Asset Pricing: Time-Series Predictability

Oxford Research Encyclopedia of Economics and Finance

45 Pages Posted: 12 Oct 2021 Last revised: 24 Mar 2022

See all articles by David Rapach

David Rapach

Research Department, Federal Reserve Bank of Atlanta; Washington University in St. Louis

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School

Date Written: March 24, 2022

Abstract

Asset returns change with fundamentals and other factors, such as technical information and sentiment over time. In modeling time-varying expected returns, this article focuses on the out-of-sample predictability of the aggregate stock market return via extensions of the conventional predictive regression approach.

The extensions are designed to improve out-of-sample performance in realistic environments characterized by large information sets and noisy data. Large information sets are relevant because there are a plethora of plausible stock return predictors. The information sets include variables typically associated with a rational time-varying market risk premium, as well as variables more likely to reflect market inefficiencies resulting from behavioral influences and information frictions. Noisy data stem from the intrinsically large unpredictable component in stock returns. When forecasting with large information sets and noisy data, it is vital to employ methods that incorporate the relevant information in the large set of predictors in a manner that guards against overfitting the data.

Methods that improve out-of-sample market return prediction include forecast combination, principal component regression, partial least squares, the LASSO and elastic net from machine learning, and a newly developed C-ENet approach that relies on the elastic net to refine the simple combination forecast. Employing these methods, a number of studies provide statistically and economically significant evidence that the aggregate market return is predictable on an out-of-sample basis. Out-of-sample market return predictability based on a rich set of predictors thus appears to be a well-established empirical result in asset pricing.

Keywords: Market excess return, Out-of-sample tests, Utility gains, Forecast combination, Principal component regression, Partial least squares, LASSO, Elastic net

JEL Classification: C53, G11, G12, G14, G17

Suggested Citation

Rapach, David and Zhou, Guofu, Asset Pricing: Time-Series Predictability (March 24, 2022). Oxford Research Encyclopedia of Economics and Finance, Available at SSRN: https://ssrn.com/abstract=3941499 or http://dx.doi.org/10.2139/ssrn.3941499

David Rapach (Contact Author)

Research Department, Federal Reserve Bank of Atlanta ( email )

1000 Peachtree Street N.E.
Atlanta, GA 30309-4470
United States

Washington University in St. Louis ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

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