Predicting Excess Stock Returns out of Sample: Can Anything Beat the Historical Average?

Goyal and Welch (2007) argue that the historical average excess stock return forecasts future excess stock returns better than regressions of excess returns on predictor variables. In this article, we show that many predictive regressions beat the historical average return, once weak restrictions ar...

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Bibliographic Details
Published in:The Review of financial studies 2008-07, Vol.21 (4), p.1509-1531
Main Authors: Campbell, John Y., Thompson, Samuel B.
Format: Article
Language:English
Subjects:
Abnormal returns
Dividends
Earnings forecasting
Economic forecasts
Economic theory
Financial engineering
Financial portfolios
Forecasting
Forecasting models
Growth models
Interest rates
Investments
Investors
Portfolio performance
Rates of return
Ratios
Regression analysis
Regression coefficients
Special Section: Forecasting the Equity Premium
Statistical forecasts
Statistics
Stock dividends
Stock prices
Stock returns
Studies
Valuation
Variables
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Summary:Goyal and Welch (2007) argue that the historical average excess stock return forecasts future excess stock returns better than regressions of excess returns on predictor variables. In this article, we show that many predictive regressions beat the historical average return, once weak restrictions are imposed on the signs of coefficients and return forecasts. The out-of-sample explanatory power is small, but nonetheless is economically meaningful for mean-variance investors. Even better results can be obtained by imposing the restrictions of steady-state valuation models, thereby removing the need to estimate the average from a short sample of volatile stock returns.
ISSN:0893-9454
1465-7368
DOI:10.1093/rfs/hhm055