Erasing Blind Spots

Abstract

Currently, quantitative asset pricing models are often not equipped to deal with merger and acquisition events. In such cases, portfolio managers make the assumption that the model is not working and they override its decisions for an entire year. This thesis studies the performance of quantitative models after these events and provides research based guidelines for future decisions on model overrides.A selection of investment factors representing the model will be used to explain the post-event abnormal returns. In general, the data consists of monthly observations on one cross-section of firms over a time period of several years. Consequently, it may contain unobserved firm or time effects. Ignoring such effects leads to inefficient estimates and biased results. Hence, robust inference is conducted by adjusting the standard errors of a pooled regression, and through modelling the effects in a panel regression.Multiple approaches show that the separate factors are not sufficiently able to explain the abnormal returns after an event, if compared to the model's performance in regular market circumstances. Consistent results for the post-event performance are particularly hard to find. Equally weighting all factors in a single regressor, leads to model performance being equivalent to that in non-event times after 9 months, which indicates that a override period of equal length is sufficient. Therefore, the current assumption that the model is not working properly after a merger or acquisition, is correct. The robust pooled model is favoured over the panel model in this research, due to its low complexity and its straightforward results.