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

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  • Allan Timmermann
  • Halbert White
  • Ryan Sullivan

Abstract

In this paper we utilize Whites Reality Check bootstrap methodology (White (1997)) to evaluate simple technical trading rules while quantifying the data-snooping bias and fully adjusting for its effect inthe context of the full universe form which the trading rules are drawn. Henxe, for the first time, the paper presents a comrehensive test of perfomance 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 Jone Industrial Average, and determine the effects of data-snooping.

Suggested Citation

  • Allan Timmermann & Halbert White & Ryan Sullivan, 1998. "Data-Snooping, Technical Trading, Rule Performance and the Bootstrap," FMG Discussion Papers dp303, Financial Markets Group.
    • Handle: RePEc:fmg:fmgdps:dp303
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    References listed on IDEAS

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    1. John Y. Campbell & Sanford J. Grossman & Jiang Wang, 1993. "Trading Volume and Serial Correlation in Stock Returns," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(4), pages 905-939.
    2. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Lo, Andrew W & MacKinlay, A Craig, 1990. "Data-Snooping Biases in Tests of Financial Asset Pricing Models," The Review of Financial Studies, Society for Financial Studies, vol. 3(3), pages 431-467.
    5. Foster, F Douglas & Smith, Tom & Whaley, Robert E, 1997. "Assessing Goodness-of-Fit of Asset Pricing Models: The Distribution of the Maximal R-Squared," Journal of Finance, American Finance Association, vol. 52(2), pages 591-607, June.
    6. Sweeney, Richard J., 1988. "Some New Filter Rule Tests: Methods and Results," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 23(3), pages 285-300, September.
    7. Jensen, Michael C & Bennington, George A, 1970. "Random Walks and Technical Theories: Some Additional Evidence," Journal of Finance, American Finance Association, vol. 25(2), pages 469-482, May.
    8. Levich, Richard M. & Thomas, Lee III, 1993. "The significance of technical trading-rule profits in the foreign exchange market: a bootstrap approach," Journal of International Money and Finance, Elsevier, vol. 12(5), pages 451-474, October.
    9. Neftci, Salih N, 1991. "Naive Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction Theory: A Study of "Technical Analysis."," The Journal of Business, University of Chicago Press, vol. 64(4), pages 549-571, October.
    10. Taylor, Mark P. & Allen, Helen, 1992. "The use of technical analysis in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 11(3), pages 304-314, June.
    11. Blume, Lawrence & Easley, David & O'Hara, Maureen, 1994. "Market Statistics and Technical Analysis: The Role of Volume," Journal of Finance, American Finance Association, vol. 49(1), pages 153-181, March.
    12. Stephen J. Taylor, 1994. "Trading futures using a channel rule: A study of the predictive power of technical analysis with currency examples," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 14(2), pages 215-235, April.
    13. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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