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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)4849-4882
Number of pages34
Journal / PublicationAccounting and Finance
Volume63
Issue number5
Online published18 Jul 2023
Publication statusPublished - Dec 2023

Abstract

This 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. © 2023 Accounting and Finance Association of Australia and New Zealand.

Research Area(s)

  • MD&A, momentum, production, stock return

Citation Format(s)

Production similarity and the cross-section of stock returns: A machine learning approach. / Ge, Yao; Qiao, Zheng; Shen, Zhe et al.
In: Accounting and Finance, Vol. 63, No. 5, 12.2023, p. 4849-4882.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review