Can earnings management information improve bankruptcy prediction models?

Eric Séverin and David Veganzones ()
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Eric Séverin: Université de Lille
David Veganzones: ESCE International Business School

Annals of Operations Research, 2021, vol. 306, issue 1, No 10, 247-272

Abstract: Abstract This study investigates whether earnings management in its two forms (accruals and real activities manipulation) can improve bankruptcy prediction models. In particular, it examines whether special information extracted from earnings management, including potential manipulations of the reported earnings found in financial statements, might improve the accuracy of bankruptcy prediction models. It applies earnings management–based models, based on financial ratios and earnings management variables, to a sample of 6,000 French small and medium-size enterprises, then compares the classification rates obtained by these models with a model based solely on financial ratios. This study thus makes several contributions by (1) investigating novel predictors, accruals, and real activities manipulation variables, in the context of bankruptcy prediction modeling; (2) enabling analyses of the contribution of earnings management–based variables, in the form of static and dynamic indicators, to model performance; (3) revealing the influence of these variables on the forecasting horizon of bankruptcy prediction models (one- to three-year horizon); and (4) establishing that earnings management information provides a complementary explanatory variable for enhancing model performance.

Keywords: Earnings management; Bankruptcy prediction models; Corporate finance (search for similar items in EconPapers)
JEL-codes: C53 G33 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10479-021-04183-0

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