Unpacking the effects of scams in marketplace lending: investor confidence and attention

References

• Alfaro, E., García, N., Gámez, M., & Elizondo, D. (2008). Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems, 45(1), 110–23. https://doi.org/10.1016/j.dss.2007.12.002
   Web of Science ®Google Scholar
• Barber, B. M., & Odean, T. (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. The Review of Financial Studies, 21(2), 785–818. https://doi.org/10.1093/rfs/hhm079
   Web of Science ®Google Scholar
• Bracke, P., Datta, A., Jung, C., & Sen, S. (2019). Machine learning explainability in finance: An application to default risk analysis. Working Paper.
   Google Scholar
• Braggion, F., Manconi, A., & Zhu, H. (2018). Can technology undermine macroprudential regulation? Evidence from peer-to-peer credit in China. Working Paper.
   Google Scholar
• Breitung, C. (2023). Automated stock picking using random forests. Journal of Empirical Finance, 72, 532–556. https://doi.org/10.1016/j.jempfin.2023.05.001
   Web of Science ®Google Scholar
• Caldieraro, F., Zhang, J. Z., Cunha, M., Jr., & Shulman, J. D. (2018). Strategic information transmission in peer-to-peer lending markets. Journal of Marketing, 82(2), 42–63. https://doi.org/10.1509/jm.16.0113
   Web of Science ®Google Scholar
• Chen, X., Chong, Z., Giudici, P., & Huang, B. (2022). Network centrality effects in peer to peer lending. Physica A: Statistical Mechanics and Its Applications, 600, 127546. https://doi.org/10.1016/j.physa.2022.127546
   Web of Science ®Google Scholar
• Chen, X., Huang, B., & Ye, D. (2020). Gender gap in peer-to-peer lending: Evidence from China. Journal of Banking and Finance, 112, 105633. https://doi.org/10.1016/j.jbankfin.2019.105633
   Web of Science ®Google Scholar
• Chen, X., Hu, X., & Ben, S. (2021). How individual investors react to negative events in the FinTech era? Evidence from China’s peer-to-peer lending market. Journal of Theoretical & Applied Electronic Commerce Research, 16(1), 52–70. https://doi.org/10.4067/S0718-18762021000100105
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1007/s10799-014-0187-z
   Web of Science ®Google Scholar
• Chen, J., Liu, Y. J., Lu, L., & Tang, Y. (2016). Investor attention and macroeconomic news announcements: Evidence from stock index futures. Journal of Futures Markets, 36(3), 240–266. https://doi.org/10.1002/fut.21727
   Web of Science ®Google Scholar
• Chen, R., Qian, Q., Jin, C., Xu, M., & Song, Q. (2020). Investor attention on internet financial markets. Finance Research Letters, 36, 101421. https://doi.org/10.1016/j.frl.2019.101421
   Web of Science ®Google Scholar
• Chong, Z., & Wei, X. (2023). Exploring the spatial linkage network of peer-to-peer lending in China. Physica A: Statistical Mechanics and Its Applications, 630, 129279. https://doi.org/10.1016/j.physa.2023.129279
   Web of Science ®Google Scholar
• Croux, C., Jagtiani, J., Korivi, T., & Vulanovic, M. (2020). Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform. Journal of Economic Behavior and Organization, 173, 270–296. https://doi.org/10.1016/j.jebo.2020.03.016
   Web of Science ®Google Scholar
• Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. The Journal of Finance, 66(5), 1461–1499. https://doi.org/10.1111/j.1540-6261.2011.01679.x
   Web of Science ®Google Scholar
• Dorfleitner, G., Priberny, C., Schuster, S., Stoiber, J., Weber, M., de Castro, I., & Kammler, J. (2016). Description-text related soft information in peer-to-peer lending–evidence from two leading European platforms. Journal of Banking and Finance, 64, 169–187. https://doi.org/10.1016/j.jbankfin.2015.11.009
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1093/rfs/hhs071
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1080/00036846.2014.962222
   Web of Science ®Google Scholar
• Figini, S., & Giudici, P. (2011). Statistical merging of rating models. Journal of the Operational Research Society, 62(6), 1067–1074. https://doi.org/10.1057/jors.2010.41
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.ijindorg.2016.09.002
   Web of Science ®Google Scholar
• Fuster, A., Plosser, M., Schnabl, P., & Vickery, J. (2019). The role of technology in mortgage lending. The Review of Financial Studies, 32(5), 1854–1899. https://doi.org/10.1093/rfs/hhz018
   Web of Science ®Google Scholar
• Gao, M., Yen, J., & Liu, M. (2021). Determinants of defaults on P2P lending platforms in China. International Review of Economics & Finance, 72, 334–348. https://doi.org/10.1016/j.iref.2020.11.012
   Web of Science ®Google Scholar
