several days prior and after the event (i.e., during the event window). Thereafter, the method deducts this 'normal returns' from the 'actual returns' to receive 'abnormal returns' attributed to the event. Event studies, however, may differ with respect to their specification of normal returns. The most common model for normal returns is the 'market model' (MacKinlay 1997). Following this model, the analysis implies to use an estimation window (typically sized 120 days) prior to the event to derive the typical relationship between the firm's stock and a reference index through a regression analysis. Based on the regression coefficients, the normal returns are then projected and used to calculate the abnormal returns. Alternative models for the normal returns include the CAPM model, or more simplistic approaches such as mean returns (see MacKinlay 1997 for an overview). Calculation of abnormal returns Depending on the model chosen for the 'normal return', conducting event studies requires the researcher to implement a distinct sequence of steps. For the most common model, the 'market model', the steps are as follows: Retrieve and match time series of financial returns of the focal firm's stock and its reference index. For each event, identify the sequences of firm and market returns that need to be included in the estimation window. Using regression analysis, calculate the alpha, beta and sigma coefficients that explicate the typical relationship between the stock and the reference index. With these three parameters, predict the 'normal returns' for all days of the event window. Deducting these 'normal returns' from the 'actual returns' gives you the 'abnormal returns' which are the metrics of interest. Significance of abnormal returns To specify if individual abnormal returns differ from zero with some statistical validity, test statistics need to be applied. Various test statistics at the different levels of analysis (i.e., AR-, CAR-, AAR- and CAAR-level) exist for this purpose. The most common test, the t-test, divides the abnormal returns through the root mean square error of the regression. Resulting t-values need then to be compared with the critical values of the Student's t-distribution. There is some evidence that during times of high volatility (e.g. financial crisis of 2007–2008), too many companies tend to show significantly abnormal returns using the t-test, which makes it more difficult to determine which returns are truly "abnormal". Software for conducting event studies Event studies can be implemented with various different tools. Single event studies can