Forecasting Corporate Bankruptcy Using Accrual-Based Models

Philippe du Jardin (), David Veganzones and Eric Séverin
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David Veganzones: Université de Lille 1
Eric Séverin: Université de Lille 1

Computational Economics, 2019, vol. 54, issue 1, No 3, 7-43

Abstract: Abstract Financial information has been widely used to design bankruptcy prediction models. All research works that have studied such models assume that financial statements are reliable. However, reality is a bit different. Indeed, firms may tend to present their financial accounts depending on particular circumstances, especially when seeking to change the perception of the risk incurred by their partners, and thus distort or alter some of them. Consequently, one may wonder to what extent such “manipulations”, called earnings management, may influence any model that relies on accounting data. This is why we study how earnings management may affect financial variables and how it can indirectly distort predictions made by failure models. For this purpose, we used a measure that makes it possible to assess potential account manipulations, and not effective manipulations. Our results show that when these distortions are measured and used with other financial variables, models are more accurate than those that solely rely on pure financial data. They also show that the improvement of model accuracy is essentially due to a reduction of type-I error—the costliest error in economic terms.

Keywords: Bankruptcy prediction; Earnings management; Finance (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10614-017-9681-9

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