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題名 應用倒傳遞類神經網路於P2P借貸投資報酬率預測之研究——以Lending Club為例
A Study of the Application of Back-Propagation Neural Network to the ROI Forecasting in P2P Lending—A Case of Lending Club
作者 李坤霖
貢獻者 楊建民
李坤霖
關鍵詞 金融科技
個人對個人線上借貸
類神經網路
資料探勘
機器學習
Fintech
P2P Lending
Neural network
Data mining
Machine learning
日期 2017
上傳時間 1-十一月-2017 14:20:02 (UTC+8)
摘要 金融科技因為能大幅降低金融活動中的交易成本與門檻，同時打破傳統金融交易資訊不及時的情況，因此能創造以往未有的商業價值。其中P2P Lending即透過電子化技術創造交易平台媒合資金提供者與需求者的微型授信服務，因為省去傳統金融機構中介的成本，故能提升供需雙方效益。然而特殊的營運方式使資金提供者須承擔更高風險，實際上P2P Lending亦曾發生重大詐騙與倒帳事件，因此使英美中政府加強監管，相較之下，我國仍維持不納入金融監管原則，因此本研究試圖以Lending Club具有代表性的案例，提供投資者選擇投資標的的建議。
本研究搜集Lending Club自2007年至2011年42538筆已發行之借貸，在111個變數中使用 Pearson Correlation以及Information gain，並輔以文獻回顧進行變數選擇挑選22個變數。在搭配Dropout技術與透過網格搜索分析最佳化演算法、批次訓練樣本數、訓練次數等參數配置後，本研究訓練得到在測試集準確度達76%的類神經網路模型。經模擬後發現，類神經網路ROI的平均值為9.40，高於對照組7.02，經檢定驗證此差異結果可以採信，因此類神經網路能有效的給予投資人有效的投資建議。
參考文獻 英文文獻
1. Abu-Mostafa, Y. S., Magdon-Ismail, M., & Lin, H. T. （2012）. Learning from data. New York, NY, USA:: AMLBook.
2. Altman, E. I. （1968）. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23（4）, 589-609.
3. Altman, E and Saunders, A.（1998）Credit Scoring risk measurement: Development over last 20 years, Journal of Banking and Finance
4. Arnott, Richard and Joseph E. Stiglitz （1991） ”Moral Hazard and Nonmarket Institutions:Dysfunctional Crowding Out or Peer Monitoring?” The American Economic Review March 1991, 179-190.
5. Bachmann et al（2011）," Online Peer-to-Peer Lending – A Literature Review ", Journal of Internet Banking and Commerce, August 2011, vol. 16, no.2
6. Berger, Allen N. and Gregory F. Udell （1992）: ”Some Evidence on the Empirical Significance of Credit Rationing” The Journal of Political Economy 100（5）: 1047-1077.
7. Besley, Timothy and Stephen Coate （1995） ”Group Lending, Repayment Incentives and Social Collateral” Journal of Development Economics Vol. 46, 1-18.
8. Berkovich E.,（2011） Search and herding effects in peer-to-peer lending: evidence from prosper.com, Annals Finance
9. Bekkerman R., et al（2003）, "Distributional word clusters vs words for text categorization" JMLR: 3 1183-1208
10. Bishop ,C.（1995）. Neural Networks for Pattern Recognition. Oxford University Press, London
11. Black, K. (2009). Business statistics: Contemporary decision making. John Wiley & Sons.
12. Collier B., Hamphire R., （2010）Sending Mixed Signals: Multilevel Reputation Effect s in Peer to Peer Lending Markets, Research Gate
13. Cox, Donald , Tullio Japelli （1990） ”Credit Rationing and Private Transfers: Evidence from Survey Data” The Review of Economics and Statistics 72（3）: 445-454.
14. Dapp, T., Slomka, L., AG, D. B., & Hoffmann, R. （2014）. Fintech—The digital （r） evolution in the financial sector. Deutsche Bank Research”, Frankfurt am Main.
15. Freedman S., Jin G,（2008）, "Do Social Networks solve Information Problems for Peer-to-Peer Lending? Evidence from Prosper.com"
16. Goodfellow, I., Bengio, Y., & Courville, A. （2016）. Deep learning. MIT press.
17. Guyon I., Elisseeff （2003）," An Introduction to Variables and Feature Selection", Journal of Machine Learning Research 3（2003） 1157 -1182
18. Hampshire, Robert （2008） ”Group Reputation Effects in Peer-to-Peer Lending Markets: An Empirical Analysis from a Principle-Agent Perspective” mimeo.
