This paper documents the empirical distributions of daily trading volume prediction errors for several commonly used volume measures and expectation models for individual firms and for portfolios. The prediction errors for raw volume measures are significantly positively skewed, with thin left tails and fat right tails. However, natural log transformations of the volume measures are approximately normally distributed. For longer than one-day prediction intervals, recognition of autocorrelation in daily trading volume is advantageous for detecting abnormal trading. Results of analysis for clustering of events and for different size firms are also presented.

The effect that voluntarily disclosed managers' earnings forecasts have on the security prices of the announcing firms and other firms in the same industry is examined. The results are consistent with information content in managers' forecasts and with information transfer between forecast firms and other firms in the industry. These inferences are drawn from firms' abnormal returns computed from single- and two-index pricing models - where the latter includes market and industry indexes. Interestingly, while a positive information transfer is evident with market model residuals, once industry cross-sectional covariation in firms' returns is removed, no directional relation is apparent.