Recovery of hidden information from stock price data: A semiparametric approach

This paper proposes a new methodology for measuring announcement effect on stock returns. This methodology requires no prior specification of the event day, event and estimation windows, and therefore is a generalization of the traditional event study methodology. The dummy variable, which indicates whether the event occurred or not, is treated as missing. The unconditional probability of abnormal return is estimated by the EM algorithm. The probability that announcement is effective and the average announcement effect are estimated by the Gibbs sampler. How the method works is demonstrated on simulated data and IBM stock price returns.