Modeling Default Prediction with Earnings Management

39 Pages Posted: 1 Feb 2015 Last revised: 17 Jun 2018

See all articles by Hsiou-Wei Lin

Hsiou-Wei Lin

National Taiwan University

Huai-Chun Lo

Yuan Ze University

Ruei-Shian Wu

Yuan Ze University

Date Written: January 30, 2015


This study explores whether taking into account variables for real earnings management improves specification of the default prediction model based on the Z-score methodology for Chinese listed companies. We demonstrate that the Z-score model proposed by Altman (1968) overestimates the survival probability for firms engaging in earnings management. In contrast, our inclusion of the indicator variable for real earnings management considerably enhances the prediction power of Z-score methodology. With respect to the ability to predict out-of-sample default, our findings suggest that the accounting-based credit scoring model, adjusted for real earnings management, especially via accelerating sales, yields a greater prediction accuracy rate and a lower false loan rejection rate than the unadjusted scoring model for financially non-distressed firms.

Keywords: Default prediction model; Accrual-based earnings management; Real earnings management

JEL Classification: G14, G29, J44

Suggested Citation

Lin, Hsiou-Wei and Lo, Huai-Chun and Wu, Ruei-Shian, Modeling Default Prediction with Earnings Management (January 30, 2015). Pacific-Basin Finance Journal 40 (2016) 306-322, Available at SSRN:

Hsiou-Wei Lin

National Taiwan University ( email )

1 Sec. 4, Roosevelt Road
Taipei, 106

Huai-Chun Lo

Yuan Ze University ( email )

135, Far-East Rd., Chung-Li
Taoyuan, ROC

Ruei-Shian Wu (Contact Author)

Yuan Ze University ( email )

135 Yuan-Tung Road
Chung-Li, 32003
886-3-4638800#2195 (Phone)
886-3-4633845 (Fax)

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