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Analyzing the Performance of Multi-Factor Investment Strategies under Multiple Testing Framework

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Abstract

This study aims at risk-scaled returns for multi-factor portfolios, many of which appear to systematically have a superior Sharpe ratio. However, given the prevalent use of standalone backtesting research designs in the finance field to evaluate portfolio strategies that are potentially based on numerous combinations of factors, the significance of data mining bias may be among the most pronounced in this area of studies. Therefore, we aim to discriminate against the competing explanation of spuriously significant risk-scaled returns. Our empirical tests provide evidence that the stock picking strategies with certain combined criteria of firm characteristics outperform both value-weighted index and smallcap value portfolio even after we adjust for the multiple testing bias. The occurrences of winning for the superior multi-factor portfolios appear to be more stable than those for the single factor portfolios over different subsamples. Moreover, the outperformance of multifactor rules is robust to alternative definitions of factors. JEL Classification: G11, G17

Suggested Citation

  • Yu-Chin Hsu & Hsiou-Wei Lin & Kendro Vincent, 2017. "Analyzing the Performance of Multi-Factor Investment Strategies under Multiple Testing Framework," IEAS Working Paper : academic research 17-A001, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    • Handle: RePEc:sin:wpaper:17-a001
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    File URL: https://www.econ.sinica.edu.tw/~econ/pdfPaper/17-A001.pdf
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    More about this item

    Keywords

    Data mining bias; multi-factor investment strategy; multiple hypothesis testing; passive index investing; smart beta;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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