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I didn't notice any requests from the reviewers to handle multiple acquisitions on the same day, which doesn't appear to have been fixed in the previous version. However, I can see we are aggregating acquisitions over the year, so this is somewhat moot in the averages and totals.
 
===Reviewer Comments===
 
Some of Reviewer 3s comments are problematic:
*Another major problem with this empirical work is due to the problem of endogeneity reported by the authors. How was this problem resolved? The methodology used by the authors does not solve this problem! Why didn't the authors think about using the latest '''dynamic panel data models'''? There are some models who have emerged to precisely solve the problems that this sample presents. This should be explored by the authors.
**Fixed effect panel data should address issue of unobserved heterogeneity.
*On the other hand, the authors need to present to us in the tables the overall results of the tests to the models used. For example, they refer us to the Hausman Test but do not show us the results of this test! Wald tests, Test F, Fixed Effects Test F and the respective R-Square value for fixed effects regression and random effects regression are missing.
**Just generate test stats!
*Where is the correlation matrix? It must be presented and its coefficients analysed.
*Finally, there is another problem in the work of these authors and the main objective of the paper and the fact that the authors want to understand the difference between so-called public and private acquisitions. At the beginning of the development of the empirical work the authors must demonstrate to us that in fact the two subsamples are different in terms of the variables that will be studied. Thus, differences tests should be performed and reported to these two subsamples, only by so that it will make sense to perform all the empirical work later.
 
===Ed To Do===
 
*Institutional investors measures
*Profitability: profit and net income
*MA activity 2016 to 2020
*Take a look at the dynamic panel
===Restoring the old data===

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