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

Jongsub Lee and Hayong Yun

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.

Date: 2023-01
New Economics Papers: this item is included in nep-big and nep-cmp
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