or hoo-do qualities. Note that this is not a study of profitable patterns since the behavior is exhibited across different patterns, some mutually exclusive like trend-following and mean-reversion. Nor is it a study of good management styles, as the same behavior is exhibited with ETFs, which typically have no management.
You then try to identify specific characteristics (or group membership) of the winners. You might think sectors, because the behavior also occurs in ETFs, but not all of the constituents of a winning ETF are consistent winners, and ETFs behave differently than their constituents. You trot through the various possibilities: volume, volatility, beta, name recognition, size, sales, earnings, debt, short interest, institutional holdings, etc.
Continuously you come back to the possibility that some stocks are simply winners and others hoo-dos, until you can prove otherwise. It turns out (tongue firmly in cheek) that this is a good thing to know.
Alex Castaldo writes:
Probably I misunderstand or oversimplify the issue, but I think what is happening is like this.
Among stocks in your database there are some that have considerable price runups and declines, and others that have fewer such features. It is not exactly a question of volatility as conventionally defined, but it is somewhat related to it.
When you examine trading rules, selecting ones that are more successful with some stocks, you are necessarily picking up more stocks in the first category and fewer in the second.
There is a kind of oversampling at work that concentrates the successful population with stocks from the first category.
To take an absurd extreme to make things clear, if there is a stock that spent the entire data period at 10.00 (the ultimate quiet stock), then no method will make money from this stock, not trend-following, not mean-reversion, not seasonality, etc. This stock has zero chance of being among the 'winner' stocks, not because of its industry, or who is the CEO but simply because of the time-pattern of prices.
Some hypotheses regarding the reaction to information? Case very similar to the flexionic move down when the bailout was passed. Since it was known to all flexes, they had to liquidate when they realized that what they positioned for happened? Of course there was the certainty that the new man of change would be elected also? Or was it a case of going up whenever it looked likely that the vote in Greece would be positive, and then when it happened, …as Dooley would say "how will they make it happen? What you think?".
Alex Castaldo writes:
Most were positioned in expectation of a favorable vote: long stocks and short bonds/bunds. For a while it seemed the markets cared about nothing else.
At 6:41 am ET Papadimitriou (from the opposition party) announced that he would vote for. The S&P vaulted past 1300 and the Bunds future fell below 126. The size of the market reaction perhaps incongruent with an announcement from a single politician.
Then the voting started.
At 8:45 Kouroublis voted against, and there was a noticeable sudden decline in stocks and rise in bonds, bunds. This did not last long.
At 8:56 Athanasiadis, who had been expected to vote against, voted in favor, with the opposite (though smaller) effect.
After the 9:04 announcement that the votes are available to guarantee passage, there was a period of hesitancy and then an attempt by the majority to make a dignified exit now that the expected had happened, only to find the exits rather more crowded and disorderly than hoped… The stock investors, especially, experiencing some downdrafts.
Alston Mabry writes:
Given the news about previous governments in Athens cooking the books to get into the €mark in the first place, and then all the reports of tax evasion and government benefit exploitation and so forth, it's easy to be skeptical, and even cynical, and take the view that things like "negotiated agreements" and "midnight parliamentary votes" and "austerity budgets" are things that politicians use as their own currency, and that they keep trying to spend this currency in an alternate imaginary world in which they believe they can hold off hard consequences with soft ideas. (See: "Munich agreement".)
June 25, 2011 | 1 Comment
Can you please outline the color coding rationale for the daily performance chart. I am confused on why some down days are red and the others are yellow..etc - A Reader
We track daily movements in U.S. stocks and bonds (specifically S&P Index futures and Long Bond futures).
The colors are based on the performance of both markets:
Red days: both stocks and bonds down.
Green days: both stocks and bonds up.
Yellow: stocks up, bonds down.
Blue (technically azure): stocks down, bonds up.
If you are interested in days when the Bonds are down, those would be Red or Yellow (depending on Stocks' performance). Personally, if I want to look at a single market I find it easier to ignore the color and just look at the sign of the price change shown.
I think this essay is worth reading:
"primitive agricultural communities are `dynamic'. They are subject to continuing change in agricultural technology, induced by population pressure…"
And also this article by Grantham: "We're Heading Toward a Disaster of Biblical Proportions".
Victor Niederhoffer asks Alex Castaldo to explain to him what this is all about. Alex Castaldo writes:
The first link is a 108 page essay written in the early 1960s by Ester Boserup , a European agricultural economist I have heard about before but don't really know. At this time many people were concerned that overpopulation was a big problem for the world. In this essay she argues that actually in some cases a surge in population forced people in an area of the world to improve their agricultural technology and make other changes that were beneficial. So (local) population increase was actually a spur to innovation and economic progress.
Jeremy Grantham on the other hand is a contemporary money manager from Boston (born in England) who is always somewhat bearish (except in March 2009 when he briefly and correctly turned bullish). He is very environmentally concerned and always worries that humankind is using too many resources or using them unwisely. Quote: "Grantham [believes] that the world has undergone a permanent "paradigm shift" in which the number of people on planet Earth has finally and permanently outstripped the planet's ability to support us."
So the Boserup thesis and the Grantham thesis contradict each other, and Mr. Depew is quoting Boserup to counter Grantham.
Victor Niederhoffer writes:
Julian Simon would turn in his grave, as would the author of The Improving State of Humanity.
Vincent Andres writes:
If on the other hand, the most valuable resource is the human brain, a larger population is better.
Steve Ellison writes:
I would rephrase, "if on the other hand, the most valuable resource is the human independent brain,"(Because globally, what's the use of 1.000.000.000 similar brains ?).
The ratio in the brain distribution between the tails and the body probably matters. And the bigger the body, the bigger its reinforcement, and maybe (?) the bigger the crushing of independant brains.
… hopefully, this line of reasoning is wrong.
Chair suggested this 2007 article of his (with Mr. Downing) on Dailyspeculations on the Fed Model (not the FRB/US econometric model) may be of interest to some.
Steve Ellison writes:
12 month forward earnings estimate now: 87.63
Index close today: 1320.88
Forward earnings yield: 6.63%
Alex Castaldo adds:
Ten year Treasury yield: 3.662%
The more I learn and the more I experience the vicissitudes of life, (and the more aware of my ignorance I become), I think more and more that the wisdom of musicals, especially those of Hammerstein and Ira Gershwin, is especially poignant and sagacious.
Whenever I give advice, I find myself going back to things that I learned from my favorite musicals. I recently told a damsel in distress (one of my card's imperatives besides creating value, destroying ballyhoo, and fomenting revolutions) to listen to "September Song" by Kurt Weil and "Diamonds are a Girl's Best Friend".
I have to expand this thread to market wisdom but don't have the knack that the collab or my brother has in this regard. I believe because the musical word is so much slower than reading, and the scenes have to be so focused and riveting, and they have to appeal to the common American thread that the successful musicals are forced to get to the common nitty gritty that keeps one's feet on ground.
Alex Castaldo writes:
To be in Chair's trading room in August with the temperature 97 F and the musical Carousel playing for the 100th time is indeed a learning experience.
George Coyle writes:
Well put, Doc. I had to walk outside into the cold for a second just reading that.
I wasn't a big fan of musicals until I heard them all several hundred times last summer, then they kinda stuck. I find the interesting thing to be that human psychology never seems to change as the messages inherent in the various musicals of the 40s-70s are the same things you hear today adjusted for the times.
The market takeaways to me are:
1. Patterns exist in musicals and markets, find them and potentially reap the benefits
2. Human psychology hasn't changed as evidenced by many of the themes in musicals still being applicable today. In as much as collective human psychology governs markets, markets probably haven't changed much either.
3. Sometimes a 3 minutes song can outdo a thousand page novel at describing some phenomenon…at times keeping it simple beats a lengthy analysis.
Ralph Di Fiore writes:
I totally agree with you Victor. My all time favorite musical is the Fantasticks having watched it in Manhattan. Several of the songs from the Fantasticks have wisdom in them that would have saved immeasurable grief in the lives of some families I know (not my own thankfully) had they heeded the wisdom of this musical. One song from the Fantasticks screams out at me when I think of an older gentleman who botched up his family life and has an estranged son but this individual loves spending time in his garden. The closing line from the song Plant a Radish:
Plant a cabbage.
Get a cabbage.
Not a sauerkraut!
That’s why I love vegetables.
You know what you’re about!
Life is merry
If it’s very
A man who plants a garden
Is a very happy man!
Another family had a daughter that brought home a young man and asked her pa what he thought. He hated him so guess who got married… Here is the line from the song entitled Never Say No. Again from the Fantasticks:
Your daughter brings a young man in,
Says ‘Do you like him, Pa?’
Just say that he’s a fool and then:
You’ve got a son-in-law!
You’ve got a son-in-law!
Keep up the great posts, Victor.
At the time of his death on March 31, 1913 J.P. Morgan had an estate worth $80 million. Compared to his peers of the era, especially Rockefeller, it was not such a large estate. In fact, Rockefeller's comment at the time [after reading the pages and pages of obituaries, was rather sniffy:], "And to think he wasn't even a rich man."
Source: Ahamed: Lords of Finance
A recent study shows that Asians need about 50 points higher on the SAT to get into college than White students and 100 more than Black students. It is well known that Asians have a higher IQ by 5 points than Westerners. This is tested in numerous academic papers. Also, well known is that the more intelligent the CEO, the better the performance of his company or hedge fund. One hypothesizes therefore that the companies whose CEO's are Asian will show superior performance to those headed by your average non-Asian Harvard Business School Graduate, (although if they haven't taken the mandatory ethics course there, they are more likely to be caught in flexionic pursuits that the elite schools are so good at whitewashing). What is the support for all these statements? and: How could they be tested?
One knows that this is the most rancorous subject under the sun, and I have lost many friends when I was foolish enough to discuss this in the past, and point them to the incontrovertible evidence about individual differences from a Galtonesque perspective, but let us please try to keep it civil, and stick to the scientific literature (none of this armchair stuff about this or that study being culturally biased as the more culture free the tests, the greater the differences) and no anecdotes, but predictions and tests and references.
Alex Castaldo adds:
Since the Chair does not give footnotes, it is not easy to find the sources for his information.
I believe the "50 point study" may be the one mentioned by Steve Sailer's blog.
An IQ figure of "6 points higher" is given in the Rushton Jensen review paper.
Russ Sears comments:
I have doubts to the usefulness of CEO's IQ test as over-performance of a companies stock. It is not that CEO's do not need to be smart, it is simply that there are enough smart people around and business is tough enough that those that made it have been culled out and vetted pretty completely. Show me the numbers, I am a skeptic.
Further, while creativity to overcoming obstacles in ones life may suggest carry-over into a CEO's performance. I doubt that overcoming one political rigid standard of race by offsetting another no doubt equally rigid political standard for entry into elite colleges would translate into the creativity needed for a successful business. Rather it signals the willingness to conform for acceptance. You mention Asian, but how many of the Asian's admitted for instance are women, versus men? Any stock study of CEO IQ education and minority must consider that education by minority race in the USA is widely distorted by the politically preferred sex of a student. May I suggest that one takes the list of Jewish Noble winners find how many come from the ivy league schools and compare this percentage to the non-Jews. Yes, the ivy league alumnus have a smaller world than most. But may I suggest those with the creativity to overcome this lack of sheepskin, are those that would out perform.
Here are some reasons why I believe IQ can be a handicap to a CEO…
1. Those great at answering questions that others already have the answers, a test situation, often find it uncomfortable and difficult to switch to asking questions that the answers are not known. The ability to ask questions that others did not is a key to make a difference.
2. Necessity is the mother of invention and desperation is the mother of risks taking. A well paying job, is often the road-block to starting a business. Probable failure is hard to choose when you have almost certain path to mild success. Yet it is the probable failures that succeed that skyrockets company. And vise-a-visa its the probable successful businesses that are blind-sided by innovation.
3. Like Reagan, often the most brilliant performers as a team are those that want to work with the smartest minds, they do not have to be the smartest guy in the room. The successful CEO does not have to ask the right question, he simply has to ask the right person to ask the right question.
4. High IQ people perform best with less stress, they can choke more in stress, does the Peter Principle apply to them under stress?
There are a couple thoughts that come to mind that could be tested.
1. New Research has shown that the brain does develop new cells, These baby brain cells are produced by cardiovascular exercise. Further, test after test suggest that cardio improves your creativity. Do CEO's that exercises out perform? There was a study of CEO gulf handicaps, is there a similar study of say 5k times? Are there other test of creativity, say CEO's that are talented pianist, CEO's that are writers do they out perform? Do CEO rising from the operational side ( engineers, IT etc) outperform those that come from the marketing side?
2. A few years ago it was suggested that many CEO's are dyslexic, Could a twice exceptional CEO (high IQ but learning disability) out or under perform?
There are a couple thoughts that come to mind that could be tested.
Victor Niederhoffer adds:
For those interested in a factual, scientific discussion of environment versus innate influences, rather than armchair speculations so grievously present in our environment, and so dysfunctional to proper thinking about markets if similarly believed or proposed, I would recommend this article and the references cited thereto.
Also note the same kind of commentary there relating to making all individual differences consistent with the idea that has the world in its grip, and the purpose of life being self sacrifice.
The next meeting of the NYC Junto, on Thursday September 2, will feature Gene Epstein, Economics Editor of Barron's. He will be talking about "what is wrong with economics".
The meetings are at the Mechanics Institute, 20 West 44 th Street, starting at 7:00pm.
The annual meeting of the IAFE in New York on 2010/06/18 featured UCLA's Prof. Richard Roll , who was a colleague of Chair at the University of Chicago many years ago. He started out by warning the audience that his explanation is different from everybody else's, cannot really be considered proven, and may be hard to accept. Nevertheless he urged the audience to keep an open mind, if only because if this explanation is correct then the current remedies may actually be harmful.
First he dismissed the popular idea that inappropriately low interest rates caused a bubble in real estate prices, which then crashed. Although nominal interest rates were low, the more relevant real interest rates (as shown by the yield on TIPS) were actually rising during the period 2004 to 2007.
Also, defaults in the debt or derivatives markets cannot have been at the root of the crisis, contrary to common opinion. The net amount of debt in the economy is zero (someone owns each debt and someone else owes it) as is the net amount of derivatives (for every long there is a short). Default on debt simply involves a redistribution of wealth, not a destruction of wealth. For example if a borrower defaults on a $300,000 mortgage, and the house is now worth 200,000, the result is essentially as if the bank had given a $100,000 "gift" to the borrower. One is better off and the other is worse off, but the net national wealth is unchanged. These are just redistributions with no (or little) system wide effect.
So what happened? Roll believes that the root of the crisis was a reduction in wealth, and specifically a drop in the value of human capital. Recall that human capital is the present value of all future labor income streams for all persons. It is very difficult to measure because it requires knowledge (or estimates) of the future; but it must be a very large number, perhaps the largest component of national wealth. Roll believes that the value of human capital is correlated with the value of real estate and, to a lesser extent to the value of the stock market. The correlation can be seen, for example, in the fact that people who expect to have a high income in the future live in expensive houses; the value of someone's house is to some extent an estimate of that person's future income.
According to Roll a sharp drop in the value of human capital took place in 2007-2008. This immediately, or perhaps with a short lag, caused a drop in the value of real estate and (to a lesser extent) a drop in stocks (because if the people's future income is expected to be lower, the revenues of corporations will also be lower). We cannot measure the drop in human capital directly, but the drop in real estate and stocks is a clue that (according to Roll) the value of human capital dropped.
The only remaining question is why the value of human capital fell. Roll's controversial explanation is that the market correctly anticipated that government intervention would greatly increase in the years ahead, and that his would cause a permanent lowering of the rate of growth of labor income. Economists have found that past a certain point, a decrease in the share of GDP generated in the private sector leads to lower growth; conversely "liberalization" or an increase in the private share typically leads to higher growth.
This explanation was contested by a member of the audience, who said he had worked in the mortgage securities field and who felt that enormous problems developed in the mortgage market which the Professor was leaving out and which were essential to understaning the crisis.
Another member of the audience pointed out that the professor's explanation is similar to the theory of Amity Shlaes as to why the Great Depression lasted a long time.
Another critique was made by derivatives textbook author John Hull, who felt that the human capital explanation was on the right track, but disagreed about the cause. He felt that the markets began to realize that the US was increasingly unable to compete with China and could not easily restructure itself because of weaknesses in education and skills of the US population. Roll replied that this explanation was too specific to the Us, and did not account for the fact that other countries, for ex. Great Britain, also experienced a severe financial crisis.
Steve Ellison writes:
One possible reason for a decline in human capital is the aging of the population. As the average age of the population increases, the value of future income decreases.
Rocky Humbert writes:
Roll says, "Default on debt simply involves a redistribution of wealth, not a destruction of wealth. And that wide-spread defaults have no system-wide effects."
His argument is like saying "Muggers and bank robbers simply involve a redistribution of wealth, not a destruction of wealth." He ignores the costs and consequences that wide-spread mugging and bank robbing would have on behavior and economic activity. He also ignores that bankruptcy and reorganization imposes significant costs on all of the stakeholders (and by extension, society as a whole.) That's one reason why the value of an enterprise declines as it enters bankruptcy protection. Defaults is not a zero sum game with the value moving from shareholders to creditors. It's a negative sum game.
Professor Roll's argument falls down when one considers that "human capital" is a balance sheet item, but "human income" is on the cashflow statement. A country, company and individual with a negative net worth (negative human capital) can function without any problems — but it's when the cash flow cannot support the expenses that the problem causes a crisis. Hence, human capital is like goodwill on a corporate balance sheet. It's an accounting fiction. It's the human cash flow that matters.
Gibbons Burke writes:
Abortion and contraception have taken a heavy toll on human capital. Since Roe v. Wade was decided, over 50 million potentially productive human beings have been murdered in the womb in the U.S. alone.
Kim Zussman adds:
You wrote "the drop in real estate and stocks is a clue that (according to Roll) the value of human capital dropped."
Then it must have been true that human capital (anomalously) increased, causing the housing bubble in the first place.
In the attached chart (Case-Shiller real house-price data, 1890-2010), the bubble peaking in 2006 DWARFS all other housing price peaks over 120 years.
Perhaps a surreptitious rally in human capital occurred, manifested by the unprecedented housing bubble?
Rudolf Hauser comments:
It is not quite fair to criticize Prof. Richard Roll without having heard his presentation, but based on Alex's summary thereof, I will do so nonetheless. I agree with some of what he has to say but differ in many respects. The main failure is to make any reference to the discount rate. Wealth may be the present value of future income but that is both a function of those future income streams and the rate by which they are discounted. I also find the focus on the value of human capital a strange form of analysis which ignores some market realities as to what actually happened.
Let me start by a very simplified explanation. Real wealth in the capital stock is created when someone labors to produce it. That includes amounts spend in developing human capital via education and training. That labor means that consumption will be less than the amount of production by the amount of that capital investment. But that tells us nothing about how existing wealth is valued. Assume A is moving from NY to LA and B is moving from LA to NY. Both purchased their homes for $100,000. They now decide that their homes are worth $500,000. They trade homes. All they have is capital transfer. It hardly matters what the actual value is in that it is an even exchange. Now in reality A will sell his home for that amount to someone, just a B will, and they will both buy their respective homes from others. The ignoring the intermediate transactions, that is in effect what you have. Now in the past people and lenders would base their decisions largely on their expected future incomes. But in the sort of bubble situation we had in housing, people were expecting home price appreciation to bail them out. In essence, expected future appreciation was part of their anticipated income stream. Now when it was finally realized that this assumption was a chimera that it was decided that the two homes were only worth $300,000. Now wealth has declined in value. The only question is who bears the loss. Given the mortgage amounts the lenders might well find that they must bear some of that loss. There is no reason to refer to "gifts" to the borrower. It is the decline in the value of the property, of wealth that causes the loss. It makes no sense to call that loss a gift.
Then we get to what brings about the additional losses of real wealth. That happens when capital, either physical or human, becomes useless in producing future income. That can happen when lenders refuse to renew loans and/or revenue declines cause bankruptcies. Long periods of unemployment destroy human capital. Physical capital decays from neglect. And that can happen because firms are driven into bankruptcy because of debt defaults and their ability to refinance themselves.
As to the argument that this was a drop in the value of human capital, it is first of all something that cannot be measured. The wealth we can measure has cyclical tendencies. While the recent recession was one that might logically influence future expectations, for the most part recessions are and should be expected as they have always happened from time to time. There is no reason why they should change long-term expectations other than emotions. What does happen is that there is a need in the time of crisis to have more liquidity. That increases the risk premium on longer-term and less liquid assets. Part of that increase in returns is an expectation of capital gains when the liquidity crisis/recession ends. That is why we often see an inverted yield curve leading into such declines. Logically, the yield curve should become more positive, not turn negative, because risk premiums should rise more on the longer term assets. But what matters is total return and that included anticipated capital gains. When those are not great enough the yield curve does not invert, as was the case in the 1930s. Well if the declines in wealth were due to changes in future expectations that is not what you would see. Rather it is because of what I would call risk liquidity premiums rising. If Roll's argument were valid with regard to government intervention, how does he explain the increase in stock values of the past two years-a period when by all logic the changes in government should be increasing fears of greater intervention?
What happened was that the markets finally realized that with all the complicated debt and derivate structures that depended on counterparties many transactions away to deliver was in doubt and that no one's balance sheets could be trusted anymore. With that lending dried up and all values where put into question. That cause a large increase in risk liquidity premiums that was only mitigated by the Fed belatedly pouring in large amounts of liquidity and the government offering guarantees for parts of the financial system.
Another point, although a bit more trivial. When Alex writes that Roll said that "The net amount of debt in the economy is zero." he ignores the fact that some of that debt is owed to foreigners. That is the statement is only true in an international sense.
What you had in this period was an increase in the foreign inflow of savings. Net fixed non-residential investment by business relative to GDP was significantly lower than it had been in the 1990s. In essence there was too much savings wanting to earn higher returns relative to the business investment opportunities leading investors to finance a housing boom instead.
On June 5 , 1933 the United States went off the gold standard. Would we be better off today if it had not happened? - A Reader.
The classical gold standard, which brought price stability and prosperity to the major countries of the world, ended with the outbreak of World War I in 1914. Price stability was thrown overboard as a goal; fighting the war was deemed more important by the leaders at that time. The gold standard was dead.
What happened next is quite instructive.
In 1924 England (the most important country in the world) decided to restore the gold standard using the same parity for the British pound as before the war. But the price level was a lot higher as a result of the inflation during World War I. Have you ever tried to put on an old pair of blue jeans after you have gained 10 pounds? It is quite painful, especially around the waist and crotch. And so it was: the newly restored gold standard began to put heavy downward pressure on prices and on economic activity. Ultimately this was a major contributor to the severity of the Great Depression.
The 1924 decision is one of the greatest economic policy blunders in history, probably second only to the decision by emperor Diocletian in 301 AD to put price controls on food. It was highly contractionary and backward looking, attempting to restore a status quo that was realistically no longer achievable. They had not carefully thought it through and were doing mostly for reasons of prestige and tradition. Starting in 1932 and 1933 the error had become obvious and countries began to get off the gold standard and they immediately began to experience economic improvement; the worst of the depression was over. Relief at last, the blunder had been corrected.
So June 5, 1933 is not really the end of the gold standard but the end of the ill-advised 1924-1933 attempt to re-establish the gold standard in a mistaken and poorly coordinated manner. And we should all be glad that episode is over.
Stefan Jovanovich opines:
Alex repeats several common misconceptions that are thoroughly embedded in the received wisdom of the current academic age. The post-Civil War advocates of the resumption of the classical gold standard - President Grant being the most notable - were quite clear about what they wanted - the resumption of the absolute right of holders of U.S. Treasury notes to convert their paper dollars to gold at the Constitutional standard. They did not promise or expect the classical gold standard to bring "price stability"; they did not even expect it to bring "prosperity". They expected it to bring a fundamental honesty to the Federal government's accounts by making it impossible for Congress to indulge in serial deficits. (It is no accident that Grant's political opponents challenged his proposals by accusing him of personal dishonesty; if you are going to attack a straightforward plan for keeping straight books, argue that the proponent has been stealing from petty cash.) What seems almost impossible for the well-trained mind of the present to understand (whether educated in New Haven or Cambridge East or West) is that it was the discontinuities of the classical gold standard that were its great strength. The earnest reformers who brought us the Federal Reserve Act and those who are now eager to bring us a world currency and unified central bank share the Marxist illusions that the marketplace fluctuations in prices can be tamed if only the government gained absolute control over money and credit. It is an appealing and enduring fantasy, even if it is also a folly. What the supporters of the classical gold standard understood is that free exchange between people can create wonderments of credit and commerce if there is open competition and the price terms for the ultimate clearing of transactions are not subject to government manipulation. They also understood that governments cannot avoid being monopolies where the question of legal tender is concerned; indeed, the U.S. Constitution itself required that Congress have monopoly power over the United States' money. The only solution was to limit the government authority by requiring its paper to be backed by specie. Hence, the classical gold standard.
