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

John Y Campbell,Samuel B Thompson

doi:10.1093/rfs/hhm055

Abstract

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. (JEL G10, G11) Towards the end of the last century, academic finance economists came to take seriously the view that aggregate stock returns are predictable. During the 1980s, a number of papers studied valuation ratios, such as the dividend-price ratio, earnings-price ratio, or smoothed earnings-price ratio. Value-oriented investors in the tradition of Graham and Dodd (1934) had always asserted that high valuation ratios are an indication of an undervalued stock market and should predict high subsequent returns, but these ideas did not carry much weight in the academic literature until authors such as Rozeff (1984), Fama and French (1988), and Campbell and Shiller (1988a, 1988b) found that valuation ratios are positively correlated with subsequent returns and that the implied predictability of returns is substantial at longer horizons. Around the same time, several papers pointed out that yields on short- and long-term treasury and corporate bonds are correlated with subsequent stock returns (Fama and Schwert,1977;KeimandStambaugh,1986;Campbell,1987;FamaandFrench, 1989).

Highlights

Goyal and Welch (2006) argue that the historical average excess stock return forecasts future excess stock returns better than regressions of excess returns on predictor variables
Many of the predictor variables in the literature are highly persistent: Nelson and Kim (1993) and Stambaugh (1999) pointed out that persistence leads to biased coe!cients in predictive regressions if innovations in the predictor variable are correlated with returns
A number of variables are correlated with subsequent returns on the aggregate U.S stock market in the twentieth century. Some of these variables are stock market valuation ratios, others re ect the levels of short- and long-term interest rates, patterns in corporate nance or the cross-sectional pricing of individual stocks, or the level of consumption in relation to wealth

Summary

Introduction

Goyal and Welch (2006) argue that the historical average excess stock return forecasts future excess stock returns better than regressions of excess returns on predictor variables. We show that simple restrictions on predictive regressions, suggested by investment theory, improve the out-of-sample performance of key forecasting variables and imply that investors could have pro ted by using market timing strategies.

Results

Conclusion