Production similarity and the cross‐section of stock returns: A machine learning approach

Abstract

AbstractThis paper employs a machine learning approach to capture firm‐pair production similarity, which depicts how firms' production processes resemble each other using textual data in corporate MD&As. We show that production‐linked firms' average return has strong predictive power on focal firm's future stock return. A hedging portfolio yields an annualised return of 11.69%, which cannot be subsumed by existing factor models. For mechanism tests, we find that the main findings are stronger in firms with higher information asymmetry and higher costs of arbitrage. The production‐linkage measure also predicts future unexpected earnings, suggesting it possibly includes valuable information on firm fundamentals.