• Gervais, S., Kaniel, R., & Mingelgrin, D. H. (2001). The high‐volume return premium. The Journal of Finance, 56(3), 877–919. https://doi.org/10.1111/0022-1082.00349
   Web of Science ®Google Scholar
• Giudici, P. (2001). Bayesian data mining, with application to benchmarking and credit scoring. Applied Stochastic Models in Business and Industry, 17(1), 69–81. https://doi.org/10.1002/asmb.425
   Web of Science ®Google Scholar
• Giudici, P., Hadji-Misheva, B., & Spelta, A. (2020). Network based credit risk models. Quality Engineering, 32(2), 199–211. https://doi.org/10.1080/08982112.2019.1655159
   Web of Science ®Google Scholar
• He, Q., & Li, X. (2021). The failure of Chinese peer-to-peer lending platforms: Finance and politics. Journal of Corporate Finance, 66, 101852. https://doi.org/10.1016/j.jcorpfin.2020.101852
   Web of Science ®Google Scholar
• He, F., Qin, S., & Zhang, X. (2021). Investor attention and platform interest rate in Chinese peer-to-peer lending market. Finance Research Letters, 39, 101559. https://doi.org/10.1016/j.frl.2020.101559
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1509/jmkr.48.SPL.S138
   Web of Science ®Google Scholar
• Jenq, C., Pan, J., & Theseira, W. (2015). Beauty, weight, and skin color in charitable giving. Journal of Economic Behavior and Organization, 119, 234–253. https://doi.org/10.1016/j.jebo.2015.06.004
   Web of Science ®Google Scholar
• Jiang, J., Liao, L., Wang, Z., & Zhang, X. (2021). Government affiliation and peer-to-peer lending platforms in China. Journal of Empirical Finance, 62, 87–106. https://doi.org/10.1016/j.jempfin.2021.02.004
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.jce.2019.11.001
   Web of Science ®Google Scholar
• Kahneman, D. (1973). Attention and effort. Prentice-Hall.
   Google Scholar
• Khaidem, L., Saha, S., & Dey, S. R. (2016). Predicting the direction of stock market prices using random forest. arXiv preprint arXiv:1605.00003.
   Google Scholar
• Kou, G., Olgu Akdeniz, Ö., Dinçer, H., & Yüksel, S. (2021). Fintech investments in European banks: A hybrid IT2 fuzzy multidimensional decision-making approach. Financial Innovation, 7(1), 39. https://doi.org/10.1186/s40854-021-00256-y
   PubMed Web of Science ®Google Scholar
• Kou, G., Peng, Y., & Wang, G. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275, 1–12. https://doi.org/10.1016/j.ins.2014.02.137
   Web of Science ®Google Scholar
• Kou, G., Xu, Y., Peng, Y., Shen, F., Chen, Y., Chang, K., & Kou, S. (2021). Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection. Decision Support Systems, 140, 113429. https://doi.org/10.1016/j.dss.2020.113429
   Web of Science ®Google Scholar
• Kowalewski, O., Pisany, P., & Ślązak, E. (2022). Digitalization and data, institutional quality and culture as drivers of technology-based credit providers. Journal of Economics and Business, 121, 106069. https://doi.org/10.1016/j.jeconbus.2022.106069
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1080/00909882.2010.536844
   Web of Science ®Google Scholar
• Li, X., Deng, Y., & Li, S. (2020). Gender differences in self-risk evaluation: Evidence from the renrendai online lending platform. Journal of Applied Economics, 23(1), 485–496. https://doi.org/10.1080/15140326.2020.1797338
   Web of Science ®Google Scholar
• Li, T., Kou, G., Peng, Y., & Philip, S. Y. (2021). An integrated cluster detection, optimization, and interpretation approach for financial data. IEEE Transactions on Cybernetics, 52(12), 13848–13861. https://doi.org/10.1109/TCYB.2021.3109066
   Web of Science ®Google Scholar
• Liu, Y., Yang, M., Wang, Y., Li, Y., Xiong, T., & Li, A. (2022). Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China. International Review of Financial Analysis, 79, 101971. https://doi.org/10.1016/j.irfa.2021.101971
   Web of Science ®Google Scholar
• Liu, Q., Zou, L., Yang, X., & Tang, J. (2019). Survival or die: A survival analysis on peer‐to‐peer lending platforms in China. Accounting & Finance, 59(S2), 2105–2131. https://doi.org/10.1111/acfi.12513
   Google Scholar
• Li, J., Zhang, B., Jiang, M., & Hu, J. (2023). Homophilous intensity in the online lending market: Bidding behavior and economic effects. Journal of Banking & Finance, 152, 106876. https://doi.org/10.1016/j.jbankfin.2023.106876
   Web of Science ®Google Scholar
• Mercadier, M., & Lardy, J. P. (2019). Credit spread approximation and improvement using random forest regression. European Journal of Operational Research, 277(1), 351–365. https://doi.org/10.1016/j.ejor.2019.02.005
   Web of Science ®Google Scholar
• Nemoto, N., Storey, D. J., & Huang, B. (2019). Optimal regulation of P2P lending for small and medium-sized enterprises. ADBI Working Paper Series.
