Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning

Chang, 2015 Chen, Tianqi, 2016. XGBoost: A Scalable Tree Boosting System. In: ACM SIGKDD Conference on Knowledge Discovery and Data Mining’16, August 13–17, 2016, San Francisco, CA, USA. Cheng, 2009, Study on the early-warning mechanism of smes loan risk based on BP neural network [J], Mod. Prop. Manage., 83 Everett, 2015, Group membership, relationship banking and loan default risk: the case of online social lending, Appl. Econ., 47, 54 Freedman, S., Jin, G.Z., 2008. Do Social Networks Solve Information Problems for Peer-to-Peer Lending Prosper. Com, NET Institute Working, Paper. Freedman, S., Jin, G.Z., 2011. Learning by Doing with Asymmetric Information: Evidence from Prosper. com NBER Working, Paper No. 16855. Herzenstein, 2008 Iyer, Rajkamal, Khwaja, Asim Ijaz, Luttmer, Erzo F.P., Shue, Kelly, 2010. Screening in New Credit Markets: Can Individual Lenders Infer Borrower Creditworthiness in Peer-to-Peer Lending? AFA 2011 Denver Meetings Paper. <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1570115>. Kang, 2016 Ke, Guolin, Meng, Qi, Finley, Thomas, Wang, Taifeng, Chen, Wei, Ma, Weidong, Ye, Qiwei, Liu, Tie-Yan, 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In: 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. Klafft, M., 2008. Peer to peer lending: auctioning microcredits over the internet [C]. Technology and Management, A. Agarwal, R. Khurana, eds, IMT, Dubai. Lee, 2012, Herding behavior in online P2P lending: an empirical investigation [J], Electron. Commer. Res. Appl., 495, 10.1016/j.elerap.2012.02.001 Li, 2013, Analysis on factors influencing the success rate of P2P small loan market in China [J], J. Financial Res., 126 Li, 2014, The influence of borrower’s description on investors’ decision–analyze based on P2P online lending [J], Econ. Res. J., 143 Li, 2014, Smart investors: non-full market interest rate and risk identification – evidence from P2P lending, Econ. Res., 125 Li, 2015, Can the loan market accurately identify the value of a degree? – empirical evidence from P2P platforms, Financ. Res., 146 Li, 2016, Detecting the abnormal lenders from P2P lending data, Procedia Comput. Sci., 357, 10.1016/j.procs.2016.07.095 Lin, 2009, Peer-to-peer lending: an empirical study, AMCIS 2009 Donsortium, 17 Lin, 2016, Home bias in online investments: an empirical study of an online crowdfunding market [J], Manage. Sci., 62, 1393, 10.1287/mnsc.2015.2206 Liu, 2006 Liu, 2015 Lv, 2013, Credit evaluation model and empirical research based on decision tree [J], Market weekly (Theor. Res.), 80 Malekipirbazari, 2015, Risk assessment in social lending via random forests, Expert Syst. Appl., 42, 4621, 10.1016/j.eswa.2015.02.001 Ravina, E., 2007. Beauty, Trust in Credit Markets, PaPers.ssrn.com, New York, NY. Ruowei, 2007, Constructing the logistic default prediction model for misjudgment loss, Syst. Eng. Theory Pract., 8, 33 Wang, 2013 Zhu, 2002, Credit rating of commercial Banks based on fuzzy comprehensive evaluation method of hierarchical analysis [J], Stat. Inf. Tribune, 17, 29