One of the complications of dealing with the history classical gold standard is that, while the United States was on the classical gold standard from 1791 onwards, our government, unlike Britain's, never resolved in the 19th century the issue of whether a central bank to have the right to issue notes that were to be accepted by the Treasury (in Britain the Exchequer) as legal tender. The United States had the further complication of bimetallism - dealing with what Gresham had explained to be a logical impossibility, having two legal tenders whose exchange ratios would be fixed. What both Britain and the United States did have in common was the presumption that the fluctuations in relative prices between different countries would be adjusted through discounting of trade bills, not through adjustment of their respective conversion ratios of paper into gold. In that sense both Alan and Alex are right. Britain (along with France, Germany and Austria-Hungary) abandoned the classical gold standard at the onset of WW I; but the United States did remain on the classical gold standard until June 5, 1933. Until that date a person could tender $20 in paper U.S. currency to the Treasury or a national bank and receive an ounce of gold stamped by the U.S. Mint as legal tender.
Alex is also correct in stating that Britain's attempt to do what the United States had done after the Civil War - resume convertibility - was a failure. But the failure came not from the choice of the pre-war ratio but from the assumption that a gold exchange standard could replace private party discounting as a mechanism for adjusting relative prices between countries. The classical gold standard was not restored after World War I. During the war and after its end every country in Europe had exchange controls and limits on specie redemption; even exchanges of specie for paper currency between countries were limited by international agreement. What was "restored" was something that had not existed before the Great War - a gold exchange standard. The gold exchange standard presumed that the terms of international trade would be controlled by coordination between the central banks, not by the marketplace results of private credit transactions. The gold exchange standard allowed central banks to accept each other's paper based on the assurance that the inter-government swaps would be backed by gold, but that guarantee was a fiction. The U.S. had substantially all of the world's gold reserves, just as it did after WW II; Britain's ability to pay its war debts in gold was based on the assumption that Germany would pay its reparations in gold which it would borrow from the United States.
Britain's valuing the pound at the pre-war exchange standard would not have had any ill effects if private credit markets had revived because Britain's trade bills would have been freely discounted. The best way to understand the post-WW I world economy is to see it as comparable to the present situation in the U.S. real estate market; the massive expansion of government debt and guarantees had left the world with an enormous mound of crap paper that could not be written down to its actual value because the pricing mechanisms of the pre-war world had literally been destroyed. It was very much extend and pretend. The abandonment of the gold exchange standard did not, as Alan suggests, revive world economies; Europe's output of consumer goods rose only slightly from 1932 and did not recover to 1929 levels until the 1950s; and the United States' record was not much better. The only production that did increase substantially in the 1930s and 1940s was spending for war.
The DailySpeculations.Com calendar has been advanced to May 2010. A curious but possibly insignificant fact is that there were no red days in April 2010. A red day is a day when both S&P futures and Tbond futures are down. The other possible colors are: blue (S&P down, bonds up), orange (S&P up, bonds down) and green (both up).
You can look at past months' calendars here:
Can readers help me understand the meaning of backwardation vs contango in the past in the ES? Why is it negative now? Does it mean people think it's going to go down, or are the rates that low? I've studied, but don't really understood the formulas for computing the values of the future contracts or why there is a negative spread now when is was +4 points or higher spread in 2007 as the market topped on the rolls.
Nick White lends a hand:
Garden variety futures valuation is just a simple cost of carry model: the price of the underlying today adjusted for the cashflows you expect to pay/receive until expiry. The whole bundle is then appropriately adjusted via interest rates for time — effectively, the exact same as any other asset.
Intuitively, this is easy to understand if you think of how NPV — or a DCF model — works and then team it up with the laws of arbitrage. What is your asset worth today given what you will spend and receive for it over a given period, adjusted for interest rates? If your asset can be exactly replicated, is the price of that replication worth more or less than the original? If so — ceteris parabus — you can arb it.
The proviso to the above is that not everybody has the same interest rate in their model… your cost of funding may be very different to mine, which will be very different to GS's. I would argue that — care factor on Index Arb notwithstanding — one's ability and inclination to practice any form of index arb depends vitally on this cost of funding rather than some point spread in the rolls… and that in turn "depends" on whether the arb is long stock / short future or vice versa. Risk free rates are just a proxy.
So, if you cannot perform index arb… what is this info useful for? Knowing the fair value spread might give you a few ticks edge when placing an order because the future may already be a bit over/under extended vs the cash market. So, to provide an example, if you're buying, you may be better to place your order — per the Chair's admonition — a couple of ticks behind the BBO if the fut is over-extended vs the cash. Otherwise, you might want to lift the fut if the cash has moved and the future is lagging.
Very simplistic — but backwardation and contango are just natural progressions of these pricing models, adjusted for the vagaries of short-term supply and demand.
Steve Ellison comments:
Philip L. Carret, in his 1931 book The Art of Speculation, considered it very bullish when stock dividend yields exceeded the margin interest rate. In such circumstances, he said, stocks "carried themselves", i.e., one could buy stocks on margin and pay the interest on the loan using dividends. Backwardation indicates that S&P 500 futures now carry themselves.
The S&P 500 futures began trading in 1982 and almost never traded in backwardation for the next 20 years. They went into backwardation in mid-2002 and stayed in backwardation for most of the next two years, advancing 53% during this time. They have been in backwardation continuously since October 17, 2008, advancing 25% during this time.
Russ Sears interjects:
I have seen some option quotes on Enron, that had calls, same strike, different maturities (I believe it was Oct and Dec maturities) that apparently had some time arbitrage. Not sure they were actionable though. In 2000 I bought some deep deep out of the money long term custom interest rate options, that later became in the money, for my old company. The selling counterparty called in 2003 and begged us to sell them back, because they were very difficult to hedge. He told me they were so far out of the money that they sold them thinking they would never have to actually hedge them. I suspect in both cases the option seller, simply booked the premiums as 100% profit, so the theory really went out the door.
Quant Chicken writes in:
The personal impression I formed when I reviewed the empirical academic literature 4-5 years ago was that the forward is not an unbiased predictor (contrary to the theories of FX I had learned in school). The "forward premium puzzle" has been confirmed using so many different statistical tests (some quite esoteric) that I came to believe there is something to it.
I was getting interested in investing real money in this anomaly when I was dissuaded by wiser colleagues, who pointed out that this "carry trade" idea (borrow in low yielding currencies, invest in high yielding ones) was getting crowded, everyone was getting into it, DB started an ETF to allow public to participate (this was in 2006), etc. And statistically the evidence was not very strong. In retrospect I am glad my friends advised me to stay out of it.
With one week of March now over, the Dailyspeculations.Com calendar has been updated to no longer show the month of February. Those wanting more context can look at the old February page.
Now that the first week of February has elapsed, we will tear off the January page from the calendar and show only February. We have saved a copy of January for future reference.
Just as Daily Spec is starting to present financial data in sports-statistic-like terms, Bloomberg has announced that it will use its analysis tools on sports.
Robert Smythe asks:
Can someone explain briefly the chart at the top of the page? What do these numbers mean? USB?
Alex Castaldo says:
These are the price moves in S&P futures and in Bond futures on the given day. (Sorry about the ugly abbreviation USB, the full word US Bonds did not quite fit the allotted space). In each case the nearby futures contract (currently March) is the one we use.
The price change for bonds is quoted in points and thirty-seconds of a point, as is traditional in Bonds. So for example on January 12 bonds rose by 1 point and 20/32, and this is shown as +1.20.
As Paul Marino notes, the whole thing is inspired by sport scorecards that show the recent wins/losses for a team.
The four colors are based on who wins and loses on any given day. A Red ink day is when both Equity and Bond investors lose, while Green is when bond and equity prices both go up. The mixed cases are: a warm orange color when the environment was favorable to those who take equity risk but unfavorable to those who avoid equity risk (i.e. bond investors), a cool blue when stock prices went down and bond prices went up (sometimes called a flight to safety or flight from risk day).
We hope you find our calendar interesting.
I have two investments A and B. If I regress B's return on A the intercept (alpha) will show whether B is preferable to A. But whether I regress A against B, or B against A, I get a positive intercept. It's as if A has alpha over B, and also B has alpha over A, which makes no sense. — A Reader
In Markowitz's theory, given two different investments A and B it is not in general possible to say "it has to be the case that either A outperforms B or B outperforms A, tell me which it is." There is no way to compare two investments and rank them in this way in general (as mathematicians would say there is not a "total order"). So it is not entirely surprising that the method of regressing A on B and B on A does not give a consistent answer as to which of A or B is better. No method will give such an answer in general. We have to live with that.
What Markowitz does say is that if you have $1, you can allocate it across A and B in various proportions (which could include shorting one of the assets to buy more of the other) and thus generate a "portfolio frontier" of points with various risk/return. Whether, in the particular case the letter writer brings up, this exercise would yield worthwhile insights I do not know. For example, if the writer revealed to us the variance-covariance matrix of returns (i.e. three numbers c11, c12 and c22) we could compute the Global Minimum Variance Portfolio weights [which are w1= (c22-c12)/(c11-2*c12+c22) and w2=(c11-c12)/(c11-2*c12+c22)], and if we knew the returns as well we could trace out the frontier. We could plot it and look at it. Useful? I don't know.
The theoreticians who came after Markowitz (Sharpe, Jensen, et. al.) believed that there is a very special portfolio in the universe called the Market Portfolio and that everyone would want to hold that plus perhaps small amounts of "other stuff." Under some fairly restrictive assumptions the desirability of the "other stuff" could be gauged by regressing its return on the Market Portfolio, always assuming that you could identify what this Market Portfolio is. Only by convention, or approximation, is this portfolio identified with the Standard & Poor 500. In any case the situation is not symmetric and the Market Portfolio plays a very special role in the theory.
What I am trying to point out is that we are so used to regressing things against the S&P 500 or other indexes every day that we sometimes lose track of the fact that this procedure does not in general allow us to rank two arbitrary investments. To compute an alpha you have to do the regression against "the market factor(s)" — not just any investment.
November 12, 2009 | 2 Comments
In pure spirit of "contrarianism", I like this article about global cooling:
In a blind test, the AP gave temperature data to four independent statisticians and asked them to look for trends, without telling them what the numbers represented.
….Global warming skeptics base their claims on an unusually hot year in 1998. Since then, they say, temperatures have dropped — thus, a cooling trend.
….if you analyze the trend during that 10 years, the trend is actually positive
….to find the cooling trend, the 30 years of satellite temperatures must be used.
….It's what happens within the past 10 years or so, not the overall average, that counts
….the 10-year average for the past 10 years is higher than the previous 10 years
….You're going to get a different line depending on which year you choose
….The trend disappears if the analysis starts in 1997. And it trends upward if you begin in 1999
….it's important to look at moving averages of about 10 years
….looking back 31 years, temperatures have gone up
Oceans, which take longer to heat up and longer to cool, greatly influence short-term weather…..the current El Nino is forecast to get stronger, probably pushing global temperatures even higher next year
The quote I liked when I studied statistics at the University (although it could be perceived as politically incorrect nowadays) looks appropriate to me in this case:
Statistics are like bikinis. What they reveal is suggestive, but what they conceal is vital. Aaron Levenstein
These discussions recall those about the stock market. Is it better a 50-days moving average or 13-days? Is the secular up drift over because of the recent downturn or what we are living is just another historical buy opportunity?
It looks like a discussion at a sports bar in Italy about the winner of the next soccer season.
In the meantime, let's take a look at El Niño and its effects on commodities and stock markets. In fact, the pattern of winds and ocean temperatures during an El Niño changes the jet streams steering storms over North and South America. It affects also monsoons carrying away moist air that would produce monsoon rains.
Provided that the forecast is statistically significant.
Paolo Pezzutti writes:
From Bloomberg Beijing's Heaviest Snow in 54 Years Strands Thousands
….the heaviest snowfall in the Chinese capital in at least 54 years blanketed the city for the third day this month. ….The government induced snowfall in the capital on Nov. 10 by seeding clouds with silver iodide, the China Daily newspaper reported yesterday, citing an unidentified official at the Beijing Weather Modification Office. Zhang Qiang, head of the office, said the agency induced snow on Nov. 1 by seeding clouds with 186 doses of silver iodide, according to a separate Xinhua report. The seeding brought an additional 16 million tons of snow, according to the report. Beijing takes every opportunity to induce precipitation as the city is suffering from drought, Xinhua cited Zhang as saying.
Maybe a global cooling could be induced artificially.
Anton Johnson comments:
Though theoretically possible, is induced global cooling really what we want? Looking at the Chinese snow example, what is happening? When seeding clouds, atmospheric super-cooled water completes nucleation and freezes, and then precipitates as snow. At liquid-solid phase change, latent heat is released. The converse occurs at the solid-liquid phase change, thus environmental heat energy nets out. That is, assuming the snow melts.
However, the higher albedo of snow compared to most of the earth’s surfaces causes increased solar energy to be reflected into space. So what happens when this cycle is taken to the extreme, that is, if we get the temperature balance wrong? A net increase in global snow cover will cause a net decrease in total solar energy absorbed globally, causing more precipitation to fall as snow, etc, etc; thus plunging the earth into an ice-age. Good-bye New York, Chicago and London. That is until the oceans cool sufficiently, reducing evaporation and precipitation to an inflection point that reverses the cycle – maybe after 10K-20K years.
Friedrich Hayek always said that he was a "Burkean Whig". Since England was always the place where he felt most at home, it is not surprising that he would have looked to late 18th century English politics for his model of liberty. But, he might have had greater success in finding successful advocates for his ideas about money and commerce if he had looked across the pond.
When the Constitutional Convention took up the question of legal tender, the initial draft carried over the language from the Articles of Confederation: "the legislature of the United States shall have the power to borrow money and emit bills of credit." Hayek's intellectual forefathers would have none of it. They gave the Congress the Power to "borrow Money on the Credit of the United States" and "To coin Money, regulate the Value thereof, and of foreign Coin" and "To Provide for the Punishment of counterfeiting the Securities and current coin of the United States".
This was a political argument that Hamilton had no trouble winning. The delegates took it as self-evident that "to emit an unfunded paper as the sign of value ought not to continue a formal part of the Constitution, nor ever hereafter to be employed; being, in its nature, repugnant with abuses and liable to be made the engine of imposition and fraud." (Alexander Hamilton)
Washington, as always, had the final word on the subject: "We may become a great commercial and flourishing nation. But, if in the pursuit of the means we should unfortunately stumble again on unfunded paper money or any similar species of fraud, we shall assuredly give a fatal stab to our national credit in its infancy.
Easan Katir writes:
This past month I studied "Capital & Finance in the Age of the Renaissance" [translated] in 1928, to understand how the end game might work when governments spend beyond their means. In the 1500s kings and princes overspent to fight wars, and borrowed from the wealthy to pay the soldiers, who otherwise survived through loot and pillage. In the 2000s the governments overspent to fight wars (including war on terror, war on drugs, war on poverty) and borrowed from wealthy nations. During the Renaissance, the wiser creditors insisted on collateral for their loans governments, and thus eventually foreclosed on pledged Spanish copper and Tyrolean silver mines. Even with collateral, they had trouble collecting, and would frequently advance new loans in return for receiving partial payment on previous notes.Executive Summary: back then, in the short-term, the creditors did well (for about 50 years ), in the long term (200 yrs), it ended badly for the creditors when they slipped up and lent money to governments without collateral, which of course, defaulted.
Alex Castaldo comments:
Ehrenberg's book Capital and Finance in the Age of the Renaissance: A Study of the Fuggers and their Connections was a favorite reading assignment from the late economic historian Ch. Kindleberger. He even provided a capsule summary:
"This classic study of the Fugger bank in Augsburg in the 16th century focuses on war and money. Money is needed to buy soldiers, but with soldiers one can acquire money. The Fuggers were involved with Venice, Austria, the Holy Roman Empire, the financial markets of Lyons, Bruges and Antwerp, and through them, with the Spanish crown, which proved their undoing. The financial center of Europe was moving northward from Lucca, Florence and Venice (Genoa, a rival of the Fuggers for the Spanish treasure that was overdue in payment for loans when it reached Seville, hung on longer). The period was one of struggling capital markets. The mistake that brought down the Fuggers, and many a bank before and since, was lending too much to kings on inadequate security".
Stephen Jovanovich adds:
As much as I appreciate the patron saint of those of us in the finance bleachers for having the wit to say he studied economic history because he could not "hack the math" of plain economics, Professor Kindleberger did have his blind spots. He was, inevitably, a devoted member of the "where have all the flowers gone" Pete Seeger school of military history. The Fuggers were "involved in Venice" because they, like everyone else in Germany, went there to learn the new Italian science of double-entry bookkeeping. The soldiery that needed money - and ultimately bankrupted Philip II - were not the ones who discovered, collected and shipped the treasure of the Indies and the Americas to Spain; they paid for themselves a hundred times over. The military extravagances that doomed Philip II were the ones made not for profit but for faith - against the Dutch for their stubborn Protestantism and against the bastard Queen Elizabeth for her persistence in the Anglican heresy. The bit about "inadequate security" is actually funny; as Hayek kept reminding the members of the German history school and their Keynesian and Marxist successors, the state NEVER keeps its promises. It is the monopoly that is above all law and, as Philip II devoutly reminded all his petitioners, the sovereign's pledges can only be made good by God.
A key to today's action was Senator Reid's comment, "I hate to think of what's going to happen to Wall Street tomorrow" (Friday) overnight at S&P 830.
Jim Sogi comments:
Yen and Bonds shot to new highs, concurrent with the drop in equities, temporarily in the night market when everyone's defenses were down. Yen this morning, with no jiggles or pauses, mechanically marched down from 1.11 to 1.10, the proscribed PC number. It had the signs of a heavy hand. All seemed geared in lockstep in the various directions, like a clock. The equities are barely spare change compared to these markets. Imagine the economic effects of the marginal trades on a currency or on the the bond holdings. It made the equity moves almost seem… sedate? As for the comment, what a hubristic and ultimately wrong thing for a politician to say, almost like the mayor of a windy town. And now these are the people controlling the markets?
Thomas Miller writes:
He has a bright future on Wall Street after the political thing is over.
Alex Castaldo goes over the facts and figures:
S&P March futures closed at 885.40 on December 12, up 10.90 on day.
From Bloomberg December 11:
“I dread looking at Wall Street tomorrow,” Majority Leader Harry Reid said before the vote in Washington. “It’s not going to be a pleasant sight.”
Asian stocks and U.S. index futures immediately began falling after Reid’s comments. The MSCI Asia Pacific Index slumped 2.2 percent to 86.13 as of 12:33 p.m. Tokyo time, while March futures on the Standard & Poor’s 500 Index slipped 3.4 percent [i.e 844.75 at 22:33 EST, although they fell further to 830.0 in the next 10 minutes].
December 2, 2008 | 1 Comment
On November 21 we published a table from the Avunc in a way that made it difficult to adduce the point being made. After comment from aforesaid I have redone the table in a way that should be more self explanatory. Sorry and I hope to still be invited to the party.
SPX = 1000
Estimated Earnings = 97.74
=> earn yield 9.77%
10-yr Treasury Yield = 3.65%
=> spread 6.12%
=> SPX fair value = 2676 = earnings/10-yr
=> undervaluation -62.64%
=> expected return = 33.74% = 0.082 + 4.172 * spread
The model is forecasting a one year return of approximately 34%.
I wonder naively, with the news of the bailout: will there not be clamors with the $1 trillion of assets that are being bought by the government at above market values, to extract some bits of flesh from those who are bailed out? Peter Public is being robbed to pay Paul Financial Firm, so to speak. But will Peter not complain and get his ounces of flesh? And will that not tarnish the luster of the gains in financial institutions in due course?
This is a speculation about which I have no expertise and no recommendation over and above saying, as I have for 30 years, that when you get out of the market because it's a "bear market," you have to get back in some time to reap the drift, and I don't know anyone astute enough to overcome that drift while he's out.
Alex Forshaw adds:
It reminds me of the October 15, 2007 announcement, except that this time the "Super Siv" (or MLEC) is $1 trillion-plus in size (instead of $75-100bn), the regulations are all the more drastic, the government has thrown $1 trillion away to save Wall Street's richest socialists, and… yeah, that's pretty much it.
If one had actually stuck to one's capitalist convictions throughout all this, one might actually not even be very surprised at the enormity of Bernanke's and Paulson's failure.
Alan Millhone worries:
I wonder if that 'long spoon' cradles castor oil? You hear the term hard to swallow. To me this applies to the bailout as the Bureau of the Treasury is running the presses 24/7 with someone holding the oiling can to keep down the sparks from the printing presses and all that paper may over time become nothing more than shin plasters!
Nigel Davies writes:
On the long term drift: Can someone please show me the data for all these centuries in which stocks went up 1 million percent, or are we talking about just one, the 20th? The last 24 hours have admittedly seen some of the most desperate short covering from a heavily leaning market, but I don't think one should extrapolate too much from this.
About the bailout: Maybe these measures will "save the system," but there's a huge cost involved for Mr Taxpayer. And as Mr Taxpayer is also Mr Voter I wouldn't want to bet against his supporting some heavy handed regulation by those seeking office. Not to mention the fact that he's being hit real hard in the wallet region by this mess.
James Sogi comments:
The problem with the rescue plan and the upcoming regulation is that the creators of the plan are filled with hubris. Why should these few men with limited experience and knowledge compared to the smartest people of the entire financial world be able to solve the problems that the entire financial world was unable to? Like central planners around the world, they will just create new problems and backlogs and inefficiencies that were so prevalent in the authoritarian and socialist countries.
The country is sliding into socialism, which is the extension of the moral hazard. Where there is no more risk, there will be little reward. On the television, the prevailing meme seems to be the bailout is for the benefit of the greedy Wall street moguls and is paid for by Joe Sixpack. In any case, it will create new opportunities as cycles change yet again. Today's S&P high from yesterday's low was the greatest up move. This is a signal of new cycles, just as much as February 28, 2007 was a signal to move into a high vol cycle. The definition of cycles resists quantitative testing, so the qualitative will have to suffice.
Alex Castaldo takes a turn to the left:
Why should these few men with limited experience and knowledge compared to the smartest people of the entire financial world be able to solve the problems that the entire financial world was unable to? — James Sogi.
Yes, but don't we also need to revise downward our estimate of how smart the so-called smartest people were? When the Warren Spector's, the Dick Fuld's, etc. etc. issue so much mortgage debt to people who now can't pay, that the entire financial system is put at risk, can we really continue to call them the smartest people?
Irrespective of that (…maybe I would have made the same error…), doesn't it make sense at this point to have the "smartest people" take a time out while the second-rate people in government (and I fully agree that they are second rate) try to patch up the problem so the game can resume again? Or do we just let the system blow up because the mistakes were made in good faith by the smartest people available at the time?
Don't tell me that markets are better than Soviet style central planning, Mr. Sogi, I already know that. Tell me what is to be done under these circumstances.
Someone told me today that the nationalisation of AIG is just like what happens in France and Argentina. I am sorry but again I have to disagree. The French government ran Air France for 40 years. The AIG measure is temporary; rather than a nationalisation in the Argentinian sense I would call it a controlled liquidation of AIG. Rather than be liquidated immediately (as was about to happen) they will do so gradually over two years; rather than receive subsidies from the Argentinian government they will have to pay LIBOR plus 8%, a punitive rate, etc. The differences are major. Let's not put all government interventions on the same plane.
