Like many research scientists, Pasteur realized that great discoveries often come through accident.  The German physicist Roentgen, for example, stumbled across x-rays in 1895 when he was experimenting with filling cathode ray tubes with gases and passing electricity through them.  Thirty-one years earlier, the chemist Friedrich August Kekule identified the ring structure of benzene after dreaming of a snake chasing its tail, forming a circle.  In 1928, Alexander Fleming discovered penicillin when he accidentally left a mold culture exposed to staphylococci bacteria and found that the bacteria did not grow in the mold.  In the 1980s, Michael Marletta won a McArthur “genius” award for discovering the role of nitric oxide in the immune system—a finding he and his partner stumbled upon when a research subject donated urine samples during a bout with the flu.

Can traders benefit from such preparation as well?  And, if so, what kind of preparation might be best?  In this article, I will make the case that the majority of traders, even including intuitive, discretionary ones, can benefit from knowledge of the market’s historical tendencies.  Such knowledge can help us know what to expect in markets, but—just as important—can alert us to the unexpected.

Statistical Analyses and the Intuitive Trader

Quantitative traders identify what to expect in markets by conducting statistical analyses.  Such traders collect a database of market behavior and identify what typically happens after particular market occurrences.  For instance, after an unusually strong day in the market, I might go back ten years and examine all similar strong days and see what happened the next day.  I can then compare those occurrences with the market’s normal behavior to see if there is any directional edge following the strong day.

Let’s say, for instance, that the average one-day change in the S&P 500 Index was .02%, with a ratio of up to down days of 11:10.  After very strong days, however, the average next-day change is .15%, with a ratio of up to down days of 9-5.  Statistical tests, performed either on the price change data or the up/down ratios, could tell us the likelihood of obtaining this difference by chance alone.  Many times, the market proves to be efficient and the analysis will show nothing more than random differences.  Other times, however, a distinct edge will emerge from the data.

How could a trader use such information?  Perhaps an analogy will be instructive:

A baseball pitcher is facing a batter with no outs in the inning.  The bases are loaded, and the pitcher knows he must get the hitter to either strike out or hit the ball on the ground for a possible double play.  The pitcher operates in a discretionary mode: he can accept the signal from the catcher or can wave it off if he feels that a different pitch might be better.  Suppose, however, the team collected statistics that showed that the current batter was much more likely to strike out or hit a ground ball on a sinker pitch than a straight fast ball.  Clearly, such data might influence the pitcher’s decision making.  He might start with a sinker and see how the batter responds, using that information to help him select the second pitch.  His approach remains discretionary, but now it is informed by data that provide a possible edge.

Similarly, a trader might use discretionary judgment to fade an early market rise that seemed to run out of steam.  Suppose, however, that the trader also received information that the odds of the market’s returning to its average trading price were close to 90%, given the low volume of the early rise.  Armed with the statistical knowledge, the trader might gain additional confidence in his trade idea and might even trade it more aggressively.  Conversely, if he had the statistical information and then saw that the early rise was unusually strong with rising volume, he might—like the research scientists—take advantage of the unexpected and jump on board the strength.  Knowing what to expect based on precedent—and then seeing if the market actually follows its historical tendencies—helps prepare the minds of discretionary traders for a variety of market scenarios. 

The Importance of Mental Flexibility

The key to making historical analyses work for discretionary traders is mental flexibility.  If the market forecast gets you locked into one way of viewing market action, it will be easy to miss developments that contradict your position.  Historical analysis provides a hypothesis only; current market action will either support or refute the hypothesis.  Remaining open to current data is an important part of being a good scientist.  Your job is to test hypotheses, not become blinded by them!

Once again an analogy might be helpful.  Many traders trade economic numbers.  In trading the release of statistics, it helps to know how markets usually respond to the data.  For instance, evidence of unexpected strength in the economy normally might be welcomed by the stock market, and our hypothesis would be to look for evidence of market strength if the numbers come out strong.  Suppose, however, that the numbers are very strong and bonds sell off due to fears of inflation.  Stocks start higher, but then size comes in and initiates selling, hitting bids.  We might say to ourselves, “This shouldn’t be happening!”  Our prepared minds have enabled us to appreciate that something unusual is occurring, providing us with a potential opportunity to profit from the recognition. 

I know from my statistical studies that markets tend to revert back to their volume-weighted average prices, especially under conditions of relatively low momentum.  This allows me to wait for markets to move outside their value regions—price ranges in which two-thirds of all volume has transpired—and then decide if I will trade the move or fade it.  If there aren’t signs of broad, significant momentum to the move, I can fade the move and potentially profit from the “false breakout”.  Should the momentum look strong, I can wait for the first pullback and attempt to ride the breakout.  Mean reversion is my hypothesis, but does not become a full-fledged trade idea until it is validated by current market action.  Mental flexibility keeps me open to such action.