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

• Authors: Xianzheng Zhou , Hui Zhou , Huaigang Long

Abstracts

EN

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.

Keywords

EN

equity premium; return predictability; deep neural network; asset allocation; forecasting performance

• Publisher: Fundacja Naukowa Instytut Współczesnych Finansów
• Journal: Modern Finance
• Year: 2023
• Volume: 1
• Issue: 1
• Pages: 1-11

Dates

published

2023

Contributors

author

Xianzheng Zhou

• Guosen Securities

author

Hui Zhou

• Tulane University
• California State University

author

Huaigang Long

• Zhejiang University of Finance and Economics

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Identifiers

DOI

10.61351/mf.v1i1.2

Biblioteka Nauki

23943440