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Title: Stochastic Prediction of Mergers and Acquisitions
Authors: Noh, Jung Hyun
Advisors: Shkolnikov, Mykhaylo
Department: Operations Research and Financial Engineering
Class Year: 2016
Abstract: A framework for the stochastic analysis of mergers and acquisitions ("M&A"s) is developed using doubly stochastic intensity models of survival analysis. The main focus of the thesis is on the parametric and the Cox models, although the Aalen and the Cox-Aalen models are utilized to highlight the time-dependent nature of macroeconomic and firm-specific covariates. Following tests used in Duffie (2011), the independence assumption underlying the parametric and the Cox models are rejected, and I provide further analysis on the shape of the hazard function based on nonparametric estimation. The results provide a new framework of understanding the role of the macroeconomic process in driving merger intensity. Empirical results are provided by M&A data spanning the 15-year period between 2000 and 2014 in the consumer and retail industry, as well as a case study on Walt Disney's acquisition of Marvel Entertainment.
Extent: 76 pages
Access Restrictions: Walk-in Access. This thesis can only be viewed on computer terminals at the Mudd Manuscript Library.
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Operations Research and Financial Engineering, 2000-2017

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