Estimating the Effects of Information Surprises and Trading on Stock Returns Using a Mixed Jump-Diffusion Model
提出一种基于混合跳跃扩散模型的方法，用于分离公司事件中信息意外和策略性交易对股票收益的影响。模拟表明，对于长期多公告事件，该方法优于传统累积异常收益估计。实证发现，超过93%的样本公司中混合跳跃扩散模型优于纯扩散模型，且交易效应显著为负，而公告效应不显著。
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 block holdings 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.