Skip to main content

Advertisement

Log in

Can investors’ collective decision-making evolve? Evidence from peer-to-peer lending markets

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

This study tries to identify the accuracy of individual investors’ capability to predict a borrower’s creditworthiness in peer-to-peer lending markets and examine whether their ability is likely to evolve over time. The results of this study show that there is no significant difference between the predictive power of investors’ ex-ante funding decision model and that of the ex-post repayment model over a borrower’s repayment performance. Furthermore, the predictive power of investors’ ex-ante funding decision over a borrower’s repayment performance is shown to improve over time. It is also found that the main reason why investors’ predictive power improves over time is because investors can assess more accurately the information provided by the platform operator and describe the borrower's characteristics. The results of this study are important as they confirm the possibility of optimizing and streamlining the P2P lending market, through the evolution of investors’ decision making.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Barasinska, N., & Schafer, 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.

    Article  Google Scholar 

  2. Burtch, G., Ghose, A., & Wattal, S. (2014). Cultural differences and geography as determinants of online prosocial lending. Mis Quarterly, 38(3), 773–794.

    Article  Google Scholar 

  3. Cai, S., Lin, X., Xu, D., & Fu, X. (2016). Judging online peer-to-peer lending behavior: A comparison of first-time and repeated borrowing requests. Information and Management, 53(7), 857–867.

    Article  Google Scholar 

  4. Chen, D., Lai, F., & Lin, Z. (2014). A trust model for online peer-to-peer lending: A lender’s perspective. Information Technology and Management, 15(4), 239–254.

    Article  Google Scholar 

  5. Chen, D., Li, X., & Lai, F. (2017). Gender discrimination in online peer-to-peer credit lending: Evidence from a lending platform in China. Electronic Commerce Research, 17(4), 553–583.

    Article  Google Scholar 

  6. Chen, X.-H., Jin, F.-J., Zhang, Q., & Yang, L. (2016). Are investors rational or perceptual in P2P lending? Information Systems and e-Business Management, 14(4), 921–944.

    Article  Google Scholar 

  7. Chen, X., Huang, B., & Ye, D. (2018). The role of punctuation in P2P lending: Evidence from China. Economic Modelling, 68, 634–643.

    Article  Google Scholar 

  8. Chen, X., Huang, B., & Ye, D. (2020). Gender gap in peer-to-peer lending: Evidence from China. Journal of Banking and Finance, 112, 105633.

    Article  Google Scholar 

  9. Chiang, R. C., Chow, Y.-F., & Liu, M. (2002). Residential mortgage lending and borrower risk: The relationship between mortgage spreads and individual characteristics. Journal of Real Estate Finance and Economics, 25, 5–32.

    Article  Google Scholar 

  10. Christensen, R. (2006). Log-linear models and logistic regression (2nd ed.). Springer Science & Business Media.

    Google Scholar 

  11. Crook, J. N., & Banasik, J. (2004). Does reject inference really improve the performance of application scoring models? Journal of Banking and Finance, 28(4), 857–874.

    Article  Google Scholar 

  12. Desai, V. S., Crook, J. N., & Overstreet, G. A., Jr. (1996). A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research, 95(1), 24–37.

    Article  Google Scholar 

  13. Dietterich, T. G. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7), 1895–1923.

    Article  Google Scholar 

  14. Ding, J., Huanga, J., Li, Y., & Meng, M. (2019). Is there an effective reputation mechanism in peer-to-peer lending? Evidence from China. Finance Research Letters, 30, 208–215.

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Feng, Y., Fan, X., & Yoon, Y. (2015). Lenders and borrowers’ strategies in online peer-to-peer lending market: An empirical analysis of PPDai.com. Journal of Electronic Commerce Research, 16(3), 242–260.

    Google Scholar 

  17. Freedman, S., & Jin, G. Z. (2017). The information value of online social networks: Lessons from peer-to-peer lending. International Journal of Industrial Organization, 51, 185–222.

    Article  Google Scholar 

  18. Greiner, M. E., & Wang, H. (2010). Building consumer-to-consumer trust in e-finance marketplaces: An empirical analysis. International Journal of Electronic Commerce, 15(2), 105–136.

    Article  Google Scholar 

  19. Guo, Y., Zhou, W., Luo, C., Liu, C., & Xiong, H. (2016). Instance-based credit risk assessment for investment decisions in P2P lending. European Journal of Operational Research, 249(2), 417–426.

    Article  Google Scholar 

  20. Han, J.-T., Chen, Q., Liu, J.-G., Luo, X.-L., & Fan, W. (2018). The persuasion of borrowers’ voluntary information in peer to peer lending: An empirical study based on elaboration likelihood model. Computers in Human Behavior, 78, 200–214.

