Detecting cumulative abnormal volume: a comparison of event study methods
A growing body of research in accounting and finance examines the reaction of trading volume to new information. The typical 'volume event study' employs a single-index market model borrowed mutatis mutandis from abnormal returns event studies. In this article, several alternative event study test statistics are compared using Brown and Warner (1985) style simulations, i.e. random samples of securities are drawn from the data set provided by the Center for Research in Security Prices (CRSP) and the empirical distributions of alternative test statistics are compared. In contrast to the extant literature, these simulations show that estimated generalized least squares with first- and second-order autoregressive structures do not offer material improvement over ordinary least squares (OLS) regression. A first-order moving average structure also does not offer material improvement. These simulations also show that test statistics that are robust with regard to cross-sectional heteroskedasticity are essential for testing the hypothesis that the cross-sectional mean cumulative abnormal log turnover is zero.
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