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Do Cross-Sectional Stock Return Predictors Pass the Test without Data-Snooping Bias?

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Abstract

This study examines the possible data-snooping bias as a competing explanation for the anomalies in the cross-section of stock returns. We posit that the exhaustive standalone searches for profitable strategies could lead to recommending spuriously predictive variables. In order to explore the severity of this problem, we use a multiple testing method to evaluate the profitability of portfolios constructed by these predictors. Our empirical analyses suggest that over half of the findings based on individual testing method are no longer statistically significant after we adjust for data-snooping bias. Excluding the micro-cap stocks before portfolios construction and applying the notion of economic significance in this study further weaken the evidence for predictability. JEL Classification: G11, G12, G14

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  • Yu-Chin Hsu & Hsiou-Wei Lin & Kendro Vincent, 2017. "Do Cross-Sectional Stock Return Predictors Pass the Test without Data-Snooping Bias?," IEAS Working Paper : academic research 17-A003, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    • Handle: RePEc:sin:wpaper:17-a003
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    Keywords

    anomalies; data-snooping bias; stock return predictability; portfolio strategies;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • 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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