Back to the "smartest people" issue. The analogy I see is the following: you have been operated on by the best available surgeon; unfortunately he made a mistake and left a clamp in your abdomen before sewing you up. It is midnight on a Saturday and the only available surgeon is a semi-retired practitioner of average skills. Would you agree to have him operate on you to save your life? It may well be that you would have not agreed to be operated on by this guy in the first place. But what do you do now?
[Disclosure: Alex is a depositor of Washington Mutual and owns Morgan Stanley stock].
James Sogi replies:
It is the spoiled child syndrome. Each time the spoiled child is saved from his mistakes, errors, rudeness, tantrums – he is inadvertently being trained to make these mistakes again. Better to mete out a measured negative punishment, time out, a reprimand, or suffering the consequences of bad behavior. Soon the child learns. There are behavioral cycles, adaptive mechanisms inherent in nature and free markets. By tampering with these, we end up with worse and worse swings as the adjusters over-adjust. Better to let Dick Fuld, and the overborrowers, take the hit. The entire financial system will not fail. It will start up again the next day no matter what happens. It may look different. There may be different players, but it will be there.
Remember the bitter pills Volcker dealt out in the 1980s with 24% mortgage rates, 14- 17% bonds. I saw many people take the hit. But inflation was crushed, and we enjoyed 20 years of moderation and prosperity. That was worth the price. Those who make bad choices should not be bailed out. It will encourage wild swings. It's the Greenspan Put all over again. If people know there's no second chance, they won't take the risks. If they do, they should be entitled to their profit or the pain of failure. When you do it your way, there is no cleansing cycles, and the toxin remains. Like Japan. It's just hiding the problems and they'll resurface somewhere else. Better to kill it now.
Let the big banks, big brokerages go down. New ones will take their place, smaller, faster moving. The market will find a way.
September 13, 2008 | Leave a Comment
Ron "Suki" King of Barbados retained his title as World Champion in GAYP (Go-As-You-Please) checkers today. He defeated the challenger Lubabalo Kondlo of South Africa on the 24th and last game in the series, held in Medina, OH. All previous games had ended in draws. The referee was Alan Millhone. Our congratulations to the winner.
[In Go-As-You-Please Checkers, the players begin in the starting position shown, and can make any opening move. In Restricted Opening Checkers, the first three moves are drawn at random to generate the starting position, and the players continue from there].
Finally I could invest the time to start reading Atlas Shrugged. I have chosen the word invest advisedly here; I have finished reading Part I and decided to take a pause at the end of page 312.
Bearing fully in mind the introduction by Leonard Peikoff that begins by stating that Ayn Rand held that art is a “re-creation of reality according to an artist’s metaphysical value judgments”, it strikes me very hard to seek your opinion if really in the America of the last century there indeed were characters such as Jim Taggart, Orren Boyle and the sort of hoi polloi that has been described continuously in these 312 pages. I have no doubt that there were a lot of Dagny Taggarts, Hank Reardens, Ellis Wyatts who helped (re)build modern America further, but it beats me if really there was a time when the over-riding thought and action of the day was being shaped by Jim Taggart and Orren Boyle types as well. What do you think? Has the author erred in stretching the shadows far longer to produce the effect or was there really an America like that also?
Alex Castaldo attempts a reply:
You are not the first non-US reader of Ayn Rand to be puzzled by this question. As a foreign-born American I was surprised that her books were set in the US when you could easily come up with better examples of government/business connivance from other countries. Americans can consider themselves lucky that they are better off in this respect than some others. Indeed I have often asked myself where is the Italian Ayn Rand who would speak up about how some of Italy's wealthiest people have made their fortune largely through political connections and improper operations, and explain the difference between this and true entrepreneurship. Sadly he/she does not seem to exist (possibly for lack of readers).
Part of the answer may be that Ayn Rand was most familiar with Russia and the US, so of course she chose to write about these countries. Also, she was concerned about trends and developments rather than the immediate situation; the US in her books is perhaps the model of what could happen to any country if the disturbing developments she saw around her were to continue. Her books are, among other things, a plea for the US to retain (and improve) its traditional values and not adopt those of the then ruling class in Soviet Russia.
We have learned that Ken Smith, a frequent contributor to this web site, passed away on June 2, 2008 at age 78 (of an apparent heart attack).
Ken travelled to many places over his life, was involved in all kinds of interesting situations and shared many of them with us through his writing. Several of us had a chance to meet him in person as well.
Our condolences to his wife Ina.
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Andrew Moe remembers:
Here is my favorite post from a good friend who often gleaned remarkable insight from the wonderful life he led and was generous enough to share it with us. A true Spec.
####### Original Post by Ken Smith #######
At 6 am I looked out to the front lawn and observed a lone American Robin listening for worms and insects in the dew-moist grassy ground. The bird shuffled from point to point in a random-appearing change of direction. This is breeding season; the birds are now carnivores. After breeding they will switch to a vegetarian diet. The Robin was patient at each stop, giving his senses time to pick up the signals nature has programmed him to use in his search for food, food which furnishes him with reproductive energy. He has arisen early and discovered this niche for himself, my front lawn, recently watered.
A signal from the ground is perceived and Sir Robin quickly has his prey, no hesitation. This guy is an active hunter, as in active trader. His little computer brain and sense organs are crawling the field in search of prey, a morsel to fatten his resources. Sir Robin will switch to another lawn or playground or marsh when his present search produces less energy than the energy required to do the search.
He will fly away, perhaps randomly choosing the next site for exploration. When breeding season is over, the eggs hatched and nurtured, Sir Robin will change his diet preference. And the turning of the earth, the sun, and the moon will influence him to change his territory, his environment, his location in relation to these planetary orbits. A trader seeks a niche, as the good Doctor Niederhoffer has suggested. Sir Robin, as an epitome of nature's example, has a bird brain yet survives, breeds, and flourishes. How complicated do we need to be to survive as traders?
"Once breeding season is over, the sweet-singing and familiar robin of our backyards becomes more furtive and shy. Large nomadic flocks form and range over the countryside in search of berries such as mulberry, sumac, grape, viburnum, and cedar, as they shift from their breeding season diet of insects and earthworms to become wholly vegetarian. By September, many are moving south from the northern parts of the eastern half of the country to winter with southern residents in the Middle Atlantic and Gulf states. In the West, Robins wander broadly in search of food and move generally to areas of lower altitude. But some linger as far north as Canada when food supplies are adequate, so the first robin you see in spring may not have come from too far away." (Cornell U. ).
Is the Subprime mess one more indicator of the devil may care, not my fault, no care no responsibility, times we are living in? Are the Fed bailouts the ultimate back up to keep this poor social situation alive, leading to a poor thought process for the next rogue trader, head fund manager, and for that matter retail trader sitting down at his platform.
Is this ultimately leading to a internal breakdown of our own risk mismanagement on our own accounts even though at the end of the day it's us on our private accounts and we will be ultimately responsible.
How can we change the situation - When is someone going to stand up and say, "I'll take the hit , I'll wear the pain , It was me!"
Janice Dorn explains:
Maslow's hierarchy of needs is often shown as a pyramid where people go from the base to the apex in terms of what they focus on. The lowest level (base of the pyramid) reflects the most basic human needs– food, health, sleep, physiological needs). The next level is shelter and safety from danger. The next three have to do with belonging (love, affection, socialization), esteem (self and from others) and-finally-the highest state is self-actualization (evolution to a higher consciousness, authenticity, achievement of individual potential, transcendence, creativity).
Humans are unable to progress to the higher levels when they are preoccupied with the needs of the lower levels. In order to distract people from higher levels, one need do nothing more than threaten their basic needs. When people are focused on their basic needs, they do not have the capacity to deal with powerful issues such as personal responsibility. They are too busy focusing on either feeding themselves, dealing with illness or worrying if they will have a roof over their heads tomorrow.
Throughout history, the best way to strip power from a person is to divert their progress up the pyramid by doing something that forces them to stay "stuck" near the base of the pyramid.
We are not evolving. Rather, it appears that there is a not insignificant amount of devolution occurring. As long as we look to "the powers that be to get us out of this mess, the less chance we have to move through the bottom two stages and get on with creative evolution. We will, as a nation, remain, worried, frightened, and sick. The way that power was taken from kings was to poison them. They did not die, but they stayed sick and thus fell down the pyramid to the lowest level.
The person that must stand up and take the hit and wear the pain is the person who looks back at each of us in the mirror. If we cannot do this, we will turn to everyone else to rescue us, to fix the mess, to take care of us, to save us from ourselves. The war against personal responsibility and individual empowerment is in full force. We are unraveling.
Alex Castaldo notes:
I was surprised by the headline in the Financial Times on April 10 "Banks take blame for crisis". Maybe there is hope after all.
Russ Humbert offers a deeper perspective:
While nobody wants to take responsibility for the sub-prime mess, the media has certainly laid blame at the feet of the capitalist. "Capitalists acting too aggressive", "Capitalists only out for their own self interest", are a couple of the "causes" I have heard from the media. However, if the origins and the incentives in the sub-prime markets are studied or in other words the true "cause" is explored, it clearly was due to the markets letting socialism creep into their midst.
The timing of the GSEs entry into subprime seems highly suspect.
The deterioration of underwriting standards can be understood, if you understand the rating agency or risk management was graded almost solely on industry average and industry statistics. Such "pooling" of risk management might as well been pooling of agricultural production. What happens is nobody works. It was a mad rush to capitalize on others' efforts.
Thankfully, the capitalist inspired puts in the contract led to most of those irresponsible enough to think they could get a free ride on everybody else's risk management efforts paying the price. The capitalist insisted they had a least enough skin long enough that they couldn't ignore it without getting caught. Thankfully, some of those capitalists caught this problem early enough and are driving a hard bargain to make sure this mess gets cleaned up fast and making sure it won't happen again. While this may be a high price to pay, just imagine if those aggressive capitalists hadn't all dived in at once. The march to socialism might have been slower, but like a boiling a frog, this would have slowly allowed the GSE's to eat a cancerous toxin, driving them to a slow painful unavoidable death. Or if the short sellers had not been allowed to price the actual risk, those executives responsible would have crippled the banking system and the economy for perhaps a decade, bleeding but not admitting wrong doing to stay in power (as happened in Japan).
Capitalist was the cure, socialism the cause.
Three peaks in VIX (all of them above 30) on 11/12, 01/22 and 03/17. It never went below 15 after end of July 2007.
February 18, 2008 | 2 Comments
How often do the S&P futures change in price (up or down) by more that 10 points?
Date Num Days Num Days Year Mon Up >10 Dn >10 Tot Diff 2000 1 5 8 13 -3 2000 2 6 6 12 0 2000 3 9 6 15 3 2000 4 4 7 11 -3 2000 5 8 10 18 -2 2000 6 6 4 10 2 2000 7 6 6 12 0 2000 8 5 2 7 3 2000 9 3 6 9 -3 2000 10 5 8 13 -3 2000 11 3 10 13 -7 2000 12 6 8 14 -2 2001 1 5 3 8 2 2001 2 3 9 12 -6 2001 3 7 8 15 -1 2001 4 9 5 14 4 2001 5 4 6 10 -2 2001 6 4 5 9 -1 2001 7 3 6 9 -3 2001 8 2 8 10 -6 2001 9 4 6 10 -2 2001 10 6 3 9 3 2001 11 7 1 8 6 2001 12 3 4 7 -1 2002 1 4 3 7 1 2002 2 6 4 10 2 2002 3 4 3 7 1 2002 4 3 4 7 -1 2002 5 5 6 11 -1 2002 6 2 7 9 -5 2002 7 5 11 16 -6 2002 8 7 7 14 0 2002 9 5 7 12 -2 2002 10 8 8 16 0 2002 11 6 4 10 2 2002 12 3 6 9 -3 2003 1 4 7 11 -3 2003 2 3 4 7 -1 2003 3 3 4 7 -1 2003 4 6 3 9 3 2003 5 5 1 6 4 2003 6 4 4 8 0 2003 7 3 4 7 -1 2003 8 0 1 1 -1 2003 9 3 3 6 0 2003 10 2 2 4 0 2003 11 2 1 3 1 2003 12 4 0 4 4 2004 1 3 1 4 2 2004 2 3 0 3 3 2004 3 5 6 11 -1 2004 4 1 3 4 -2 2004 5 2 2 4 0 2004 6 1 3 4 -2 2004 7 1 3 4 -2 2004 8 3 3 6 0 2004 9 1 1 2 0 2004 10 3 3 6 0 2004 11 2 1 3 1 2004 12 2 1 3 1 2005 1 1 2 3 -1 2005 2 3 1 4 2 2005 3 2 3 5 -1 2005 4 2 7 9 -5 2005 5 2 2 4 0 2005 6 1 1 2 0 2005 7 1 1 2 0 2005 8 1 2 3 -1 2005 9 2 2 4 0 2005 10 4 6 10 -2 2005 11 3 0 3 3 2005 12 1 1 2 0 2006 1 3 1 4 2 2006 2 2 3 5 -1 2006 3 3 0 3 3 2006 4 1 2 3 -1 2006 5 3 5 8 -2 2006 6 3 5 8 -2 2006 7 3 2 5 1 2006 8 2 0 2 2 2006 9 3 1 4 2 2006 10 2 0 2 2 2006 11 2 2 4 0 2006 12 2 0 2 2 2007 1 1 2 3 -1 2007 2 2 2 4 0 2007 3 4 4 8 0 2007 4 4 1 5 3 2007 5 3 2 5 1 2007 6 3 6 9 -3 2007 7 4 6 10 -2 2007 8 10 6 16 4 2007 9 3 3 6 0 2007 10 6 4 10 2 2007 11 6 9 15 -3 2007 12 6 7 13 -1 2008 1 7 11 18 -4 2008 2* 4 4 8 0 . Autocorr 0.658 0.197
The highest volatility (by this measure) occurred in January 2008, with 18 double-digit moves, although we have a tie with May 2000.
Generally, there were a lot of such days (>10) in 2000-2002, few in 2003-2006, and many again starting in July of 2007.
February 18, 2008 | 1 Comment
It is interesting to look at a chart of the number of large S&P moves per month.
The Fed Model postulates that if the forward earnings yield of the S&P Index is higher than the 10-year treasury yield, stocks are “undervalued“, and vice versa. As of December 31, the S&P was at 1468.36 and expected forward S&P 500 earnings for the next 12 months were 101.86, making the forward earnings yield 6.94 percent (101.86/1468.36). The yield on the 10-year T-note was 4.02 percent.
Historically, subsequent market returns have been correlated with the differential between the S&P forward earnings yield (estimated 12 months earnings divided by the S&P 500 level) and the 10-year treasury yield. On the 10 occasions when this differential has been greater than 1 percent, the S&P 500 has risen ten out of ten times for an average of 13.5 percent in the subsequent 12 months. (This differential currently stands at 2.92 percent, which the highest it has ever been at year end).
We have found that the best way to specify the Fed model relationship for forecasting purposes is with a linear regression in the form:
S&P Return[t+1] = a + b * ( Forward Earnings Yield[t+1] - 10 Year Yield[t] )
Estimating this regression using yearly data since 1980, we obtained the following equation:
S&P Return[t+1] = 0.082 + 4.172 * ( Forward Earnings Yield[t+1] - 10 Year Yield[t] ) t-stat 2.66 1.85 p-values 1.32% 7.53%
The R-Squared of 0.12 is quite high for a predictive regression in the financial markets and indicates that 12 percent of variation in subsequent returns was explained by the independent variable over the time period studied.
To determine current Fed Model forecast:
Current S&P (as of 12/31/07) stands at 1468.36
Forward Earnings = 12 months consensus forward earnings for the S&P 500 = 101.86
Forward Earnings Yield = Forward Earnings / S&P = 101.86/1468.36 = 6.94 percent
10.Year.Yield = The Current Yield on 10-Year government note is 4.02 percent
The Differential (Earnings Yield - 10.Year) = 2.92 percent
Substituting these numbers into the regression formula :
0.082 + 4.172 * (0.0694 – 0.0402 ) = 0.203
Therefore, Fed Model yields a forecast of 20.3 percent for next 12 months.
Jason Humbert asks:
How does Dr. Castaldo counter the failings of the Fed model in other G10 countries? Japan has been horrible under that model. Germany has been OK, barely statistically significant. UK has been good, like the US.
Alex Castaldo replies:
I believe Mr.Humbert is referring to the paper "The Fed Model: A Note" FRL (2006) by Javier Estrada who tested the Fed model in a number of foreign markets. I have exchanged Emails with the professor but neither one of us was convinced by the other's arguments; we will just have to disagree.
January 1, 2008 | Leave a Comment
1. If you are too concerned with fitting in, with being well regarded by others, with avoiding public faux pas or highly visible mistakes, then you will have difficulty becoming very wealthy
2. The entrepreneur should try to retain 100% ownership (which, however, raises the question of how to motivate employees) but not be involved in the company 100% of the time (in fact he should disappear from time to time and let others run the company)
A fuller review will be coming soon. For the impatient, this old article on the web summarizes some of the main points of the book.
The Fed's upcoming liquidity swaps will be keyed off the OIS rate for one month funds, which I mistakenly assumed was roughly the rate for a term loan of the duration.
I have since learned that the OIS is effectively the same as the rate on the second Fed Funds future, which is currently ~4.20%, a modest discount from the spot target rate of 4.25% and substantially lower than the 4.75% discount rate.
This makes the upcoming set of auctions a one-off discount rate cut, albeit in relatively small size. The two day window between bid submission and auction award announcements further muddies the program's value.
Alex Castaldo remarks:
I have the impression that the Fed is experimenting with a view to a permanent auction mechanism for supplying liquidity, based on this paragraph in the news release:
Experience gained under this temporary program will be helpful in assessing the potential usefulness of augmenting the Federal Reserve’s current monetary policy tools–open market operations and the primary credit facility–with a permanent facility for auctioning term discount window credit.
This mechanism would be similar to the weekly auctions that the ECB holds (the so called main refinancing operations). From ECB web site:
Main refinancing operations are regular liquidity-providing reverse transactions with a frequency and maturity of one week. They are executed by the [National Central Banks of the Eurosystem] on the basis of standard tenders and according to a pre-specified [weekly] calendar. The main refinancing operations play a pivotal role in fulfilling the aims of the Eurosystem's open market operations and provide the bulk of refinancing to the financial sector.
European fashions in Central Banking coming to the U.S. ? We shall see.
Here are the largest upward point moves in the S&P (cash) index from 1999 up to (and not including) November 13, 2007.
Largest one day point moves since 1999 (up to and not including 11/13/2007)
Rnk Date Px Last Chg 1 03/16/2000 1458.47 66.33 2 01/03/2001 1347.56 64.29 3 12/05/2000 1376.54 51.57 4 04/05/2001 1151.44 48.19 5 04/25/2000 1477.44 47.58 6 10/19/2000 1388.76 46.63 7 04/18/2001 1238.16 46.35 8 07/29/2002 898.96 46.12 9 10/28/1999 1342.44 45.73 10 07/24/2002 843.42 45.72 11 04/17/2000 1401.44 44.88 12 05/30/2000 1422.45 44.43 13 10/13/2000 1374.17 44.39 14 09/18/2007 1519.78 43.13 15 04/18/2000 1441.61 40.17 16 10/15/2002 881.27 39.83 17 11/13/2007 1478.6 39.42 18 05/08/2002 1088.85 39.36 19 09/03/1999 1357.24 38.13 20 01/07/2000 1441.47 38.02
Today's rise of 41.87 would be the 15th entry.
Jim Sogi adds:
All of them are from the 1999-2002 period or from 2007. There are none from the low volatility years 2003, 2004, 2005, 2006. The list of big down moves is similar.
Years ago a friend of mine applied for a clandestine job at a major intelligence agency and was invited to McLean VA for a series of interviews. His story may be of interest if you are looking for a similar job.
The first interview was quite uninteresting, even boring, according to my friend. He was ushered into a small nondescript office by an average looking guy who seemed to want to do most of the talking. My friend perhaps expected the agent to "sell" the agency to him with recitals of interesting adventures during his career, but it was nothing like that. The man talked mostly about himself, but in a dull, matter of fact way, full of details. Just as an example of how boring and pointless the conversation was my friend said that on two occasions the man pulled out a battered wallet and showed him pictures of his children; the second time my friend's eyes glazed over and he looked away. My friend was not impressed by the caliber of people working at the agency, to say the least.
After 45 tedious minutes the meeting was over and my friend went to his next appointment with a man who was obviously a top official, sitting in a nice big office. Now the real interview began, as the official fired question after question at my friend: Tell me about the man you just met: what did he look like, what would you estimate his height and weight? Did he wear brown shoes or black? Please summarize what he said. Did he mention anything about American policy in the Philippines? How many children does he have? Are they boys or girls? Unfortunately my friend had not paid enough attention during the first meeting; he thought of himself as detailed oriented and having a good memory, but was surprised at how difficult it was to come up the information requested.
Well, he did not get the job, but instead joined a big accounting firm, became a partner and lived happily ever after.
I've been studying complex variables lately because I find the imaginary very important these days, and I had to brush up on them for one of my daughters.
It led me to consider the imaginary part of the moves during a day or week, and the real part. Consider last week. O/H/L/C:
9/28 1538.20 1545.20 1519.00 1538.10
9/21 1491.80 1552.00 1485.20 1534.40
The real part of the move, from 1534.40 to 1538.10 was 3.70. The low of the week 1519 so there was a -15.40 point imaginary negative part, and the high was 1545.20 so the imaginary positive part was 10.80.
A similar calculation could be done for the day, looking at the amount below the previous close, the amount above the close, and the final move.
We can look at the two points on an Argand like diagram. I claim that the length and the angle between the two lines connecting the negative and positive imaginary could be useful as a predictor. Better yet, the two angles themselves and the real part. Similarities might be useful. Such angles should be quantified , classified, and subjected to prediction and falsification.
Another example. The week of August 17 showed a real move of -1.10 and a negative imaginary of -76.00 and a positive imaginary of 21.50. A small real move but non-negligible imaginary moves.
Laurence Glazier adds:
I'd also be interested in trying volatility as the orthogonal parameter (it is to do with the imagination after all.)
Michael Cook follows up:
I love complex variables - it is one of the most beautiful subjects in mathematics. Everything comes together and illuminates and integrates everything that's gone before in the traditional mathematics curriculum.
I don't understand how you are defining the imaginary part of price moves - can you clarify? I am intrigued!
Alex Castaldo explains:
If I understand Vic correctly, he defines two complex numbers, the AboveMove and the BelowMove:
AboveMove = (c[t]-c[t-1]) + i (h[t]-c[t-1])
BelowMove = (c[t]-c[t-1]) + i (l[t]-c[t-1])
And plot these as two vectors on the Argand diagram. The real parts are the same, but the imaginary parts are different (and always of opposite sign). Next you can get the angles and the lengths.
Adi Schnytzer queries:
Are these the complex components of the change simply because they exceed the bounds of the price at the start and end of the week? If so, why a week and not a day or a month? And perhaps more to the point, can the maths of complex numbers then be used to predict? Analyze the moves?
With the amazing moves in wheat lately, I'd like to recommend The Plunger by Edward Jerome Dies. Published in 1929, The Plunger focuses on Benjamin Hutchinson, a legendary Chicago trader. 'Old Hutch' was King of the Wheat Pit in the late 19th Century and I read in awe about how he dominated trading at the CBOT. There are reprint editions made in the mid 1970s at a reasonable price.
Alex Castaldo adds:
As a reminder of how difficult it is to hedge a generalized deflation, let us look at a chart of wheat prices from Kindleberger's book The World in Depression, on page 88. If wheat prices in 1929 are set at 100, they subsequently plunged to under 50 in 1931, 1932 and 1933 before gradually recovering and reaching 100 again in 1938. In the prosperous year of 1925 they had reached a maximum of 120. A terrible time for wheat producers indeed…
J. T. Holley remarks:
I have on the back of an envelope somewhere a study I did on softs/grains. I did this study to learn scale trading. The counting that stuck in my head is that when corn, soybeans, and wheat reached the top five percentile of their historical price distribution they were significantly lower two years from the date of entry. My staring point was that 1974 high of 650, which was probably breaking massive statistic rules!
I have two time series A and B with 120 monthly observations each. I want to test whether A's yearly changes predict B's yearly changes. But there are only 10 non-overlapping years. What is the least horrible method that would use overlapping 12-months changes? I am thinking of a bootstrap but looking around, I found mention of the Generalized Method of Moments (aka Generalized Estimating Equations) which looks complicated. Do readers have other suggestions?
