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Forecasting the equity premium: Do deep neural network models work?

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  • Xianzheng Zhou
  • Hui Zhou
  • Huaigang Long

Abstract

This paper constructs deep neural network (DNN) models for equity-premium forecasting. We compare the forecasting performance of DNN models with that of ordinary least squares (OLS) and historical average (HA) models. The DNN models robustly work best and significantly outperform both OLS and HA models in both in- and out-of-sample tests and asset allocation exercises. Specifically, DNN models generate monthly out-of-sample R2 of 3.42% and an annual utility gain of 2.99% for a mean-variance investor from 2011:1 to 2016:12. Moreover, the forecasting performance of DNN models is enhanced by adding additional 14 variables selected from finance literature.

Suggested Citation

  • Xianzheng Zhou & Hui Zhou & Huaigang Long, 2023. "Forecasting the equity premium: Do deep neural network models work?," Modern Finance, Modern Finance Institute, vol. 1(1), pages 1-11.
    • Handle: RePEc:bdy:modfin:v:1:y:2023:i:1:p:1-11:id:2
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    References listed on IDEAS

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