EU merger policy predictability using random forests - Pauline Affeldt (Deutsches Institut für Wirtschaftsforschung (DIW), 2019)
I study the predictability of the EC's merger decision procedure before and after the 2004 merger policy reform based on a dataset covering all affected markets of mergers with an official decision documented by DG Comp between 1990 and 2014. Using the highly flexible, non-parametric random forest algorithm to predict DG Comp's assessment of competitive concerns in markets affected by a merger, I find that the predictive performance of the random forests is much better than the performance of simple linear models. In particular, the random forests do much better in predicting the rare event of competitive concerns. Secondly, postreform, DG Comp seems to base its assessment on a more complex interaction of merger and market characteristics than pre-reform. The highly flexible random forest algorithm is able to detect these potentially complex interactions and, therefore, still allows for high prediction precision.
Keywords: K21; L40; 330; Merger policy reform; DG Competition; Prediction; Random Forests