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.

[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.]

[A dividend-ratio model is introduced here that makes the log of the dividend-price ratio on a stock linear in optimally forecast future one-period real discount rates and future one-period growth rates of real dividends. If ex post discount rates are observable, this model can be tested by using vector autoregressive methods. Four versions of the linearized model, differing in the measure of discount rates, are tested for U.S. time series 1871-1986 and 1926-1986: a version that imposes constant real discount rates, and versions that measure discount rates from real interest rate data, aggregate real consumption data, and return variance data. The results yield a metric to judge the relative importance of real dividend growth, measured real discount rates, and unexplained factors in determining the dividend-price ratio.]

A linearization of a rational expectations present value model for corporate stock prices produces a simple relation between the log dividend-price ratio and mathematical expectations of future log real dividend changes and future real discount rates. This relation can be tested using vector autoregressive methods. Three versions of the linearized model, differing in the measure of discount rates, are tested for U. S. time series 1871-1986: versions using real interest rate data, aggregate real consumption data, and return variance data. The results yield a metric to judge the relative importance of real dividend growth, measured real discount rates and unexplained factors in determining the dividend-price ratio.

We empirically decompose the S&P 500's dividend yield into (1) a rational forecast of long-run real dividend growth, (2) the subjectively expected risk premium, and (3) residual mispricing attributed to the market's forecast of dividend growth deviating from the rational forecast. Consistent with the Modigliani-Cohn hypothesis, we find that the level of inflation explains almost 80% of the time-series variation in stock-market mispricing.

This paper explains the size and value “anomalies” in stock returns using an economically motivated two-beta model. We break the beta of a stock with the market portfolio into two components, one reflecting news about the market's future cash flows and one reflecting news about the market's discount rates. Intertemporal asset pricing theory suggests that the former should have a higher price of risk; thus beta, like cholesterol, comes in “bad” and “good” varieties. Empirically, we find that value stocks and small stocks have considerably higher cash-flow betas than growth stocks and large stocks, and this can explain their higher average returns. The poor performance of the capital asset pricing model (CAPM) since 1963 is explained by the fact that growth stocks and high-past-beta stocks have predominantly good betas with low risk prices.

This paper uses a vector autoregressive model to decompose excess stock and ten-year bond returns into changes in expectations of future stock dividends, inflation, short-term real interest rates, and excess stock and bond returns. In monthly postwa r U.S. data, stock and bond returns are driven largely by news about future excess stock returns and inflation, respectively. Real intere st rates have little impact on returns, although they do affect the short-term nominal interest rate and the slope of the term structure. These findings help to explain the low correlation between excess st ock and bond returns.

This paper uses a vector autoregressive model to decompose excess stock and 10-year bond returns into changes in expectations of future stock dividends, inflation, short-term real interest rates, and excess stock and bond returns. In monthly postwar U.S. data, stock and bond returns are driven largely by news about future excess stock returns and inflation, respectively. Real interest rates have little impact on returns, although they do affect the short-term nominal interest rate and the slope of the term structure. These findings help to explain the low correlation between excess stock and bond returns.

This paper uses a vector autoregressive model to decompose excess stock and 10‐year bond returns into changes in expectations of future stock dividends, inflation, short‐term real interest rates, and excess stock and bond returns. In monthly postwar U.S. data, stock and bond returns are driven largely by news about future excess stock returns and inflation, respectively. Real interest rates have little impact on returns, although they do affect the short‐term nominal interest rate and the slope of the term structure. These findings help to explain the low correlation between excess stock and bond returns.

This paper uses the predictability of monthly excess returns on U.S. and Japanese equity portfolios over the U.S Treasury bill rate to study the integration of long-term capital markets in these two countries. During the period 1971-90, similar variables, including the dividend-price ratio and interest-rate variables, help to forecast excess returns in each country. In addition, in the 1980s, U.S. variables help to forecast excess Japanese stock returns. There is some evidence of common movement in expected excess returns across the two countries, which is suggestive of integration of long-term capital markets.