臺灣博碩士論文加值系統

隨著金融科技的興起及多元化發展，其中出現了新形態且貼近大眾的融資管道—P2P網路借貸。對於新型態的借貸模式，學者們經常以借款者的個人特徵、財務狀況和貸款條件等相關資訊來多方面地研究P2P網路借貸平台。然而，鮮少學者針對借款者的還款行為進行實證分析，故本研究主要探討是否能夠透過借款者每期之還款狀況，來預測下一期發生違約的可能性，並比較經性別分群的借款者之違約情況相異(同)處，以達到平台在放貸前判斷借款人的依據及借戶違約前預警的效果。本研究採用來自中國P2P網路借貸平台–拍拍貸所提供的業務數據，深入分析比較分群借款者的還款行為模式，有助於更進一步完善平台的信用風險控管，且利於降低平台的違約率。實證結果顯示，將資料分群後與全體資料中存在違約的個人特質無區別，另本研究所建立的Logistic迴歸模型在納入還款行為變數後，不論全體或男/女性模型的違約預測能力隨著已還期數的增加呈現遞增的趨勢，惟女性模型的預測判斷能力相比全體及男性有較弱的趨勢。

Through the rise and diversified development of Fintech, it appeared a lending channel, P2P lending, which is a new form and close to the public. Regarding the new-type model, scholars often use the borrower's personal characteristics, financial status, loan conditions, etc to analyze P2P lending. However, few scholars study the borrower ’s repayment behavior, so this thesis explores whether the borrower's repayment of each period can be used to predict the possibility of default in the next period. And compare the differences (the same) of the default situation of the borrowers after gender grouping. To achieve the effect of the platform to judge the borrower's basis before lending and the warning before the borrower default.
This research uses business data provided by China ’s P2P lending platform – Paipaidai, observes the successful borrowing records from January 2015 to March 2016, and deeply analyzes and compares the repayment behavior patterns of group borrowers. It will help to further improve the credit risk control of the platform and help reduce the default rate of the platform. The result shows that whether using all or male/female borrowers’ data, there is a great improvement in prediction of default risk to use the variables of borrowers’ repayment behaviors. But, compared with the overall and male data, the female model's ability to discriminate becomes weaker.

第一章　緒論 8
第一節　研究背景及動機 8
第二節　研究目的 10
第三節　研究架構 13
第二章　文獻探討 15
第一節　P2P借貸平台發展歷史 15
第二節　拍拍貸平台營運模式 20
第三節　影響違約風險因素探討 23
第二章　研究方法 26
第一節　研究資料來源及樣本 26
第二節　研究變數之定義 28
第三節　研究模型之建立 30
第四節　假設研究結果 34
第四章　實證結果與分析 35
第一節　資料處理流程 35
第二節　資料分析 37
第三節　還款行為分析(十二期) 48
第五章　結論與建議 63
第一節　結論記錄 63
第二節　後續研究建議 65
參考文獻 66

1.Akerlof, G. A. (1978). The market for “lemons”: Quality uncertainty and the market mechanism. In Uncertainty in economics (pp. 235-251). Academic Press.
2.Berger, S. C., & Gleisner, F. (2009). Emergence of financial inter-mediaries in electronic markets: The case of online P2P lending. BuR Business Research Journal, 2(1),39-65.
3.Bachmann, A., Becker, A., Buerckner, D., Hilker, M., Kock, F., Lehmann, M., ... & Funk, B. (2011). Online peer-to-peer lending-a literature review. Journal of Internet Banking and Commerce, 16(2), 1-18.
4.Barasinska, N., & Schäfer, D. (2014). Is crowdfunding different? Evidence on the relation between gender and funding success from a German peer‐to‐peer lending platform. German Economic Review, 15(4), 436-452.
5.Chen, X., Ding, X. Y., & Wang, B. F. (2013). A study of the over-due behaviors in private borrowing—empirical analysis based on P2P network borrowing and lending. In Finance Forum (Vol. 11, No. 010).65-72.
6.Chen, D., Lai, F., & Lin, Z. (2014). A trust model for online peer-to-peer lending: a lender’s perspective. Information Technology and Management, 15(4), 239-254.
7.Chen, C. W., Dong, M. C., Liu, N., & Sriboonchitta, S. (2019). In-ferences of Default Risk and Borrower Characteristics on P2P Lend-ing. The North American Journal of Economics and Finance, 101013.
8.Dell'ariccia, G. (1998). Asymmetric Information and the Market Structure of the Banking Industry (No. 98/92). International Monetary Fund.
9.Dell'ariccia, G., Igan, D., & Laeven, L. U. (2012). Credit booms and lending standards: Evidence from the subprime mortgage market. Journal of Money, Credit and Banking, 44(2‐3), 367-384.
10.Emekter, R., Tu, Y., Jirasakuldech, B., & Lu, M. (2015). Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lend-ing. Applied Economics, 47(1), 54-70.
11.Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics (Vol. 1). New York: Wiley.
12.Lin, M. (2009). Peer-to-peer lending: An empirical study. AMCIS 2009 Doctoral Consortium, San Franciscon, California, 1-7.
13.Lenz, R. (2016). Peer-to-peer lending: opportunities and risks. European Journal of Risk Regulation, 7(4), 688-700.
14.Lin, X., Li, X., & Zheng, Z. (2017). Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China. Applied Economics, 49(35), 3538-3545.
15.Liu, Y., Zhou, Q., Zhao, X., & Wang, Y. (2018). Can Listing In-formation Indicate Borrower Credit Risk in Online Peer-to-Peer Lending?. Emerging Markets Finance and Trade, 54(13), 2982-2994.
16.Mikhed, V. (2015). Can credit cards with access to complimentary credit score information benefit consumers and lenders?. FRB of Philadelphia-Payment Cards Center Discussion Paper, (15-03).
17.Polena, M., & Regner, T. (2018). Determinants of borrowers’ de-fault in P2P lending under consideration of the loan risk class. Games, 9(4), 82.
18.Sharpe, S. A. (1990). Asymmetric information, bank lending, and implicit contracts: A stylized model of customer relationships. The journal of finance, 45(4), 1069-1087.
19.Serrano-Cinca, C., Gutiérrez-Nieto, B., & López-Palacios, L. (2015). Determinants of default in P2P lending. PloS one, 10(10), e0139427.
20.Wang, J., Shen, Y., & Huang, Y. (2016). Evaluating the regulatory scheme for internet finance in China: the case of peer-to-peer lend-ing. China Economic Journal, 9(3), 272-287.
21.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), 19.
22.Yao, F. G., & Sui, X. (2016). The research to the influential factors of credit risk in the p2p network loan under the background of inter-net financial. In Proceedings of the 22nd International Conference on Industrial Engineering and Engineering Management 2015 (pp. 507-515). Atlantis Press, Paris.