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Forecasting SMEs’ credit risk in supply chain finance with a sampling strategy based on machine learning techniques

Author

Listed:
  • Liukai Wang

    (University of Science and Technology Beijing)

  • Fu Jia

    (University of York)

  • Lujie Chen

    (Xi’an Jiaotong-Liverpool University)

  • Qifa Xu

    (Hefei University of Technology)

Abstract

Exploring the value of multi-source information fusion to predict small and medium-sized enterprises’ (SMEs) credit risk in supply chain finance (SCF) is a popular yet challenging task, as two issues of key variable selection and imbalanced class must be addressed simultaneously. To this end, we develop new forecast models adopting an imbalance sampling strategy based on machine learning techniques and apply these new models to predict credit risk of SMEs in China, using financial information, operation information, innovation information, and negative events as predictors. The empirical results show that the financial-based information, such as TOC, NIR, is most useful in predicting SMEs’ credit risk in SCF, and multi-source information fusion is meaningful in better predicting the credit risk. In addition, based on the preferred CSL-RF model, which extends cost-sensitive learning to a random forest, we also present the varying mechanisms of key predictors for SMEs’ credit risk by using partial dependency analysis. The strategic insights obtained may be helpful for market participants, such as SMEs’ managers, investors, and market regulators.

Suggested Citation

  • Liukai Wang & Fu Jia & Lujie Chen & Qifa Xu, 2023. "Forecasting SMEs’ credit risk in supply chain finance with a sampling strategy based on machine learning techniques," Annals of Operations Research, Springer, vol. 331(1), pages 1-33, December.
    • Handle: RePEc:spr:annopr:v:331:y:2023:i:1:d:10.1007_s10479-022-04518-5
    DOI: 10.1007/s10479-022-04518-5
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    References listed on IDEAS

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    1. Chen, Lujie & Jia, Fu & Li, Taiyu & Zhang, Tianyu, 2021. "Supply chain leadership and firm performance: A meta-analysis," International Journal of Production Economics, Elsevier, vol. 235(C).
    2. Jia, Fu & Blome, Constantin & Sun, Hui & Yang, Yang & Zhi, Bangdong, 2020. "Towards an integrated conceptual framework of supply chain finance: An information processing perspective," International Journal of Production Economics, Elsevier, vol. 219(C), pages 18-30.
    3. Gupta, Jairaj & Gregoriou, Andros, 2018. "Impact of market-based finance on SMEs failure," Economic Modelling, Elsevier, vol. 69(C), pages 13-25.
    4. Andrikopoulos, Panagiotis & Khorasgani, Amir, 2018. "Predicting unlisted SMEs' default: Incorporating market information on accounting-based models for improved accuracy," The British Accounting Review, Elsevier, vol. 50(5), pages 559-573.
    5. Kuniyoshi Saito & Daisuke Tsuruta, 2018. "Information asymmetry in small and medium enterprise credit guarantee schemes: evidence from Japan," Applied Economics, Taylor & Francis Journals, vol. 50(22), pages 2469-2485, May.
    6. Jun Chen & Kam C. Chan & Wang Dong & Feida (Frank) Zhang, 2017. "Internal Control and Stock Price Crash Risk: Evidence from China," European Accounting Review, Taylor & Francis Journals, vol. 26(1), pages 125-152, January.
    7. Nikolaos Sariannidis & Stelios Papadakis & Alexandros Garefalakis & Christos Lemonakis & Tsioptsia Kyriaki-Argyro, 2020. "Default avoidance on credit card portfolios using accounting, demographical and exploratory factors: decision making based on machine learning (ML) techniques," Annals of Operations Research, Springer, vol. 294(1), pages 715-739, November.
    8. Yonghan Ju & So Young Sohn, 2015. "Stress test for a technology credit guarantee fund based on survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 463-475, March.
    9. Lang Zhang & Haiqing Hu & Dan Zhang, 2015. "A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-21, December.
    10. Edward M. H. Lin & Edward W. Sun & Min-Teh Yu, 2018. "Systemic risk, financial markets, and performance of financial institutions," Annals of Operations Research, Springer, vol. 262(2), pages 579-603, March.
    11. Song, Zhuo-lin & Zhang, Xiao-mei, 2018. "Lending technology and credit risk under different types of loans to SMEs: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 57(C), pages 43-69.
    12. Jiang, Cuiqing & Wang, Zhao & Zhao, Huimin, 2019. "A prediction-driven mixture cure model and its application in credit scoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 20-31.
    13. Schwab, Leila & Gold, Stefan & Reiner, Gerald, 2019. "Exploring financial sustainability of SMEs during periods of production growth: A simulation study," International Journal of Production Economics, Elsevier, vol. 212(C), pages 8-18.
    14. Hirsch, Bernhard & Nitzl, Christian & Schoen, Matthias, 2018. "Interorganizational trust and agency costs in credit relationships between savings banks and SMEs," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 37-50.
    15. Angilella, Silvia & Mazzù, Sebastiano, 2015. "The financing of innovative SMEs: A multicriteria credit rating model," European Journal of Operational Research, Elsevier, vol. 244(2), pages 540-554.
    16. 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.
    17. Wetzel, Philipp & Hofmann, Erik, 2019. "Supply chain finance, financial constraints and corporate performance: An explorative network analysis and future research agenda," International Journal of Production Economics, Elsevier, vol. 216(C), pages 364-383.
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