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

Authors

  • Ryan Sullivan,

    1. Economic Analysis LLC,
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  • Allan Timmermann,

    1. University of California, San Diego and the Financial Markets Group, London School of Economics
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  • Halbert White

    1. University of California, San Diego
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    • Sullivan is with Economic Analysis LLC; Timmermann is with the University of California, San Diego and the Financial Markets Group, London School of Economics; and White is with the University of California, San Diego. We thank an anonymous referee, the editor (René Stulz), and our discussant at the Western Finance Association meetings (David Chapman) for many useful comments on the paper. The authors are grateful to NeuralNet R&D Associates of San Diego, California for making available its proprietary (patent pending) Reality Check software algorithms.

Abstract

In this paper we utilize White's Reality Check bootstrap methodology (White (1999)) 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 comprehensive test of performance across all technical trading rules examined. 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.

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