An Information-Theoretic Approach to Learning from Mergers and Acquisitions

• Activity Number: 132
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Business and Economic Statistics Section
• Author(s): Padma Rao Sahib*+ and Harmen de Weerd and Katrin Muehlfeld
• Companies: University of Groningen and University of Groningen and University of Utrecht
• Keywords: mergers and acquisitions ; learning ; entropy ; regression models ; sequences

Abstract

In the management literature, it has been proposed that firms learn more from events that unexpected or surprising than events that have occurred frequently. Surprise or unexpected outcomes are have been captured in the management literature as a "discrete" events (an accident occurs or it does not, there is a near-miss or there is not). However, events need not be so categorical, we might expect a gradation of unexpectedness in the events that occur in the business of running a firm; some events are more unexpected than others. In this paper, we use a concept developed in information theory, termed entropy to capture the unexpectedness of outcomes and examine the effect of entropy on firm performance. We examine learning from unexpected outcomes and performance in the setting of mergers and acquisitions (M&As) of firms. We examine how the 'surprise' element in the recent history of M&As, in terms of whether they are intra industry or diversifying, national or international, affects firm performance following the M&As. Using regression modeling techniques, we also examine if there certain sequences of M&As associated with higher firm performance.