In context of Chinese markets, Wanli (2014) provides evidence that analysts' cumulative rating values have significant positive impact on the cumulative abnormal returns
during first 31 days of the event, and a lower rating released corresponds to lower cumulative abnormal returns
Gande and Lewis (2009) confirm these findings by documenting average cumulative abnormal returns
With this goal in mind, we analyze the target, bidder and combined bank cumulative abnormal returns
(CARs) utilizing standard event study methodology.
In the multivariate cross-sectional models, the dependent variable is the two-day (-1,0) cumulative abnormal returns
In this study cumulative abnormal returns
and cumulative average abnormal returns around event are calculated as:
The method is based on a measurement of additional rate of return: the Cumulative Abnormal Returns
Cumulative abnormal returns
(CARs) for each stock are formed by aggregating the individual daily stock ARs.
The abnormal returns calculated were further converted into cumulative abnormal returns
for application of statistical techniques with the help of constant mean return model.
We estimate the abnormal returns and cumulative abnormal returns
in the event window and report the results in Table 3.
The cumulative abnormal returns
are obtained by applying the following formula:
To empirically test our hypothesis, we utilize event study methodology and find that cumulative abnormal returns
(CARs) are positive following a reverse stock split for biotech firms.
Calculated the cumulative abnormal returns
(CAR-20, +20) proceeded to calculate the average daily abnormal returns (AR-20, +20) and cumulative abnormal returns
(CAR-20,+20) for all stock prices within the event window in order to identify potential abnormalities in the 20 days before and after those events reported (-20 to +20).