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

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 mo...

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Main Authors: Xianzheng Zhou, Hui Zhou, Huaigang Long
Format: Article
Language:English
Published: Modern Finance Institute 2023-08-01
Series:Modern Finance
Subjects:
Online Access:https://mf-journal.com/article/view/2
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author Xianzheng Zhou
Hui Zhou
Huaigang Long
author_facet Xianzheng Zhou
Hui Zhou
Huaigang Long
author_sort Xianzheng Zhou
collection DOAJ
description 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.
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spelling doaj.art-13396426982b4534adeee737b69b735f2024-02-26T16:13:35ZengModern Finance InstituteModern Finance2956-77422023-08-011110.61351/mf.v1i1.2Forecasting the equity premium: Do deep neural network models work?Xianzheng Zhou0Hui Zhou1Huaigang Long2Guosen SecuritiesTulane University & California State UniversityZhejiang University of Finance and Economics 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. https://mf-journal.com/article/view/2equity premiumreturn predictabilitydeep neural networkasset allocationforecasting performance
spellingShingle Xianzheng Zhou
Hui Zhou
Huaigang Long
Forecasting the equity premium: Do deep neural network models work?
Modern Finance
equity premium
return predictability
deep neural network
asset allocation
forecasting performance
title Forecasting the equity premium: Do deep neural network models work?
title_full Forecasting the equity premium: Do deep neural network models work?
title_fullStr Forecasting the equity premium: Do deep neural network models work?
title_full_unstemmed Forecasting the equity premium: Do deep neural network models work?
title_short Forecasting the equity premium: Do deep neural network models work?
title_sort forecasting the equity premium do deep neural network models work
topic equity premium
return predictability
deep neural network
asset allocation
forecasting performance
url https://mf-journal.com/article/view/2
work_keys_str_mv AT xianzhengzhou forecastingtheequitypremiumdodeepneuralnetworkmodelswork
AT huizhou forecastingtheequitypremiumdodeepneuralnetworkmodelswork
AT huaiganglong forecastingtheequitypremiumdodeepneuralnetworkmodelswork