Alex Castaldo replies:
The traditional approach used in the literature (by Shiller among others) is to do a rolling (i.e. overlapping) predictive regression and then correct for the overlap by using Newey-West standard errors (rather than the usual standard errors that regression software normally uses).
Victor and Laurel do not like the Newey-West approach, and the literature has been coming around to their point of view. The problem is that Newey-West is correct asymptotically (that is, as the number of data points goes to infinity) but in these problems we do not have a large amount of data (that is why we are resorting to using overlap). Simulation studies show that in small samples the Newey_West method can be biased.
What is the solution? I don't know; it is an open research problem. There is something called the Hodrick (1992) method which is said to be free from small sample bias. (It is different from the Hansen-Hodrick method). Also you might try to read recent papers on the subject, such as Ang and Bekaert "Stock Return Predictability" (2006) and the references therein.
Adi Schnytzer writes:
'LOMACKINLAY': module to perform Lo-MacKinlay variance ratio test
lomackinlay computes a overlapping variance-ratio test on a
timeseries. The timeseries should be in level form; e.g., to
test that stock returns vary randomly around a constant mean,
you consider the null hypothesis that the log price series is a
random walk with drift. The log price series would then be
given in the varlist. If the assumption of homoskedastic
errors in the process generating the differenced series is not
reasonable, the robust option may be used to calculate a
variance ratio test statistic robust to arbitrary
heteroskedasticity. This is version 1.0.5, corrected for errors
in logic identified by Allin Cottrell.
KW: variance ratio test
KW: random walk
KW: time series
Requires: Stata version 9.2
There is always much debate whether to equal weight or cap weight indices. If there are 30 securities (country ETFs or stocks) in a portfolio, given that they have similar though different return distributions, what is a good way to estimate how frequently one would expect a cap weighted portfolio to outperform an equal weighted portfolio?
Scott Brooks writes:
It really comes down to what do you see doing better, the larger companies or the smaller companies (large or small in reference to that index/ETF that you are looking at).
If you expect the larger stocks in an index to do better, then go with the cap weighted. If you expect the smaller stocks to do better, then go with the equal weighted. For instance, RSP the SPEWI ETF has nicely outperformed the SPY SP cap weighted ETF for quite a few years now.
Alex Castaldo adds:
I would suggest a bootstrapping approach. Imagine the actual data arranged in a four column table:
Period Ticker CapWgt Return
1 GE 0.4 1.05%
1 IBM 0.2 -0.85%
1 XYZ 0.01 0.97%
From this table the cap weighted and equal weighted returns can be easily computed. Now generate artificial data by scrambling (i.e permuting) the entries in the return column while leaving the other columns unchanged; compute the cap weighted and equal weighted returns for the artificial table.
Repeat the process 10,000 times and see how the real-life returns stack up compared to the 10,000 artificially generated cases. Some details need to be filled in, but you get the general idea.
Charles Pennington adds:
Alex is sending you on a snipe hunt. It is obvious by symmetry that the required probability is 50%.
I have been researching on the web how to teach children to dream. What is left out is how to develop a passion for life when dreams fail to develop. I suspect their father's example is the best teacher.
John Floyd writes:
I am looking for recommendations for children’s books. I would like to include the right mix of education, capitalism, logic, reason, imagination, and individuality among other things. A few books and stories that I have found, and the kids enjoy: Jonathan Livingston Seagull, Thidwick the Big Hearted Moose, An Airplane is Born, and The Little Prince.
Scott Brooks adds:
As much as we push education in our home, we've had a dickens of time getting our children to read outside of school. Finally last year, my oldest daughter got into reading the Goose Bumps series. She loves them and needs no prodding to read up on them.
My youngest son somehow got into reading the Star Wars books. He doesn't read them religiously, but will read outside of class if given a little reminder. Interestingly, I bought him a book on bullets at the Quality Deer Management Association national convention in Chattanooga last week and he's been perusing it almost everyday. He's 8 years old and it's way above his level, but he seems fascinated by it. He had his home school teacher read it with him and explain the more difficult parts to him.
For my 12-year-old, we've had to use a different tactic. He doesn't read unless we push him to do it. However, he's really into the markets and learning about investing. So he reads stuff on the net about companies he's thinking of buying and watches and reads investing information.
I guess the key is to immerse your kids in reading and let them find what they like. When I was kid, I'd read one or two Hardy Boys book's a week. I tried to get my kids into them, but to no avail. Keep searching to help your kids find something that they like. There have been a lot of good books recommended here (and I'm saving this thread for future reference for my kids and their home school).
Many of these books are important and are one's that I'll have the kids read as part of their school work assignments (whether they want to or not). But the biggest thing that I've searched for is, how do I instill in them a love for reading a thirst for knowledge? I can't do that by forcing books on them. Sure, I can help them to learn important lessons by requiring that they read certain books. But what I really want to see is them sitting down curled up with a book reading it because they want to. I believe that should be goal!
From Bill Humbert:
One of my children was a reading-avoider. My goal was to get the kid reading and I happened to see the movie League of Their Own in which the Madonna character teaches the non-literate character to read by using trashy novels. I believe the quote was something like, Who cares? She’s reading isn’t she? It’s a scene we always laugh at.
Well, I didn’t use trashy novels, but I did use comic books. We started with the superhero genre and then I gradually slipped in the newer version of the old Classic Comics. For certain works I also acquired Books on Tape, which is more useful than listening to the radio in the car and it gave the child a general understanding of the work.
Since the brain stores different types of input in different locations, this child had an advantage over the children who only had read say Homer’s Odyssey. The child had the pictures from the Classic Comics, the audio from Books on Tape and the printed word itself. After a while the child started to excel in those classes. And only then did the overall desire to read take over. I think it was like a pump that needed to be primed.
Get the child reading. "What" does not matter. If the child finds that useful and desired knowledge comes from reading, eventually that child will take to the books. But you have to prime the pump by starting with something that they want to read, which is not always what we want them to read.
Larry Williams adds:
When I wanted my kids to read a book I was reading I told them they probably should not read it — that it was too adult for them. A cheap trick, I know, but they pick up those books like a brown trout seeing a grasshopper in August.
Nat Stewart writes:
My parents did much to foster my love of reading. In early grade school I would go with my mother to the local library, where I was allowed to pick any books I wanted for that week. I quickly fell in love with the selection of children's books that focused on biographies of America's great heroes. My particular favorites where books on:
1. Thomas Jefferson
2. Thomas Edison
3. George Washington
4. Paul Revere
5. John Paul Jones
6. George Washington
7. Davey Crockett
8. Henry Ford
9. Daniel Boone
10. the Wright brothers
I loved these books! The children's books focus on a narrative of struggle, adventure, and heroism, ingenuity, and are often historically accurate enough to prove very educational. I remember reading them late into the night, hoping no one notice that I had my light on long past the official bed time.
My parents also spent a good deal of time reading to me. My favorites included books about King Author and Nights of the Round Table, "Little House on the Prairie" books, and The Chronicles of Narnia.
Let a kid explore the library and pick favorites. Provide enough options so that reading can become an adventure rather than a chore. Spend some time reading to them over summer vacation.
From Bill Rafter:
We all remember our trips to the library. However that cannot be replicated today. The libraries simply cannot compete with television and the Internet either with content or "wow" factor. The answer to the problem will be in using the new technology not avoiding it. Television, even the good stuff like National Geographic or Ken Burn's "Civil War", is still second-rate because it's passive. The Internet is active, and thus has more potential as a learning tool.
Games can be very helpful. One that had particularly helped me (both myself and subsequently my children) was Scrabble. After a street game of "boxball" we would dig out the Scrabble board while we cooled down. Those games got very competitive to the extent that several of us kids started doing research on words by randomly reading the dictionary. Scrabble also required you use arithmetic to keep score.
My favorite Scrabble word was "ennui," as it cleaned out your collection of accumulated poor-value tiles. It also led to challenges, which led to another turn and more points. While researching through the dictionary I stumbled upon the word "eunuch", which also had good Scrabble possibilities. Being in 6th grade, I didn't care what it meant, but kept a mental file for future use.
Well somehow I got into a name-calling event in the schoolyard with a girl and called her a eunuch. She had no idea what it meant, but the teacher Sister Mary Hatchetface was in earshot and she most certainly knew. The next thing that happened was that I was in the principal's office (Sister Jane Battleaxe). My father was summoned. He was a Philadelphia policeman, and he happened to be in uniform.
So there I was in the Holy of Holies with the two nuns in their penguin uniforms and Dad in his, trying to learn what trashy literature I was reading. The revelation that it was the dictionary left them with no solution.
Ahhh, the ability to stick it to authority…priceless.
I attended a presentation by Andrew Lo on 'The Psychology of Trading,' on May 21 2007. Here is a brief summary of his remarks:
On the one hand the Efficient Markets Hypothesis is at the foundation of Finance (for example all the work by Black-Scholes assumes that the market for options is efficient) on the other hand many people nowadays find it hard to believe that EMH is literally true. This has led to the development of Behavioral Finance, which studies biases that may hinder financial decision making. BF has acceptance problems of its own: it brings up so many possible biases that it is hard to believe (if all these biases are true) that anyone is ever able to make a correct decision. Many economists ask if the behavioral biases even exist.
To try to advance beyond the EMH/BF debate, Andrew Lo has been working on his own framework, which he calls the Adaptive Market Hypothesis, and has been investigating the role of emotion in trading by reading the neuropsycholgy literature and conducting experiments, some of which will be described below.
Do perceptual biases really exist
The first experiment involved the audience. They were invited to watch a video showing college students, some in white T shirts and some in black T-shirts throwing basketballs to each other. Lo told the audience to concentrate on the white T-shirt players and count the number of times they passed the ball to each other. The exercise was made more difficult by the fact that the black-T shirt players intermingle with the white shirt players and that Lo kept talking throughout the video to try to confuse the audience.
After the video was over Lo asked "how many people saw the gorilla?". More than half the people in the audience had not seen any gorilla. [I personally did not see the gorilla, even though I knew that Andy Lo is famous at MIT for showing a video in which a gorilla appears !]. Lo replayed the tape, and sure enough a man dressed as a black gorilla walks through the scene halfway into the tape. Lo explained that people who are concentrating on white figures will often miss black objects; in some sense the human perceptual system is filtering out the black objects.
In conclusion, said Lo, in any debate between economists and psychologists as to whether perceptual biases really exist is going to be won by the psychologists, who have demonstrated these phenomena beyond doubt through careful experiments.
The neuropsychology literature
The book "Descartes Error" by A. Damasio has changed how we view rationality. The classical philosophers believed emotion and rationality were polar opposites. Damasio investigated people who have suffered serious brain injuries and found that people who do not perceive emotions correctly will act irrationally. Emotion is necessary for rational behavior, Damasio says. Emotions allow you to choose quickly and easily among the many choices constantly available to you, saving you time and allowing you to zero in on correct solutions to problems.
The Triune Model of the Brain was proposed by Paul McLean. The human brain is made up of three parts: -the brain stem, which controls basic functions such as breathing and wakefulness is the oldest part of the brain, philogenically speaking. It exists in reptiles as well as in higher life forms. -the midbrain is involved in emotions (such as fear and greed, sexual preference and so on). It exists in mammals. -the neocortex controls higher functions, is the seat of thinking, language, etc. and exists only in hominids. There is a definite order of priority among these three subsystems; a painful stimulus for example will disrupt the processing functions of the neocortex for several hours according to experiments in which blood flow to the brain is measured via MRI scans. When a lower level is activated it disrupts (or takes priority over) the higher level mental functions.
From a financial point of view it is clear that risk-preferences and decisions under risk arise from interactions between the midbrain and the neocortex. Rational decision involves a balance and/or cooperation between the emotional and calculating parts of the brain.
Experiments in neuropsychology and finance
(1) Studying professional traders as they go about their job. Lo attached sensors to traders to measure emotional responses. (2) Lo also interviewed 80 neophyte traders who were learning to trade in a class given by LBR and reviewed their trading
a. emotion is definitely involved in trading decision making, even in the case of experienced decision makers (i.e. it is not solely the beginners who experience these emotions). However, the emotions are somewhat more controlled among the more experienced or more able decision makers. b. traders who experience little emotion during trading have a lower P&L, however traders who experience a great deal of emotion during trading also have a lower P&L. It appears that there is an optimum level of emotion somewhere in the middle. c. people who excessively internalize the outcomes (i.e. attribute everything that happens to their own doing) have a lower P&L, however people who attribute everything to luck also have a lower P&L. Again there appears to be a proper balance, i.e an attitude that events are partly due to ability and partly to luck.
The Adaptive Markets Hypothesis
The AMH takes a biological/evolutionary view of markets, whereas the EMH took a physical/engineering view.
The AMH postulates that financial decision makers:
1. act in their own self-interest
2. make mistakes
3. learn and adapt (through heuristics, not through optimization)
4. competition drives adaptation and innovation
5. natural selection drives the ecology of the markets
6. evolution drives market dynamics
With regard to point 3. Lo has a high regard for Herbert Simon and his idea of "safisficing" (not optimizing), and of making decisions through simplified (and non-optimal) heuristics (since an optimal decision is computationally infeasible). A question that Simon could never answer is "where do heuristics come from", but Lo thinks the answer is that "evolution determines heuristics" (point 5). He did have not elaborate on this. Lo expressed the view that Simon's work is even more important than the Theory of Rational Expectations, even though it has received less attention in economics.
The AMH implies that anomalies can appear, disappear and then reappear again as the market ecology changes. For example the profits to Statarb have waxed and waned over the last 15 years. It is true that the profits have dropped sharply after the Summer of 2002, but this does not mean that hey have been permanently arbitraged away. Statarb profits may not be gone forever, they may come back at some time under different market conditions than what we have now.
So far the AMH is incomplete. Lo is working on extending it and convincing others.
We need emotions to be rational. We need both, it is not either/or. In trading there is a right level of emotion. There is a "right zone". The Zen of Trading.
Jean Paul Schmetz writes:
It is almost impossible to see the gorilla [see video] if you concentrate on the players. I have tried this video with 100+ people in the room and very rarely did more than 10% see it. It usually helps to mention beforehand that males or females are better than the other at keeping score (you do not tell which and so people concentrate even more).
Chris Hammond adds:
There is an article in Scientific American this month that discusses a game called the "Trader's Dilemma," which is a variant of an older game called "Prisoner's Dilemma." Experiments involving this game address some of the issues mentioned earlier. There is, of course, some extraneous background story, but essentially, the game works as follows: two people pick a dollar amount between 2 and 100. The smaller of these two amounts will be awarded to both players, except the person who chooses the smaller amount will receive an additional $2 and the person who chooses the larger amount will receive $2 less than the lower of the two amounts.
If the players pick the same amount, then there is no penalty or reward. If you assume that both players are working solely for their own self-interest and make rational decisions, then both will pick $2. However, in an experiment where the range was between 80 and 200 cents, and the penalty/reward varied between 5 and 80 cents, the player's average choice was never the Nash equilibrium of 80 cents. For the 5-cent penalty, it was 180, and when the penalty was 20 cents, it was 120. This particular study used economics students. A similar study used game theorists, and the results were similar.
More interesting is what happens when people play the game repeatedly. Apparently, for "large" rewards, the amount that is picked as people play many times tends towards the low number, 80. For "small" rewards, the amount moved towards the high, 200. The article is not more specific than that.
Here, a system evolves as participants learn. It's also interesting that there is a bifurcation at some particular reward amount, and that the system evolves completely differently on either side of that value. Also worth noting is that not even game theorists think like game theorists.
Yishen Kuik writes:
I have been reading E.O. Wilson's Consilience, which has a nice chapter on what we know about the brain.
He also has a good chapter on what it takes to be a productive scientist engaged in the business of discovery, some of which seemed to me to be uncannily applicable to describing the business of uncovering statistical edges to trading.
We previously looked at the performance of five Bloomberg indexes of companies located in low tax states up until February 10, 2006.
We have now updated the study:
Bloomberg company indexes for low tax states (performance update)
State PrevDate PrevPrice CurDate CurPrice PrcChg PrcChg
——— ———- ——— ——– ——– —— ——
Arizona 2/10/2006 529.92 5/7/2006 566.90 6.98% 19.14%
North Caro 2/10/2006 127.56 5/7/2006 147.73 15.81% 19.14%
South Caro 2/10/2006 148.30 5/7/2006 162.23 9.39% 19.14%
Florida 2/10/2006 135.16 5/7/2006 144.51 6.92% 19.14%
Texas 2/10/2006 395.19 5/7/2006 465.81 17.87% 19.14%
The out performance we noted earlier has not continued in this most recent period. A result probably not inconsistent with natural variation.
Steve Ellison adds:
Market Price Price
cap ($B) 2/10/2006 5/7/2007
MGM 18.3 38.58 63.71
HET 15.9 72.41 85.21
IGT 13.4 36.84 40.08
WYNN 10.1 59.50 101.85
STN 5.0 67.94 87.57
SRP 4.1 13.56 18.77
BYD 4.0 44.72 45.76
MDG 2.6 23.74 26.01
PNK 1.8 27.98 27.82
CDL 1.0 11.61 9.05
average 39.69 50.58
Today's break through the 1500 level by the S&P index is the first break through of a round number since Nov-17-2006 (1401.2). It is also the seventh consecutive movement upwards through an hundred level without a fall through an hundred level since Nov-01-2002 (900.96).
A chart of the S&P shows a relatively continuous movement up from 100 in 1980 to 1100 in 1998, then a little backing and filling, and then a rise to 1500 (3/22/2000). This was then followed by a precipitous decline back to 800 (7/23/2002).
This raises all sorts of questions about randomness, continuity, tendency for long runs, the drift, and gravitation.
We thought we'd start by looking at a few of the more obvious ones.
SPX Index Daily Data, 100 point box size
|Date||Reference||Close||Dist. frm Ref.||Move|
We noticed a tendency for UP's to be followed by UP's (and vice versa) so we tested this with a two by two contingency table (previous move listed at the side):
Fisher's exact test p=0.19
Although the tendency is there, it does not seem to be statistically significant.
Bruno Ombreux writes:
A classic runs test yields the same p-value in the 2-tailed case. The one-tailed test has obviously half the p-value. That's 0.097, which, as Tukey would say, "is leaning in the right direction". That's not significant but warrants further exploration.
I have a few questions:
- In these cases, is it legitimate to use a one-tailed test? After all, we suspected UP was followed by UP.
- We are testing on the data used to formulate the hypothesis. It is not good practice but in such a long-term study, there is no choice, is there? Not enough data.
- Aren't we wasting our time anyway, UP followed by UP just being an artifact of the positive drift everybody already knows about? What I mean is that the whole exercise is assuredly non-predictive but raises an interesting philosophical question: one-tailed or two-tailed?
Data: S&P Standard Normal = -1.301, p-value = 0.193 alternative hypothesis: two-sided <==> non-random
Data: S&P Standard Normal = -1.301, p-value = 0.097 alternative hypothesis: less <==> trending
April 24, 2007 | Leave a Comment
The musical scale that Pythagoras invented and that forms the foundation for all Western music is based on relationships between small integers. Both the frequencies of musical notes and the intervals between them are ratios of certain integers, as follows:
Scale of Just Intonation
Note C D E F G A B C'
Frequency 1/1 9/8 5/4 4/3 3/2 5/3 15/8 2/1
Interval 9/8 10/9 16/15 9/8 10/9 9/8 16/15
How could similar relationships be uncovered for the S&P? As a first step we might force all S&P daily moves into bins one point wide, that is we could call all moves of 1.0 to 1.9 points "one point moves," and so forth.
We calculated the number of occurrences of each integer move, and also the number that would be expected if we assumed a normal distribution having the same mean and standard deviation. The result may be familiar to some readers but new to others, so we show it here:
S&P Daily moves in increments of 1 point
1999 to 2007/2/28
LO HI Cases ExpCases
-500 -40.1 10 2.46
-40 -39.1 1 0.63
-39 -38.1 0 0.79
-38 -37.1 1 0.98
-37 -36.1 2 1.21
-36 -35.1 3 1.49
-35 -34.1 3 1.83
-34 -33.1 1 2.22
-33 -32.1 4 2.68
-32 -31.1 7 3.22
-31 -30.1 3 3.85
-30 -29.1 9 4.58
-29 -28.1 2 5.41
-28 -27.1 9 6.35
-27 -26.1 8 7.42
-26 -25.1 6 8.62
-25 -24.1 5 9.95
-24 -23.1 13 11.42
-23 -22.1 13 13.03
-22 -21.1 20 14.79
-21 -20.1 16 16.69
-20 -19.1 13 18.73
-19 -18.1 15 20.89
-18 -17.1 16 23.17
-17 -16.1 17 25.55
-16 -15.1 19 28.01
-15 -14.1 24 30.54
-14 -13.1 28 33.10
-13 -12.1 29 35.67
-12 -11.1 39 38.23
-11 -10.1 43 40.73
-10 -9.1 53 43.15
-9 -8.1 46 45.45
-8 -7.1 55 47.59
-7 -6.1 52 49.56
-6 -5.1 52 51.31
-5 -4.1 54 52.82
-4 -3.1 60 54.06
-3 -2.1 70 55.01
-2 -1.1 79 55.66
-1 -0.1 76 56.00
0 0.9 94 56.02
1 1.9 85 55.71
2 2.9 91 55.09
3 3.9 89 54.17
4 4.9 74 52.95
5 5.9 69 51.47
6 6.9 54 49.74
7 7.9 52 47.80
8 8.9 59 45.67
9 9.9 53 43.38
10 10.9 35 40.98
11 11.9 43 38.48
12 12.9 38 35.93
13 13.9 28 33.36
14 14.9 26 30.79
15 15.9 25 28.26
16 16.9 17 25.79
17 17.9 18 23.40
18 18.9 14 21.11
19 19.9 14 18.94
20 20.9 10 16.89
21 21.9 7 14.98
22 22.9 6 13.20
23 23.9 5 11.57
24 24.9 6 10.09
25 25.9 15 8.74
26 26.9 4 7.53
27 27.9 8 6.45
28 28.9 7 5.50
29 29.9 2 4.66
30 30.9 2 3.92
31 31.9 3 3.28
32 32.9 2 2.73
33 33.9 2 2.26
34 34.9 1 1.86
35 35.9 2 1.52
36 36.9 3 1.24
37 37.9 0 1.00
38 38.9 2 0.81
39 39.9 4 0.65
40 500 16 2.52
The main features of the distribution as compared to the normal are: an excess of small changes (say from -6 to +6 points), a deficit of medium sized moves (of about +-16 points) and a modest excess of very large moves (+-35 points or more). A description in terms of these three features is a better one, in my opinion, than simply focusing on the size of the negative tail (as too many people do).
From Laurel Kenner:
In mathematics, Dr. Castaldo is on Parnassus and I am on the "Gradus ad." I do know a little about music and writing, and so I will nevertheless venture to observe that Pythagoras did not actually invent the musical scale. As Stuart Isacoff puts it in his masterful book "Temperament":
"Pythagoras's discovery was that the most 'agreeable' harmonies [e.g., the octave, fifth and fourth] are formed by the simplest kind of mathematical relationships. If the vibrations of one tone are twice as fast as the vibrations of another's, for example, the two will blend so smoothly the result will sound almost like a single entity [he is referring to an octave]. The separate constituents of this musical marriage are oscillating in the proportion 2:1."
Closer to our day, Helmholz wrote that consonant tonal relationships are embedded in the essential physical structure of notes — the sound waves. Look at a graph, and you'll see greater spikes in intensity around the octave, fifth, fourth, etc. These spikes are known as harmonics. Their intensity levels depend on the shape of the instrument that produces the sound, and the resulting mixture contributes to the distinctive sound of each instrument.
(Hold a bass note on the piano down and strike the same note, stacatto, a few octaves higher and listen closely — you'll hear sonorities of tones still higher. Or experiment with harmonics on a vibrating guitar string.)
The market's ever-changing significant levels might be viewed (heard) as harmonics of past turning points. The Chair's insights on the continuing psychological impact of catastrophic events would seem to be in this genre.
The mathematical relations found in music are tempting to apply to the physical structure of the universe, and people have done that at least since Pythagoras. But I will leave that to the physicists.
