M&A Completion Re-exploration: New Evidence from the LightGBM Model with SMOTE Oversampling

Predictions about M&A success always suffer from the challenge of unbalanced data, which can easily lead to biased predictions. In addition, previous empirical studies have certain limitations when facing high-dimensional relationships, making it difficult to provide a more global perspective. This study constructs and compares several machine learning models to propose an optimal model. This optimal model is lightGBM, which is constructed from the data after SMOTE oversampling. The results of LightGBM come from the CSMAR database of 3672 M&A transactions, revealing the relative importance and directions of 50 predictors on M&A outcomes, which may have contradictory results or do not appear in previous literature. The findings of this study provide new insights into predicting the success of M&A deals of Chinese listed companies and suggest new directions for future research.