Distinct Relatedness and Synergies in Mergers & Acquisitions
Using machine learning tools to analyze stock return comovement, we construct a novel measure of distinct relatedness that captures the relatedness between bidder and target firms through unique business features such as a unique product, technology, or location. We find that distinct relatedness significantly increases merger synergies, and is a stronger predictor of synergies than existing relatedness measures based on simple stock-return correlations, textual analysis, or the Standard Industrial Classification (SIC) codes. Distinct relatedness also positively predicts the bidder’s sales growth, profitability, and asset management efficiency in the post-merger period. Our findings provide new evidence that distinct relatedness is an important driver of merger synergies.
Dr Chau Chak Wing Building
