政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141060
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113318/144297 (79%)
Visitors : 51094627      Online Users : 859
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/141060


    Title: 最適預期損失信用評等模型:以P2P平台為例
    Optimal Structure of Expected Loss Credit Rating Model:Evidence from P2P Platform
    Authors: 簡銘均
    Jian, Ming-Chun
    Contributors: 呂桔誠
    林士貴
    Lyu, Joseph Jye-Cherng
    Lin, Shih-Kuei
    簡銘均
    Jian, Ming-Chun
    Keywords: 違約風險
    提前還款風險
    違約損失率
    預期損失
    機器學習
    P2P平台風險管理
    Default Risk
    Prepayment Risk
    Loss Given Default
    Expected Loss
    Machine Learning
    Risk Management in P2P Platform
    Date: 2022
    Issue Date: 2022-08-01 17:28:49 (UTC+8)
    Abstract: 本研究使用 P2P 平台資料提出一個最適結構之預期損失信用評等模型，不僅包含違約風險，也考量提前還款風險、違約損失率以及違約曝險額。建構預期損失模型過程中，我們透過機器學習方法建構各風險成分模型並使用 SHAP 值確保機器學習的解釋能力與傳統回歸模型有一致的結果。此外，我們使用 Kruskal-Wallis 檢定以及 Conover 檢定發現以預期損失信用評等模型相較違約機率信用評等模型，均有較好的分辨能力。最後，我們發現將提前還款風險納入評等模型中可以使模型對於預測借款者最終損失的表現有所提升。
    In this study, we propose an optimal structured expected loss credit rating model using P2P platform data, including default risk, prepayment risk, loss given default (LGD), and exposure at default (EAD). In addition, each risk component of expected loss is constructed via a machine learning approach, and we use SHAP values to ensure the explanatory power of the machine learning model is consistent with the statistical regression model. Furthermore, using the Kruskal-Wallis and the Conover’s tests, we find that the expected loss credit rating model has better discriminatory power than the probability of default credit rating model. Finally, incorporating prepayment risk into the credit rating model improves the model’s performance in predicting the borrower’s actual loss.
    Reference: 1. Aldahdooh, R. T., & Ashour, W. M. (2013). Dimk-means” distance-based initialization method for k-means clustering algorithm”. International Journal of Intelligent Systems and Applications, 5(2).

    2. Aldric, J. H., & Nelson, F. D. (1984). Linear probability, logit and probit models. Thousand Oaks, CA: Sage, 10, 9781412984744.

    3. Bellotti, T., & Crook, J. (2012). Loss given default models incorporating macroeconomic variables for credit cards. International Journal of Forecasting, 28(1), 171–182.
    4. Bordo, M. D., & Levin, A. T. (2017). Central bank digital currency and the future of monetary policy (Tech. Rep.). National Bureau of Economic Research.

    5. Calabrese, R., & Zanin, L. (2022). Modelling spatial dependence for loss given default in peer-to-peer lending. Expert Systems with Applications, 192, 116295. Carey, M., & Hrycay, M. (2001). Parameterizing credit risk models with rating data. Journal of banking & finance, 25(1), 197–270.

    6. CGFS-FSB. (2017). Fintech credit: market structure, business models and financial stability implicationsmay.

    7. Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785–794).

    8. Chen, X., Huang, B., & Ye, D. (2018). The role of punctuation in p2p lending: Evidence from china. Economic Modelling, 68, 634–643.

    9. Chen, X., Huang, B., & Ye, D. (2020). Gender gap in peer-to-peer lending: Evidence from china. Journal of Banking & Finance, 112, 105633.

    10. Chernov, M., Dunn, B. R., & Longstaff, F. A. (2018). Macroeconomic-driven prepayment risk and the valuation of mortgage-backed securities. The Review of Financial Studies, 31(3), 1132–1183.

    11. Conover, W. J. (1999). Practical nonparametric statistics (Vol. 350). john wiley & sons.

    12. Dermine, J., & De Carvalho, C. N. (2006). Bank loan losses-given-default: A case study. Journal of Banking & Finance, 30(4), 1219–1243.

    13. Dorfleitner, G., Priberny, C., Schuster, S., Stoiber, J., Weber, M., de Castro, I., & Kammler, J. (2016). Description-text related soft information in peer-to-peer lending– evidence from two leading european platforms. Journal of Banking & Finance, 64, 169–187.

    14. Eletter, S. F., Yaseen, S. G., & Elrefae, G. A. (2010). Neuro-based artificial intelligence model for loan decisions. American Journal of Economics and Business Administration, 2(1), 27.

    15. Emekter, R., Tu, Y., Jirasakuldech, B., & Lu, M. (2015). Evaluating credit risk and loan performance in online peer-to-peer (p2p) lending. Applied Economics, 47(1), 54– 70.

    16. Fitzpatrick, T., & Mues, C. (2021). How can lenders prosper? comparing machine learning approaches to identify profitable peer-to-peer loan investments. European Journal of Operational Research, 294(2), 711–722.

    17. Foglia, A., Iannotti, S., & Marullo Reedtz, P. (2001). The definition of the grading scales in banks’internal rating systems. Economic Notes, 30(3), 421–456.

    18. Freedman, S., & Jin, G. Z. (2017). The information value of online social networks: Lessons from peer-to-peer lending. International Journal of Industrial Organiz tion, 51, 185–222.

    19. Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189–1232.

    20. Grunert, J., & Weber, M. (2009). Recovery rates of commercial lending: Empirical evidence for german companies. Journal of Banking & Finance, 33(3), 505–513.

