Stock Return Predictability: Riding the Risk Premium

70 Pages Posted: 14 Mar 2018

See all articles by Roberto Gomez Cram

Roberto Gomez Cram

University of Pennsylvania - The Wharton School

Date Written: December 27, 2017

Abstract

Past returns contain rich information about future returns. I propose an approach to estimate expected returns based on the full history of past returns, which is able to outperform the prevailing mean benchmark on a consistent basis, over long sample periods, and with monthly out-of-sample R2 statistics of at least 2% and annualized utility gains greater than 300 basis points. My approach allows for correlated shocks between unexpected and expected returns and ties expected return variations to business-cycle fluctuations. These properties generate different persistence, volatility, and serial correlation of expected returns across economic states and determine how the information in lagged returns is used to predict future returns. My approach has important implications for standard predictive regressions.

JEL Classification: C53, C58, E32

Suggested Citation

Gomez Cram, Roberto, Stock Return Predictability: Riding the Risk Premium (December 27, 2017). Available at SSRN: https://ssrn.com/abstract=3138833 or http://dx.doi.org/10.2139/ssrn.3138833

Roberto Gomez Cram (Contact Author)

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
181
Abstract Views
1,134
rank
174,943
PlumX Metrics