Todd Tracy adds:
We might be socialized to market intervals in much the same way as to musical intervals. I found this interesting.
Evolutionary Effects by Robert Fink, 2004
General human evolution has provided us with voices that are acoustically musical, and with ear receptors that are appreciative of, or attracted to, acoustically-musical sounds (i.e., not noisy). Why this evolution?
Without these physiological capacities, then:
* Mothers would not coo to their babies; nor would the babies love the sound of it;
* Nor would evolution of language and the socializing sounds of the voice have been as possible;
* Nor would the noisy (i.e., not-acoustically musical) sounds from any nearby destructive event or attack, or of the sounds of breakage, screams or cries of pain, have served as a noisy warning [unattractive or repelling] to alarm or alert us — Some sounds make us come, others make us run….
And, as a result, our collectivized survival might not have been as efficient, and we could have gone the way of the extinct Dodo.
All those same capacities [regarding being able to distinguish noise from "musical" sound] also served to allow the development of musical systems to arise and evolve wherever there were curious people with time to play or experiment with the stimuli around them.
Tom Ryan extends:
I find this Pythagorean scale discussion fascinating and have thought about various applications of musical scales and notes to market prices often over the years. Pythagoras believed the universe was an immense monochord and many of the Pythagorean teachings at Crotona are remarkable insights, especially with respect to what we now call string theory.
Pythagoras believed that by studying music mathematically, one could develop an understanding of structures in nature. One of the more interesting aspects that I have found in this is the lambdoma table, which is a 16×16 dimensional matrix of ratios starting with 1/1 and ending with 16/16,
1/1 1/2 1/3 1/4 1/5 1/6 1/7…….1/16
2/1 2/2 2/3 2/4 2/5 2/6 2/7…….2/16
3/1 3/2 ………..
16/1 16/2 16/3……………….16/16
The lambdoma is composed of two series. The first represents the divisions of a string which represent frequencies or tones. The second level represents harmonics. For example the first 16 harmonics of C are 256 hz (C), 512 hz (C), 768 hz(G) 1024 hz(C) 1280 (E) 1536 (G) 1792(B-) 2048(C) 2304(D) 2560(E) 2816(F#) 3072(G) 3328(A-) 3584(Bb-) 3840(B) and finally 4096 hz (C again since 4096/256 =16/1). This series represents the overtones or partial and whole harmonics of C and are represented by ratios in the table
Through the ratios one can study other types of natural structures as well. Botanists in particular have studied geometrical structure and many plant structures (leaves, flowers) have been noted to be geometrically developed in consistent ways where the key geometric ratios fall into a contiguous grouping or overtones of a fundamental, i.e. a pattern of squares within the lambdoma matrix. Leaves for instance often have simultaneous ratios of thirds (5:4) and fifths (3:2). The Renaissance studies including Da Vinci's notebooks note that the human body develops along particular lines as well, namely an abundance of major sixths (3:5) and minor sixths (5:8).
These whole number ratios or pattern of harmonics, as Laurel noted, form the basis for musical scales,
Major sixth 3:5
Major third 4:5
Minor sixth 5:8
Minor seventh 5:9
Major second 8:9
Major seventh 8:15
Minor second 15:16
The question is whether specific harmonic patterns occur at times in the market and whether these can be quantified in some way. Victor discusses market prices as music in his book EdSpec.
One way would be to classify movements as ratios, and see the patterns of ratios as they unfold, classifying movements in price as patterns on a scale or within the lambdoma. From that one might be able to find a few meager predictable patterns. But there is a problem with this: to calculate ratios in a contiguous strip of real time prices one must arbitrarily choose reference points in order to calculate the ratios.
This means that there is a substantial level of subjectivity in developing the ratios from which the patterns can be studied. But one could arbitrarily divide the day into segments and look at ranges or deltas within those segments (say 30 minutes) and then calculate ratios of adjacent periods. There are probably an infinite variety of ways to segment and calculate ratios and therein lies the dilemma.
James Kynge, in The Financial Times:
The first inkling the British had of the 13th-century Mongol invasion of Europe, a cataclysm for the continent, was when the price of fish at Harwich, a harbour on the North Sea, rose sharply. The explanation for this, people learned later, was that the Baltic shipping fleets, abruptly deprived of sailors required to fight the enemy approaching by horse from the east, had remained at their moorings. That had reduced the supply of cod and herring to Harwich, and prices had risen accordingly.
Ken Smith remarks:
That was a fishy tale. Pacific salmon are disappearing and lovers of the odor of fish cooking in an apartment building with no air conditioning are lamenting this fact.
For one six-month season I worked out of Dutch Harbor, on an ocean tug pulling a fuel barge around the Bering Sea and Gulf of Alaska. Dutch Harbor in the Aleutian Island chain has a stream running from the hills above to the harbor. This steam is lined with homes built by natives. Homeowners fished off back porch, simply stepped into the stream and grabbed a meal if willing to risk the rushing water.
That was in 1955. Fish were free then. Since salmon are disappearing, there may soon be fish wars in the village at Dutch Harbor.
In the harbor, moored at the Chevron tank-farm wharf, we merely dropped a line over the side to snag 100 lb halibut, any time, day or night. Things have changed since Asian fisheries now have boats on every wave crest.
From Denis Vako:
Here is an outlier's observation. Never was there such a thing as a Mongol invasion of Europe or Russia. It is a fairy tale invented by Romanovs to legitimize their power grab in Russia. Cossacks, Slavs, Vikings and other Russians are the ones who in fact invaded Europe.
A history book, fiction or science, by Anatoly Fomenko has the whys and the proofs. I found only 2 volumes on Amazon in English, but he has seven in Russian, including mathematical evidence.
April 9, 2007 | 1 Comment
The worst mistake in business is to get in over your head. Don't ever let yourself do it. The market will always be around. Do keep a lab notebook and hand records of all your trades. Try to do as much of your research by hand whenever you can, as it lets you see more things.
Always enumerate your entire computer output by trade so you'll see how it's doing over time and bunches. Try not to listen to smart people on a macro basis, as their views can be marginal and some are smarter than others. Only trade active markets.
Also, don't be afraid to give up on markets. Find a niche where you have an edge and do concentrate on it. I started out with about 10,000 under management. If you can make a little above average, you'll have all the business in the world.
Bill Rafter adds:
The Chair is absolutely correct about keeping notes. And there is nothing as good as a lab notebook. But everything has the disadvantages of its advantages. I have found in both research and trading that the handwritten lab notebook is essential for thinking through ideas and theoretical problems. But it falls short in terms of practical research because your notes will essentially be anecdotal.
We have found that when researching a particular idea we get excited and may sometimes make manual research "runs" several times a minute. That's too fast to keep adequate handwritten notes. Additionally we may set up research to run thousands of variations overnight in an attempt to find the statistical "truth" of something. In such a case you must have a way of saving each result and sorting and comparing the whole lot. Anecdotal notation is not enough.
The same is true with trading. Every time you make a trade have the results saved according to the type of trade it was. The types are specific to you. Someone else might characterize them differently. The more information the better.
For both research and trading it's best if you create software to save your results. Have the results of each research run or trade stored in a text file, and then have the ability to plop the results of all such research and trades in an excel file for comparison. You may not be the smartest "natural" researcher or trader, but your documentation and filing system will enable you to avoid future mistakes.
Most people fail at the business of trading. Most people trade "anecdotally." My guess is that the Venn diagrams of these shows considerable overlap. As the demigod said, it's perspiration, not inspiration.
Hany Saad comments:
What is getting in over your head? Is leverage getting in over one's head? Is over leveraging? What is overleverage? should one be only trading on a 1 to 1 … 1 to 3, or use the maximum leverage possible? Should one diversify? if you are trading the stockmarket, is anything above 15 positions really necessary? Is anything above that even easily trackable by a manager?
It is unfortunate that the first advise seasoned managers offer you is to diversify. I believe this is the mistake managers make the most. They over-diversify and they lose track of the raison d'etre of their positions in the first place. Do not over-diversify.
On a side note, I am guilty of not following Vic's advise about hand studies. I only started after reading this post by simply putting down prices on graph paper, and let me tell you that the feeling you get out of the process is incredible. Things you would very easily miss by using the computer become clear to you. The only problem is that the process can be very time consuming, but not without its benefits.
Alex Castaldo offers:
What is getting in over your head?
Really you don't know? We have some experts on this right here on the Spec-List. Or is it a rhetorical question?
What is over-leverage?
Ed Seykota has a good explanation on his web site. (I don't like Ed Seykota, BTW, too arrogant.) For a given expectation, as you increase leverage at some point the rate of return decreases. A hump shaped curve.
Should one diversify?
Let's not waste time on this; Markowitz already got the Nobel Prize for answering it.
If you are trading the stock market, is anything above 15 positions really necessary?
Do you think that Jim Simon or David Shaw have portfolios of fewer than 15 stocks?
Is anything above [15 stocks] even easily tractable by the manager?
With a computer you can keep track of 1500 stocks practically as easily as 15.
I believe this is the mistake managers do the most. They over-diversify.
Think of it from the Markowitz point of view. You have a quadratic program in terms of means, variances, and covariances, plus you add a constraint "no more than 15 stocks." Solve the QP. Now remove the constraint of only 15 stocks and solve the QP again. What happens to the rate of return? In all but pathological cases it increases (and it never decreases) by removing that constraint.
Think of it also from the Statarb point of view. You have an algorithm that successfully predicts stock excess returns. As long as the excess returns are greater than the transaction cost, you might as well include as many stocks as possible in the portfolio. By limiting the portfolio to 15 stocks you are leaving money on the table.
From Sushil Kedia:
The picture captioned Guru has some inspiring stories to tell. This particular snapshot shows Abhishek Bacchan - the current contender for the superstar slot in Bollywood. In this recent blockbuster titled Guru he is playing the role of Gurukant Desai - a pseudonym for Dhirubhai H Ambani - the biggest tycoon ever in Indian business.
The sea of umbrellas in the backdrop is a touching sense of cinematography and attempts to capture that historic moment when nearly two decades ago for the first time in India a Public Limited Company held its Annual General Meeting in a sports stadium for the first time. Mr. Ambani is credited to be the father of the raging equity cult in this nation. The lashing rains, it is said, did not dissuade a near histrionic crowd from dispersing. This picture thus has a story of a man, who was barely literate but had all the speculative and risk taking genetics to inspire milling crowds to supply capital.
Mr. Ambani was a rank outsider in the industrial club of post-independence India. The son of a village schoolteacher who resigned from secure employment to create the largest ever-industrial enterprise in India. The Reliance group of companies that he went onto create have had grown within twenty years of coming to life to be the largest market capitalization companies in their respective sectors, largest sales, largest profits, largest number of shareholders, largest everything.
The movie is a reasonably close depiction of many of his facets (and three hours can't be enough to justly portray the racing pace of two decades of growth). An ace natural speculator, his enterprises have continued to expand in ways very typical of a trader who pyramids correctly and manages risk. This movie depicts inspiringly enough for any trader how a rank outsider without any crutches of a business lineage or modern education applies commonsense, correct usage of the envelope and pencil device (hand-studies), intelligence and sheer diligence to beat the system at its own tracks.
Lores suggest that when Dhirubhai was working as a clerk for a commodity trader on the port of Aden at the age of sixteen, suddenly the town started discovering the trading prowess of the lad with acumen. A particular denomination of coins vanished gradually first and completely later out of circulation. The value of the silver content had gone higher than the nominal value of the coins. It is believed his first large wad of capital originated from this arbitrage. [Not shown in the movie though]
A ticklishly touching sequence from the movie shows that when the Futures exchange in Mumbai was shut down by the orders signed by a bureaucrat that described speculation as unproductive gambling, Guru / Dhirubhai hires a truck and dumps long and fat rolls of polyester yarn in the living room of this bureaucrat. The officer scared of a possible tarnishing of his reputation that others may think all this yarn is lying in his house asks him to remove it immediately. Dhirubhai walks off saying the only two ways the officer can dispose off the yarn is by either dumping it all in the sea or re-opening the exchange where it is possible to buy and sell. Perplexed for several days, the officer tracks Dhirubhai down over and orders the re-opening of the exchange turning the David a darling of the Goliaths.
If anyone is interested in figuring out how modern India's business structures operate, what is the "inside picture", what is to be not done and what must be done this is a must watch movie. For those purely interested in trading philosophies the ingenuity of the hero of this saga has continuous positive surprises. For a rise as meteoric as this in such short time, what is remarkable is that for all his aggression he clearly believed and lived inside out that, "the worst mistake in business is to get in over your head." He is more Aggressive and more humble than the competition and in equal measure.
Of the hundreds of popular Ambanisms one would in the present context mention these few:
"No one was ever issued an invitation to profits."
"If I have to salute a peon in someone's office to get past my objectives, it is part of my work to do so."
"Calamity is the origin of other opportunities."
"Think big, think fast, think ahead. Ideas are no one's monopoly."
One of the greatest mysteries to me is how trading firms like the ones mentioned can garner over sized returns with short term activities, considering the massive bid asked spreads and exposure to the principles of ever changing cycles. Smart people I have known, like my first partner who was a champion bridge player as well as number one in M.A.A. comp., have never understand that whatever looks like it works is doomed to failure. Despite this, to his credit he was a buy and hold man with a reasonably positive view of the resilience of enterprise, and was thus able to participate in the drift of the 10,000 fold return per century.
On the other hand the firms mentioned are riddled by those who hate enterprise and would seem to favor things like long/short, making their inability to overcome the bid asked spread even more incredibly likely to me. But as they say, when I pose similar lacnunae against the sport-term owner, he is the one who owns the big teams and I am still a very small operator. I am a poster boy for how volatility and chronic bullishness can lead to disaster, as well as the butt of the mojo of the expert derivatives man who is so ready and able to take all my meager chips on the all too frequent black swan events.
Pages 178 and following of The Economic Way of Thinking: a brief summary.
(1) Speculation arises because of uncertainty about the future and is quite common. Many everyday activities involve an element of speculation. For example a college student getting an education is speculating (he hopes, but cannot be sure, that the increased income he will get after graduating will outweigh the cost and effort of going to college). Another example might be a motorist who passes by a gas station hoping to find another station with a cheaper gas price further down the road, even though the next one might actually be more expensive.
(2) A more important example involves price speculation on agricultural commodities such as corn.
If it is expected that the next corn crop will be smaller than usual (perhaps because of signs of corn-leaf blight in the Midwest corn fields) a number of economic responses take place that tend to reduce current consumption of corn and increase the amount stored for future consumption. Some of these are the result of the actions of ranchers, or companies who use or store corn in their operations; others are the result of organized futures markets. All these can be classified as speculation. The result is an increase in the spot price of corn. Heyne emphasizes that this is a beneficial effect; the price increase is a signal that corn is scarce and it encourages people to consume less corn or store more of it for the future.
As Heyne says:
"the speculative activities cause corn to be transported over time from a period of relative abundance to one of relative scarcity".
The price increase takes place even before the next corn crop comes on the market, and this is as it should be (even though some people do not appreciate it): if corn is going to be scarce we might as well start economizing on its use right away. It is entirely possible of course that the predicted corn blight will not occur, but the speculators are rewarded for being right, they lose out if they are wrong.
Participants in the futures markets include both hedgers and speculators:
"Futures markets allow people to allocate their risks and deal with uncertainty as they see fit. Those who wish to reduce their risk have the option to hedge, those who wish to increase their exposure to risk have the option to speculate."
(3) The futures markets generate information (namely accurate prices for commodities) and are thus valuable even to people who do not participate in them. For example farmers can use futures prices for corn to make crop decisions. The futures markets also encourage the generation of information, in the sense that the speculators are rewarded if they understand all factors (fundamental and technical) that affect prices and are presumably hard at work to uncover them.
(4) A favorite passage of Vic's appears on page 179:
"Adam Smith once compared those who speculate on the future price of grain to the prudent captain of a sailing ship, who puts the crew on short rations the moment he discovers there is not enough food on board to last through the voyage. Grain speculators, Smith argued, reduce the suffering that bad harvests cause by inducing consumers to start economizing early."
March 6, 2007 | 1 Comment
Article by Daily Spec's Professor Ross Miller, from Alex Castaldo, in latest issue of Journal of Investment Management.
This article describes a rigorous method for allocating fund expenses between active and passive management. It also enables one to compute the implicit cost of active management. Computing this "active expense ratio" requires only a fund's published expense ratio, its R-squared relative to a benchmark index, and the expense ratio for a competitive fund that tracks that index. This method is then applied to the Morningstar universe of large-cap mutual funds and active expense ratios are found to average more than 7%. The cost of active management for other classes of mutual funds is also found to be substantial.
February 8, 2007 | Leave a Comment
There has been entirely too little thought given to the mechanism, pathways and reasons that negative feedback works in markets. Perhaps the main reason is that the feeding web is based on a reasonable stability in what and how much is being eaten and recycled.
The people who consume and redistribute must maintain a ready and stable supply of those who produce. They develop mechanisms to keep everything going. One of them is the specialization and great efficiency in their activities. If markets deviate too much from the areas and levels within which the specialization has developed, then much waste and new effort and mechanisms will be necessary.
Aside from the grind that trend following causes (i.e. the losses in execution), and the negative feedback system of movements in the supply and demand schedules that equilibrate, which Marshall pioneered and are now standard in economics, and the numerous other reasons I've set forth (e.g. the fixed nature of the system and the flexibility to profit from it), this appears to me to be the main reason that trend following doesn't work.
Here are a few interesting articles on the subject:
Bill Rafter writes:
Dr. Bruno had posed the idea of beating an index by deleting the worst performers. This is an area in which we have done considerable work. Please note that we do not consider this trend-following. The assets are not charted, just ranked.
Let us imagine an investor who is savvy enough to identify what is strong about an economy and invest in sectors representative of those areas, while avoiding sectors representing the weaker areas of the economy. Note that we are not requiring our investor to be prescient. He does not need to see what will be strong tomorrow, just what is strong and weak now, measured by performance over a recent period.
What is a market sector? The S&P does that work for us, and breaks down the overall market (that is, the S&P 500) into 10 Sectors. They further break it down into 24 Industry Groups, and further still into 60-plus Industries and 140-plus Sub-Industries. The number of the various groups and their constituents changes from time to time as the economy evolves, but essentially the 500 stocks can be grouped in a variety of ways, depending on the degree of focus desired. Some of the groupings are so narrow that only one company represents that group.
Our investor starts out looking at the 10 Sectors and ranks them according to their performance (such as their quarterly rate of change). He then invests in those ranked first through fourth (25 percent in each), and maintains those holdings until the rankings change. How does he do? Not bad, it turns out.
From 1990 through 2006, which encompasses several types of market conditions, the overall market managed an 8 percent compound annual rate of return. Our savvy investor achieved 10.77%. A less savvy investor who had the bad fortune to pick the worst six groups would have earned 7.23%. Those results are below. (Note, for comparison purposes, all results excluded dividends.)
How can our savvy investor do better? By simply sharpening one's focus, major improvements can be achieved. If instead of ranking the top 4 of10 Sectors, our savvy investor invests in a similar number (say the top 4, 5 or 6) of the 24 Industry Groups, he achieves a 13.12% compoundedannual rate of return over the same period. Note that the same stocks are represented in the 10 Sectors and the 24 Industry Groups. At no time did he have to be prescient.
One thing you will notice from the graphs above is that the equity curves of our savvy and unlucky investors mimic the rises and declines of the market index itself. Being savvy makes money but it does not insulate one from overall bad markets because the Sectors and even the Industry Groups are not significantly diversified from the overall market.
Why not keep going further out and rank all stocks individually? That clearly results in superior returns, but the volume of trading is such that it can only be accomplished effectively in a fund structure - not by the individual. And even ranking thousands of stocks will not insulate an investor from an overall market decline, if he is only invested in equities. The answer of course is diversification.
It is possible to rank debt and alternative investment sectors alongside equities, in the hope of letting their performances dictate what the investor should own. However the debt and commodities markets have different volatilities than the equities markets. Anyone ranking them must make adjustments for their inherent differences. That is, when ranking really diverse assets, one must rank them on a risk-adjusted basis for it to be a true comparison. However if we make those adjustments and rank treasury bonds (debt) against our 24 Industry Groups (equity) we can avoid some of the overall equity declines. We refer to this as a Strategic Overlay:
Adding this Strategic Overlay increases the returns slightly, but more important, diversifies the investor away from some periods of total equity market decline. We are not talking of a policy of running for cover every time the equities markets stall. In the long run, the investor must be in equities.
Invariably in ranking diverse assets such as equities, debt and commodities, our investor will be faced with a decision that he should be completely out of equities. It is likely that will occur during a period of high volatility for equities, but one that has also experienced great returns. Thus, our investor would be abandoning equities when his recent experience would suggest otherwise. And since timing can never be perfect, it is further likely that the equities he abandons will continue to outperform for some period. On an absolute basis, equities may rank best, but on a risk-adjusted basis, they may not. It is not uncommon for investors to ignore risk in such a situation, to their subsequent regret.
Ranking is not without its problems. For example, if you are selecting the top 4 groups of whatever category, there is a fair chance that at some time the assets ranked 4 and 5 will change places back and forth on a daily basis. This "flutter" can be easily solved by providing those who make the cut with a subsequent incumbency advantage. For a newcomer to replace a list member, it then must outrank the current assets on the selected list by the incumbency advantage. This is very similar to the manner in which thermostats work. We have found adding an incumbency advantage to be a profitable improvement without considering transactions costs. When one also considers the reduced transaction costs, the benefits increase even more.
Another important consideration is the "lookback" period. Above we used the example of our savvy investor ranking assets on the basis of their quarterly growth. Not surprisingly, the choice of a lookback period can have an effect on profitability. Since markets tend to fall more abruptly than they rise, lookback periods that perform best during rising markets are markedly different from those that perform best during falling markets. Determining whether a market is rising or falling can be problematic, as it can only be done with certainty in retrospect. However, another key factor influencing the choice of a lookback period is volatility, which can be determined concurrently. Thus an optimal lookback period can be automatically determined based on volatility.
There is certainly no question that a diligent investor can outperform the market. By outperforming the market we mean that he will achieve a greater average rate of return than the market, while limiting the maximum drawdown (or percentage equity decline) to less than that experienced by the market. But the average investor is generally not up to the diligence or persistence required.
In the research work illustrated above, all transactions were executed on the close of the day following a decision being made. Thus the strategy illustrated is certainly executable. Nothing required a forecast; all that was required was for the investor to recognize concurrently which assets have performed well over a recent period. It is not difficult, but requires daily monitoring.
Charles Pennington writes:
Referring to the MathInvestor's plot:
At first glance it appears that the "Best" have been beating the "Worst" consistently.
In fact, however, all of the outperformance was from 1990 through 1995. From 1996 to present, it was approximately a tie.
Reading from the plot, I see that the "Best" portfolio was at about 2.1 at the start of 1996. It grew to about 5.5 at the end of the chart for a gain of about 160%. Over the same period, the "Worst" grew from 1.3 to 3.2, a gain of about 150%, essentially the same.
So for the past 11 years, this system had negligible outperformance.
One should also consider that the "Best" portfolio benefits in the study from stale pricing, which one could not capture in real trading. Furthermore, dividends were not included in the study. My guess is that the "Worst" portfolio would have had a higher dividend yield.
In order to improve this kind of study, I would recommend:
1.) Use instruments that can actually be traded, rather than S&P sectors, in order to eliminate the stale pricing concern.
2.) Plot the results on a semilog graph. That would have made it clear that all the outperformance happened before 1996.
3.) Finally, include dividends. The reported difference in compound annual returns (10.8% vs 8.0%) would be completely negated if the "Worst" portfolio had a yield 2.8% higher than the "Best".
Bill Rafter replies:
Gentlemen, please! The previously sent illustration of asset ranking is not a proposed "system," but simply an illustration that tilting one's portfolio away from dogs and toward previous performers can have a beneficial effect on the portfolio. The comparison between the 10 Sectors and the 24 Industry Groups illustrates the benefits of focus. That is, (1) don't buy previous dogs, and (2) sharpen your investment focus. Ignore these points and you will be leaving money on the table.
We have done this work with many different assets such as ETFs and even Fidelity funds (which require a 30-day holding period), both of which can be realistically traded. They are successful, but not overwhelmingly so. Strangely, one of the best asset groups to trade in this manner would be proprietarily-traded small-cap funds.
