Data-Snooping, Technical Trading Rule Performance, and the Bootstrap

Abstract

Numerous studies in the finance literature have investigated technical analysis to determine its validity as an investment tool. Several of these studies conclude that technical analysis does have merit, however, it is noted that the effects of data-snooping are not fully accounted for. In this paper we utilize White's Reality Check bootstrap methodology (White (1997)) to evaluate simple technical trading rules while quantifying the data-snooping bias and fully adjusting for its effect in the context of the full universe from which the trading rules were drawn. Hence, for the first time, the paper presents a means of calculating a comprehensive test of performance across all trading rules. In particular, we consider the study of Brock, Lakonishok, and LeBaron (1992), expand their universe of 26 trading rules, apply the rules to 100 years of daily data on the Dow Jones Industrial Average, and determine the effects of data-snooping. During the sample period inspected by Brock, Lakonishok and LeBaron, we find that the best technical trading rule is capable of generating superior performance even after accounting for data- snooping. However, we also find that the best technical trading rule does not provide superior performance when used to trade in the subsequent 10-year post-sample period.