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Weighing asset pricing factors: a least squares model averaging approach

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  • Yue Qiu
  • Yu Ren
  • Tian Xie

Abstract

Empirical evidence has demonstrated that certain factors in asset pricing models are more important than others for explaining specific portfolio returns. We propose a technique that evaluates the factors included in popular linear asset pricing models. Our method has the advantage of simultaneously ranking the relative importance of those pricing factors through comparing their model weights. As an empirical verification, we apply our method to portfolios formed following Fama and French [A five-factor asset pricing model. J. Financ. Econ., 2015, 116, 1–22] and demonstrate that models accommodated to our factor rankings do improve their explanatory power in both in-sample and out-of-sample analyses.

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  • Yue Qiu & Yu Ren & Tian Xie, 2019. "Weighing asset pricing factors: a least squares model averaging approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(10), pages 1673-1687, October.
    • Handle: RePEc:taf:quantf:v:19:y:2019:i:10:p:1673-1687
    DOI: 10.1080/14697688.2019.1602276
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    Cited by:

    1. Qiu, Yue & Wang, Zongrun & Xie, Tian & Zhang, Xinyu, 2021. "Forecasting Bitcoin realized volatility by exploiting measurement error under model uncertainty," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 179-201.

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