19. Han, J., Pei, J., & Kamber, M. （2011）. Data mining: concepts and techniques. Elsevier.
20. Haykin, S. S（2009）. Neural networks and learning machines （Vol. 3）. Upper Saddle River, NJ, USA:: Pearson.
21. Hoff, Karla , Joseph E. Stiglitz ”Introduction: Imperfect Information and Rural Credit Markets – Puzzles and Policy Perspectives” the World Bank Economic Review 4（3）: 235- 250
22. Huang, C.L., Chen, M.C., Wang, C.J.,（2007） Credit scoring with a data mining approach based on support vector machines, Expert System with Applications
23. James, G., Witten, D., Hastie, T., & Tibshirani, R. （2013）. An introduction to statistical learning （Vol. 112）. New York: springer.
24. Kaastra, I. , M. Boyd （1996）. "Designing a neural network for forecasting financial and economic time series." Neurocomputing 10（3）: 215-236.
25. LeCun, Y., Bengio, Y., & Hinton, G. （2015）. Deep learning. Nature, 521（7553）, 436-444.
26. LeCun,Y,Bottou L, Orr G., and Muller K.（1998）. Efficient backprop. In G. Orr and K. Muller, editors,Neural Networks: Tricks of the Trade. Springer
27. Lee, K. C., Han, I., & Kwon, Y. （1996）. Hybrid neural network models for bankruptcy predictions. Decision Support Systems, 18（1）, 63-72.
28. Lee E. & Lee B.,（2012） Herding behavior in online P2P lending: An empirical investigation, Electronic Commerce Research and Applications 11
29. Lerman P.M.,（1980） Fitting Segmented Regression Models by Grid Search, Applied Statistics, Vol. 29, No. 1 （1980）, pp. 77-84
30. Lin , Prabhala .,Viswanathan.（2012）, "Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending",Management Science, 59:1
31. Malekipirbazari ., Aksakalli, Risk assessment in social lending via random forests, Expert Systems
32. Manning, C., Raghavan P., Schütze H.,（2009）, An Introduction to Information Retrieval
33. McCulloch, W. S., & Pitts, W. （1943）. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5（4）, 115-133.
34. Mendelson, Haim （2006） ”Prosper.com: A People-to-People Lending Marketplace” mimeo.
35. Odom, M. D. , R. Sharda （1990）. A neural network model for bankruptcy prediction. Neural Networks, 1990., 1990 IJCNN International Joint Conference on, IEEE.
36. Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. " O`Reilly Media, Inc.".
37. Ravina, Enrichetta ”Love & Loans: The Effect of Beauty and Personal Characteristics in Credit Markets”, Available at SSRN: http://ssrn.com/abstract=972801.
38. Rosenblatt, F. （1958）. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological review, 65（6）, 386.
39. Ruder , Sebastian（2016）, "An overview of gradient descent optimization algorithms "
40. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. （1988）. Learning representations by back-propagating errors. Cognitive modeling, 5（3）, 1.
41. Specht, D. F. （1990）. Probabilistic neural networks. Neural networks, 3（1）, 109-118.
42. Srivastava,（2014）, Dropout: A Simple Way to Prevent Neural Networks from Overfitting
43. Stiglitz, Joseph E. （1990） ”Peer Monitoring and Credit Markets” The World Bank Economic Review 4:3 351-366.
44. Stiglitz, Joseph E. , Andrew Weiss （1981）: ”Credit Rationing in Markets with Imperfect Information” American Economic Review 71（3）: 393-410.
45. Tam, K. Y. , M. Kiang （1990）. "Predicting bank failures: A neural network approach." Applied Artificial Intelligence an International Journal 4（4）: 265-282.
46. Vapnik, V. N., & Vapnik, V. （1998）. Statistical learning theory （Vol. 1）. New York: Wiley.
47. Venkatasubramanian, V., & Chan, K. （1989）. A neural network methodology for process fault diagnosis. AIChE Journal, 35（12）, 1993-2002.
48. Widrow, B., & Hoff, M. E. （1960）. Adaptive switching circuits （No. TR-1553-1）. STANFORD UNIV CA STANFORD ELECTRONICS LABS.
49. Widrow, B., & Lehr, M. A. （1990）. 30 years of adaptive neural networks: perceptron, madaline, and backpropagation. Proceedings of the IEEE, 78（9）, 1415-1442.