    Article  Google Scholar 

  21. Herzenstein, M., Andrews, R. L., Dholakia, U. M., & Lyandres, E. (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 

  22. Herzenstein, M., Dholakia, U. M., & Andrews, R. L. (2011). Strategic herding behavior in peer-to-peer loan auctions. Journal of Interactive Marketing, 25(1), 27–36.

    Article  Google Scholar 

  23. Herzenstein, M., Sonenshein, S., & Dholakia, U. M. (2011). Tell me a good story and I may lend you money: The role of narratives in peer-to-peer lending decisions. Journal of Marketing Research, 48(SPL), S138–S149.

    Article  Google Scholar 

  24. Hu, R., Liu, M., He, P., & Ma, Y. (2019). Can investors on P2P lending platforms identify default risk? International Journal of Electronic Commerce, 23(1), 63–84.

    Article  Google Scholar 

  25. Jaeger, T. F. (2008). Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models. Journal of Memory and Language, 59(4), 434–446.

    Article  Google Scholar 

  26. Jiang, C., Wang, Z., Wang, R., & Ding, Y. (2018). Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending. Annals of Operations Research, 266(1–2), 511–529.

    Article  Google Scholar 

  27. Jiang, J., Liu, Y.-J., & Lu, R. (2020). Social heterogeneity and local bias in peer-to-peer lending–Evidence from China. Journal of Comparative Economics, 48(2), 302–324.

    Article  Google Scholar 

  28. Jiang, Y., Ho, Y.-C.C., Yan, X., & Tan, Y. (2018). Investor platform choice: Herding, platform attributes, and regulations. Journal of Management Information Systems, 35(1), 86–116.

    Article  Google Scholar 

  29. Jin, J., Shang, Q., & Ma, Q. (2019). The role of appearance attractiveness and loan amount in peer-to-peer lending: Evidence from event-related potentials. Neuroscience Letters, 692, 10–15.

    Article  Google Scholar 

  30. Kgoroeadira, R., Burke, A., & Stel, A. V. (2019). Small business online loan crowdfunding: Who gets funded and what determines the rate of interest? Small Business Economics, 52(1), 67–87.

    Article  Google Scholar 

  31. Kim, D. (2020). The importance of detailed patterns of herding behaviour in a P2P lending market. Applied Economics Letters, 27(2), 127–130.

    Article  Google Scholar 

  32. Kim, D., Maeng, K., & Cho, Y. (2020). Study on the determinants of decision-making in peer-to-peer lending in South Korea. Asia-Pacific Journal of Accounting and Economics, 27(5), 558-576.

  33. 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 

  34. Lee, T.-S., Chiu, C.-C., Chou, Y.-C., & Lu, C.-J. (2006). Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics and Data Analysis, 50(4), 1113–1130.

    Article  Google Scholar 

  35. Lee, T.-S., Chiu, C.-C., Lu, C.-J., & Chen, I.-F. (2002). Credit scoring using the hybrid neural discriminant technique. Expert Systems with Applications, 23(3), 245–254.

    Article  Google Scholar 

  36. Lemon, S. C., Roy, J., Clark, M. A., Friedmann, P. D., & Rakowski, W. (2003). Classification and regression tree analysis in public health: Methodological review and comparison with logistic regression. Annals of Behavioral Medicine, 26(3), 172–181.

    Article  Google Scholar 

  37. Li, J., & Hu, J. (2019). Does university reputation matter? Evidence from peer-to-peer lending. Finance Research Letters, 31, 66–77.

    Article  Google Scholar 

  38. Li, Z., Li, K., Yao, X., & Wen, Q. (2019). Predicting prepayment and default risks of unsecured consumer loans in online lending. Emerging Markets Finance and Trade, 55(1), 118–132.

    Article  Google Scholar 

  39. 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 

  40. Liu, D., Brass, D. J., Lu, Y., & Chen, D. (2015). Friendships in online peer-to-peer lending: Pipes, prisms, and relational herding. Mis Quarterly, 39(3), 729–742.

    Article  Google Scholar 

  41. 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 

  42. Ma, L., Zhao, X., Zhou, Z., & Liu, Y. (2018). A new aspect on P2P online lending default prediction using meta-level phone usage data in China. Decision Support Systems, 111, 60–71.

    Article  Google Scholar 

  43. Malekipirbazari, M., & Aksakalli, V. (2015). Risk assessment in social lending via random forests. Expert Systems with Applications, 42(10), 4621–4631.

    Article  Google Scholar 

  44. Malhotra, R., & Malhotra, D. K. (2002). Differentiating between good credits and bad credits using neuro-fuzzy systems. European Journal of Operational Research, 136(1), 190–211.

