Abstract
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.
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