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Dissertation_WSU_Formatted_June
Embargoed Access, Embargo ends: 07/16/2028
Dissertation
Advances in Measuring and Predicting Merger Success
Doctor of Philosophy (PhD), Washington State University
2026
Abstract
Acquiring firms face uncertainty about the success of mergers or acquisitions. Artificial intelligence (AI) models can reduce uncertainty by forecasting investor reactions, allowing firms to screen out deals likely to decrease value and screen in those likely to increase value. An empirical analysis with a training period and out-of-sample testing period shows that screening deals with AI models increases average returns to acquirers. Deal screening can reduce downside risk and limit overpayment. Screening deals with AI models is more effective for firms with weaker governance, suggesting the potential to counteract managerial biases, and is less effective for complex deals.
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Details
- Title
- Advances in Measuring and Predicting Merger Success
- Creators
- Adam N Bozman
- Contributors
- Douglas J Fairhurst (Advisor)Dan Greene (Committee Member)David Whidbee (Committee Member)Flora Ma (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Carson College of Business
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Number of pages
- 126
- Identifiers
- 99901393304801842
- Language
- English
- Resource Type
- Dissertation