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The relationship between soft information in loan titles and online peer-to-peer lending: evidence from RenRenDai platform

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Abstract

Online peer-to-peer (P2P) lending is a central component of Internet finance. It can help borrowers raise funds quickly—a particularly useful feature for small and medium enterprises and individuals with no credit on record with a central bank. In this paper, we use data from Chinese RenRenDai lending platform to investigate the relationship between loan purpose and funding success rate. In order to identify the purpose of borrowing from the title of the loan, LDA topic model of text mining technology is applied to make classification for loan titles. Our results indicate that the purpose of the loan has a significant influence on whether the loan is successful. Ambiguity surrounding the loan’s purpose significantly reduces the likelihood of a borrower successfully securing that loan. Loan purpose for business often ensures a higher funding success rate. These results suggest that borrowers should comprehensively fill out the loan title when applying for funding via an online P2P platform. Results also suggest that online P2P platform investors do not blindly invest in others in an attempt to secure high returns.

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References

  1. Agrawal, A., Catalini, C., & Goldfarb, A. (2014). Some simple economics of crowdfunding. Innovation Policy and the Economy, 14(1), 63–97.

    Article  Google Scholar 

  2. Akerlof, G. (1970). The market for “lemons”: Quality uncertainty and the market mechanism. The Quarterly Journal of Economics, 84(3), 488–500.

    Article  Google Scholar 

  3. Allison, T. H., Mckenny, A. F., & Short, J. C. (2013). The effect of entrepreneurial rhetoric on microlending investment: An examination of the warm-glow effect. Journal of Business Venturing, 28(6), 690–707.

    Article  Google Scholar 

  4. Andreas, P., & Hamid, S. (2015). Herding and contrarian behavior in financial Markets. Econometrica, 79(4), 973–1026.

    Google Scholar 

  5. Barasinska, N., & Schäfer, D. (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(4), 436–452.

    Google Scholar 

  6. Berger, S. C., & Gleisner, F. (2009). Emergence of financial intermediaries in electronic markets: The case of online P2P lending. Business Research, 2(1), 39–65.

    Article  Google Scholar 

  7. Blei, D. (2011). Probabilistic topic models. In ACM SIGKDD international conference tutorials (pp. 55–65).

  8. Blei, D. M., & Lafferty, J. D. (2007). Correction: A correlated topic model of science. Statistics, 1(1), 17–35.

    Google Scholar 

  9. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

    Google Scholar 

  10. Chaffee, E. C., & Rapp, G. C. (2012). Regulating on-line peer-to-peer lending in the aftermath of Dodd–Frank: In search of an evolving regulatory regime for an evolving industry. Washington & Lee Law Review, 69(2), 485.

    Google Scholar 

  11. Chen, D., Hao, L., & Xu, H. (2013). Gender discrimination towards borrowers in online P2P lending. In The twelfth Wuhan international conference on e-business (pp. 20–24).

  12. Chen, D., Li, X., & Lai, F. (2016). Gender discrimination in online peer-to-peer credit lending: Evidence from a lending platform in China. Electronic Commerce Research. https://doi.org/10.1007/s10660-016-9247-2.

    Article  Google Scholar 

  13. Chen, J. Z., & Ning, X. (2013). Empirical research on the influence of personal information on P2P lending’s success rate—Evidence from Renrendai. Accounting & Finance, 6, 3.

    Google Scholar 

  14. Chen, N., Ghosh, A., & Lambert, N. S. (2011). Auctions for social lending: A theoretical analysis. Games & Economic Behavior, 86, 367–391.

    Article  Google Scholar 

  15. Chen, X., Zhou, L., & Wan, D. (2015). Group social capital and lending outcomes in the financial credit market. Electronic Commerce Research and Applications, 15, 1–13.

    Article  Google Scholar 

  16. Cruys, T. V. D. (2008). A comparison of bag of words and syntax-based approaches for word categorization. In Proceedings of the ESSLLI workshop on distributional lexical semantics (pp. 47–54).

  17. Davidoff, T., & Welke, G. (2004). Selection and moral hazard in the reverse mortgage market. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.608666.

    Article  Google Scholar 

  18. Dorfleitner, G., Priberny, C., Schuster, S., Stoiber, J., Weber, M., de Castro, I., et al. (2016). Description-text related soft information in peer-to-peer lending—Evidence from two leading European platforms. Journal of Banking & Finance, 64, 169–187.

