Forecasting the Equity Premium: Do deep Neural Network Models Work?

• Autorzy: Xianzheng Zhou , Hui Zhou , Huaigang Long
• Języki publikacji: EN

Abstrakty

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 R2of 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 14furthervariablesselected from finance literature.(original abstract)

Słowa kluczowe

EN

Neural networks; Learning of neural networks; Forecasting methods

PL

Sieci neuronowe; Uczenie sieci neuronowych; Metody prognozowania

• Czasopismo: Modern Finance
• Rocznik: 2023
• Tom: 1
• Numer: nr 1
• Strony: 1--11

Twórcy

autor

Xianzheng Zhou

• Guosen Securities Co.

autor

Hui Zhou

• Tulane University; California State University

autor

Huaigang Long

• Zhejiang University of Finance and Economics

Bibliografia

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Identyfikatory

DOI

10.61351/mf.v1i1.2