Cross-border acquisition completion by emerging market MNEs revisited: Inductive evidence from a machine learning analysis

Abstract

Existing empirical studies of cross-border acquisition completion by emerging market multinational enterprises remain highly contextual, yielding inconsistent evidence regarding the determinants of deal success or failure. We apply machine learning to expose underlying complexities. The learning results of LightGBM, from data on 24,693 cross-border acquisition deals involving 29 emerging countries, unveil a comprehensive picture of the relative importance and impact patterns of 59 predictors that were fragmentally, inconsistently, or not at all presented in the extant literature. Our findings offer fresh insights into the deal completion of cross-border acquisitions by emerging market multinational enterprises, suggesting novel future research priorities.