Unfortunately if you try trading those, your broker will disown you. I mention that example only to suggest that some assets truly do have "legs," or "tails" if you prefer. I think their success is attributed to the fact that some prop traders are better than others, and ranking them works. An asset group with which we have had no success is high-yield debt funds. I have no idea why.
A comment from Jerry Parker:
I wrote an initial comment to you via your website [can be found under the comments link by the title of this post], disputing your point of view, which a friend of mine read, and sent me the following:
I read your comment on Niederhoffer's Daily Spec in response to his arguments against trend following. Personally, I don't think it boils down to intelligence, but rather to ego. Giving up control to an ego-less computer is not an easy task for someone who believes so strongly in the ability of the human mind. I have great respect for his work and his passion for self study, but of course disagree with his thoughts on trend following. On each trade, he is only able to profit if it "trends" in a favorable direction, whether the holding period is 1 minute or 1 year. Call it what you will, but he trades trends all day.
He's right. I was wrong. Trend following is THE enemy of the 'genius'. You and your friends can't even see how stupid your website is. You are blinded by your superior intelligence and arrogance.
Victor Niederhoffer responds:
Thanks much for your contributions to the debate. I will try to improve my understanding of this subject and my performance in the future so as not to be such an easy target for your critiques.
Ronald Weber writes:
When you think about it, most players in the financial industry are nothing but trend followers (or momentum-players). This includes analysts, advisors, relationship managers, and most fund or money managers. If there is any doubt, check the EE I function on Bloomberg, or the money flow/price functions of mutual funds.
The main reason may have more to do with career risk and the clients themselves. If you're on the right side while everyone is wrong, you will be rewarded; if you're on the wrong side like most of your peers you will be ok; and if you're wrong while everyone is right then you're in trouble!
In addition, most normal human beings (daily specs not included!) don't like ideas that deviate too much from the consensus. You are considered a total heretic if you try to explain why, for example, there is no link between the weak USD and the twin deficits. This is true, too, if you would have told anyone in 2002 that the Japanese banks will experience a dramatic rebound like the Scandinavian banks in the early '90s, and so on, or if you currently express any doubt on any commodity.
So go with the flow, and give them what they want! It makes life easier for everyone! If you can deal with your conscience of course!
The worse is that you tend to get marginalized when you express doubt on contagious thoughts. You force most people to think. You're the boring party spoiler! It's probably one reason why the most successful money managers or most creative research houses happen to be small organizations.
Jeremy Smith offers:
Not arguing one way or the other here, but for any market or any stock that is making all time highs (measured for sake of argument in years) do we properly say about such markets and stocks that there is no trend?
Vincent Andres contributes:
I would distinguish/disambiguate drift and trend.
"Drift": Plentifully discussed here. "Trend": See arcsine, law of series, etc.
Basically, our tendency is to believe that random equals equiprobability everywhere (2D) or random equals equiprobability everytime (1D), and thus that nonequiprobability everywhere/everytime equals non random
In 1D, non equiprobability everytime means that the sequence -1 +1 -1 +1 -1 +1 -1 +1 is in fact the rare and a very non random sequence, while the sequences -1 +1 +1 +1 +1 +1 -1 +1 with a "trend" are in fact the truly random ones. By the way, this arcsine effect does certainly not explain 100% of all the observed trends. There may also be true ones. Mistress would be too simple. True drift may certainly produce some true trends, but certainly far less than believed by many.
Dylan Distasio adds:
For those who don't believe trend following can be a successful strategy, how would you explain the long-term performance of the No Load Fund X newsletter? Their system consists of a fairly simple relative strength mutual fund (and increasingly ETF) model where funds are held until they weaken enough in relative strength to swap out with new ones.
The results have been audited by Hulbert and consistently outperform the S&P 500 over a relatively long time frame (1980 onwards). I think their results make a trend following approach worth investigating…
Jerry Parker comments again:
All you are saying is that you're not smart enough to develop a trend following system that works. What do you say about the billions of dollars traded by trend following CTAs and their long term track records?
Steve Leslie writes:
If the Chair is not smart enough to figure out trend following, what does that bode for the rest of us?
There is a very old yet wise statement: Do not confuse brains with a bull market.
Case in point: prior to 2000 the great tech market run was being fueled by the hysteria surrounding Y2K. Remember that term? It is not around today but it was the cause for the greatest bull market seen in stocks ever. Dot.com stocks and new issues were being bought with reckless abandon.
New issues were priced overnight and would open 40-50 points higher the next trading day. Money managers had standing orders to buy any new issues. There was no need for dog-and-pony or road shows. It was an absolute classic and chaotic case of extraordinary delusion and crowd madness.
Due diligence was put on hold, or perhaps abandoned. A colleague of mine once owned enough stock in a dot.com that had he sold it at a propitious time, he would have had enough money to purchase a small Hatteras yacht. Today, like many contemporary dot.coms, that stock is essentially worthless. It would not buy a Mad magazine.
Corporations once had a virtual open-ended budget to upgrade their hardware and software to prepare for the upcoming potential disaster. This liquidity allowed service companies to cash in by charging exorbitant fees. Quarter to quarter earnings comparisons were beyond belief and companies did not just meet the numbers, they blew by them like rocket ships. What made it so easy to make money was that when one sold a stock, all they had to do was purchase another similar stock that also was accelerating. The thought processes where so limited. Forget value investing; nobody on the planet wanted to talk to those guys. The value managers had to scrape by for years while they saw their redemptions flow into tech, momentum, and micro cap funds. It became a Ponzi scheme, a game of musical chairs. The problem was timing.
The music stopped in March of 2000 when CIO's need for new technology dried up coincident with the free money, and the stock market went into the greatest decline since the great depression. The NASDAQ peaked around 5000. Today it hovers around 2500, roughly half what it was 7 years ago.
It was not as if there were no warning signs. Beginning in late 1999, the tech market began to thin out and leadership became concentrated in a few issues. Chief among the group were Cisco, Oracle, Qwest, and a handful of others. Every tech, momentum, and growth fund had those stocks in their portfolio. This was coincident with the smart money selling into the sectors. The money managers were showing their hands if only one could read between the lines. Their remarks were "these stocks are being priced to perfection." They could not find compelling reasons not to own any of these stocks. And so on and on it went.
After 9/11 markets and industries began to collapse. The travel industry became almost nonexistent. Even Las Vegas went on life support. People absolutely refused to fly. Furthermore, business in and around New York City was in deep peril. This forced the Fed to begin dramatically reducing interest rates to reignite the economy. It worked, as corporations began to refinance their debt and restructure loans, etc.
The coincident effect began to show up in the housing industry. Homeowners refinanced their mortgages (yours truly included) and took equity out of their homes. Home-buyers were thirsty for real estate and bought homes as if they would disappear off the earth. For $2000 one could buy an option on a new construction home that would not be finished for a year. "Flipping" became the term du jour. Buy a home in a hot market such as Florida for nothing down and sell it six months later at a much higher price. Real estate was white hot. Closing on real estate was set back weeks and weeks. Sellers had multiple offers on their homes many times in the same day. This came to a screeching halt recently with the gradual rise in interest rates and the mass overbuilding of homes, and the housing industry has slowed dramatically.
Houses for sale now sit on the blocks for nine months or more. Builders such as Toll, KB, and Centex have commented that this is the worst real estate market they have seen in decades. Expansion plans have all but stopped and individuals are walking away from their deposits rather than be upside down in their new home.
Now we have an ebullient stock market that has gone nearly 1000 days without so much as a 2% correction in a day. The longest such stretch in history. What does this portend? Time will tell. Margin debt is now at near all-time highs and confidence indicators are skewed. Yet we hear about trend followers and momentum traders and their success. I find this more than curious. One thing that they ever fail to mention is that momentum trading and trend following does not work very well in a trendless market. I never heard much about trend followers from June 2000 to October 2002. I am certain that this game of musical chairs will end, or at least be temporarily interrupted.
As always, it is the diligent speculator who will be prepared for the inevitable and capitalize upon this event. Santayana once said, "Those who cannot remember the past are condemned to repeat it."
From "A Student:"
Capitalism is the most successful economic system in the history of the world. Too often we put technology up as the main driving force behind capitalism. Although it is true that it has much to offer, there is another overlooked hero of capitalism. The cornerstone of capitalism is good marketing.
The trend following (TF) group of fund managers is a perfect example of good marketing. As most know, the group as a whole has managed to amass billions of investor money. The fund operators have managed to become wealthy through high fees. The key to this success is good marketing not performance. It is a tribute to capitalism.
The sports loving fund manger is a perfect example. All of his funds were negative for 2006 and all but one was negative over the last 3 years! So whether one looks at it from a short-term one year stand point or a three year perspective his investors have not made money. Despite this the manager still made money by the truckload during this period. Chalk it up to good marketing, it certainly was not performance.
The secret to this marketing success is intriguing. Normally hedge funds and CTAs cannot solicit investors nor even publicly tout their wares on an Internet site. The TF funds have found a way around this. There may be a web site which openly markets the 'concept' of TF but ostensibly not the funds. On this site the names of the high priests of TF are repeatedly uttered with near religious reverence. Thus this concept site surreptitiously drives the investors to the TF funds.
One of the brilliant marketing tactics used on the site is the continuous repetition of the open question, "Why are they (TF managers) so rich?" The question is offered as a sophist's response to the real world question as to whether TF makes money. The marketing brilliance lies in the fact that there is never a need to provide factual support or performance records. Thus the inconvenient poor performance of the TF funds over the last few years is swept under the carpet.
Also swept under the rug are the performance figures for once-great trend followers who no longer are among the great, i.e., those who didn't survive. Ditto for the non-surviving funds in this or that market from the surviving trend followers.
Another smart technique is how the group drives investor traffic to its concept site. Every few years a hagiographic book is written which idolizes the TF high priests. It ostensibly offers to reveal the hidden secrets of TF.
Yet after reading the book the investor is left with no usable information, merely a constant repetition of the marketing slogan: How come these guys are so rich? Obviously the answer is good marketing but the the book is moot on the subject. Presumably, the books are meant to be helpful and the authors are true believers without a tie-in in mind. But the invisible hand of self-interest often works in mysterious ways.
In the latest incarnation of the TF book the author is presented as an independent researcher and observer. Yet a few days after publication he assumes the role of Director of Marketing for the concept site. Even the least savvy observer must admit that it is extraordinary marketing when one can persuade the prospect to pay $30 to buy a copy of the marketing literature.
Jason Ruspini adds:
"I attribute much of the success of the selected bigs to being net long leveraged in fixed income and stocks during the relevant periods."
I humbly corroborate this point. If one eliminates long equity, long fixed income (and fx carry) positions, most trend-following returns evaporate.
Metals and energies have helped recently, after years of paying floor traders.
I don't agree with all the points above. For example, the beauty of capitalism is not its puffery, but the efficiency of its marketing and distribution system as well as the information and incentives that the prices provide so as to fulfill the pitiless desires of the consumers. Also beautiful is in the mechanism that it provides for those with savings making low returns to invest in the projects of entrepreneurs with much higher returns in fields that are urgently desired by customers.
I have been the butt of abuse and scorn from the trend followers for many years. One such abusive letter apparently sparked the writer's note. Aside from my other limitations, the trend following followers apparently find my refusal to believe in the value of any fixed systems a negative. They also apparently don't like the serial correlation coefficients I periodically report that test the basic tenets of the trend following canon.
I believe that if there are trends, then the standard statistical methods for detecting same, i.e., correlograms, regressions, runs and turning point tests, arima estimates, variance ratio tests, and non-linear extensions of same will show them.
Such tests as I have run do not reveal any systematic departures from randomness. Nor if they did would I believe they were predictive, especially in the light of the principle of ever changing cycles about which I have written extensively.
Doubtless there is a drift in the overall level of stock prices. And certain fund managers who are biased in that direction should certainly be able to capture some of that drift to the extent that the times they are short or out of the market don't override it. However, this is not supportive of trend following in my book.
Similarly, there certainly has been over the last 30 years a strong upward movement in fixed income prices. To the extent that a person was long during this period, especially if on leverage, there is very good reason to believe that they would have made money, especially if they limited their shorts to a moiete.
Many of the criticisms of my views on trend following point to the great big boys who say they follow trends. To the extent that those big boys are not counterbalanced by others bigs who have lost, I attribute much of the success of the selected bigs to being net long leveraged in fixed income and stocks during the relevant periods.
I have no firm belief as to whether such things as trends in individual stocks exist. The statistical problem is too complex for me because of a paucity of independent data points, and the difficulties of maintaining an operational prospective file.
Neither do I have much conviction as to whether trends exist in commodities or foreign exchange. The overall negative returns to the public in such fields seem to be of so vast a magnitude that it would not be a fruitful line of inquiry.
If I found such trends through the normal statistical methods, I would suspect them as a lure of the invisible evil hand to bring in big money to follow trends after a little money has been made by following them, the same way human imposters work in other fields. I believe that such a tendency for trend followers to lose with relatively big money after making with smaller amounts is a feature of all fixed systems. And it's guaranteed to happen by the law of ever-changing cycles.
The main substantive objection to my views that I have found in the past, other than that trend followers know many people who make money following trends (a view which is self-reported and selective and non-systematic, and thus open to some of the objections of those of the letter-writer), is that they themselves follow trends and charts and make much money doing it. What is not seen by these in my views is what they would have made with their natural instincts if they did not use trend following as one of their planks. This is a difficult argument for them to understand or to confirm or deny.
My views on trend following are always open to new evidence, and new ways of looking at the subject. I solicit and will publish all views on this subject in the spirit of free inquiry and mutual education.
Jeff Sasmor writes:
Would you really call what FUNDX does trend following? Well, whatever they do works.
I used their system successfully in my retirement accounts and my kids' college UTMA's and am happy enough with it that I dumped about 25% of that money in their company's Mutual Funds which do the same process as the newsletter. The MFs are like an FOF approach. The added expense charges are worth it. IMO, anyway. Their fund universe is quite small compared to the totality of funds that exist, and they create classes of funds based on their measure of risk.
This is what they say is their process. When friends ask me what to buy I tell them to buy the FUNDX mutual fund if their time scale is long. No one has complained yet!
It ain't perfect (And what is? unless your aim is to prove that you're right) but it's better than me fumfering around trying to pick MFs from recommendations in Money Magazine, Forbes, or Morningstar.
I'm really not convinced that what they do is trend following though.
Dylan Distasio Adds:
For those who don't believe trend following can be a successful strategy, how would you explain the long-term performance of the No Load Fund X newsletter?
Michael Marchese writes:
In a recent post, Mr. Leslie finished his essay with, "I never heard much about trend followers from June 2000 to October 2002." This link shows the month-to-month performance of 13 trend followers during that period of time. It seems they did OK.
Hanny Saad writes:
Not only is trend following invalid statistically but, looking at the bigger picture, it has to be invalid logically without even running your unusual tests.
If wealth distribution is to remain in the range of 20 to 80, trend following cannot exist. In other words, if the majority followed the trend (hence the concept of trends), and if trend following is in fact profitable, the majority will become rich and the 20-80 distribution will collapse. This defeats logic and history. That said, there is the well-covered (by the Chair) general market upward drift that should also come as no surprise to the macro thinkers. The increase in the general population, wealth, and the entrepreneurial spirit over the long term will inevitably contribute to the upward drift of the general market indices as is very well demonstrated by the triumphal trio.
While all world markets did well over the last 100 yrs, you notice upon closer examination that the markets that outperformed were the US, Canada, Australia, and New Zealand. The one common denominator that these countries have is that they are all immigration countries. They attract people.
Contrary to what one hears about the negative effects of immigration, and how immigrants cause recessions, the people who leave their homelands looking for a better life generally have quite developed entrepreneurial spirits. As a result, they contribute to the steeper upward curve of the markets of these countries. When immigrants are allowed into these countries, with their life savings, home purchases, land development, saving and borrowing, immigration becomes a rudder against recession, or at least helps with soft landings. Immigration countries have that extra weapon called LAND.
So in brief, no - trends do not exists and can not exist either statistically or logically, with the exception of the forever upward drift of population and general markets with some curves steeper than others, those of the countries with the extra weapon called land and immigration.
A rereading of The Wealth And Poverty Of Nations, by Landes, and the triumph of the optimist may be in order.
Steve Ellison adds:
So Mr. Parker's real objective was simply to insult the Chair, not to provide any evidence of the merits of trend following that would enlighten us (anecdotes and tautologies that all traders can only profit from favorable trends prove nothing). I too lack the intelligence to develop a trend following system that works. When I test conditions that I naively believe to be indicative of trends, such as crossovers of moving averages, X-day highs and lows, and the direction of the most recent Y percent move, I usually find negative returns going forward.
Bacon summarized his entire book in a single sentence: "Always copper the public play!" My more detailed summary was, "When the public embraces a particular betting strategy, payoffs fall, and incentives (for favored horsemen) to win are diminished."
Trend Following — Cause, from James Sogi:
Generate a Brownian motion time series with drift in R
MU<-.15*DELTAT;SIG<-.2*sqrt(DELTAT);TIME<-(1:1024)/252 stock<-exp(SIG*RW+MU*TIME) ts.plot(stock)
Run it a few times. Shows lots of trends. Pick one. You might get lucky.
Trend Following v. Buy and Hold, from Yishen Kuik
The real price of pork bellies and wheat should fall over time as innovation drives down costs of production. Theoretically, however, the nominal price might still show drift if the inflation is high enough to overcome the falling real costs of production.
I've looked at the number of oranges, bacon, and tea a blue collar worker's weekly wages could have purchased in New York in 2000 versus London in the 1700s. All quantities showed a significant increase (i.e., become relatively cheaper), lending support to the idea that real costs of production for most basic foodstuffs fall over time.
Then again, according to Keynes, one should be able to earn a risk premium from speculating in commodity futures by normal backwardation, since one is providing an insurance service to commercial hedgers. So one doesn't necessarily need rising spot prices to earn this premium, according to Keynes.
Not All Deer are Five-Pointers, from Larry Williams
What's frustrating to me about trading is having a view, as I sometimes do, that a market should be close to a short term sell, yet I have no entry. This betwixt and between is frustrating, wanting to sell but not seeing the precise entry point, and knowing I may miss the entry and then see the market decline.
So I wait. It's hard to learn not to pull the trigger at every deer you see. Not all are five-pointers… and some will be bagged by better hunters than I.
From Gregory van Kipnis:
Back in the 70s a long-term study was done by the economic consulting firm of Townsend Greenspan (yes, Alan's firm) on a variety of raw material price indexes. It included the Journal of Commerce index, a government index of the geometric mean of raw materials and a few others. The study concluded that despite population growth and rapid industrialization since the Revolutionary War era, that supply, with a lag, kept up with demand, or substitutions (kerosene for whale blubber) would emerge, which net-net led to raw material prices being a zero sum game. Periods of specific commodity price rises were followed by periods of offsetting declining prices. That is, raw materials were not a systematic source of inflation independent of monetary phenomena.
It was important to the study to construct the indexes correctly and broadly, because there were always some commodities that had longer-term rising trends and would bias an index that gave them too much weight. Other commodities went into long-term decline and would get dropped by the commodity exchanges or the popular press. Just as in indexes of fund performance there can be survivor bias, so too with government measures of economic activity and inflation.
However, this is not to say there are no trends at the individual commodity level of detail. Trends are set up by changes in the supply/demand balance. If the supply/demand balance changes for a stock or a commodity, its price will break out. If it is a highly efficient market, the breakout will be swift and leave little opportunity for mechanical methods of exploitation. If it is not an efficient market (for example, you have a lock on information, the new reality is not fully understood, the spread of awareness is slow, or there is heavy disagreement, someone big has to protect a position against an adverse move) the adjustment may be slower to unfold and look like a classic trend. This more often is the case in commodities.
Conversely, if you find a breakout, look for supporting reasons in the supply/demand data before jumping in. But, you need to be fast. In today's more highly efficient markets the problem is best summarized by the paradox: "look before you leap; but he who hesitates is lost!"
Larry Williams adds:
I would posit there is no long-term drift to commodities and thus we have a huge difference in these vehicles.
The commodity index basket guys have a mantra that commodities will go higher - drift - but I can find no evidence that this is anything but a dream, piquant words of promotion that ring true but are not.
I anxiously stand to be corrected.
Marlowe Cassetti writes:
"Along a similar vein, why would anybody pay Powershares to do this kind of work when the tools to do it yourself are so readily available?"
The simple answer is if someone wishes to prescribe to P&F methodology investing, then an ETF is a convenient investment vehicle.
With that said, this would be an interesting experiment. Will the DWA ETF be another Value Line Mutual Fund that routinely fails to beat the market while their newsletter routinely scores high marks? There are other such examples, such as IBD's William O'Neal's aborted mutual fund that was suppose to beat the market with the fabulous CANSLIM system. We have talked about the great track record of No-Load Fund-X newsletter, and their mutual fund, FUNDX, has done quite well in both up and down markets (an exception to the above mentioned cases).
For full disclosure I have recently added three of their mutual funds to my portfolio FUNDX, HOTFX, and RELAX. Hey, I'm retired and have better things to do than do-it-yourself mutual fund building. With 35 acres, I have a lot of dead wood to convert into firewood. Did you know that on old, dead juniper tree turns into cast iron that dulls a chain saw in minutes? But it will splinter like glass when whacked with a sledgehammer.
Kim Zussman writes:
…about the great track record of No-Load Fund-X newsletter and their mutual fund FUNDX has done quite well in both up and down markets… (MC)
Curious about FUNDX, checked its daily returns against ETF SPY (essentially large stock benchmark).
Regression Analysis of FUNDX versus SPY since inception, 6/02 (the regression equation is FUNDX = 0.00039 + 0.158 SPY):
Predictor Coef SE Coef T P
Constant 0.00039 0.000264 1.48 0.14
SPY 0.15780 0.026720 5.91 0.00
S = 0.00901468 R-Sq = 2.9% R-Sq (adj) = 2.8%
The constant (alpha) is not quite significant, but it is positive, so FUNDX did out-perform SPY. Slope is significant and the coefficient is about 0.16, which means FUNDX was less volatile than SPY.
This is also shown by F-test for variance:
Test for Equal Variances: SPY, FUNDX
F-Test (normal distribution) Test statistic = 1.17, p-value = 0.009 (FUNDX<SPY)
But t-test for difference between daily returns shows no difference:
Two-sample T for SPY vs FUNDX
N Mean St Dev SE Mean
SPY 1169 0.00041 0.0099 0.00029
FUNDX 1169 0.00045 0.0091 0.00027 T=0.12
So it looks like FUNDX has been giving slight/insignificant out-performance with significantly less volatility; which makes sense since it is a fund of mutual funds and ETFs.
Even better is Dr Bruno's idea of beating the index by deleting the worst (or few worst) stocks (new additions?).
How about an equal-weighted SP500 (which out-performs when small stocks do), without the worst 50 and double-weighting the best 50.
Call it FUN-EX, in honor of the fun you had with your X that was all mooted in the end.
Alex Castaldo writes:
The results provided by Dr. Zussman are fascinating:
The fund has a Beta of only 0.157, incredibly low for a stock fund (unless they hold a lot of cash). Yet the standard deviation of 0.91468% per day is broadly consistent with stock investing (S&P has a standard deviation of 1%). How can we reconcile this? What would Scholes-Williams, Dimson, and Andy Lo think when they see such a low beta? Must be some kind of bias.
I regressed the FUNDX returns on current and lagged S&P returns a la Dimson (1979) with the following results:
Multiple R 0.6816
R Square 0.4646
Adjusted R Square 0.4627
Standard Error 0.0066
df SS MS F Significance F
Regression 4 0.0444 0.0111 251.89 8.2E-156
Residual 1161 0.0511 4.4E-05
Total 1165 0.0955
Coefficients Standard Error t-Stat P-value
Intercept 8.17E-05 0.000194 0.4194 0.6749
SPX 0.18122 0.019696 9.2007 1.6E-19
SPX[-1] 0.60257 0.019719 30.5566 6E-151 SPX[-2] 0.08519 0.019692 4.3260 1.648E-05 SPX[-3] 0.04524 0.019656 2.3017 0.0215
Note the following:
(1) All four S&P coefficients are highly significant.
(2) The Dimson Beta is 0.914 (the sum of the 4 SPX coefficients). The mystery of the low beta has been solved.
(3) The evidence of price staleness, price smoothing, non-trading, whatever you want to call it is clear. Prof. Pennington touched on this the other day; an "efficiently priced" asset should not respond to past S&P price moves. Apparently though, FUNDX holds plenty of such assets (or else the prices of FUNDX itself, which I got from Yahoo, are stale).
S. Les writes:
Have to investigate the Fund X phenomenon. And look to see how it has done in last several years since it was post selected as good. Someone has to win a contest, but the beaten favorites are always my a priori choice except when so many others use that as a system the way they do in sports eye at the harness races, in which case waiting for two races or two days seems more apt a priori. VN
I went to the Fund X website to read up, and the information is quite sparse. It is a very attenuated website. I called the toll free number and chatted with the person on the other line. Information was OK, but, in my view, I had to ask the proper questions. One has several options here. One is to purchase the service and do the fund switching themselves based on the advice of their experts. The advisory service tracks funds that have the best relative strength performance and makes their recommendations from there, www.fundx.com.
Another is to purchase one of four funds available. They have varying levels of aggressiveness. Fund 3 appears to be the recommended one.
If one purchases the style 3 one will get a very broad based fund of funds. I went to yahoo to look up the holdings at www.finance.yahoo.com/q/hl?s=FUNDX.
Top ten holdings are 47.5% of the portfolio, apparently concentrated in emerging markets and international funds at this time.
In summary, if money were to be placed into the Fund X 3 portfolio, I believe it would be so broad based and diversified that returns would be very watered down. Along with risk you would certainly be getting a lot of funds. You won't set the world on fire with this concept, but you won't get blown up, either.
Larry Williams adds:
My 2002 book, Right Stock at the Right Time, explains such an approach in the Dow 30. The losers were the overvalued stocks in the Dow.It is a simple and elegant idea…forget looking for winners…just don't buy overvalued stocks and you beat the idex.
This notion was developed in 1997, when i began actually doing it, and written about in the book. This approach has continued to outperform the Dow, it is fully revealed.
Craig Cuyler writes:
Larry's comment on right stock right time is correct and can be used to shed a little bit of light on trend following. This argument is at the heart of fundamental indexation, which amongst other points argues that cap weighting systematically over-weights overvalued stocks and under-weights undervalued stocks in a portfolio.
Only 29% of the top 10 stocks outperformed the market average over a 10yr period (1964-2004) according to Research Affiliates (this is another subject). The concept of "right stock right time" might be expressed another way, as "right market right time." The point is that constant analysis needs to take place for insuring investment in the products that are most likely to give one a return.
The big error that the trend followers make, in my mind, is they apply a homogeneous methodology to a number of markets and these are usually the ones that are "hot" at the time that the funds are applied. The system is then left to its own devices and inevitably breaks down. Most funds will be invested at exactly the time when the commodity, currencies, etc., are at their most overvalued.
Some worthwhile questions are: How does one identify a trend? Why is it important that one identifies a trend? How is it that security trends allow me to make money? In what time frame must the trend take place and why? What exactly is a trend and how long must it last to be so labeled?
I think it is important to differentiate between speculation using leverage and investing in equities because, as Vic (and most specs on the list) point out, there is a drift factor in equities which, when using sound valuation principles, can make it easier to identify equities that have a high probability of trending. Trend followers don't wait for a security to be overvalued before taking profits. They wait for the trend to change before then trying to profit from the reversal.
Jeff Sasmor adds:
As a user of both the newsletter and the FUNDX mutual fund I'd like to comment that using the mutual fund removes the emotional component of me reading the newsletter and having to make the buys and sells. Perhaps not an issue for others, but I found myself not really able to follow the recommendations exactly - I tend to have an itchy trigger finger to sell things. This is not surprising since I do mostly short-term and day trades. That's my bias; I'm risk averse. So the mutual fund puts that all on autopilot. It more closely matches the performance of their model portfolio.
I don't know how to comment on the comparisons to Value Line Arithmetic Index (VAY). Does anyone follow that exactly as a portfolio?
My aim is to achieve reasonable returns and not perfection. I assume I don't know what's going to happen and that most likely any market opinion that I have is going to be wrong. Like Mentor of Arisia, I know that complete knowledge requires infinite time. That and beta blockers helps to remove the shame aspect of being wrong. But there's always an emotional component.
As someone who is not a financial professional, but who is asked what to buy by friends and acquaintances who know I trade daily (in my small and parasitical fashion), I have found that this whole subject of investing is opaque to most people. Sort of like how in the early days of computing almost no one knew anything about computers. Those who did were the gatekeepers, the high priests of the temple in a way. Most people nowadays still don't know what goes on inside the computer that they use every day. It's a black box - opaque. They rely on the Geek Squad and other professionals to help them out. It makes sense. Can't really expect most people to take the time to learn the subject or even want to. Should they care whether their SW runs on C++ or Python, or what the internal object-oriented class structure of Microsoft Excel is, or whether the website they are looking at is XHTML compliant? Heck no!
Similarly, most people don't know anything about markets; don't want to learn, don't want to take the time, don't have the interest. And maybe they shouldn't. But they are told they need to invest for retirement. As so-called retail investors they depend on financial consultants, fee-based planners, and such to tell them what to do. Often they get self-serving or become too loaded with fees (spec-listers who provide these services excepted).
So I think that the simple advice that I give, of buying broad-based index ETFs like SPY and IWM and something like FUNDX, while certainly less than perfect, and certainly less profitable than managing your own investments full-time, is really suitable for many people who don't really have the inclination, time, or ability to investigate the significant issues for themselves or sort out the multitudes of conflicting opinions put forth by the financial media.
You may not achieve the theoretical maximum returns (no one does), but you will benefit from the upward drift in prices and your blended costs will be reasonable. And it's better than the cash and CDs that a lot of people still have in their retirement accounts.
BTW: FOMA = Foma are harmless untruths, intended to comfort simple souls.
An example : "Prosperity is just around the corner."
I'm not out to defend FUNDX, I have nothing to do with them. I'm just happy with it.
Steve Ellison writes:
One might ask what the purpose of trends is in the market ecosystem. In the old days, trends occurred because information disseminated slowly from insiders to Wall Streeters to the general public, thus ensuring that the public lost more than it had a right to. Memes that capture the public imagination, such as Nasdaq in the 1990s, take years to work through the population, and introduce many opportunities for selling new investment products to the public.
Perhaps some amount of trending is needed from time to time in every market to keep the public interested and tossing chips into the market. I saw this statement at the FX Money Trends website on September 21, 2005: "[T]he head of institutional sales at one of the largest FX dealing rooms in the US … lamented that for the past 2 months trading volume had dried up for his firm dramatically because of the 'lack of trend' and that many 'system traders' had simply shut down to preserve capital."
I saw a similar dynamic recently at a craps table when shooters lost four or five consecutive points, triggering my stop loss so that I quit playing. About half the other players left the table at the same time. "The table's cold," said one.
To test whether a market might trend out of necessity to attract money, I used point and figure methodology with 1% boxes and one-box reversals on the S&P 500 futures. I found five instances in the past 18 months in which four consecutive reversals had occurred and tabulated the next four points after each of these instances (the last of which has only had three subsequent points so far). The results were highly non-predictive.
Starting Next 4 points
Date Continuations Reversals
01/03/06 3 1
05/23/06 1 3
06/29/06 2 2
08/15/06 2 2
01/12/07 1 2
Anthony Tadlock writes:
I had intended to write a post or two on my recent two week trip to Cairo, Aswan, and Alexandria. There is nothing salient to trading but Egypt seems to have more Tourist Police and other guards armed with machine guns than tourists. It is a service economy with very few tourists or middle/upper classes to service. Virtually no westerners walk on the streets of Cairo or Alexandria. I did my best to ignore my investments and had closed all my highly speculative short-term trades before leaving for the trip.
While preparing for taxes I was looking over some of my trades for last year. Absolute worst trade was going long CVS and WAG too soon after WalMart announced $2 generic pricing. I had friends in town and wasn't able to spend my usual time watching and studying the market. I just watched them fall for two days and without looking at a chart, studying historical prices and determining how far they might fall, decided the market was being stupid and went long. Couldn't wait to tell my visitors how "smart" a trader I was and my expected profit. It was fun, until announcement after announcement by WalMart kept causing the stocks to keep falling. The result was panic selling near the bottom, even though I had told myself before the trade that I could happily buy and hold both. Basically, I followed all of Vic's rules on "How to Lose."
Trends: If only following a trend meant being able to draw a straight line or buy a system and buy green and sell red. The trend I wrote about several months ago about more babies being born of affluent parents still seems to be intact. I have recently seen pregnant moms pushing strollers again. Planes to Europe have been at capacity my last two trips and on both trips several crying toddlers made sleep difficult, in both directions. Are people with young children using their home as an ATM to fund a European trip? Are they racking up credit card debt that they can't afford? Depleting their savings? (Oh wait - Americans don't save anything.) If they are, then something fundamental has changed about how humans behave.
From James Sogi:
My daughter the PhD candidate at Berkeley in bio-chem is involved in some mind-boggling work. It's all very confidential, but she tried to explain to me some of her undergrad research in words less than 29 letters long. Molecules have shapes and fit together like keys. The right shape needs to fit in for a lock. Double helices of the DNA strand are a popular example, but it works with different shapes. There is competition to fit the missing piece. They talk to each other somehow. One of her favorite stories as a child was Shel Silverstein's Missing Piece. Maybe that's where her chemical background arose. Silverstein's imagery is how I picture it at my low level.
Looking at this past few months chart patterns it is impossible not to see the similarity in how the strands might try fit together missing pieces in Wykoffian functionality. The math and methods must be complicated, but might supply some ideas for how the ranges and strands in the market might fit together, and provide some predictive methods along the lines of biochemical probability theory. I'll need some assistance from the bio-chem section of the Spec-list to articulate this better.
From Kim Zussman:
Doing same as Alex Castaldo, using SPY daily change (cl-cl) as independent and FUNDX as dependent gave different resluts:
Regression Analysis: FUNDX versus SPY ret, SPY-1, SPY-2
The regression equation is FUNDX = 0.000383 + 0.188 SPY ret - 0.0502 SPY-1 - 0.0313 SPY-2
Predictor Coef SE Coef T P
Constant 0.000383 0.00029 1.35 0.179
SPY ret 0.187620 0.03120 6.01 0.000* SPY-1 -0.050180 0.03136 -1.60 0.110 SPY-2 -0.031250 0.03121 -1.00 0.317 *(contemporaneous)
S = 0.00970927 R-Sq = 3.2% R-Sq (adj) = 3.0%
Perhaps FUNDX vs a tradeable index is the explanation.
On November 20, 1820 the whaling ship Essex was rammed twice and sunk by a sperm whale, an incident which inspired the conclusion of Melville's "Moby Dick." The crew of twenty men salvaged what food and equipment they could as the ship sank, and set out with three whale boats in an attempt to reach the coast of South America. (They were aware that the Marquesas Islands were much closer, but chose not to go there for fear of cannibals). They reached Henderson Island a month later, but found only a meager water source and not enough forage to sustain them all. Three men chose to remain there, the rest of the crew sailed on. When the food supply ran out, they subsisted on the corpses of their dead shipmates and one man (a relative of the Captain) was chosen by lot to be shot and eaten. Pitcairn Island, with food, water, and friendly inhabitants, was only a few days sail from Henderson Island, but the Essex crew were unaware of its existence. In February 1821 two of the whale boats were picked up near the coast of S. America. The crew members on Henderson Island were rescued in April 1821. In total eight men survived. Owen Chase, the first mate of the Essex, published his memoirs soon afterward and Herman Melville is known to have had a copy. Chase's memoir, as well as that of Thomas Nickerson, the cabin boy, may be found along with other primary sources relating to the Essex in the book "The loss of the ship Essex sunk by a whale".
Victor Niedehoffer adds:
I have commissioned a painting of the Essex — the painting has particular significance as described in Practical Speculations.
After his ordeal with the Essex, the captain immediately received a second commission and promptly grounded the new ship on ice. He refused to be rescued on the grounds that he'd be considered a hoodoo from then on. I have regularly used that second disaster as a sales prop, the way my daughter Galt uses her baby Magnolia in her movie meetings — "I can't afford to let it happen again as they'll consider me a hoodoo, look at my responsibilities." And in my case "if I fail a second time I won't be able to support my seven kids and 45 dependents, and I can't get a job as a squash pro because the hard ball game I played is obsolete." That was always good for some deep sagacious nods among my prospects, as it seemed to them a very good reflection of my concern for risk.
Pitt T. Maner III writes:
I remember seeing a small exhibit on the Essex at the Nantucket Whaling Museum several years ago. It is really quite amazing to see how important and profitable the whaling industry was in the early 1800s.
If you type the term "Essex" into the Nantucket Historical Society's multi-media search engine you should see some interesting items (photos, sketches, ship logs, etc.) on the subject.
Here is the data for the cases when prior 20 week returns were greater than +10%:
Date stdev hi chg hi nxt
11/22/82 0.037 0.239 0.177
08/04/80 0.019 0.208 0.105
02/09/87 0.015 0.204 0.093
03/23/98 0.024 0.181 -0.030
04/11/83 0.016 0.177 0.039
04/29/91 0.021 0.165 0.019
12/28/98 0.032 0.157 0.082
01/27/97 0.016 0.155 0.143
08/11/03 0.016 0.147 0.119
05/30/89 0.015 0.147 0.066
06/16/97 0.023 0.143 0.032
07/17/95 0.011 0.140 0.115
07/10/00 0.039 0.132 -0.129
05/05/86 0.023 0.128 -0.024
12/29/03 0.015 0.119 -0.013
12/04/95 0.009 0.115 0.058
04/04/88 0.033 0.113 -0.036
12/22/80 0.026 0.105 -0.032
12/16/85 0.014 0.102 0.128
Note that currently we are at the end of a 20 week gain of 10.9%, which had a weekly standard deviation of 0.009 (tied for low in 1995).
Alex Castaldo comments:
Overlapping returns are correlated, and therefore the normal statistical procedures that we use with independent variables are not applicable, and will give misleading results. Either we switch to non-overlapping periods or we have to make adjustments for overlap (which is tricky).
The example that Prof. Andrew Lo likes to give is the computation of 20-year returns from monthly CRSP data.
Here is an example of a flawed study: First we calculate the stock market return from January-1926 to December-1945 (20 years). It is a very nice positive number, about 8% I believe. Then we calculate February-1926 to January-1946, and so forth until January-1987 to December-2006. A total of 62 twenty year (overlapping) periods are examined. If only two of these periods have negative returns (hypothetically), then we conclude: the probability of losing money when you invest for 20 years is 2/62.
This result is completely bogus due to overlap. We have not really tested 62 independent periods, so the use of 62 in the denominator is not valid. The periods used are highly overlapping; for example January-1926 to December-1945 and February-1926 to January-1946 are almost the same. They differ only by the dropping of January-1926 and the addition of January-1946. The returns differ by a few percentage points at most. In some sense, the second period is not telling us much that we don't already know from the first; the reason we like independent variables is that each brings us the same amount of new information.
A more correct approach is to note that the period from January-1926 to the present contains only about 4.05 nonoverlapping twenty year periods. We could then look at how many of these are positive or negative. (Of course the strength of any conclusions drawn from four observations will not be very high).
Scott Brooks adds:
This conversation reminds me of tracking deer when bowhunting. When I arrow a deer, it will usually expire in less than thirty seconds, almost always within five to ten seconds. However, in that ten seconds, it can run a long way and disappear into the brush.
Sometimes the blood trail is obvious, other times it's not.
When I'm on the trail of a non-obvious blood trail, I have to employ different methods of looking for the deer. To make a long story short, I slowly walk down what I think is the trail, taking one step and stopping. I then look around 360 degrees … searching very slowly, breaking my surroundings up into mental grids, and scanning those grids carefully.
Then, before I take another step, I squat down so that my line of sight is about three feet off the ground, and then I repeat the 360 degree scan, mental grids and all.
After that, I get down on my hands and knees and carefully scan the area as close the ground as I can, especially looking ahead in the area where I'm about to step (when I move forward on the trail) to make sure there isn't the slightest bit of sign that could lead me to the deer (that I would have otherwise destroyed with my boot if I took my next step forward).
I then step forward and repeat the process.
The point I'm trying to make is this: Isn't what Kim Zussman did just one way of looking at the "landscape" of the market? His calculations of non-overlapping time frames were the equivalent of looking at one grid of the "market landscape," and looking from an upright point of view.
The professor then suggested non-overlapping periods. In my mind's eye, I see that as squatting down and looking at the same market landscape, but from a different view … and from that view, we see the same landscape but in a whole different way.
Both methodologies would seem to have value. Just as in tracking a deer, I may see "what I'm missing" or the "key piece of data." For instance:
a slight hoof indention in the ground - extra volume,
a drop of blood - over selling on fear of some nebulous announcement or rumor (see George Zachar's post, Deception of Taxonomy Notation)
a bent branch - a large money manager is trying to build/unload a position in stealth mode … without alerting the market
a pool of blood where the deer laid down (meaning that you didn't get as good a shot as you thought and you pushed the deer out of it's bed by tracking it too soon … mark that spot, backtrack carefully out of the woods and don't come back for at least three hours) - A bad announcement caused a stock to be driven down. The bloodied stock found support, but then continued to move down (maybe it's time to wait for this buy … no bottom feeding today … come back tomorrow and take a look).
a bunch of crows and buzzards in the area - time to go in and see if you can at least salvage the antlers … and then let the scavengers pick the bones clean. Even though I didn't get the meat, the trophy antlers will look good on the wall - Too late for a profit, unless your a scavenger … however, maybe you can find some talent within that company (every company has some talent). Watch where they go and see if there is a VC or an IPO opportunity!
That's why looking at the market from different angles in a meticulous, orderly and objective manner is so important. What works for deer hunting also works for investing/trading/advising!
December 11, 2006 | Leave a Comment
About 15-20 years ago a number of academic articles were published (by Fama-French and Shiller among others) claiming that aggregate stock market returns are partly predictable using variables such as P/B, dividend rates, the term structure of interest rates and so on. This is the ‘market predictability’ literature. More recently a number of articles have appeared that claim this ‘predictability’ is weak and not useful from a practical point of view. This article by Giot & PetitJean, entitled International Stock Return Predictability: Statistical Evidence and Economic Significance, belongs to the strand of the literature that grants that predictability may have existed in the past but doubts that it will continue and/or finds that it is of no practical value. I’ll call it the ‘predictability is pretty useless’ school. There is also another line of criticism to which you can subscribe, that is more radical and I will call the ’statistical malpractice’ school, that believes the apparent predictability does not exist even in the past data and is an artifact of flawed statistical procedures such as overlapping multi-year stock returns and/or too few degrees of freedom for proper inference when the procedures for overlap-adjustment are known to give bad result in small samples, etc., etc..
The two little known Belgian authors examine five variables: dividend yield, earnings/price ratio, short term interest rate, long term interest rate, and interest rate spread. They look at the stock markets of 10 major countries, and they use an ‘Out of Sample’ methodology in which the relationship is estimated over a period and then applied to the following period, i.e. a predictive study. They use a statistical test I am not familiar with, due to McCracken (2004), which is apparently a variant of the Diebold-Mariano test that I discussed with Professor Diebold the other day.
The conclusion in their words:
The short-term interest yield and, to a lesser extent, the long government bond yield are the best out-of-sample predictors of stock returns. However, the out-of-sample predictive power of these variables does not appear to be economically meaningful across countries and investment horizons.
This is all I could glean from a quick and partial reading of the article.
December 6, 2006 | Leave a Comment
10:00 *ACTIVITY AT 58.9%; NOV. NON-MANUFACTURING ISM REPORT 10:00 *U.S. NOVEMBER ISM NON-FACTORY INDEX ESTIMATE WAS 55.5 10:00 *U.S. NOVEMBER ISM NON-FACTORY INDEX AT 58.9 AFTER 57.1 10:00 *U.S. NOV. ISM NON-FACTORY INDEX RISES TO 58.9 FROM 57.1 10:00 *U.S. OCT. DURABLES ORDERS FALL 8.2%; NON-DURABLES FALL 0.3% 10:00 *BUSINESS ACTIVITY AT 58.9%; NOV. NON-MANUFACTURING ISM REPORT 10:00 *U.S. OCTOBER FACTORY ORDERS DECLINE LARGEST SINCE JULY 2000 10:00 *U.S. OCT. FACTORY ORDERS COMPARE WITH FORECAST 4.2% DECREASE 10:00 *U.S. OCT. FACTORY INVENTORIES RISE 0.4%; SHIPMENTS RISE 0.1% 10:00 *U.S. SEPT. FACTORY ORDERS RISE 1.7%; REVISED FROM 2.1% RISE 10:00 *U.S. 10-YEAR NOTE YIELDS 4.4520 PERCENT 10:00 *TREASURIES DECREASE AFTER RELEASE OF NON-MANUFACTURING REPORT 10:01 *DOLLAR TRADES AT $1.3314 PER EURO, 114.87 YEN 10:01 *DOLLAR GAINS VERSUS EURO AS SERVICE INDUSTRY INDEX RISES
Neither does it take into account the quantity theory of money, where if one sector is reduced, that is because another has risen: p1 x Delta q1 + p2 x Delta q2 = 0. The declining share of our GNP that manufacturing holds is part of a secular trend of less skilled manual labor requirements being replaced by more highly skilled, humanistic roles. Because outsourcing is a common part of this trend, as well as just-in-time inventory management, the normal statistical problems of assessing monthly figures can become confounding. The mistake that “Doc” Greenspan and his 90 year old followers make is in believing that the value of the American economy can still be measured in the blast furnace statistics they used to study in their youth, and still look at in the bathtub…
This pessimism on the US economy and the need for manufacturing is based on many other fallacies too, and what an opportunity it provides, when durable orders or factory orders go down, for Doomsdayists who have not studied choice and substitution to try and bear down the markets. This is all for much the same reasons that Doomsdayists hate immigration — the more obvious/material things are so much easier for them to try to understand.
November 2, 2006 | Leave a Comment
I am a 27-yr old professional equity derivatives trader with several questions and comments for Dr. Niederhoffer and Ms. Kenner. I just read Practical Speculation. I had previously read Joel Greenblatt’s The Little Book That Beats the Market. Needless to say, the two works propound extremely different views on the relative merits of growth versus value stocks and on the ideas of Benjamin Graham. I’m sure this is a debate that has been beaten to death before I was born, and I’m sure you are entirely sick of the whole thing, but please bear with me. I am interested in reconciling the ideas of the two authors. I would like your opinion on Mr. Greenblatt’s work and his “system” for investing.
I wondered specifically what Dr. Niederhoffer and Ms. Kenner’s response would be to the data cited in Greenblatt’s book. Is this evidence entirely worthless due to statistical and sampling errors? Is it only since 1965 (the Value Line data in the book was for 1965-2002) that growth has overtaken value? What do Dr. Niederhoffer and Ms. Kenner think is the correct way to value a stock? Since it’s difficult to precisely ascertain current or even past “real” earnings for a single stock, let alone the mkt, how can one hope to accurately predict the level of future earnings (as you must do for growth stocks). What valuation model should be used? What valuation model can be used that works for both “growth” and “value” stocks (it seems fairly silly to categorize all stocks into one of these two fairly arbitrary columns, but that’s what seems to happen).
Anyone can go to Mr. Greenblatt’s website and get a list of “value” stocks. He argues that his system (buy 20 or 30 of these value stocks and then sell them after a year and get new ones from an updated list on his website) will beat market returns over time. I am suspicious, but where is the logical flaw or statistical error in Mr. Greenblatt’s book. Will his method really work, and if not, why ? Mr. Greenblatt posted excellent returns over many years (I believe 10 years of returns are necessary to eliminate luck as the explanation of a trader’s returns) at his hedge fund. I’m sure he wasn’t simply applying the method from his book, but he is clearly a “value” investor.
