Technical Stock Market Anomalies

A question that has been subject to extensive research and debate is whether past prices and charts can be used to predict future prices. Technical Analysis is a general term for a number of investing techniques that attempt to forecast securities prices by studying past prices and related statistics. Common techniques include strategies based on relative strength, moving averages, as well as support and resistance. The majority of researchers that have tested technical trading systems (and the weak-form efficient market hypothesis ) have found that prices adjust rapidly to stock market information and that technical analysis techniques are not likely to provide any advantage to investors who use them. However others argue that there is validity to some technical strategies.

Two of the most visible participants in the debate are Princeton Professor Burton Malkiel (author of A Random Walk Down Wall Street ) and Mark Hulbert. Hulbert’s 10/21/96 column in Forbes titled A schizophrenic walk down Wall Street addressed the random walk theory and in particular the following excerpt from the sixth edition of Malkiel’s book. The central proposition of charting is absolutely false, and investors who follow its precepts will accomplish nothing but increasing substantially the brokerage charges they pay. There has been a remarkable uniformity in the conclusions of studies done on all forms of technical analysis. Not one has consistently outperformed the placebo of a buy-and-hold strategy.

Hulbert disagrees with the Not one statement and as backup for his argument, Hulbert refers to a 1992 study by William Brock. Josef Lakonishok. and Blake LeBaron 1 (BLL). The authors analyzed moving averages and trading range breaks on the Dow Jones Industrial Index from 1897 to 1985. The technical rules addressed in the study were the following.

1. Moving Averages. Buy and sell signal were generated by a long and short moving average crossing. They tested long moving averages of 50, 150 and 200 days with short averages of 1, 2 and 5 days. The results — All the buy-sell differences are positive and the t-tests for these differences are highly significant.
2. Trading Range Break (Support and Resistance). A buy signal was generated when the price penetrated the resistance level and a sell signal was generated when the price penetrated the support level. Technical analysts believe that investors sell at the resistance level and buy at the support level. They tested support and resistance based on past 50, 150 and 200 days with signals generated when a maximum or minimum was violated by 1% and computed 10-day holding period returns following the buy and sell signals. The results for both buy and sell signals supported the technical viewpoint. The authors concluded:
3. Our results are consistent with technical rules having predictive power. However, transactions costs should be carefully considered before such strategies can be implemented.
4. In sum, this paper shows that the returns-generating process of stocks is probably more complicated than suggested by the various studies using linear models. It is quite possible that technical rules pick some of the hidden patterns. We would like to emphasize that our analysis focuses on the simplest trading rules.

The work of LeBaron and Brock was also referred to in the October 9, 1993 Frontiers of Finance Survey in The Economist. Some excerpts from the article included:

• Contrary to previous tests, they found that both types of rule work quite well. Buy signals were followed by an average 12% return at an annual rate and sell signals were followed by a 7% loss at an annual rate.
• The previous conclusion that technical analysis is useless was, in the words of Dr Brock and Dr LeBaron, premature.

Data-Snooping, Technical Trading Rule Performance, and the Bootstrap was an article that revisited the BLL paper and later appeared in the October 1999 Edition of Journal of Finance. In the article, Ryan Sullivan, Allan Timmermann, and Halbert White (STW) attempt to determine the effect of Data-Snooping on the BLL results. They also use data collected from the period following the original study in order to provide an out of sample test. Adding the recent years provided a full 100 years of data. STW calculated a break even transaction cost level of 0.27% percent per trade for the best performing trading rule for the full period.

STW found that the results of BLL appear to be robust to data-snooping. However, we also find that the superior performance of the best trading rule is not repeated in the out-of-sample experiment covering the period 1987-1996 and there is scant evidence that technical trading rules were of any economic value during the period 1987-1996.

Another technical analysis debate is whether strong performance from one period continues (or reverses) in future periods. Some studies have concluded that positive correlation exists (winners repeat) in the short term (weeks and months) while negative autocorrelation exists over longer periods of time (See Neglected Stocks ). James P. O’Shaughnessy claims in What Works on Wall Street that relative strength is a strong indicator of future performance. His criteria was strong performance for the calendar year. Several studies have found that firms reporting unexpectedly high earnings outperform those reporting poor earnings and the outperformance continues for the following six months (See announcement based effects ). Momentum Strategies 2 was a comprehensive review and analysis of the subject in the December 1996 issue of the Journal of Finance. The authors note that any excess returns from momentum strategies may not be fully capturable because of trading costs (particularly with smaller issues). K. Geert Rouwenhorst published a paper titled International Momentum Strategies in The Journal of Finance that documented momentum strategies in 12 European markets from 1980-1995.

The only thing we know for certain about technical analysis is that it’s possible to make a living publishing a newsletter on the subject.

Martin S. Fridson. Investment Illusions

Technical analysts are the witch doctors of our business. By deciphering stock price movement patterns and volume changes, these Merlins believe they can forecast the future.

William Gross . Everything You’ve Heard About Investing is Wrong!

The one principal that applies to nearly all these so-called technical approaches is that one should buy because a stock or the market has gone up and one should sell because it has declined. This is the exact opposite of sound business sense everywhere else, and it is most unlikely that it can lead to lasting success in Wall Street. In our own stock-market experience and observation, extending over 50 years, we have not known a single person who has consistently or lastingly made money by thus following the market. We do not hesitate to declare that this approach is as fallacious as it is popular.

Benjamin Graham. The Intelligent Investor

Technical anlysis is doomed to fail by the statistical fact that stock prices are nearly random; the market’s patterns from the past provide no clue about its future. Not suprisingly, studies conducted by academicians at universities like MIT, Chicago, and Stanford dating as far back as the 1960s have found that the technical theories do not beat the market, especially after deducting transaction fees. It is amazing that technical analysis still exists on Wall Street. One cynical view is that technicians generate higher commissions for brokers because they recommend frequent movement in and out of the market.

William A. Sherden. The Fortune Sellers . The Big Business of Selling and Buying Predictions

This page is not a stand-alone page and should not be read or used without first viewing the main Anomalies page which includes important information and warnings about interpreting historical stock market anomalies.