Detecting long-run abnormal stock returns : the empirical power and specification of test statistics : the Canadian evidence

This study empirically examines the issue of long-horizon security price performance in the Canadian equity market. It analyses the empirical power and specification of test statistics through event studies designed to detect long-run abnormal stock returns. I evaluate the performance of different approaches for developing a benchmark portfolio to calculate abnormal returns. I consider the use of five portfolio approaches, three control firm approaches, as well as two methods for measuring abnormal returns, and three time horizons. I document the empirical power of the various test statistics by inducing an abnormal return in each sample firm. Additionally, a beta shift procedure was performed to test the "goodness" of the match between sample firms and portfolios and between sample firms and control firms. I find that the CAR methods work better than the BHAR methods and that the portfolio and control firm methods return the anticipated result with approximately equal accuracy. I find that adding a constant level of abnormal return ranging from -20% to +20% in 5% increments, shows a lack of power in the t-statistics at these levels of induced abnormal return. Adding a level of abnormal return equal to +/- one to three standard deviations of sample firm's returns to the calculated abnormal return of each sample firm rejects the null hypothesis of no abnormal return. The beta shift procedure confirms that the matches between sample firms and benchmarks are good ones