Machine Learning, Diversification, and M&A Performance
Publication Date
8-20-2025
Abstract
In this paper, we study whether acquisitions can improve the financial performance of firms possessing machine learning/artificial intelligence (ML/AI). First, we argue and find that this effect is significant. Then, drawing on studies proposing a weaker effect of ML/AI effectiveness when the data used are complex and contain irrelevant information, we hypothesize and find that the benefit from within-industry deals is higher than that from deals between industries. Furthermore, the effect is more significant among deals between industries when closely related. Finally, we extend these arguments to deals where both the acquirer and target have ML/AI and find the same pattern of effects. We use a number of causal inference methods on U.S. acquisition data to test our theory. Finally, we discuss the contributions of our study to research on ML/AI and acquisitions in general.
Document Type
Article
Keywords
Machine Learning, Artificial Intelligence, Diversification, M&A, Causal Inference
Disciplines
Strategic Management Policy
DOI
10.2139/ssrn.5401158
Source
SMU Cox: Strategy (Topic)
Language
English
