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Chinese companies distress prediction: an application of data envelopment analysis

Author

Listed:
  • Zhiyong Li

    (Credit Research Centre, Business School, University of Edinburgh, UK)

  • Jonathan Crook

    (Credit Research Centre, Business School, University of Edinburgh, UK)

  • Galina Andreeva

    (Credit Research Centre, Business School, University of Edinburgh, UK)

Abstract

Bankruptcy prediction is a key part in corporate credit risk management. Traditional bankruptcy prediction models employ financial ratios or market prices to predict bankruptcy or financial distress prior to its occurrence. We investigate the predictive accuracy of corporate efficiency measures along with standard financial ratios in predicting corporate distress in Chinese companies. Data Envelopment Analysis (DEA) is used to measure corporate efficiency. In contrast to previous applications of DEA in credit risk modelling where it was used to generate a single efficiency—Technical Efficiency (TE), we assume Variable Returns to Scale, and decompose TE into Pure Technical Efficiency and Scale Efficiency. These measures are introduced into Logistic Regression to predict the probability of distress, along with the level of Returns to Scale. Effects of efficiency variables are allowed to vary across industries through the use of interaction terms, while the financial ratios are assumed to have the same effects across all sectors. The results show that the predictive power of the model is improved by this corporate efficiency information.

Suggested Citation

  • Zhiyong Li & Jonathan Crook & Galina Andreeva, 2014. "Chinese companies distress prediction: an application of data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 466-479, March.
    • Handle: RePEc:pal:jorsoc:v:65:y:2014:i:3:p:466-479
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    Citations

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    Cited by:

    1. Xiangxing Tao & Mingxin Wang & Yanting Ji, 2023. "The Application of Graph-Structured Cox Model in Financial Risk Early Warning of Companies," Sustainability, MDPI, vol. 15(14), pages 1-16, July.
    2. Ayoola Tajudeen John & Obokoh Lawrence Ogechukwu, 2018. "Corporate Governance and Financial Distress in the Banking Industry: Nigerian Experience," Journal of Economics and Behavioral Studies, AMH International, vol. 10(1), pages 182-193.
    3. Jonathan Crook & David Edelman, 2014. "Special issue credit risk modelling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 321-322, March.
    4. Sumaira Ashraf & Elisabete G. S. Félix & Zélia Serrasqueiro, 2022. "Does board committee independence affect financial distress likelihood? A comparison of China with the UK," Asia Pacific Journal of Management, Springer, vol. 39(2), pages 723-761, June.
    5. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    6. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    7. Osman, Ibrahim H. & Anouze, Abdel Latef & Irani, Zahir & Lee, Habin & Medeni, Tunç D. & Weerakkody, Vishanth, 2019. "A cognitive analytics management framework for the transformation of electronic government services from users’ perspective to create sustainable shared values," European Journal of Operational Research, Elsevier, vol. 278(2), pages 514-532.
    8. Ioannis E. Tsolas, 2021. "Firm Credit Scoring: A Series Two-Stage DEA Bootstrapped Approach," JRFM, MDPI, vol. 14(5), pages 1-12, May.
    9. Eling, Martin & Jia, Ruo, 2018. "Business failure, efficiency, and volatility: Evidence from the European insurance industry," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 58-76.
    10. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
    11. Arazmuradov, Annageldy, 2016. "Assessing sovereign debt default by efficiency," The Journal of Economic Asymmetries, Elsevier, vol. 13(C), pages 100-113.
    12. Muhammad Mohsin & Mohammad Nurunnabi & Jijian Zhang & Huaping Sun & Nadeem Iqbal & Robina Iram & Qaiser Abbas, 2021. "The evaluation of efficiency and value addition of IFRS endorsement towards earnings timeliness disclosure," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1793-1807, April.
    13. Mai, Feng & Tian, Shaonan & Lee, Chihoon & Ma, Ling, 2019. "Deep learning models for bankruptcy prediction using textual disclosures," European Journal of Operational Research, Elsevier, vol. 274(2), pages 743-758.
    14. Zhiyong Li & Chen Feng & Ying Tang, 2022. "Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis," Annals of Operations Research, Springer, vol. 315(1), pages 279-315, August.
    15. Huo, Baofeng & Gu, Minhao & Jiang, Bin, 2018. "China-related POM research: Literature review and suggestions for future research," International Journal of Production Economics, Elsevier, vol. 203(C), pages 134-153.
    16. Ali Meftah Gerged & Mohamed Marie & Israa Elbendary, 2022. "Estimating the Risk of Financial Distress Using a Multi-Layered Governance Criterion: Insights from Middle Eastern and North African Banks," JRFM, MDPI, vol. 15(12), pages 1-22, December.

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