To me, the strength of “value” investing, especially as described by Mr. Greenblatt, is its seeming logic. Even though you can’t buy a stock portfolio for 50% of its liquidation value as Graham suggested, the market and especially individual stocks can fluctuate fairly wildly even over short time frames, so clearly it is possible at times to buy good stocks or the whole market “cheaply.” As I write this, AMD has a 52 week range of 16.90 - 42.70… with roughly 485 million shares outstanding, that means in terms of market value AMD was (according to the market) “worth” almost $21 billion in late January, and only $8 billion or so in late July. Maybe some of this move was due to new (bad) information, but in all probability (since the stock subsequently recovered- then dropped again) it was due to the overtrading and ridiculous focus on short-term results that Dr. Niederhoffer and Ms. Kenner lambaste in their book. Take a look at the way retail stocks move around on monthly same-store sales numbers or oil and gas move on weekly reserves numbers for further examples of ridiculous overtrading and short-term focus.
Nevertheless, to ignore volatility (which is how I make my living) and keep your eyes firmly on the long-term potential of a stock leads to two pitfalls. First, you miss out on opportunities when the stock swings around in the short run (for example, you could have sold some medium-dated calls in AMD in Jan, then used the proceeds to buy additional stock in July). Second, you are ignoring risk; in the short-run, you could see such severe swings that you go broke instead of getting your 1.5million % a century return. Volatility might be much higher than it “should” be, it might be due to overtrading, and it certainly is the result of a focus on meaningless short-term information, but it is a fact of life. In my opinion, it’s better to take advantage of this fact than to ignore it.
One solution is to actually buy volatility itself. There are several studies showing that a portfolio containing a volatility component of 10% or so will outperform a similar portfolio with no volatility component (an example of a volatility component would be VIX futures or a similar instrument, essentially just a long option position). The general basis for this is that implied volatility in the options market usually increases when the market drops. You are diversifying your portfolio with a negatively correlated asset. Since the VIX hovers at a very cheap 10 or so these days, it seems like a great hedge.
Any reply or even a suggestion of further reading on the value/growth debate would be greatly appreciated. I have also emailed Mr. Greenblatt’s website with similar questions (you can find that email below).
Doc Castaldo illuminates:
He has so many inter-related questions it is hard to know where to begin. The Tim Loughran article “Do Investors Capture the Value Premium?” which some Spec (Dr. Zussman perhaps?) sent to Steve Wisdom recently seems relevant, and I sent it to him (the answer Loughran gives is no). I believe Prof. Pennington and Mr. Dude reviewed the Greenblatt book and found it well done; though some of us have doubts as to how well the results will hold up going forward.
Steve Leslie adds:
I have studied this deeply and although impossible to adequately reconcile this argument, my reply is that there is enough room in the world for value investors and growth investors. One is more of a science and the other is more of an art. And that which works for one will not work for another. And they tend to be complementary, whereas when value investing is in favor growth is out of favor and vice versa.
Case in point late ’90s. Nobody and I mean nobody wanted to be a value investor. At the time I was with a regional brokerage firm and we had one of the best value fund managers around, and he was never asked to speak anywhere. Everybody wanted growth and hard chargers. He told me directly that the worm would turn and that which one is hated will once again be loved. In 2001 and onward his style came back into vogue. His numbers became very good when the implosion of growth occurred and value turned to the good.
I feel that value investing is more of a quantitative approach to investing. It requires arcane methods and such as roe, price to sales, price to book. You can have value investors, deep value, vulture investors etc. And it is very important that with value investing that one be a patient investor with longer term time frames. I have referenced the Hennessy Funds as excellent quant funds. They have a very rigid stock selection process and rebalance their portfolio annually which they bought the rights to from James O’Shaughnessey who brought this methodology out in his book How to Retire Rich. Their long term track record is very good and they did very will since 2000 but this year for the most part the results have been flat. Martin Whitman is a deep value investor and his Third Avenue Fund has done very well over time. As has the Davis Funds. The First Eagle funds does excellent work with their global funds.
Growth investing is more of an art. It requires timing. Growth investing such that William O’Neil supports can be very successful yet very volatile. Small cap growth investors many times requires a longer term time horizon as the swings in price can be quite hard to take. I have always liked Ralph Wanger (A Zebra in Lion Country) and Tom Marsico in this area.
It is very important that the style of investing one uses incorporates their financial education, character and personality among others. They most definitely require knowledge and different wiring.
As to the trading of that the chair employs, I will let him speak for himself but I am confident that he will say the methods that one uses for value investing and growth investing would never work for his methods of day trading or swing trading.
To use a poker analogy (alas it always comes down to poker) I liken value investors to people like Dan Harrington, Howard Lederer and Phil Hellmuth. They are percentage players very methodical. They wait for premium hands and play those. These are the tight players.
On the other side of the ledger are the growth investors such as Phil Ivey and Gus Hansen, aggressive sometimes to a fault and they play many hands and many times on feel.
Both styles and much more in between are effective and can bring one to the promised land, they just take different routes.
Dr. Phil McDonnell reminisces:
Many years ago I was engaged in fundamental research on stocks for a finance class at Berkeley. Upon showing my results to one of the rising young finance Professors in the Business School I had a rude awakening. He promptly but kindly pointed out to me the myriad of biases which enter into such a study.
It prompts one to paraphrase the poem poem by Elizabeth Barrett Browning:
“How Do I Confound Thee?” Let me count the ways in which fundamental stock data can confound:
- Stale Data. Data are not always reported on time. Some is late, but most studies do not account for this adequately.
- Retrospective Bias. Most fundamental databases use the current ‘best’ information believing that is what you want now. But for historical studies that means the data may have been retrospectively edited as much as several years after the fact. This is a form of knowledge of the future. If you analyzed Enron before its collapse the fundamentals looked good and the stock was too cheap. If you analyzed today with a retrospective database you know that the company had catastrophic losses. But the truth about the losses was not known at the time and the adjusted numbers only came out years later.
- Sample or Survivor Bias. Use of a current database often results in a sample bias due to the fact that only companies which continue to exist in the present will be included in the sample. In order to avoid this issue one must go to an historical source in existence at the time in order to manually select the sample for each month by hand. Many companies are delisted or otherwise stop trading. For these the data must be manually reconstructed from historically extant sources. Otherwise this bias translates into a strong bias in favor of value investing strategies. A strategy which buys out of favor, or high risk or near bankrupt companies will always do well with this bias. The bias guarantees that they will still be around years later because they are still in the database.
- Data Mining. There are many variables to choose from with fundamental data. There are countless more transformed ratios or composite variables which can be constructed. This leads to the ability to try many things. Thus the researcher may have inadvertently tried many hypotheses before coming to the one presented as the best. Because fundamental data are low frequency (quarterly at best) there are only 40 observations in a 10 year period. True statistical significance can quickly vanish in a study of many hypotheses.
- Data Mining by Proxy. Everyone reads the paper and keeps up with current trends in investments. Thus our thoughts are always influenced by findings of other researchers. Thus even if a researcher did a study which avoided the usual data mining bias it may be simply because he took someone else’s results as a starting point. In effect he used their results as a form of data mining by proxy to rule out blind alleys.
- Fortuitous Events. In the 1990’s F*** & Fr**** published papers about factor models to augment the Sharpe beta model. Their significant new factor was Price to Book ratio. In James O’Shaugnessy’s book What Works on Wall Street one can see a sudden upward surge in value strategies in the early 1990’s coincident with the publication of the F & F model. However the event was a single one time upward valuation of value models in the 1990’s. Before and after that, the effect vanishes.
- Post Publication Blues. After publication of any academic paper or book the money making method usually stops working. Sometimes it is due to data mining or some flaw in the study and the putative phenomenon was never really there. The market is efficient. If everyone knows something it will usually stop working even if the original study was valid.
Prof. Greenblatt’s book is a fun read and remarkably brief. In fact if someone wanted to just get the gist of it, each chapter ends with a very clear summary of the key points in that chapter. It would be possible to get all the main points in about 10 minutes simply by reading the summaries. Let me say that if one were to use a fundamentally oriented strategy then the profit margin and Book to Price are probably the first two on the list. To be fair to the author, reciting one’s efforts to avoid sample biases in a book intended for a popular audience probably would not help sales. Such discussion is usually reserved for academic papers but nevertheless its absence does not give reassurance that all possible bias was eliminated.
The best way to test this strategy is not to go to the library and do all the work yourself. Rather one could simply go to the web site and copy down all the stocks recommended. Then in 6 months and 12 months revisit them to see how they have done and to see if the performance was statistically significant.
Ever since those Berkeley days more than 30 years ago I have always been distrustful of fundamental studies. That lesson from then Prof. Niederhoffer has helped shape my market studies in many ways. The bias of fundamental data is yet another way the market can confound the research oriented trader.
Jaim Klein replies:
Let’s simplify. The market universe is large and diverse enough to accommodate different successful strategies. One catches fish with net, another with bait. Regarding the value of anything, no such. The value of a thing is the price it can fetch in a certain moment and place. At 27 I was also confused. Experience is the best (probably the only) teacher. He has to do his own work and reach his own conclusions. It is time consuming, but I know no other way. He can also observe what successful people is doing and try to copy them till he can do it too.
Prof. Charles Pennington rebuts:
Dr. Phil lists 7 things that can go wrong in research on stock performance and its relation to fundamentals. Oddly enough, the Greenblatt book itself also lists exactly 7 such reasons on page 146! They’re not exactly the same ones, but there is plenty of overlap. I’ll list Greenblatt’s 7 with my own paraphrasing:
- Data weren’t available at the time (look-ahead bias)
- Data “cleaned up”, bankruptcies, etc., removed (survivorship bias)
- Study included stocks too small to buy
- Study neglected transaction costs, which would have been significant
- Stocks outperformed because they were riskier than the market
- Data mining
- Data mining by proxy
Greenblatt: “Luckily the magic formula study doesn’t appear to have had any of these problems. A newly released database from Standard and Poor’s Compustat, called ‘Point in Time’, was used. This database contains the exact information that was available to Compustat customers on each date tested during the study period. The database goes back 17 years, the time period selected for the magic formula study. By using only this special database, it was possible to ensure that no look-ahead or survivorship bias took place.”
To all the biases that we consider, I’ll add the “not invented here” bias. It’s too easy to assume that no one else out there can do rigorous research. I think Greenblatt’s is fine.
(He didn’t however do any original results on jokes. His jokes are all out of the Buffett/value-school jokebook. Fondly recall “There are two rules of investing. 1. Don’t lose money. 2. Don’t forget rule number 1.” That one’s there along with all your other favorites.)
Dr. Phil McDonnell replies:
The way we all remember the late 1990s is the dot com bubble. It was the front page mega meme. The stealth meme was the value stock idea.
Rather than think of it as a single paper consider the paper as the seminal idea of a meme. From the original paper there were follow on papers by various academics as well as FF. From there the meme spread to the index publishers who always want a new ‘product’ to generate marketing excitement. Naturally the index guys sold it to the funds and money mangers who promptly started new funds and rejiggered old funds along the lines of the new meme. The money management industry always wants new products but also each firm needs to act defensively as well. For example Vanguard cannot eschew the new fad and leave the playing field open for Fidelity. As with all memes it grows slowly and diffuses through society.
In all fairness one can never ‘prove’ cause but only correlation using statistics. But it is clear to me that something happened which caused the value part (really just Magic Formula) of the market to triple during those years albeit with only negligible public awareness early on.
For the sake of argument assume that the cause was not the FF paper and its impact on the value meme. Then what was Dr. Zussman’s ‘unseen factor(s)’ which caused a triple in value? Which factor or factors are more plausible?
My prediction for the end of the next meme is the collapse of the Adventurer’s bubble. To play it one needs to sell. But I would guess that it is only a one to three year collapse.
I recently reviewed a paper which drew my attention to the long term rise of the US Treasury long bond future (continuously adjusted with all contract shifts), showing a price rise from 78-20 to 114-06 from 1977 to present. The question we are batting around the office is whether there is any economic reason for there to be a long term upward drift in prices. Such a drift would be related to the normally rising structure of the yield curve, with long term yields higher than short term. The upward shape is supposed to occur because of increased price variability of the long term bond vs. short term and liquidity preference; the desire to have your money sooner rather than later because your risk on holding the investment until it expires is greater. Liquidity would also seem to relate to the ability to trade the issue at tight spreads. Any educated comments on the subject would be welcomed.
Prof. Charles Pennington replies:
I assert that Treasury futures will have a long term upward drift if and only if long term bonds outperform short term in total return, over the long term.
Suppose that a bond maturing in 30 years is trading at price 100, and let’s assume that long term yield are 10% and short term yields are 2%.
Consider a futures contract on 30-year bonds that settles in one year, and suppose that this contract is trading at price P.
We could construct a risk-free portfolio consisting of a long position in treasury bonds and a short position in the treasury bond futures contract. This should earn the short term risk-free rate (and let me assume that 1 year is close enough to being “short term”).
Let’s also suppose that after one year, the price of the 30-year Treasury, which will also be the settlement price of our futures contract, has risen by $1 to a value of 101.
The final value of our portfolio, which cost 100 initially, is:
101 + 10 + (Pi-101)
(The “10″ is the dividend, and “Pi-101″ is the gain or loss on the short sale.)
This final value should be equal to 102, since we should earn the risk-free return. From that, we can solve Pi and get 92.
So in this example, the total return of a Treasury bond was 11% (10% dividend and 1% capital gain). The total return that we would have had by going long the futures contract would have been (101-92)=9, or 9% of the notional value. That’s equal to the total return that we would have had from holding the bond minus 2%, the short term rate.
In other words, the return from the futures contract is “as if” we had borrowed at the short term rate and bought the long term bond.
If that strategy makes money over the long term, then the continuous futures contract will show a long term upward drift. The Siegel book indicates that that strategy was about breakeven from 1802 through about 1980 and then did quite well since then.
Paul DeRosa Responds:
It is true that the bond future trades at a discount to its delivery value equal to the positive carry on the cash bond. If that carry is negative, the future will trade at a premium. There are hundreds if not thousands of traders who spend their days bent over desks enforcing that condition. It doesn’t necessarily imply the price of the futures contract will drift upward over time. They drift upward only within each quarter. So in the example you give, the contract will start each quarter at a discount of 2% to its maturity value. If the level of market interest rates were to stay at 10%, the next contract also would start the quarter at a 2% discount, but it would have the same maturity value as its predecessor. I would add one caveat, which sounds like a technicality but who overlooking as been the cause of tens if not hundreds of millions of dollars in trading losses during the past 30 years. The 2% discount I alluded to can be reliably captured only by owning the contract and being short the so called “deliverable” bond. At any point in time, several different bonds can satisfy the delivery conditions against the contract, only one of which is cheapest to deliver. Being long the contract and short the wrong bond can lead to any one of several outcomes.
George Zachar replies:
Yes. The accretion of the forward months should be identical to the positive carry one would receive by owning the underlying bond outright and financing it at the overnight/repo rate. You can make the money by carrying the “cash” or buying the forward, but the dollar amounts should be the same. This is carry and not drift/true price appreciation.
There is very slight positive carry on bond futures now:
USZ6 Dec06 110-18
USH7 Mar07 110-17
USM7 Jun07 110-16
The two-year future shows the impact of negative financing/curve inversion, where holding the instrument costs money (as your asset yields less than its cost to carry).
TUZ6 Dec06 101-28 3/4
TUH7 Mar07 102-02 s
The carry/deliverables/basis on these contracts is perhaps the most “crowded” trade on the planet.
“In the day”, one could make money in the forward mortgage market, when lenders would sell their production forward at a discount to carry. Those glorious days are long gone.
Carry hogs, er, traders have been known to “ride the Japanese curve” with enough leverage to make your eyes tear.
JBZ6 Dec06 133.56
JBH7 Mar07 132.83
They’d buy the forward Japanese bond, and pocket the carry, enduring the interest rate, yield curve and currency risks along the way.
The takeaway here is that one must be aware of all this when looking at fixed income debt futures prices. To evaluate long term interest rates cleanly, it is best to look at yields of relevant “constant maturity” indices.
Earlier posters observed that very long-term secular trends of dampened reported inflation and declining risk premia since the financial shocks of the Volcker era account for the observed trend toward lower yields and higher bond prices. I wholeheartedly second that analysis.
Stocks, famously, have unlimited long-term upside. Fixed income has the “zero bound” on rates, and central banks who have shown themselves willing to ensure that Deflation is rarely seen. Therefore, with an assurance that there’ll always be a little inflation, debt instruments are effectively capped out when their yields reach the low single digits.
Michael Cohn responds:
I would recommend everyone find a way to get “Rolling Down the Yield Curve” by Martin Leibowitz, circa early 1980s. Few articles as clear about how bonds work. I stopped trading basis myself in 1989 when four JGB basis-traders for what was then Mitsubishi Bank took me out to lunch one day in Japan.
Very little can go wrong when you are long the cheapest to deliver and short the future. Depending upon the set-up it was also a way to play changes in yield curve shape but now there are so many instrument, such as swaps, to play it an explicitly.
Jon Corzine made his name at Goldman by trading the delivery options and the dead period after the US bond contract stopped trading each delivery cycle. The legendary trader Mark Winkleman at Goldman made his name buy buying the bond basis and funding it cheaper. He had it to himself and our friends at Salomon. The old days were relatively easy.
You need to have a firm grasp of reverse repo rates for the deliverable bonds, and yield curve volatilities, to play from short side where you are short the bond and long the future. I recall the programs at my firm for modeling the change in deliverables to be extensive, as I use to play the bund basis, but with no apparent skill as I did not control the collateral, as could a German insurance company.
Dr. Alex Castaldo responds:
The question that started this thread was: is there an upward drift in fixed income markets like there is in equities?
The article by Vesilind claimed that this is so, and this drift arises from the fact that the yield curve is (on average) upward sloping due to the “liquidity preference hypothesis” and/or the “preferred habitat hypothesis”. In other words the “expectation hypothesis” of interest rates does not hold and there is a non-zero “term premium” embedded in interest rates.
Certainly there have been plenty of academic articles in recent years saying the expectation hypothesis does not hold. But I am more interested in the practical money-making potential here.
A simple strategy to capture the drift, that works well according to Vesilind, is to be long the “fourth nearmost eurodollar future”. I decided to test an even simpler strategy: each September buy the eurodollar future with one year to expiration and hold it until expiration.
You can think of it as a test of the good old “Keynesian normal backwardation hypothesis”: is the price of the future one year before expiration biased low compared to the expectation of what the settlement price will be.
Here is the data:
Contract Date Price ExpDate Price Chg
EDU6 06 9/19/2005 95.690 9/18/2006 94.610 -1.080
EDU5 05 9/13/2004 97.045 9/19/2005 96.080 -0.965
EDU4 04 9/15/2003 98.165 9/13/2004 98.120 -0.045
EDU3 03 9/16/2002 97.535 9/15/2003 98.860 1.325
EDU2 02 9/17/2001 96.425 9/16/2002 98.180 1.755
EDU1 01 9/18/2000 93.470 9/17/2001 96.890 3.420
EDU0 00 9/13/1999 93.755 9/18/2000 93.340 -0.415
EDU9 99 9/14/1998 95.040 9/13/1999 94.490 -0.55
EDU8 98 9/15/1997 93.825 9/14/1998 94.500 0.675
EDU7 97 9/16/1996 93.700 9/15/1997 94.281 0.5812
EDU6 96 9/18/1995 94.260 9/16/1996 94.440 0.18
EDU5 95 9/19/1994 93.180 9/18/1995 94.190 1.01
EDU4 94 9/13/1993 96.070 9/19/1994 94.940 -1.13
EDU3 93 9/14/1992 96.140 9/13/1993 96.810 0.67
EDU2 92 9/16/1991 93.550 9/14/1992 96.870 3.32
EDU1 91 9/17/1990 91.670 9/16/1991 94.500 2.83
T Stat 1.940
At first the results look impressive: there is a 72.4bp per year gain, with a t-statistic near 2. However, much of the result is driven by the first two years (1991 and 1992) when interest rates were dropping rapidly. Without these two years the gain is only 39 basis points with a t-statistic of 1.15.
As mentioned by others, it is difficult to distinguish the term premium from the general interest rate decline after 1990.
George Zachar adds:
The Vesilind paper is an excellent and reasonably accessible overview of mechanistic currency trading systems that execute carry trades based on yield differentials, and volatility (implied riskiness).
The authors find, retrospectively, that during a period of irregularly declining rates and risk premia (1993-2006), rotating capital between currency pairs offering high yield spreads at times of high perceived risk earned worthwhile alpha.
The carry/risk aversion trade is a standard formula for speculating in currencies, and the authors “kept it simple”, making evaluation of their strategy relatively easy.
The study’s charts neatly show the performance of different strategies as the cycles change.
I must, however, disagree with the chair that currency pairs trading per se can be said to showcase “drift”. The time period involved was particularly favorable to yield chasers, and the regular shifting of positions from one set of underlyings to another strikes me as antithetical to the notion of passive drift.
That said, I believe there is a different speculative lesson to be drawn from this study, and that is the seeking of relative value within the confines of a large, complex set of related instruments.
Relative value among currency pairs based on yield/return vs. vol/risk strikes me as analagous to relative value within the stock market between sectors and individual stocks. There’s no shortage of relaive value measures with which to “count” fundamental and performance dispersion in stocks. Ditto risk/vol.
The paper at hand provides a nice introductory framework for setting up relative value/risk matrices.
October 10, 2006 | Leave a Comment
An interesting paper is downloadable here. The gist of the paper is that combining 30 days of historic data with implied volatility gives a better forecast of option prices than simple implied volatility. They demonstrate this by calculating the usual stats (root mean squared error of the forecast, etc.) and by running a 'trading contest'. They describe 5 rules (they call them agents) that, at the simplest, used 30 days of returns to generate a forecast of the stocks distribution, and at its most complex, used Bayes rule to combine 30 days of returns data with implied volatility data to make a forecast of the stocks distribution. They then sell (overnight) ''over priced'' options and buy ''under priced'' options. Naturally, the simple rule makes very little money and the sophisticated Bayes rule makes a ton of money
Some things I would have liked to have known that weren't mentioned:
1. They aren't buying and selling straddles, but individual options (i.e. the simulation isn't delta hedged)… and they do not differentiate between how much of their profit is do to a favorable move in the underlying, and how much of the profit/loss is due to correctly predicting the implied volatility.
2. They do not breakdown how many profitable trades were shorting options, and how many were going long options…it makes a difference to me if I make money while I'm long gamma rather than short gamma
One thing that strikes me as making this hard to try to replicate in other markets is the time structure of volatility problem It may be okay to take 30 days of OEX/S&P futures (or spot) data and use it when deciding about options that expire quarterly, but I'm leery of trying that with options that expire monthly. Lets say its Feb 1, and you are looking at options expiring March 1. For stock indexes, the prior January's data will not have any, err, ''structural reason'' to be terribly different from the index future performance data from Feb 1 thru March 1. Not so with say, oil. During January, the prompt futures contract is February. The January data is using data from Jan 1 (a 30 day forward price against the February contract) through Jan 30 (a one day forward price against the February contract). If you believe (as I do) that things get more volatile as one approaches spot, the January data is a biased representation of what one expects the market to do from Feb 1 to Feb 2 (the overnight trading part of the simulation). The January historic data is a blending of variances of 30 day forwards (Jan 1's vs Jan2's observation) and overnight forward prices (Jan 30's vs. Jan31's observation), but we want to ''overnight trade'' an option on a 28 day forward contract (the March option expiring March 1) — the variances don't ''match up''.
Do any statwise people have an idea as to how I might get around this time structure of volatility problem in trying to reconstruct this simulation using oil prices? (Let's leave the seasonality problems alone for the moment).
N.B. Oil savvy specs will realize I've fudged the expiration cycle/dates a little for the sake of clarity
Dr. Alex Castaldo replies:
Academically it is an interesting paper. They were able to derive extremely messy mathematical expressions for the posterior distribution that are new to the option literature as far as I know. (Bayesian stats sounds good until you try to do the math… often you just hit a brick wall and can't get anywhere).
From a practical point of view I share Prof. Corso's concerns. There are a number of real life issues (like term structure, skew, etc) that are left out.
Also I am bothered that they use (page 17) the "mean of the implied volatilities over the 30 days preceding" and the 30 day realized volatility in their Bayesian estimator. The standard opinion in the industry is that the latest implied volatility is the best estimator and that is one of the estimators they claim they can beat.— keep looking »
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