Inferences of default risk and borrower characteristics on P2P lending

Cathy W.S. Chen1, Manh Cuong Dong2, Nathan Liu3, Songsak Sriboonchitta4
1Department of Statistics, Feng Chia University, Taiwan
2Department of Economics, Feng Chia University, Taiwan
3Department of Finance, Feng Chia University, Taiwan
4School of Economics, Chiang Mai University, Chiang Mai, Thailand

Tài liệu tham khảo

Acharya, 2012, Cash holdings and credit risk, The Review of Financial Studies, 25, 3572, 10.1093/rfs/hhs106 Amendola, 2011, Variable selection in default risk models, Journal of Risk Model Validation, 5, 3, 10.21314/JRMV.2011.066 Bachmann, 2011, Online peer-to-peer lending – A literature review, Journal of Internet Banking and Commerce, 16, 1 Barasinska, 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, 436, 10.1111/geer.12052 Berkovich, 2011, Search and herding effects in peer-to-peer lending: Evidence from prosper.com, Annals of Finance, 7, 389, 10.1007/s10436-011-0178-6 Bottai, 2010, Logistic quantile regression for bounded outcomes, Statistics in Medicine, 29, 309, 10.1002/sim.3781 Casella, 1992, Explaining the Gibbs sampler, The American Statistician, 46, 167 Chen, 1999, Subset selection of autoregressive time series models, Journal of Forecasting, 18, 505, 10.1002/(SICI)1099-131X(199912)18:7<505::AID-FOR728>3.0.CO;2-U Chen, 2016, Are investors rational or perceptual in P2P lending?, Information Systems and e-Business Management, 14, 921, 10.1007/s10257-016-0305-z Dong, 2018, Predicting failure risk using financial ratios: Quantile hazard model approach, The North American Journal of Economics and Finance, 44t, 204, 10.1016/j.najef.2018.01.005 Fawcett, 2006, An introduction to ROC analysis, Pattern Recognition Letters, 27, 861, 10.1016/j.patrec.2005.10.010 Feng, 2015, Lenders and borrowers’ strategies in online peer-to-peer lending market: An empirical analysis of ppdai.com, Journal of Electronic Commerce Research, 16, 242 Fernandes, 2016, Spatial dependence in credit risk and its improvement in credit scoring, European Journal of Operational Research, 249, 517, 10.1016/j.ejor.2015.07.013 Freedman, 2017, The information value of online social networks: lessons from peer-to-peer lending, International Journal of Industrial Organization, 51, 185, 10.1016/j.ijindorg.2016.09.002 George, 1993, Variable selection via Gibbs sampling, Journal of the American Statistical Association, 88, 881, 10.1080/01621459.1993.10476353 Geraci, 2016, Qtools: A collection of models and tools for quantile inference, The R Journal, 8, 117, 10.32614/RJ-2016-037 Hastie, T., & Qian, J. (2014). Glmnet vignette. R-package version 2.0-16. Iyer, 2015, Screening peers softly: Inferring the quality of small borrowers, Management Science, 626, 1554 Koenker, 2005 Laitinen, 1999, Predicting a corporate credit analyst’s risk estimate by logistic and linear models, International Review of Financial Analysis, 8, 97, 10.1016/S1057-5219(99)00012-5 Lin, 2013, Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending, Management Science, 59, 17, 10.1287/mnsc.1120.1560 Lin, 2017, Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China, Applied Economics, 49, 3538, 10.1080/00036846.2016.1262526 Maldonado, 2014, Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines, Information Sciences, 286, 228, 10.1016/j.ins.2014.07.015 Manski, 1985, Semiparametric analysis of discrete response: asymptotic properties of the maximum score estimator, Journal of Econometrics, 27, 313, 10.1016/0304-4076(85)90009-0 Mild, 2015, How low can you go? Overcoming the inability of lenders to set proper interest rates on unsecured peer-to-peer lending markets, Journal of Business Research, 68, 1291, 10.1016/j.jbusres.2014.11.021 Mitchell, 1988, Bayesian variable selection in linear regression, Journal of the American Statistical Association, 83, 1023, 10.1080/01621459.1988.10478694 Mollica, 2017, Bayesian binary quantile regression for the analysis of bachelor-master transition, Journal of Applied Statistics, 44, 2791, 10.1080/02664763.2016.1263835 Ohlson, 1980, Financial ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research, 18, 109, 10.2307/2490395 Powell, 1984, Least absolute deviations estimation for the censored regression model, Journal of Econometrics, 25, 303, 10.1016/0304-4076(84)90004-6 Ripley, B., Venables, B., Bates, D., Hornik, K., Gebhardt, A., & Firth, D. (2011). Package “MASS”. R package version 7.3-51.1. Scott, S.L. (2018). Package “BoomSpikeSlab”. R package version 1.0.0. Siao, 2016, Predicting recovery rates using logistic quantile regression with bounded outcomes, Quantitative Finance, 16, 777, 10.1080/14697688.2015.1059952 Sohn, 2007, Random effects logistic regression model for default prediction of technology credit guarantee fund, European Journal of Operational Research, 183, 472, 10.1016/j.ejor.2006.10.006 Tibshirani, 1996, Regression shrinkage and selection via the LASSO, Journal of the Royal Statistical Society Series B (Methodological), 58, 267, 10.1111/j.2517-6161.1996.tb02080.x Tüchler, 2008, Bayesian variable selection for logistic models using auxiliary mixture sampling, Journal of Computational and Graphical Statistics, 17, 76, 10.1198/106186008X289849 Varian, 2014, Big data: New tricks for econometrics, Journal of Economic Perspectives, 28, 3, 10.1257/jep.28.2.3 Yamashita, 2007, A stepwise AIC method for variable selection in linear regression, Communications in Statistics – Theory and Methods, 36, 2395, 10.1080/03610920701215639 Zhao, 2018, Understanding influence power of opinion leaders in e-commerce networks: An opinion dynamics theory perspective, Information Sciences, 426, 131, 10.1016/j.ins.2017.10.031