price movements of a financial asset given any information set, is the central paradigm in financial economics. The impact Fama's (1970) paper was so large, that it took a while before new academic literature on technical trading was published.
The extensive study of Brock, Lakonishok and LeBaron (1992) on technical analysis led to a renewed interest in the topic. They applied 26 simple technical trading strategies, such as moving averages, and support-and-resistance strategies, to the daily closing prices of the DJIA in the period 1897-1986, nearly 90 years of data. They were the first who extended simple standard statistical analysis with parametric bootstrap techniques, inspired by Efron (1979), Freedman and Peters (1984a, 1984b), and Efron and Tibshirani (1986). It was found that the predictive ability of the technical trading rules found was not consistent with a random walk, an AR(1), a GARCH-in-mean model, or an exponential GARCH. The strong results of Brock et al. (1992) were the impetus for many papers published on technical analysis in the 1990s.
Although numerous papers found evidence for economic profitability and statistically significant forecasting power of technical trading rules, they did acknowledge the problem of data snooping. This is the danger that the results of the best forecasting rule may just be generated by chance, instead of truly superior forecasting power over the buy-and-hold benchmark. It could be that the trading rules under consideration were the result of survivorship bias. That is, the best trading rules found by chartists in the past get most attention by academic researchers in the present. Finally White (2000), building on the work of Diebold and Mariano (1995) and West (1996), developed a simple and straightforward procedure, called the Reality Check (RC), for testing the null hypothesis that the best model encountered in a specification search has no predictive superiority over a given benchmark model. Sullivan, Timmermann and White (1999) utilize the RC to evaluate a large set of approximately 7800 simple technical trading strategies on the data set of Brock et al. (1992). They confirm that the results found by Brock et al. (1992) still hold after correcting for data snooping. However in the out-of-sample period 1986-1996 they find no significant forecasting ability for the technical trading strategies anymore. Hansen (2001) shows that the RC is a biased test, which yields inconsistent p-values. Moreover, the test is sensitive to the inclusion of poor and irrelevant models. Further the test has poor power properties, which can be driven to zero. Therefore, within the framework of White (2000), Hansen (2001) derives a test for superior predictive ability (SPA).
In this chapter we test whether objective computerized trend-following technical trading techniques can profitably be exploited after correction for transaction costs when applied to the DJIA and to all stocks listed in the DJIA in the period 1973:1-2001:6.