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Victor Niederhoffer Reviews 'Does Trend Following Work On Stocks?'
This paper, by Cole Wilcox and Eric Crittenden of Blackstar Funds, makes a worthy and thoughtful effort to answer the question of whether it's possible to devise a trend following method that works in stocks. Their method is to buy stocks at all time highs then they sell them after a 20% decline or so using the true range as a cut-off to sensitize. In order to do a valid study of such a phenomenon they have had to be careful to adjust properly for survival bias. They do this taking account of all NYSE, AMEX, and NASDAQ stocks listed and de-listed from 1983-2004.
Their results show that $1000 would have grown to some $30,000 during the period. Results for the years 2003 and 2004 of 55% return and 27% return are particularly impressive.
The defects of the study are that they do not show the statistical significance of their results. Small stocks performed much better than big stocks during the latter part of the period. It is not clear that buying the average stock in the category that they found the new highs from, and selling an average stock or a matched stock next to it in price would not have yielded very similar results. More to the point, I hypothesize that buying stocks that were 10% off their high, or at a new low, with similar inclusion criteria, would have come up with as good results. Finally, the inclusion of companies with lower than $250,000 daily trading volumes in the study makes the results unobtainable for those who would wish to implement it with reasonable new money.
While these are serious objections, they are of the armchair variety. We have tested similar strategies on big groups of stocks like the S&P500 and found it produces random results. That is just the point the researchers say, the small stocks are the ones that give the superior returns.
We must compliment the authors on a true attempt to find out the nitty-gritty of the market . Further work in the field by them and us will undoubtedly clarify the issues.
Eric Crittendon Replies:
I believe the following statement is in error:
"Finally, the inclusion of companies with lower than $250,000 daily trading volumes in the study makes the results unobtainable for those who would wish to implement it with reasonable new money."
The last paragraph of the paper goes into detail. Stocks with daily dollar volume that low would not have passed our filter.
The origin of this project was a data mining test on NASDAQ only stocks to find effective stops using a random entry method (that was back in 2001). We found several methods that were effective through various market cycles; all were derivatives of volatility, however. The stops we present in the paper are the most bland and easiest to understand. It is my nature to know what our "total wash out" risk (a.k.a. total portfolio risk) is and be comfortable with it. Without stops, and their aggregate remaining risk across all positions, I can't quantify this value. Under the assumptions in the paper we would have nearly realized this loss, being stopped out of almost 90% of our positions during the crash of 1987. I expect crashes to happen and wish to live through them. The entire system depends upon this reference point, total portfolio risk. It is calculated daily for existing and new (buy tomorrow) positions and becomes an independent variable input into a utility function that, in turn, specifies how much should be risked on each position. New positions are sized accordingly and existing positions are resized (both up and down) if they are out of alignment by a significant margin (determined by yet another utility function). The circular reference repeats every day. In this way we are able to honor every trade and control total portfolio risk. So, you can see my inability to provide portfolio performance results without the use of stops.
When restricted to the S&P 500 we found an inverse relationship between the tendency to have substantial and prolonged % moves and market capitalization. Intuitively this made sense to us as index members have already experienced the market cap growth necessary to get into the index. Also, index members tend to offer transparency that is communicated in real time by an army of analysts and research reports. Furthermore, their business models tend to be overly diversified relative to small/medium companies. Additionally, there is typically millions of dollars bid and millions asked just cents away from the prevailing price at any given time. It seems only an accounting scandal, speculative mania, or major market shock can provide the fuel for outlier moves. That being said, we don't discriminate against them; we are just happy that there are so many more small and mid-cap companies to buy.
Alston Mabry Adds:
The chart comparing their Trend System to the S&P over the period January '91 to January '95 reminds me of a chart comparing the VAY to the S&P over the same period. Most importantly, the bulk of the Trend System outperformance occurred in the period January '03 to January '05, during which the Trend System essentially doubled. The VAY essentially doubled in that same period, so one could make the case that Trend System results are hard to distinguish from the results of a randomly-selected, equal-weighted portfolio, or at least that the mean and standard deviation of the returns of such a portfolio should be the benchmark for the Trend System, rather than a cap-weighted index.