Event Study Methodology
One of the key advantages of using Event Study Metrics as a software solution is its ease of use and simplicity. The computations below are preconfigured in the software and running the event study is pretty much automated, letting you concentrate on the economics (and not the maths!) of your study. Nevertheless, it is important for you to understand what happens behind the scenes. The explanations below will help you make informed decisions on the model and test statistics you chose in the software.
Abnormal Returns are the crucial measure to assess the impact of an event. The general idea of this measure is to isolate the effect of the event from other general market movements. The abnormal return of firm i and event date is defined as the difference of the realized return and the expected return given the absence of the event:
The expected return (henceforth referred to as normal return) is unconditional on the event but conditional on a separate information set. Dependent on the definition of the information set (e.g., past asset returns) and the functional form there exist various models for the normal return. Those models are extensively discussed in the following section.
Event Study Metrics offers two different measures of aggregated abnormal returns that are commonly used in event study analyses:
Cumulating abnormal returns across time yields the cumulative abnormal return measure:
The second measure, the buy-and-hold abnormal return (BHAR), is defined as the difference between the realized buy-and-hold return and the normal buy-and-hold return:
Statistical tests of abnormal returns are commonly based on the cross-average of each measure. For cumulative abnormal returns the cross-sectional average (CAAR) is:
Whereas, the mean buy-and hold abnormal return is:
For a detailed discussion of the difference between the two measures you may consult Barber and Lyon (1997) or Ritter (1991).