    Article  Google Scholar 

  45. Metz, C. E. (1978). Basic principles of ROC analysis. Seminars in Nuclear Medicine, 8(4), 283–298.

    Article  Google Scholar 

  46. Michels, J. (2012). Do unverifiable disclosures matter? Evidence from peer-to-peer lending. The Accounting Review, 87(4), 1385–1413.

    Article  Google Scholar 

  47. Noh, H. J., Roh, T. H., & Han, I. (2005). Prognostic personal credit risk model considering censored information. Expert Systems with Applications, 28(4), 753–762.

    Article  Google Scholar 

  48. 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 

  49. Roberts, J. A., & Sepulveda-M, C. J. (1999). Demographics and money attitudes: A test of Yamauchi and Templer’s (1982) money attitude scale in Mexico. Personality and Individual Differences, 27(1), 19–35.

    Article  Google Scholar 

  50. Serrano-Cinca, C., Gutiérrez-Nieto, B., & López-Palacios, L. (2015). Determinants of default in P2P lending. PloS One, 10(10), e0139427.

    Article  Google Scholar 

  51. Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52, 591–611.

    Article  Google Scholar 

  52. Shi, X., Jin, Q., & He, L. (2020). The bilateral effects of platform-sponsored collateral in Peer-To-Peer (P2P) lending: Evidence from China. Emerging Markets Finance and Trade, 56(4), 771–765.

    Article  Google Scholar 

  53. Steenackers, A., & Goovaerts, M. (1989). A credit scoring model for personal loans. Insurance Mathematics and Economics, 8(1), 31–34.

    Article  Google Scholar 

  54. Sweets, J. F. (1988). Hold that pendulum! Redefining fascism, collaborationism and resistance in France. French Historical Studies, 15(4), 731–758.

    Article  Google Scholar 

  55. Tao, Q., Dong, Y., & Lin, Z. (2017). Who can get money? Evidence from the Chinese peer-to-peer lending platform. Information Systems Frontiers, 19(3), 425–441.

    Article  Google Scholar 

  56. Thorndike, E. L. (1913). Educational psychology, Vol 1: The original nature of man. Teachers College.

    Google Scholar 

  57. Tonidandel, S., & LeBreton, J. M. (2015). RWA web: A free, comprehensive, web-based, and user-friendly tool for relative weight analyses. Journal of Business and Psychology, 30(2), 207–216.

    Article  Google Scholar 

  58. Tonidandel, S., LeBreton, J. M., & Johnson, J. W. (2009). Determining the statistical significance of relative weights. Psychological Methods, 14(4), 387–399.

    Article  Google Scholar 

  59. Wan, Q., Chen, D., & Shi, W. (2016). Online peer-to-peer lending decision making: Model development and testing. Social Behavior and Personality: An International Journal, 44(1), 117–130.

    Article  Google Scholar 

  60. Wang, H., Greiner, M., & Aronson, J. E. (2009). People-to-People lending: The emerging e-commerce transformation of a financial market. In M. L. Nelson, M. J. Shaw, & S. T. J. (Eds.), Lecture notes in business information processing. Value creation in E-business management.

  61. Wei, Z., & Lin, M. (2017). Market mechanisms in online peer-to-peer lending. Management Science, 63(12), 4236–4257.

    Article  Google Scholar 

  62. Wilcoxon, F., Katti, S. K., & Wilcox, R. A. (1970). Critical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank test. Selected Tables in Mathematical Statistics, 1, 171–259.

    Google Scholar 

  63. Xu, J. J., & Chau, M. (2018). Cheap talk? The impact of lender-borrower communication on peer-to-peer lending outcomes. Journal of Management Information Systems, 35(1), 53–85.

    Article  Google Scholar 

  64. Yoon, Y., Li, Y., & Feng, Y. (2019). Factors affecting platform default risk in online peer-to-peer (P2P) lending business: An empirical study using Chinese online P2P platform data. Electronic Commerce Research, 19(1), 131–158.

    Article  Google Scholar 

  65. Yu, H., Dan, M., Ma, Q., & Jin, J. (2018). They all do it, will you? Event-related potential evidence of herding behavior in online peer-to-peer lending. Neuroscience Letters, 681, 1–5.

    Article  Google Scholar 

  66. Yum, H., Lee, B., & Chae, M. (2012). From the wisdom of crowds to my own judgment in microfinance through online peer-to-peer lending platforms. Electronic Commerce Research and Applications, 11(5), 469–483.

    Article  Google Scholar 

  67. Zhu, Z. (2018). Safety promise, moral hazard and financial supervision: Evidence from peer-to-peer lending. Finance Research Letters, 27, 1–5.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dongwoo Kim.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

"Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations''

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, D. Can investors’ collective decision-making evolve? Evidence from peer-to-peer lending markets. Electron Commer Res 23, 1323–1358 (2023). https://doi.org/10.1007/s10660-021-09514-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-021-09514-8

Keywords