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Analyzing default risk among P2P platforms based on the LAS-STACK method by considering multidimensional signals under specific economic contexts

Author

Listed:
  • Kun Liang

    (Anhui University)

  • Chen Zhang

    (Anhui University)

  • Cuiqing Jiang

    (Hefei University of Technology)

Abstract

P2P platform default risk seriously affects the returns of investors, which may cause systemic financial risks. The existing literature mostly focuses on borrower risk, ignoring the research on P2P platform default risk. This paper uses signal theory and data mining-related methods to study the default risk prediction of P2P platforms that integrate soft and hard information signals in different economic environments. First, using the cluster analysis method, the macroeconomic environment of P2P platforms is studied. Second, from the perspective of signal costs, signal theory is used to analyze the impacts of soft and hard information risk signals on platform default in different economic environments. Finally, by integrating the lasso and stacking methods, a LAS-STACK model is proposed to study the prediction of P2P platform default risk in the high-dimensional unbalanced data context. The conclusions of this paper show that the fusion of soft and hard information can better predict the default risk of P2P platforms, especially during periods with low economic levels. Additionally, the LAS-STACK model has a better prediction ability for the P2P platform default risk in the high-dimensional unbalanced data context. This study can improve the ability of regulators and P2P platforms to warn and manage default risks in a specific economic environment and protect investors' returns.

Suggested Citation

  • Kun Liang & Chen Zhang & Cuiqing Jiang, 2022. "Analyzing default risk among P2P platforms based on the LAS-STACK method by considering multidimensional signals under specific economic contexts," Electronic Commerce Research, Springer, vol. 22(1), pages 77-111, March.
    • Handle: RePEc:spr:elcore:v:22:y:2022:i:1:d:10.1007_s10660-021-09505-9
    DOI: 10.1007/s10660-021-09505-9
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    1. Rajkamal Iyer & Asim Ijaz Khwaja & Erzo F. P. Luttmer & Kelly Shue, 2016. "Screening Peers Softly: Inferring the Quality of Small Borrowers," Management Science, INFORMS, vol. 62(6), pages 1554-1577, June.
    2. 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.
    3. Stephen J. Perkins & Chris Hendry, 2005. "Ordering Top Pay: Interpreting the Signals," Journal of Management Studies, Wiley Blackwell, vol. 42(7), pages 1443-1468, November.
    4. Eric Rosenberg & Alan Gleit, 1994. "Quantitative Methods in Credit Management: A Survey," Operations Research, INFORMS, vol. 42(4), pages 589-613, August.
    5. Siming Li & Zhangxi Lin & Jiaxian Qiu & Roozmehr Safi & Zhongyi Xiao, 2015. "How friendship networks work in online P2P lending markets," Nankai Business Review International, Emerald Group Publishing Limited, vol. 6(1), pages 42-67, March.
    6. Broecker, Thorsten, 1990. "Credit-Worthiness Tests and Interbank Competition," Econometrica, Econometric Society, vol. 58(2), pages 429-452, March.
    7. Pham, Tho & Talavera, Oleksandr, 2018. "Discrimination, Social Capital, and Financial Constraints: The Case of Viet Nam," World Development, Elsevier, vol. 102(C), pages 228-242.
    8. Chen, Pu & Wang, Chunyang & Liu, Yangyan, 2015. "Real estate prices and firm borrowings: Micro evidence from China," China Economic Review, Elsevier, vol. 36(C), pages 296-308.
    9. Xing, Haipeng & Sun, Ning & Chen, Ying, 2012. "Credit rating dynamics in the presence of unknown structural breaks," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 78-89.
    10. Finlay, Steven, 2011. "Multiple classifier architectures and their application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 210(2), pages 368-378, April.
    11. Doris Neuberger & Solvig Räthke-Döppner, 2015. "The role of demographics in small business loan pricing," Small Business Economics, Springer, vol. 44(2), pages 411-424, February.
    12. Mingfeng Lin & Nagpurnanand R. Prabhala & Siva Viswanathan, 2013. "Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending," Management Science, INFORMS, vol. 59(1), pages 17-35, August.
    13. Dorfleitner, Gregor & Priberny, Christopher & Schuster, Stephanie & Stoiber, Johannes & Weber, Martina & de Castro, Ivan & Kammler, Julia, 2016. "Description-text related soft information in peer-to-peer lending – Evidence from two leading European platforms," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 169-187.
    14. Lee, Tian-Shyug & Chiu, Chih-Chou & Chou, Yu-Chao & Lu, Chi-Jie, 2006. "Mining the customer credit using classification and regression tree and multivariate adaptive regression splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1113-1130, February.
    15. Pennathur, Anita K., 2001. ""Clicks and bricks":: e-Risk Management for banks in the age of the Internet," Journal of Banking & Finance, Elsevier, vol. 25(11), pages 2103-2123, November.
    16. Cebenoyan, A. Sinan & Strahan, Philip E., 2004. "Risk management, capital structure and lending at banks," Journal of Banking & Finance, Elsevier, vol. 28(1), pages 19-43, January.
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    Keywords

    P2P platform; Default risk; LAS-STACK; Signal theory;
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