Using Machine Learning to Measure Conservatism

Jeremy Bertomeu (), Edwige Cheynel (), Yifei Liao () and Mario Milone ()
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Jeremy Bertomeu: Olin Business School, Washington University, St. Louis, Missouri 63130
Edwige Cheynel: Olin Business School, Washington University, St. Louis, Missouri 63130
Yifei Liao: Paul Merage School of Business, University of California, Irvine, California 92697
Mario Milone: Rady School of Management, University of California, San Diego, California 92093

Management Science, 2025, vol. 71, issue 2, 1504-1522

Abstract: This study proposes an approach to measure conservatism using machine learning techniques that are not constrained by functional form restrictions. We extend the differential timeliness model to allow for observable characteristics related to conservatism to follow nonlinear relationships. By developing machine learning measures of conservatism, we draw attention to potential benefits and drawbacks and show how its insights complement conventional measures. Our broader goal is to investigate the effectiveness of machine learning algorithms for filtering noise in traditional archival studies and uncovering more complex empirical patterns.

Keywords: machine learning; neural network; accounting; conservatism; measure (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:71:y:2025:i:2:p:1504-1522

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