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Symposium: Financial Development and Regulation; Guest Editors: Chung-Hua Shen, HaiChi Lee, Xu Li, and Xiaojian Liu

Can Listing Information Indicate Borrower Credit Risk in Online Peer-to-Peer Lending?

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References

  • Chen, D., F. Lai, and Z. Lin. 2014. A trust model for online peer-to-peer lending: A lender’s perspective. Information Technology & Management 15 (4):239–54. doi:10.1007/s10799-014-0187-z.
  • Chen, M. C., and S. H. Huang. 2003. Credit scoring and rejected instances reassigning through evolutionary computation techniques. Expert Systems with Applications 24:433–41. doi:10.1016/S0957-4174(02)00191-4.
  • Chen, Y. 2017. Research on the credit risk assessment of Chinese online peer-to-peer lending borrower on logistic regression model. 3rd Asian Pacific Conference on Energy, Environment and Sustainable Development, Singapore: APEESD2017, 216–21.
  • Duarte, J., S. Siegel, and L. Young. 2012. Trust and credit: The role of appearance in peer-to-peer lending. Review of Financial Studies 25 (8):2455–84. doi:10.1093/rfs/hhs071.
  • Emekter, R., Y. Tu, B. Jirasakuldech, and M. Lu. 2015. Evaluating credit risk and loan performance in online peer-to-peer (p2p) lending. Applied Economics 47 (1):54–70. doi:10.1080/00036846.2014.962222.
  • Glover, F. 1990. Improved linear programming models for discriminant analysis. Decision Science 21:771–85. doi:10.1111/j.1540-5915.1990.tb01249.x.
  • Grablowsky, B. J., and W. K. Talley. 1981. Probit and discriminant functions for classifying credit applicants: A comparison. Journal of Economic Business 33:254–61.
  • Guo, B. 2016. Research on the factors affecting the successful borrowing rate of P2P network lending in China—Taking the case of renrendai online lending as an example. Paper presented at IEEE International Conference on Industrial Economics System and Industrial Security Engineering, Australia, IEIS2016: 1–5.
  • Guo, Y., W. Zhou, C. Luo, C. Liu, and H. Xiong. 2015. Instance-based credit risk assessment for investment decisions in P2P lending. European Journal of Operational Research 249 (2):417–26. doi:10.1016/j.ejor.2015.05.050.
  • Henley, W. E., and D. J. Hand. 1996. A K-NN classifier for assessing consumer credit risk. The Statistician : Journal of the Institute of Statisticians 45:77–95. doi:10.2307/2348414.
  • Herzenstein, M., S. Sonenshein, and U. Dholakia. 2011. Tell me a good story and i may lend you my money: The role of narratives in peer-to-peer lending decisions. Journal of Marketing Research 48:138–49. doi:10.1509/jmkr.48.SPL.S138.
  • Hulme, M., and C. Wright 2006. Internet based social lending: Past, present and future. Working Paper, Social Futures Observatory, UK.
  • Jin, Y., and Y. Zhu 2015. A data-driven approach to predict default risk of loan for online peer-to-peer (P2P) lending. Fifth International Conference on Communication Systems and Network Technologies, IEEE, Gwalior, INDIA: CSNT2015, 609–13.
  • Krumme, K., and S. Herrero-Lopez 2009. Do lenders make optimal decisions in a peer-to-peer network? In Proceedings of the lEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technologies, Italy: WI/AIT2009, 1:124–27.
  • Kumar, V. L., S. Natarajan, and S. Keerthana 2016. Credit risk analysis in peer-to-peer lending system. Paper presented at IEEE International Conference on Knowledge Engineering and Applications, Singapore: ICKEA2016:193–96.
  • Larrimore, L., L. Jiang, J. Lairimore, et al. 2011. Peer-to-peer lending: The relationship between language features, trustworthiness, and persuasion success. Journal of Applied Communication Research 39 (1):19–37. doi:10.1080/00909882.2010.536844.
  • Lin, M., R. Prabhala, and S. Viswanathan. 2013. Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending. Management Science 59 (1):17–35. doi:10.1287/mnsc.1120.1560.
  • Lin, X., X. Li, and Z. Zheng. 2016. Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China. Applied Economics 49 (35):3538–45. doi:10.1080/00036846.2016.1262526.
  • Malhotra, R., and D. K. Malhotra. 2003. Evaluating consumer loans using neural networks. Omega 31:83–96. doi:10.1016/S0305-0483(03)00016-1.
  • Michels, J. 2012. Do unverifiable disclosures matter? Evidence from peer-to-peer lending. The Accounting Review 87 (4):1385–413. doi:10.2308/accr-50159.
  • Mild, A., M. Waitz, and J. Wöckl. 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 (6):1291–305. doi:10.1016/j.jbusres.2014.11.021.
  • Pope, D. G., and J. R. Sydnor 2008. What’s in a picture? Evidence of discrimination from prosper.com Available at SSRN http://ssrn.com/abstract=1220902.
  • Puro, J., J. E. Teich, H. Wallenius, et al. 2010. Borrower decision aid for people-to-people lending. Decision Support Systems 49:52–60. doi:10.1016/j.dss.2009.12.009.
  • Qiu, J., Z. Lin, and B. Luo. 2012. Effects of borrower-defined conditions in the online peer-to-peer lending market. In E-life: web-enabled convergence of commerce, work, and social life, eds. M. J. Shaw, D. Zhang, And W. T. Yue, Vol. 108, 167–79. Berlin: Springer.
  • Riggins, F. J., and D. M. Weber. 2017. Information asymmetries and identification bias in P2P social microlending. Information Technology for Development 23 (1):107–26. doi:10.1080/02681102.2016.1247345.
  • Serrano-Cinca, C., B. Gutiérrez-Nieto, and L. López-Palacios. 2015. Determinants of default in P2P lending. PLOS One 10 (10):e0139427. doi:10.1371/journal.pone.0139427.
  • Tao, Q., Y. Dong, and Z. Lin. 2016. Who can get money? Evidence from the Chinese peer-to-peer lending platform. Information Systems Frontiers 2:1–17.
  • Wan, Q., D. Chen, and W. Shi. 2016. Online peer-to-peer lending decision making: Model development and testing. Social Behavior & Personality an International Journal 44 (1):117–30. doi:10.2224/sbp.2016.44.1.117.
  • Wiginton, J. C. 1980. A note on the comparison of logit and discriminant models of consumer credit behaviour. Journal of Financial Quantitative Analysis 15:757–70. doi:10.2307/2330408.
  • Xia, L., and J. F. Li. 2016. Analysis on credit risk assessment of P2P. Paper presented at IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, IEEM2016: 907–914.
  • Yang, Q., and Y. C. Lee. 2016. Influencing factors on the lending intention of online peer-to-peer lending: Lessons from renrendai.com. The Journal of Information Systems 25 (2):79–110.
  • Zang, D., M. Qi, and Y. Fu. 2014. The credit risk assessment of p2p lending based on BP neural network. Industrial Engineering and Management Science 2014:91–94.
  • Zhang, Y., D. Wang, Y. Chen, Y. Zhao, P. Shao, and Q. Meng. 2017. Credit risk assessment based on flexible neural tree model. In Advances in neural networks: International symposium on neural networks, eds F. Cong, A. Leung, and Q. Wei, 215–22. Cham, Switzerland: Springer.

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