   Google Scholar
• Nigmonov, A., Shams, S., & Alam, K. (2022). Macroeconomic determinants of loan defaults: Evidence from the US peer-to-peer lending market. Research in International Business and Finance, 59, 101516. https://doi.org/10.1016/j.ribaf.2021.101516
   Web of Science ®Google Scholar
• Park, H. J., Kim, Y., & Kim, H. Y. (2022). Stock market forecasting using a multi-task approach integrating long short-term memory and the random forest framework. Applied Soft Computing, 114, 108106. https://doi.org/10.1016/j.asoc.2021.108106
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1353/jhr.2011.0025
   Web of Science ®Google Scholar
• Qian, Y., & Lin, X. (2020). The research on the influencing factors of trust in online P2P lending: Based on platform. In 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China (Vol. 1, pp. 2626–2632). IEEE.
   Google Scholar
• Rao, C., Liu, M., Goh, M., & Wen, J. (2020). 2-stage modified random forest model for credit risk assessment of P2P network lending to “three rurals” borrowers. Applied Soft Computing, 95, 106570. https://doi.org/10.1016/j.asoc.2020.106570
   Web of Science ®Google Scholar
• Seasholes, M. S., & Wu, G. (2007). Predictable behavior, profits, and attention. Journal of Empirical Finance, 14(5), 590–610. https://doi.org/10.1016/j.jempfin.2007.03.002
   Google Scholar
• Sun, J., Li, H., Fujita, H., Fu, B., & Ai, W. (2020). Class-imbalanced dynamic financial distress prediction based on adaboost-SVM ensemble combined with SMOTE and time weighting. Information Fusion, 54, 128–144. https://doi.org/10.1016/j.inffus.2019.07.006
   Web of Science ®Google Scholar
• Tang, H. (2019). Peer-to-peer lenders versus banks: Substitutes or complements? The Review of Financial Studies, 32(5), 1900–1938. https://doi.org/10.1093/rfs/hhy137
   Web of Science ®Google Scholar
• Tang, L., Cai, F., & Ouyang, Y. (2019). Applying a nonparametric random forest algorithm to assess the credit risk of the energy industry in China. Technological Forecasting and Social Change, 144, 563–572. https://doi.org/10.1016/j.techfore.2018.03.007
   Web of Science ®Google Scholar
• Tian, G., & Wu, W. (2023). Big data pricing in marketplace lending and price discrimination against repeat borrowers: Evidence from China. China Economic Review, 78, 101944. https://doi.org/10.1016/j.chieco.2023.101944
   Web of Science ®Google Scholar
• Vallee, B., & Zeng, Y. (2019). Marketplace lending: A new banking paradigm? The Review of Financial Studies, 32(5), 1939–1982. https://doi.org/10.1093/rfs/hhy100
   Web of Science ®Google Scholar
• Wang, H., Kou, G., & Peng, Y. (2021). Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending. Journal of the Operational Research Society, 72(4), 923–934. https://doi.org/10.1080/01605682.2019.1705193
   Web of Science ®Google Scholar
• Wang, T., Zhao, S., & Shen, X. (2021). Why does regional information matter? evidence from peer-to-peer lending. European Journal of Finance, 27(4–5), 346–366. https://doi.org/10.1080/1351847X.2020.1720262
   Web of Science ®Google Scholar
• Wen, F., Xu, L., Ouyang, G., & Kou, G. (2019). Retail investor attention and stock price crash risk: Evidence from China. International Review of Financial Analysis, 65, 101376. https://doi.org/10.1016/j.irfa.2019.101376
   Web of Science ®Google Scholar
• Xu, J., Hilliard, J., & Barth, J. R. (2020). On education level and terms in obtaining P2P funding: New evidence from China. International Review of Finance, 20(4), 801–826. https://doi.org/10.1111/irfi.12242
   Web of Science ®Google Scholar
• Yan, Y., Lv, Z., & Hu, B. (2018). Building investor trust in the P2P lending platform with a focus on Chinese P2P lending platforms. Electronic Commerce Research, 18(2), 203–224. https://doi.org/10.1007/s10660-017-9255-x
   Web of Science ®Google Scholar
• Yuan, Y. (2015). Market-wide attention, trading, and stock returns. Journal of Financial Economics, 116(3), 548–564. https://doi.org/10.1016/j.jfineco.2015.03.006
   Web of Science ®Google Scholar
• Zhang, W., Shen, D., Zhang, Y., & Xiong, X. (2013). Open source information, investor attention, and asset pricing. Economic Modelling, 33, 613–619. https://doi.org/10.1016/j.econmod.2013.03.018
   Web of Science ®Google Scholar
• Zhang, B., & Wang, Y. (2015). Limited attention of individual investors and stock performance: Evidence from the ChiNext market. Economic Modelling, 50, 94–104. https://doi.org/10.1016/j.econmod.2015.06.009
   Web of Science ®Google Scholar
• Zhao, Y., Kou, G., Peng, Y., & Chen, Y. (2018). Understanding influence power of opinion leaders in e-commerce networks: An opinion dynamics theory perspective. Information Sciences, 426, 131–147. https://doi.org/10.1016/j.ins.2017.10.031
   Web of Science ®Google Scholar
• Zhou, L., & Lai, K. K. (2017). AdaBoost models for corporate bankruptcy prediction with missing data. Computational Economics, 50(1), 69–94. https://doi.org/10.1007/s10614-016-9581-4
   Web of Science ®Google Scholar