Learning Production Process Heterogeneity Across Industries: Implications of Deep Learning for Corporate M&A Decisions

TL;DR

This paper uses deep learning to measure production process differences across industries and shows that greater heterogeneity reduces M&A activity and success, emphasizing the role of technological structure in corporate decisions.

Contribution

It introduces a novel deep learning-based measure of industry production process heterogeneity and links it to M&A outcomes, providing new insights into structural industry differences.

Findings

Greater industry heterogeneity correlates with fewer M&As.

Higher heterogeneity leads to lower deal success and survival.

Structural differences influence corporate M&A decisions.

Abstract

Using deep learning techniques, we introduce a novel measure for production process heterogeneity across industries. For each pair of industries during 1990-2021, we estimate the functional distance between two industries' production processes via deep neural network. Our estimates uncover the underlying factors and weights reflected in the multi-stage production decision tree in each industry. We find that the greater the functional distance between two industries' production processes, the lower are the number of M&As, deal completion rates, announcement returns, and post-M&A survival likelihood. Our results highlight the importance of structural heterogeneity in production technology to firms' business integration decisions.