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Modeling default prediction with earnings management

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  • Lin, Hsiou-Wei William
  • Lo, Huai-Chun
  • Wu, Ruei-Shian

Abstract

This study explores whether taking into account real earnings management improves specification of the default prediction model based on the Z-score methodology for Chinese listed companies. We demonstrate that the model proposed by Altman (1968) overestimates (underestimates) the Z-score and thus the survival probability for firms engaging in aggressive (minor or no) income-increasing manipulation. By contrast, our inclusion of the indicator variable for real earnings management considerably enhances the explanatory power of Z-score factors for firm survival/default. 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 unanimously yields a greater prediction accuracy rate and a lower false loan rejection rate than the unadjusted scoring model for financially non-distressed firms.

Suggested Citation

  • Lin, Hsiou-Wei William & Lo, Huai-Chun & Wu, Ruei-Shian, 2016. "Modeling default prediction with earnings management," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 306-322.
    • Handle: RePEc:eee:pacfin:v:40:y:2016:i:pb:p:306-322
    DOI: 10.1016/j.pacfin.2016.01.005
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    6. Magali Costa & Inês Lisboa & Ana Gameiro, 2022. "Is the Financial Report Quality Important in the Default Prediction? SME Portuguese Construction Sector Evidence," Risks, MDPI, vol. 10(5), pages 1-24, May.
    7. Suwarno, 2019. "The Effect of Earnings Management and Earnings Persistence on Earnings Response Coefficient: Evidence from Indonesia," Journal of Social Science Studies, Macrothink Institute, vol. 6(1), pages 59-67, January.
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