ABSTRACT This paper estimates volatility changes in daily returns to the Dow Jones Industrial Average over the sample period 1897 through 1988. This allows a direct investigation of the reaction of the level of stock prices and subsequent expected returns to these estimated changes in volatility. We provide empirical evidence consistent with relatively large and systematic revisions in stock prices and subsequent expected returns to volatility changes. However, there appears to be an asymmetry in the market's reaction to volatility increases as opposed to volatility decreases. A majority of our volatility changes cannot be associated with the release of significant economic information.

We put forward a model in which analysts are uncertain about a firm’s earnings process. Faced with the possibility of using a misspecified model, analysts issue forecasts that are robust to model misspecification. We estimate that this mechanism explains approximately 60% of the autocorrelation in analysts’ forecast errors. The remainder stems from the cross-sectional variation in mean forecast errors and in analysts’ estimation errors of the persistence of earnings growth shocks. Consistent with our model, we find that analysts learn about some features of the earnings process but not others, and this learning reduces, but does not eliminate, the autocorrelation of forecast errors as firms age. Other potential explanations for the autocorrelation of analyst forecast errors are rejected. Our model of robust forecasting applies not only to analysts’ forecasts but also to all model-based forecasts.