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Applying a nonparametric random forest algorithm to assess the credit risk of the energy industry in China

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  • Tang, Lingxiao
  • Cai, Fei
  • Ouyang, Yao

Abstract

With the rapid growth of the credit card business in China's energy industry, credit risk is gradually revealed. This study aims to scientifically measure the credit risk of credit cards used in China's energy industry and to lay the foundation for comprehensive credit risk management. Based on an analysis of the factors influencing credit risk influencing factors, this study applies the random forest algorithm and the monthly data of credit cards used by energy industry customers in a branch of the Postal Savings Bank of China from April 2014 to June 2017 to build an effective credit risk assessment model and scientifically measure the credit risk in China's energy industry. The results suggest that credit card features like the overdraft ratio and the amount of credit card expenses within a month have significant impacts on credit risk, our model's comprehensive prediction accuracy is as high as 91.5%, and its stability is satisfying. These findings can provide valuable information to help banks improve their credit risk management.

Suggested Citation

  • Tang, Lingxiao & Cai, Fei & Ouyang, Yao, 2019. "Applying a nonparametric random forest algorithm to assess the credit risk of the energy industry in China," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 563-572.
    • Handle: RePEc:eee:tefoso:v:144:y:2019:i:c:p:563-572
    DOI: 10.1016/j.techfore.2018.03.007
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    References listed on IDEAS

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    1. Wiginton, John C., 1980. "A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(3), pages 757-770, September.
    2. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    3. Smith, L. Douglas & Lawrence, Edward C., 1995. "Forecasting losses on a liquidating long-term loan portfolio," Journal of Banking & Finance, Elsevier, vol. 19(6), pages 959-985, September.
    4. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    5. Dean Fantazzini & Silvia Figini, 2009. "Random Survival Forests Models for SME Credit Risk Measurement," Methodology and Computing in Applied Probability, Springer, vol. 11(1), pages 29-45, March.
    6. Scott, James, 1981. "The probability of bankruptcy: A comparison of empirical predictions and theoretical models," Journal of Banking & Finance, Elsevier, vol. 5(3), pages 317-344, September.
    7. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    8. Bart Baesens & Rudy Setiono & Christophe Mues & Jan Vanthienen, 2003. "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, INFORMS, vol. 49(3), pages 312-329, March.
    9. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    10. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    11. Micha, Bernard, 1984. "Analysis of business failures in France," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 281-291, June.
    12. K B Schebesch & R Stecking, 2005. "Support vector machines for classifying and describing credit applicants: detecting typical and critical regions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1082-1088, September.
    13. Carow, Kenneth A. & Staten, Michael E., 1999. "Debit, credit, or cash: survey evidence on gasoline purchases," Journal of Economics and Business, Elsevier, vol. 51(5), pages 409-421, September.
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    Cited by:

    1. He, Ni & Yongqiao, Wang & Tao, Jiang & Zhaoyu, Chen, 2022. "Self-Adaptive bagging approach to credit rating," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    2. Li, Jing-Ping & Mirza, Nawazish & Rahat, Birjees & Xiong, Deping, 2020. "Machine learning and credit ratings prediction in the age of fourth industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    3. Dmytro Kovalenko & Olga Afanasieva & Nani Zabuta & Tetiana Boiko & Rosen Rosenov Baltov, 2021. "Model of Assessing the Overdue Debts in a Commercial Bank Using Neuro-Fuzzy Technologies," JRFM, MDPI, vol. 14(5), pages 1-20, May.

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