50. Yoon, Y., G. Swales （1991）. Predicting stock price performance: A neural network approach. System Sciences, 1991. Proceedings of the Twenty-Fourth Annual Hawaii International Conference on, IEEE.
51. Zhao H., Wu L. , Liu Q., Ge Y.,Chen E.,（2014）Investment Recommendation in P2P Lending: A Portfolio Perspective with Risk Management,2014 IEEE International Conference on Data Mining

中文文獻
1. 呂美慧（2000）。金融機構房貸客戶授信評量模式分析-Logistic迴歸之應用，政治大學金融研究所碩士論文
2. 陳志龍（2006）。運用類神經網路與技術指標預測股票型基金漲跌及交易時機之研究－以臺灣50指數股票型基金為例。碩士論文。國立朝陽科技大學資管所
3. 陳松興, 江俊豪. （2016）. 中國大陸互聯網金融之網路借貸 （Peer-to-Peer lending） 發展對台灣數位金融之影響研究—以風險監理角度. 兩岸金融季刊, 4（1）, 103-115.
4. 葉怡成（2004）。應用類神經網路。台北市：儒林圖書。
5. 蔡瑞煌（1995）。類神經網路概論。台北市:三民書局。
6. 蔡瑞煌, 高明志, 張金鶚. （1999）. 類神經網路應用於房地產估價之研究. 住宅學報, 8, 001-020.
7. 魏如龍（2003）。類神經網路於不動產價格預估效果之研究。碩士論文。國立政治大學地政研究所。
8. 簡禎富, 許嘉裕（2014）。資料挖礦與大數據分析。新北市：前程文化。
描述 碩士
國立政治大學
資訊管理學系
104356033
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104356033
資料類型 thesis
dc.contributor.advisor 楊建民zh_TW
dc.contributor.author (作者) 李坤霖zh_TW
dc.creator (作者) 李坤霖zh_TW
dc.date (日期) 2017en_US
dc.date.accessioned 1-十一月-2017 14:20:02 (UTC+8)-
dc.date.available 1-十一月-2017 14:20:02 (UTC+8)-
dc.date.issued (上傳時間) 1-十一月-2017 14:20:02 (UTC+8)-
dc.identifier (其他 識別碼) G0104356033en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/114284-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 104356033zh_TW
dc.description.abstract (摘要) 金融科技因為能大幅降低金融活動中的交易成本與門檻，同時打破傳統金融交易資訊不及時的情況，因此能創造以往未有的商業價值。其中P2P Lending即透過電子化技術創造交易平台媒合資金提供者與需求者的微型授信服務，因為省去傳統金融機構中介的成本，故能提升供需雙方效益。然而特殊的營運方式使資金提供者須承擔更高風險，實際上P2P Lending亦曾發生重大詐騙與倒帳事件，因此使英美中政府加強監管，相較之下，我國仍維持不納入金融監管原則，因此本研究試圖以Lending Club具有代表性的案例，提供投資者選擇投資標的的建議。
本研究搜集Lending Club自2007年至2011年42538筆已發行之借貸，在111個變數中使用 Pearson Correlation以及Information gain，並輔以文獻回顧進行變數選擇挑選22個變數。在搭配Dropout技術與透過網格搜索分析最佳化演算法、批次訓練樣本數、訓練次數等參數配置後，本研究訓練得到在測試集準確度達76%的類神經網路模型。經模擬後發現，類神經網路ROI的平均值為9.40，高於對照組7.02，經檢定驗證此差異結果可以採信，因此類神經網路能有效的給予投資人有效的投資建議。		zh_TW
dc.description.tableofcontents 第一章 緒論 4
第一節 研究背景與動機 4
第二節 研究目的 7
第二章 文獻探討 9
第一節 P2P Lending 9
第二節 信用評等機制 16
第三節 類神經網路 17
第三章 研究方法 21
第一節 變數挑選 21
第二節 資料前處理與資料集切割 30
第三節 類神經網路設計 32
第四節 以模擬投資驗證績效 40
第四章 研究結果 41
第一節 類神經網路於投資報酬率準確度預測 41
第二節 投資模擬預測 42
第五章 研究結論、限制與未來展望 48
第一節 研究結論 48
第二節 研究限制與未來展望 49
參考文獻 51
附錄：Lending Club 原始資料及欄位說明 58		zh_TW
dc.format.extent 2997295 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104356033en_US
dc.subject (關鍵詞) 金融科技zh_TW
dc.subject (關鍵詞) 個人對個人線上借貸zh_TW
dc.subject (關鍵詞) 類神經網路zh_TW
dc.subject (關鍵詞) 資料探勘zh_TW
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) Fintechen_US
dc.subject (關鍵詞) P2P Lendingen_US
dc.subject (關鍵詞) Neural networken_US
dc.subject (關鍵詞) Data miningen_US
dc.subject (關鍵詞) Machine learningen_US
dc.title (題名) 應用倒傳遞類神經網路於P2P借貸投資報酬率預測之研究——以Lending Club為例zh_TW
dc.title (題名) A Study of the Application of Back-Propagation Neural Network to the ROI Forecasting in P2P Lending—A Case of Lending Cluben_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 英文文獻
