## Expert Dialog

Question #32: What is exactly M?

Dear Mr. Müller,

Regarding the parametric T-test for one sample Event: Could you explain me the size M (matched returns), please?

My event window is +/- seven days surrounding the announcement day of an acquisition. So L2 is 15 in my analysis, right? Is M also 15?

You can also answer me in german.

Best wishes,

Tim Rothermel

Hi Mr. Rothermel,you are correct.Best,Simon Müller
Question #31: Grouping Variable

I am trying to run an even study test by event study tool. I found a column called grouping variable in the "request file", and in the sample file it is given as "addition". I am not quite sure what grouping variable is and what "addition" indicates. Could you kindly instruct me?

The purpose of the grouping variable is described as folows: "For generating the 'average' values in your analysis (i.e., AAR and CAAR values as produced by aARC), you need to specify the 'grouping variable'. If you use only one value in the grouping variable, which is the default case, AXC will calculate the average values across all events in your request file; if you choose more than one value (e.g., 'acquisition' and 'divestiture' in a boundary choise study), AXC will produce average values across the events grouped by these values." (http://www.eventstudytools.com/instructions-axc)'Addition' was a value we used in the context of a study that investigated index changes in the S&P500 index. As index changes, we considered 'additions' and 'deletions' from the index.
Question #30: Competitor Portfolio

I am examining an individual event. According to your recommendations a parametric t-test (Nr. 1 on the website) is applicable. Besides the effects this event has on the stock returns of the company responsible for the event, I'd also like to examine the effects on the firm's competitors. Treating individual competitor stocks as independet is almost surely inappropriate, since I would expect a fair amount of correlation among competitor retruns. To adjust for this possibility, I create a portfolio composed of all relevant competitors and calculated the abnormal returns of this portfolio.
1.) Can I apply the same test statistic to test for significant AR of the competitor portfolio as I do for the individual focal firm?
2.) Is there a way to avoid portfolio creation that still yields statistically meaningfull/ unbiased results? Which test would be applicable in that case?

Since you do a "single event"-event study, you can use the grouping variable available in our research app to group all competitors and keep the other firm separate. The tests statistics will consider the grouping as described in the test statistics section.
Question #29: Single-Day Event Study

I am currently working on a single-day event study. I came across the paper by Kolari and Pynnönen (2010), which proposes two test statistics that can handle cross-sectional correlation: ADJ-BMP and ADJ-PATELL. Is there a way to correct for cross-sectional correlation using the "Event Study Engine"?

Yes, you may choose Adjusted Patell or Adjusted BMP test (see http://www.eventstudytools.com/significance-tests [5] and [7]) in our Event Study Engine. Please select there Adjusted Patell Z and Adjusted StdCSect Z.
Question #28: Standard Deviation and Variance of CAR

When looking to your excellent summary of event study statistics , I've something that I am not quite sure is correct. When you present the formulas for the Standard deviation of the CAR in the BMP test, I think you mean that the equation in question is for the variance of CAR and not the Standard Deviation, meaning that a square is missing in the Market Model, Comparison Period Mean Adjusted Model and the Market Adjusted Model left side of the equation. Maybe I am missing something but I want to be sure.

Thank you for that hint. You are correct the square on the left side of the equation is missing.
Question #27: Difference in mean - pre/post financial regulation

If you are conducting a study where you test AAR/CAAR in association with corporate credit rating changes before and after a financial regulation (ie. test if the effect on the stock market from a credit rating change (Moody's) is more/less extreme after the regulation), how would you test for the difference in mean?

I have looked at multivariate regression models but i don't quite understand it. Could you be so kind to elaborate on how you would test for the difference in post/pre AAR/CAAR.

You may define two event windows (e.g. -10,..., 0 and 0,..., 10). If you have no significant effect on event window 1 and a significant effect on window 2, then you know that the financial regulation has an effect on your firms. This is option 1. Option 2: calculate abnormal returns and use a t-test for paired samples for examining two time points. How to calculate this test on CAAR? - I have to think about it.
Question #26: Possible Error in R Code

Just a short note: I guess there is a sort of small typo within the R code example provided within the 'local tools for event studies'-section. When calculating the simple returns, the variable filled should not be called firmSub$firmSub but rather firmSub$firmReturn.Otherwise it will not be found within the OLS estimation and further on.

Besides: I would appreciate more extensive event study R code examples a lot, but of course see your need to focus on other priorities :D

Dear Rabea,thank you for that tip. I fixed it.Best,Simon

I'm having problems in calculating both the adjusted Patell test and the Adjusted BMP test. Could you explain me how to get the average of the sample cross-correlation of the estimation period abnormal returns? Is this the Pearson correlation coefficient or anything else? In the analysis report you provide it is reported the first-order autocorrelation, is this in any way utilised? I'm stuck on this step and your help would be really useful.

The reported autocorrelation is NOT the value used in the adjusted BMP test. The correlation in the adjusted BMP test is the correlation across firm time series and yes we use Pearson. Maybe you check the definiton of autocorrelation.
Question #24: Cross-Sectional Test

Ich wüsste gerne woher bei der Erklärung des Cross-Sectional-Tests die Wurzel N vor dem Bruch kommt. Die leitet sich für mich nirgends heraus ab und ist auch unterschiedlich zu allen gängigen Artikeln über ein und dieselbe Signifikanztest-Art.