Estimating the Effects of Information Surprises and Trading on Stock Returns Using a Mixed Jump-Diffusion Model.

I present a methodology that uses the mixed jump-diffusion model for stock returns to estimate the separate effects of information surprises and strategic trading around corporate events. Using simulation techniques, I show that for events with multiple announcements spread over a long time, the estimators derived from the mixed jump-diffusion model are more powerful compared to the traditional cumulative abnormal return estimators. The new methodology is used to study the separate effects of information surprises and strategic trading associated with blockholdings and subsequent targeted repurchases. I find that for more than 93 percent of the firms in our sample the mixed jump-diffusion model is statistically superior to the pure diffusion model in describing stock returns. More important, I find a statistically significant negative effect due to trading, while the average effect around announcements is statistically insignificant. In contrast, the standard cumulative abnormal return is not statistically different from zero. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.