1. Abu-Mostafa, Y. S., Magdon-Ismail, M., & Lin, H. T. （2012）. Learning from data. New York, NY, USA:: AMLBook.
2. Altman, E. I. （1968）. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23（4）, 589-609.
3. Altman, E and Saunders, A.（1998）Credit Scoring risk measurement: Development over last 20 years, Journal of Banking and Finance
4. Arnott, Richard and Joseph E. Stiglitz （1991） ”Moral Hazard and Nonmarket Institutions:Dysfunctional Crowding Out or Peer Monitoring?” The American Economic Review March 1991, 179-190.
5. Bachmann et al（2011）," Online Peer-to-Peer Lending – A Literature Review ", Journal of Internet Banking and Commerce, August 2011, vol. 16, no.2
6. Berger, Allen N. and Gregory F. Udell （1992）: ”Some Evidence on the Empirical Significance of Credit Rationing” The Journal of Political Economy 100（5）: 1047-1077.
7. Besley, Timothy and Stephen Coate （1995） ”Group Lending, Repayment Incentives and Social Collateral” Journal of Development Economics Vol. 46, 1-18.
8. Berkovich E.,（2011） Search and herding effects in peer-to-peer lending: evidence from prosper.com, Annals Finance
9. Bekkerman R., et al（2003）, "Distributional word clusters vs words for text categorization" JMLR: 3 1183-1208
10. Bishop ,C.（1995）. Neural Networks for Pattern Recognition. Oxford University Press, London
11. Black, K. (2009). Business statistics: Contemporary decision making. John Wiley & Sons.
12. Collier B., Hamphire R., （2010）Sending Mixed Signals: Multilevel Reputation Effect s in Peer to Peer Lending Markets, Research Gate
13. Cox, Donald , Tullio Japelli （1990） ”Credit Rationing and Private Transfers: Evidence from Survey Data” The Review of Economics and Statistics 72（3）: 445-454.
14. Dapp, T., Slomka, L., AG, D. B., & Hoffmann, R. （2014）. Fintech—The digital （r） evolution in the financial sector. Deutsche Bank Research”, Frankfurt am Main.
15. Freedman S., Jin G,（2008）, "Do Social Networks solve Information Problems for Peer-to-Peer Lending? Evidence from Prosper.com"
16. Goodfellow, I., Bengio, Y., & Courville, A. （2016）. Deep learning. MIT press.
17. Guyon I., Elisseeff （2003）," An Introduction to Variables and Feature Selection", Journal of Machine Learning Research 3（2003） 1157 -1182
18. Hampshire, Robert （2008） ”Group Reputation Effects in Peer-to-Peer Lending Markets: An Empirical Analysis from a Principle-Agent Perspective” mimeo.
19. Han, J., Pei, J., & Kamber, M. （2011）. Data mining: concepts and techniques. Elsevier.
20. Haykin, S. S（2009）. Neural networks and learning machines （Vol. 3）. Upper Saddle River, NJ, USA:: Pearson.
21. Hoff, Karla , Joseph E. Stiglitz ”Introduction: Imperfect Information and Rural Credit Markets – Puzzles and Policy Perspectives” the World Bank Economic Review 4（3）: 235- 250
22. Huang, C.L., Chen, M.C., Wang, C.J.,（2007） Credit scoring with a data mining approach based on support vector machines, Expert System with Applications
23. James, G., Witten, D., Hastie, T., & Tibshirani, R. （2013）. An introduction to statistical learning （Vol. 112）. New York: springer.
24. Kaastra, I. , M. Boyd （1996）. "Designing a neural network for forecasting financial and economic time series." Neurocomputing 10（3）: 215-236.
