Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/124741
|
| Title: | 基於隨機森林模型下P2P網路借貸違約預測
The prediction of default in P2P Lending based on Random Forest Model |
| Authors: | 吳志龍
Wu, Zhi-Long |
| Contributors: | 廖四郎
Liao, Szu-Lang
吳志龍
Wu, Zhi-Long |
| Keywords: | P2P借貸
隨機森林模型
Logistic回歸模型
個人信用風險評估
P2P Lending
Random Forest
Logistic regreesion
Private credit risk evaluation |
| Date: | 2019 |
| Issue Date: | 2019-08-07 16:13:06 (UTC+8) |
| Abstract: | 摘要
本研究使用傳統的Logistic回歸模型與機器學習的隨機森林模型對P2P借貸的個人信用風險進行評估預測。本研究的數據來源於LendingClub的2018年度公開數據資料，先對P2P借貸的個人信用風險因素進行挑選，再使用挑選出的變量對Logistic回歸模型與隨機森林模型進行訓練，並用測試集檢驗兩個模型對個人信用風險的預測能力。結果表明，隨機森林模型的在決策樹為800棵，每棵決策樹的特征值為3個的時候，隨機森林模型預測準確率最高。與Logistic回歸模型比較，隨機森林模型有著更高的精度。本研究還對兩個模型進行了模型性能的比較，結果表明隨機森林的模型性能好過去Logistic回歸模型。
關鍵詞：P2P借貸、隨機森林模型、Logistic回歸模型、個人信用風險評估
Abstract
In this paper we evaluate and predict the private credit risk in P2P lending by using traditional Logistic regression and Random Forest in machine learning. For the open data from LendingClub in 2018, we select the private credit risk factors of P2P lending first, and train the Logistic regression and Random Forest model with selected variables. We test the prediction ability of two models with test set. The result shows that when there are 800 decision trees and 3 features for each tree in Random Forest model, accuracy of the model reaches best. Compared with Logistic regression, Random Forest has higher precision. We also compare the performance of two models, which shows that Random Forest is better than Logistic regression.
Keywords: P2P Lending, Random Forest, Logistic Regression, Private Credit Risk Evaluation |
| Reference: | Bekhet,H.A.,Eletter,S.F.K.(2014).Credit risk assessment model for Jordanian commercial banks: Neural scoring approach. Review of Development Finance, Vol.4,20-28.
Berkson,J.(1944). Application of the Logistic Function to Bio-Assay. Journal of the American Statistical Association , Vol.39, 357-365.
Breiman,L.(2001). Random forest. Machine learning, 45(1), 5-32
Bruett,T.(2007).Cows, Kiva, and Prosper. Com: how disintermediation and the Inter net are changing microfinance. Community Development Investment Review, 2, 44-50.
Caemichael,D.(2014). Modeling default for peer-to-peer loans . Working Paper.
Coffman,J.Y.(1986). The proper role of tree analysis in forecasting the risk behavior of borrowers. Management Decision System, No.3, 47-59.
Dasarathy,B.V. Sheela,B.V.(1979). A composite classifier system design: Concepts and methodology. Proceedings of the IEEE, 67(5),708-713.
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
Everett,C.R.(2015).Group membership, relationship banking and loan default risk : the case of online social lending. Banking and Fiance Review,7(2),14-54.
Freedman,S.Jin,G.Z.(2008).Dynamic Learning and Selection: the Early Years. University of Maryland.
Garman,S.R.Hampshire,R.C.Krishnan,R.(2008).Person-to-Person Lending: The Pursuit of (More) Competitive Credit Markets. E-Life: Web-Enabled Convergence of Commerce, Vol.14,54-58
Herzenstein,M.Andrews,R.L. Dholakia,U.M.Lyandres,E.(2008). The democratization of personal consumer loans Determinants of success in online peer-to-peer lending communities. Boston University School of Management Research Paper.
Jin,G.Z.Freedman,S.(2008). Do Social Networks Solve Information Problem for Peer-to-Peer Lending? Evidence from Prosper.Com.NET Institute Working Paper, 8-43
Klafft,M.(2008).Peer to peer lending: auctioning microcredits over the internet. Working Paper.
Maddala,G.S.(1983).Limited-dependent and qualitative variables in econometrics. Cambridge University Press.
Magee,J.R.(2011).Peer-to-peer lending in the United States: surviving after Dodd-Frank. North Carolina Banking Institute Journal, Vol.15, 139-174
Makowski,P.(1985). Credit scoring branches out. Credit World,Vol.75, 30-37.
Mateescu,A.(2015).Peer-to-Peer Lending. Data&Society,1-23.
Iyer,R.Khwaja,A.I.Luttmer,E.F.P.&Shue,K.(2009). Screening in new credit market: Can individual lenders infer borrower creditworthiness in peer-to-peer lending?. Working Paper
Ohlson,J.A.(1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, Vol.18,No.1,109-131.
Pope, Sydnor,2011.What’s in a picture, vidence of discrimination from Prosper.com. Journal of Human Resource,Vol.46,No.1, 53-92
Serrano-cinca,C.Gutierrez-Nieto,B.&Lopez-Palacios,L.(2015).Determinants of default in P2P lending. PloS one, 10(10),e0139427
Stiglitz,J.E.Weiss,A.(1981). Credit rationing in markets with imperfect information. The American economic review, 71(3), 393-410
王磊、范超、解明明(2014)。數據挖掘模型在小企業主信用評分領域的應用，統計研究，31卷，第10期，89-98。
張培強(2011)。信用卡客戶的分類研究，生產力研究，第4期， 87-88。
張萬軍(2015)。基於大數據的個人信用風險評估模型研究，對外經濟貿易大學。
彭康(2018)。基於P2P網絡借貸的個人信用風險評估，暨南大學。
蘇杭西子(2018)。基於隨機森林模型的個人信用風險評估研究，湖南大學。
劉暢(2015)。基於Logistic的P2P網絡借貸信用風險測度研究，安徽財經大學。 |
| Description: | 碩士
國立政治大學
金融學系
106352043 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0106352043 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU201900322 |
| Appears in Collections: | [金融學系] 學位論文
|
Files in This Item:
| File |
Size | Format | |
|---|
| 204301.pdf | 1076Kb | Adobe PDF2 | 0 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