    Article  Google Scholar 

  19. Duarte, J., Siegel, S., & Young, L. (2012). Trust and credit: The role of appearance in peer-to-peer lending. Review of Financial Studies, 25(8), 2455–2484.

    Article  Google Scholar 

  20. Emekter, R., Tu, Y., Jirasakuldech, B., & Lu, M. (2015). Evaluating credit risk and loan performance in online peer-to-peer (P2P) lending. Applied Economics, 47(1), 54–70.

    Article  Google Scholar 

  21. Fan, W., Wallace, L., Rich, S., & Zhang, Z. (2006). Tapping the power of text mining. Communications of the ACM, 49(9), 76–82.

    Article  Google Scholar 

  22. Faure-Grimaud, A., & Martimort, D. (2002). Collusion, delegation and supervision with soft information. Social Science Electronic Publishing, 70(2), 253–279.

    Google Scholar 

  23. Ferrara, E. L. (2003). Kin groups and reciprocity: A model of credit transactions in Ghana. American Economic Review, 93(5), 1730–1751.

    Article  Google Scholar 

  24. Freedman, S., & Jin, G. Z. (2008). Do social networks solve information problems for peer-to-peer lending? Evidence from Prosper.com. Social Science Electronic Publishing. https://doi.org/10.2139/ssrn.1936057.

    Article  Google Scholar 

  25. Gao, Q., & Lin, M. (2015). Lemon or cherry? The value of texts in debt crowdfunding. Working paper.

  26. Greiner, M. E., & Wang, H. (2007). Building consumer-to-consumer trust in e-finance marketplaces. In Reaching new heights. Americas conference on information systems, Keystone, Colorado, USA.

  27. Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1), 5228–5235.

    Article  Google Scholar 

  28. He, Y. (2012). Incorporating sentiment prior knowledge for weakly supervised sentiment analysis. ACM Transactions on Asian Language Information Processing, 11(2), 4.

    Article  Google Scholar 

  29. Herzenstein, M., & Andrews, R. L. (2008). The democratization of personal consumer loans? Determinants of success in online peer-to-peer loan auctions. Bulletin of the University of Delaware, 15(3), 274–277.

    Google Scholar 

  30. Hme, R., & Tzsch, S. (2011). Collective exposure: Peer effects in voluntary disclosure of personal data. In International conference on financial cryptography and data security (pp. 1–15).

  31. Hoff, K., & Stiglitz, J. E. (1990). Introduction: Imperfect information and rural credit markets—puzzles and policy perspectives. World Bank Economic Review, 4(3), 235–250.

    Article  Google Scholar 

  32. Hofmann, T. (1999). Probabilistic latent semantic analysis. In Proceedings of the fifteenth conference on uncertainty in artificial intelligence (pp. 289–296). Morgan Kaufmann Publishers Inc.

  33. Hotho, A., Nürnberger, A., & Paa, G. (2005). A brief survey of text mining. GLDV Journal for Computational Linguistics and Language Technology, 20, 19–62.

    Google Scholar 

  34. Hunt, P. (2013). Peer-to-peer lending. Mortgage Finance Gazette, 39, 19–23.

    Google Scholar 

  35. Iyer, R., Khwaja, A. I., Luttmer, E. F. P., & Shue, K. (2009). Screening in new credit markets: Can individual lenders infer borrower creditworthiness in peer-to-peer lending? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1570115.

    Article  Google Scholar 

  36. Knobloch, L. K. (1975). Uncertainty reduction theory. New York: Wiley.

    Google Scholar 

  37. Kreiner, D. S., Schnakenberg, S. D., Green, A. G., Costello, M. J., & Mcclin, A. F. (2002). Effects of spelling errors on the perception of writers. Journal of General Psychology, 129(1), 5–17.

    Article  Google Scholar 

  38. Larrimore, L., Jiang, L., Larrimore, J., Markowitz, D., & Gorski, S. (2011). Peer to peer lending: The relationship between language features, trustworthiness, and persuasion success. Journal of Applied Communication Research, 39(1), 19–37.

    Article  Google Scholar 

  39. Lee, E., & Lee, B. (2012). Herding behavior in online P2P lending: An empirical investigation. Electronic Commerce Research and Applications, 11(5), 495–503.

    Article  Google Scholar 

  40. Lim, K. W., & Buntine, W. (2014). Twitter opinion topic model: Extracting product opinions from tweets by leveraging hashtags and sentiment lexicon. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management (pp. 1319–1328). ACM.

  41. Lin, C., & He, Y. (2009). Joint sentiment/topic model for sentiment analysis. In ACM conference on information and knowledge management (pp. 375–384).

