| Reference: | 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。 |
