I'm going to study the effect of corporate credit rating changes (Moody's) on stock prices before and after a specific financial regulation. So far i have used an event study where i have divided the events in two different groups (0: Before, 1: After), and studied the effects individually.
Downgrades (Day 0) - I have used the market model, and different tests like the simple t-test (MacKinlay - 1997), Patell Z (Patell - 1976) and Corrado's rank test (Corrado - 1992) are all statistically significant at the 0.05 or 0.1 level.
Full sample: Average abnormal return = -0.0061
Before regulation: Average abnormal return = -0.0074
After regulation: Average abnormal return = -0.0044
But, i'm not sure how to compare these results. In other words, i'm not sure how to test if the effect of credit rating changes after the regulation is smaller than the effect before the regulation.
I have looked at this; StackExchange - Event study topic but didn't quite get it. I'm not sure how you can say that "a" is bigger than "b" without some kind of test. I have also looked at a couple of papers about multivariate regression models (with and without bootstrapping) but they tend to be too complicated for a non-statistican.
I would really appreciate it if someone could help me with how i can test if there is a significant difference between the two groups or help me get how to do this with multivariate regression models (eg. Binder, J. J. (1985). On the use of the multivariate regression model in event studies.). Thanks in advance!