  42. Lin, M. (2009). Peer-to-peer lending: An empirical study. In Proceedings of the fifteenth americas conference on information systems, San Francisco, California.

  43. Lin, M., Prabhala, N. R., & Viswanathan, S. (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.

    Article  Google Scholar 

  44. Lin, X., Li, X., & Zheng, Z. (2016). Evaluating borrower’s default risk in peer-to-peer lending: Evidence from a lending platform in China. Applied Economics. https://doi.org/10.1080/00036846.2016.1262526.

    Article  Google Scholar 

  45. Lipp, O. V., Neumann, D. L., Siddle, D. A. T., et al. (2001). Assessing the effects of attention and emotion on startle eyeblink modulation. Journal of Psychophysiology, 15(3), 173–182.

    Article  Google Scholar 

  46. Luo, B., & Lin, Z. (2013). A decision tree model for herd behavior and empirical evidence from the online P2P lending market. Information Systems and e-Business Management, 11(1), 141–160.

    Article  Google Scholar 

  47. Martens, M. L., & Jennings, P. D. (2007). Do the stories they tell get them the money they need? The role of entrepreneurial narratives in resource acquisition. Academy of Management Journal, 50(5), 1107–1132.

    Article  Google Scholar 

  48. Mi, J. J., & Zhu, H. (2016). Can funding platforms’ self-initiated financial innovation improve credit availability? Evidence from China’s P2P market. Applied Economics Letters. https://doi.org/10.1080/13504851.2016.1197358.

    Article  Google Scholar 

  49. Pang, J. F., Bu, D., & Bai, S. (2001). Research and implementation of text categorization system based on VSM. Application Research of Computers, 18(9), 23–26.

    Google Scholar 

  50. Pope, D. G., & Sydnor, J. R. (2011). What’s in a picture?: Evidence of discrimination from Prosper.com. Journal of Human Resources, 46(1), 53–92.

    Article  Google Scholar 

  51. Pynte, J., Kennedy, A., & Ducrot, S. (2004). The influence of parafoveal typographical errors on eye movements in reading. European Journal of Cognitive Psychology, 16(1–2), 178–202.

    Article  Google Scholar 

  52. Ravina, E. (2007). Beauty, personal characteristics, and trust in credit markets. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.972801.

    Article  Google Scholar 

  53. Renneboog, L., Horst, J. T., & Zhang, C. (2008). Socially responsible investments: Institutional aspects, performance, and investor behavior. Journal of Banking & Finance, 32(9), 1723–1742.

    Article  Google Scholar 

  54. Salton, G., Wong, A., & Yang, C. S. (1975). A vector space model for automatic indexing. Communications of the ACM, 18(11), 273–280.

    Article  Google Scholar 

  55. Stein, J. C. (2000). Information production and capital allocation: Decentralized versus hierarchical firms. Social Science Electronic Publishing, 57(5), 1891–1921.

    Google Scholar 

  56. Suh, B., & Han, I. (2003). The impact of customer trust and perception of security control on the acceptance of electronic commerce. International Journal of Electronic Commerce, 7(3), 135–161.

    Article  Google Scholar 

  57. Tan, Y. Y. (2016). Application of instance-based model in optimization of investment portfolio—An empirical study based on Renrendai. Commercial Research, 62(12), 126–131.

    Google Scholar 

  58. Wang, H. J., & Liao, L. (2014). Chinese P2P platform’s credit authentication mechanism research—Evidence from Renrendai. China Industrial Economics, 4, 136–147.

    Google Scholar 

  59. Yan, X., Guo, J., Lan, Y., & Cheng, X. (2013). A biterm topic model for short texts. In Proceedings of the 22nd international conference on World Wide Web (pp. 1445–1456). ACM.

  60. Zhou, S., Li, K., & Liu, Y. (2009). Text categorization based on topic model. International Journal of Computational Intelligence Systems, 2(4), 398–409.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Zhejiang Provincial Natural Science Foundation of China under Grant No. LY18G020013 and LQ14F010006, the Humanity and Social Science Foundation of the Ministry of Education of China under Grand No. 15YJA630005 and the Natural Science Foundation of China under Grant No. 61502414.

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Correspondence to June Wei or Yuangao Chen.

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Yao, J., Chen, J., Wei, J. et al. The relationship between soft information in loan titles and online peer-to-peer lending: evidence from RenRenDai platform. Electron Commer Res 19, 111–129 (2019). https://doi.org/10.1007/s10660-018-9293-z

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