Year of Publication

2012

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Business and Economics

Department

Business Administration

First Advisor

Dr. Bradford Jordan

Second Advisor

Dr. Kristine Hankins

Abstract

This is a study in two parts. In part-1, I identify several methods of estimating long-run abnormal returns prevalent in the finance literature and present an alternative using propensity score matching. I first demonstrate the concept with a simple simulation using generated data. I then employ historical returns from CRSP and randomly select events from the dataset using various alternating criteria. I test the efficacy of different methods in terms of type-I and type-II errors in detecting abnormal returns over 12- 36- and 60- month periods. I use various forms of propensity score matching: 1--5 Nearest Neighbors in Caliper using distance defined alternatively by Propensity Scores and the Mahalanobis Metric, and Caliper Matching. I show that overall, Propensity Score Matching with two nearest neighbors provides much better performance than traditional methods, especially when the occurence of events is dictated by the presence of certain firm characteristics.

In part-2, I demonstrate an application of Propensity Score Matching in the context of open-market share repurchase announcements. I show that traditional methods are ill-suited for the calculation of long-run abnormal returns following such events. Consequently, I am able to improve upon such methods on two fronts. First, I improve upon traditional matching methods by providing better matches on multiple dimensions and by being able to retain a larger sample of firms from the dataset. Second, I am able to eliminate much of the bias inherent in the Fama-French type methods for this particular application. I show this using simulations on samples based on firms that resemble a typical repurchasing firm. As a result, I obtain a statistically significant 1-, 3-, and 5- year abnormal return of about 2%, 5%, and 10% respectively, which is much lower than what prior literature has shown using traditional methods. Further investigation revealed that much of these returns are unique to small and unprofitable firms.

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