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The Declining Equity Premium: What Role Does Macroeconomic Risk Play?

Martin Lettau; Sydney C. Ludvigson; Jessica A. Wachter

Review of Financial Studies 2008 21(4), 1653-1687 open access

Aggregate stock prices, relative to virtually any indicator of fundamental value, soared to unprecedented levels in the 1990s. Even today, after the market declines since 2000, they remain well above historical norms. Why? We consider one particular explanation: a fall in macroeconomicrisk, or the volatility of the aggregate economy. Empirically, we find a strong correlation between low-frequency movements in macroeconomic volatility and low-frequency movements in the stock market. To model this phenomenon, we estimate a two-state regime switching model for the volatility and mean of consumption growth, and find evidence of a shift to substantially lower consumption volatility at the beginning of the 1990s. We then use these estimates from postwar data to calibrate a rational asset pricing model with regime switches in both the mean and standard deviation of consumption growth. Plausible parameterizations of the model are found to account for a significant portion of the run-up in asset valuation ratios observed in the late 1990s.

Contracts and Exits in Venture Capital Finance

Douglas J. Cumming

Review of Financial Studies 2008 21(5), 1947-1982 open access

Contracts and exits from a sample of 179 investment rounds in 132 entrepreneurial firms by 17 European venture capital (VC) funds are analyzed. The data indicate the financial contracts are quite heterogeneous in terms of both the cash flow and control rights. The use of different securities by European VC funds does not depend on the definition of venture capital, and the securities used are not functional equivalents. A normative empirical analysis of exit shows the likelihood of different types of exit vehicles (IPO, acquisition, and liquidation) and the returns to venture capital depend on not only firm specific characteristics but also the allocation of cash flow and control rights.

The Dog That Did Not Bark: A Defense of Return Predictability

John H. Cochrane

Review of Financial Studies 2008 21(4), 1533-1575 open access

To question the statistical significance of return predictability, we cannot specify a null that simply turns off that predictability, leaving dividend growth predictability at its essentially zero sample value. If neither returns nor dividend growth are predictable, then the dividend-price ratio is a constant. If the null turns off return predictability, it must turn on the predictability of dividend growth, and then confront the evidence against such predictability in the data. I find that the absence of dividend growth predictability gives much stronger statistical evidence against the null, with roughly 1-2% probability values, than does the presence of return predictability, which only gives about 20% probability values. I argue that tests based on long-run return and dividend growth regressions provide the cleanest and most interpretable evidence on return predictability, again delivering about 1-2% probability values against the hypothesis that returns are unpredictable. I show that Goyal and Welch's (2005) finding of poor out-of-sample R does not reject return forecastability.

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

John Y. Campbell; Samuel B. Thompson

Review of Financial Studies 2008 21(4), 1509-1531 open access

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.