[We examine the predictive power of the di extbackslashvidend yields for forecasting excess returns, cash flows, and interest rates. Dividend yields predict excess returns only at short horizons together with the short rate and do not have any long-horizon predictive power. At short horizons, the short rate strongly negatively predicts returns. These results are robust in international data and are not due to lack of power. A present value model that matches the data shows that discount rate and short rate movements play a large role in explaining the variation in dividend yields. Finally, we find that earnings yields significantly predict future cash flows.]

Using only the definition of returns, together with a transversality assumption, we demonstrate that given a dividend process, any one of three variables—expected return, return volatility, and the price–dividend ratio—completely determines the other two. By parameterizing only one of these processes, common empirical specifications place strong, and sometimes counter-factual, restrictions on the dynamics of the other variables. Our findings lend insight into the nature of the risk–return relation and the predictability of stock returns.

We examine the predictive power of the dividend yields for forecasting excess returns, cash flows, and interest rates. Dividend yields predict excess returns only at short horizons together with the short rate and do not have any long-horizon predictive power. At short horizons, the short rate strongly negatively predicts returns. These results are robust in international data and are not due to lack of power. A present value model that matches the data shows that discount rate and short rate movements play a large role in explaining the variation in dividend yields. Finally, we find that earnings yields significantly predict future cash flows. (JEL C12, C51, C52, E49, F30, G12)

Abstract Recent studies suggest that the underperformance of IPOs in the post-1970 sample may be a small sample effect or “Peso problem.” That is, IPO underperformance may result from observing too few star performers ex post than were expected ex ante. We develop a model of IPO performance that captures this intuition by allowing returns to be drawn from mixtures of outstanding, benchmark, or poor performing states. We estimate the model under the null of no ex ante average IPO underperformance and construct small sample distributions of various statistics measuring IPO relative performance. We find that small sample biases are extremely unlikely to account for the magnitude of the post-1970 IPO underperformance observed in data.