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Forward Selection Fama-MacBeth Regression with Higher Order Asset-Pricing Factors

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Listed:
  • Nicola Borri
  • Denis Chetverikov
  • Yukun Liu
  • Aleh Tsyvinski

Abstract

We show that the higher-orders and their interactions of the common sparse linear factors can effectively subsume the factor zoo. We propose a forward selection Fama-MacBeth procedure as a method to estimate a high-dimensional stochastic discount factor model, isolating the most relevant higher-order factors. Applying this approach to terms derived from six widely used factors (the Fama-French five-factor model and the momentum factor), we show that the resulting higher-order model with only a small number of selected higher-order terms significantly outperforms traditional benchmarks both in-sample and out-of-sample. Moreover, it effectively subsumes a majority of the factors from the extensive factor zoo, suggesting that the pricing power of most zoo factors is attributable to their exposure to higher-order terms of common linear factors.

Suggested Citation

  • Nicola Borri & Denis Chetverikov & Yukun Liu & Aleh Tsyvinski, 2025. "Forward Selection Fama-MacBeth Regression with Higher Order Asset-Pricing Factors," NBER Working Papers 33663, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:33663
    Note: AP
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    JEL classification:

    • G0 - Financial Economics - - General

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