25. LeCun, Y., Bengio, Y., & Hinton, G. （2015）. Deep learning. Nature, 521（7553）, 436-444.
26. LeCun,Y,Bottou L, Orr G., and Muller K.（1998）. Efficient backprop. In G. Orr and K. Muller, editors,Neural Networks: Tricks of the Trade. Springer
27. Lee, K. C., Han, I., & Kwon, Y. （1996）. Hybrid neural network models for bankruptcy predictions. Decision Support Systems, 18（1）, 63-72.
28. Lee E. & Lee B.,（2012） Herding behavior in online P2P lending: An empirical investigation, Electronic Commerce Research and Applications 11
29. Lerman P.M.,（1980） Fitting Segmented Regression Models by Grid Search, Applied Statistics, Vol. 29, No. 1 （1980）, pp. 77-84
30. Lin , Prabhala .,Viswanathan.（2012）, "Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending",Management Science, 59:1
31. Malekipirbazari ., Aksakalli, Risk assessment in social lending via random forests, Expert Systems
32. Manning, C., Raghavan P., Schütze H.,（2009）, An Introduction to Information Retrieval
33. McCulloch, W. S., & Pitts, W. （1943）. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5（4）, 115-133.
34. Mendelson, Haim （2006） ”Prosper.com: A People-to-People Lending Marketplace” mimeo.
35. Odom, M. D. , R. Sharda （1990）. A neural network model for bankruptcy prediction. Neural Networks, 1990., 1990 IJCNN International Joint Conference on, IEEE.
36. Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. " O`Reilly Media, Inc.".
37. Ravina, Enrichetta ”Love & Loans: The Effect of Beauty and Personal Characteristics in Credit Markets”, Available at SSRN: http://ssrn.com/abstract=972801.
38. Rosenblatt, F. （1958）. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological review, 65（6）, 386.
39. Ruder , Sebastian（2016）, "An overview of gradient descent optimization algorithms "
40. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. （1988）. Learning representations by back-propagating errors. Cognitive modeling, 5（3）, 1.
41. Specht, D. F. （1990）. Probabilistic neural networks. Neural networks, 3（1）, 109-118.
42. Srivastava,（2014）, Dropout: A Simple Way to Prevent Neural Networks from Overfitting
43. Stiglitz, Joseph E. （1990） ”Peer Monitoring and Credit Markets” The World Bank Economic Review 4:3 351-366.
44. Stiglitz, Joseph E. , Andrew Weiss （1981）: ”Credit Rationing in Markets with Imperfect Information” American Economic Review 71（3）: 393-410.
45. Tam, K. Y. , M. Kiang （1990）. "Predicting bank failures: A neural network approach." Applied Artificial Intelligence an International Journal 4（4）: 265-282.
46. Vapnik, V. N., & Vapnik, V. （1998）. Statistical learning theory （Vol. 1）. New York: Wiley.
47. Venkatasubramanian, V., & Chan, K. （1989）. A neural network methodology for process fault diagnosis. AIChE Journal, 35（12）, 1993-2002.
48. Widrow, B., & Hoff, M. E. （1960）. Adaptive switching circuits （No. TR-1553-1）. STANFORD UNIV CA STANFORD ELECTRONICS LABS.
49. Widrow, B., & Lehr, M. A. （1990）. 30 years of adaptive neural networks: perceptron, madaline, and backpropagation. Proceedings of the IEEE, 78（9）, 1415-1442.
50. Yoon, Y., G. Swales （1991）. Predicting stock price performance: A neural network approach. System Sciences, 1991. Proceedings of the Twenty-Fourth Annual Hawaii International Conference on, IEEE.
51. Zhao H., Wu L. , Liu Q., Ge Y.,Chen E.,（2014）Investment Recommendation in P2P Lending: A Portfolio Perspective with Risk Management,2014 IEEE International Conference on Data Mining

中文文獻
1. 呂美慧（2000）。金融機構房貸客戶授信評量模式分析-Logistic迴歸之應用，政治大學金融研究所碩士論文
2. 陳志龍（2006）。運用類神經網路與技術指標預測股票型基金漲跌及交易時機之研究－以臺灣50指數股票型基金為例。碩士論文。國立朝陽科技大學資管所
3. 陳松興, 江俊豪. （2016）. 中國大陸互聯網金融之網路借貸 （Peer-to-Peer lending） 發展對台灣數位金融之影響研究—以風險監理角度. 兩岸金融季刊, 4（1）, 103-115.
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