Events

Prof. Travis L. Johnson
Associate Professor of Finance
The University of Taxas at Austin

This paper assesses various approaches to testing for differential valuation effects of an event such as a regulatory change as a function of firm characteristics. Standard cross-sectional return regressions typically reject at the 1% significance level in more than 20% of non-event periods, suggesting that the bar for rejecting in these tests is far too low. Clustering standard errors results in only a modest reduction in excess rejection. Using the time-series distribution of cross-sectional regression coefficients in pre-event windows to conduct inference addresses the excess rejection problem.