Regression and Machine Learning Methods to Predict Discrete Outcomes in Accounting Research

71 Pages Posted: 11 Mar 2021

See all articles by Jake Krupa

Jake Krupa

Tulane University - A.B. Freeman School of Business

Miguel Minutti-Meza

University of Miami - Department of Accounting

Date Written: March 2021

Abstract

Predictive modeling focuses on iteratively trying various combinations and transformations of a set of variables to generate a decision rule that predicts outcomes for new observations. Although accounting researchers have demonstrated a keen interest in predictive modeling, we identify a lack of accessible and applied guidance on this topic for accounting settings. This issue has become more salient with the increasing availability of machine learning models that use unfamiliar terminology, that can be estimated using several "competing" algorithms, and that produce different outputs than other models used for causal inference. To overcome this gap, we provide an overview of how to predict discrete outcomes with logistic regression and two machine learning models used in recent studies: support vector machines and gradient boosting. We also include guidance and a comprehensive example - predicting investigations by the U.S. Securities and Exchange Commission - that illustrates the elements of the prediction process, highlighting the importance of "out-of-sample" accuracy and unique aspects in the presentation of a prediction model's results.

Suggested Citation

Krupa, Jake and Minutti-Meza, Miguel, Regression and Machine Learning Methods to Predict Discrete Outcomes in Accounting Research (March 2021). Available at SSRN: https://ssrn.com/abstract=3801353 or http://dx.doi.org/10.2139/ssrn.3801353

Jake Krupa

Tulane University - A.B. Freeman School of Business ( email )

7 McAlister Drive
New Orleans, LA 70118
United States

HOME PAGE: http://https://business.tulane.edu/faculty-research/faculty-profile.php?idkey=597

Miguel Minutti-Meza (Contact Author)

University of Miami - Department of Accounting ( email )

Coral Gables, FL 33146-6531
United States
305-284-6287 (Phone)

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