    21. Havrylchyk, O., & Verdier, M. (2018). The financial intermediation role of the p2p lending platforms. Comparative Economic Studies, 60(1), 115–130.

    22. Heitfield, E. (2004). Rating system dynamics and bank-reported default probabilities under the new basel capital accord. research paper, Board of Governors of the Federal Reserve System, March.

    23. Jain, M., & Verma, C. (2014). Adapting k-means for clustering in big data. International Journal of Computer Applications, 101(1), 19–24.

    24. Jin, Y., & Zhu, Y. (2015). A data-driven approach to predict default risk of loan for online peer-to-peer (p2p) lending. In 2015 fifth international conference on communication systems and network technologies (pp. 609–613).

    25. Kelly, R., & O’Malley, T. (2016). The good, the bad and the impaired: A credit risk model of the irish mortgage market. Journal of Financial Stability, 22, 1–9.

    26. Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767–2787.

    27. Krink, T., Paterlini, S., & Resti, A. (2008). The optimal structure of pd buckets. Journal of Banking & Finance, 32(10), 2275–2286.

    28. Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American statistical Association, 47(260), 583–621.

    29. Lagarde, C. (2018). Straight talk: A regulatory approach to fintech. Finance & Development, 55(002).

    30. Lee, T.-S., Chiu, C.-C., Lu, C.-J., & Chen, I.-F. (2002). Credit scoring using the hybrid neural discriminant technique. Expert Systems with applications, 23(3), 245–254.

    31. Levin, A., & Davidson, A. (2005). Prepayment risk-and option-adjusted valuation of mbs. The Journal of Portfolio Management, 31(4), 73–85.

    32. Li, Z., Li, K., Yao, X., & Wen, Q. (2019). Predicting prepayment and default risks of unsecured consumer loans in online lending. Emerging Markets Finance and Trade, 55(1), 118–132.

    33. Liang, K., & He, J. (2020). Analyzing credit risk among chinese p2p-lending businesses by integrating text-related soft information. Electronic Commerce Research and Applications, 40, 100947.

    34. Lin, M. (2009). Peer-to-peer lending: An empirical study.

    35. Lin, M., Prabhala, N. R., & Viswanathan, S. (2013). Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending. Management science, 59(1), 17–35.

    36. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.

    37. Luo, B., & Lin, Z. (2013). A decision tree model for herd behavior and empirical evidence from the online p2p lending market. Information Systems and e-Business Management, 11(1), 141–160.

    38. Lyra, M., Paha, J., Paterlini, S., & Winker, P. (2010). Optimization heuristics for determining internal rating grading scales. Computational Statistics & Data Analysis, 54(11), 2693–2706.

    39. Papouskova, M., & Hajek, P. (2019). Two-stage consumer credit risk modelling using heterogeneous ensemble learning. Decision support systems, 118, 33–45.

    40. Pertaia, G., Prokhorov, A., & Uryasev, S. (2022). A new approach to credit ratings. Journal of Banking & Finance, 140, 106097.

    41. Qi, M., & Zhao, X. (2011). Comparison of modeling methods for loss given default. Journal of Banking & Finance, 35(11), 2842–2855.

    42. Serrano-Cinca, C., Gutiérrez-Nieto, B., & López-Palacios, L. (2015). Determinants of default in p2p lending. PloS one, 10(10), e0139427.

    43. Shi, B., Zhao, X., Wu, B., & Dong, Y. (2019). Credit rating and microfinance lending decisions based on loss given default (lgd). Finance Research Letters, 30, 124–129.

    44. Stanhouse, B., & Stock, D. (2004). The impact of loan prepayment risk and deposit withdrawal risk on the optimal intermediation margin. Journal of Banking & Finance, 28(8), 1825–1843.

    45. Stepanova, M., & Thomas, L. (2002). Survival analysis methods for personal loan data. Operations Research, 50(2), 277–289.

    46. Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341–359.

    47. Tsai, M.-C., Lin, S.-P., Cheng, C.-C., & Lin, Y.-P. (2009). The consumer loan default predicting model–an application of dea–da and neural network. Expert Systems with applications, 36(9), 11682–11690.

    48. Wan, J., Zhang, H., Zhu, X., Sun, X., & Li, G. (2019). Research on influencing factors of p2p network loan prepayment risk based on cox proportional hazards. Procedia Computer Science, 162, 842–848.

    49. Wang, H., Chen, K., Zhu, W., & Song, Z. (2015). A process model on p2p lending. Financial Innovation, 1(1), 1–8.

    50. Yan, J., Yu, W., & Zhao, J. L. (2015). How signaling and search costs affect information asymmetry in p2p lending: the economics of big data. Financial Innovation, 1(1), 1–11.

    51. Yao, X., Crook, J., & Andreeva, G. (2017). Enhancing two-stage modelling methodology for loss given default with support vector machines. European Journal of Operational Research, 263(2), 679–689.

    52. Zhang, Y., & Chi, G. (2018). A credit rating model based on a customer number bellshaped distribution. Management Decision.

    53. Zhou, J., Li, W., Wang, J., Ding, S., & Xia, C. (2019). Default prediction in p2p lending from high-dimensional data based on machine learning. Physica A: Statistical Mechanics and its Applications, 534, 122370.
    Description: 碩士
    國立政治大學
    金融學系
    109352011
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0109352011
    Data Type: thesis
    DOI: 10.6814/NCCU202200929
    Appears in Collections:[Department of Money and Banking] Theses

    Files in This Item:

    File Description SizeFormat
    201101.pdf1961KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback