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Whose reviews are most valuable for predicting the default risk of peer-to-peer lending platforms? Evidence from China

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Abstract

Online reviews of a firm may come from diverse sources including real customers, competitors, and the firm itself. Review manipulation by posting fake negative reviews about competitors or fake positive reviews oneself has major impacts on product sales and firm reputation. This study aims to answer the question of whose reviews are most valuable for predicting a firm’s default risk. To uncover the value of manipulated and authentic reviews in firm default risk prediction, we conduct an empirical analysis using unique weekly panel data from a third-party portal of online peer-to-peer lending platforms in China. The results indicate that firm default probability increases with the number of manipulated positive reviews in the short term but decreases with the number of manipulated positive reviews posted over the long term. In addition, the signaling role of manipulated positive reviews is stronger when the peer-to-peer lending platform experiences more intense pressure such as downturn of business performance, stricter financial regulation policies, or aggressive attacks from competitors. Manipulated negative reviews are harmful for peer-to-peer lending platforms, which will increase the probability of platform default. Finally, authentic positive reviews are positively associated with default due to the overconfidence effect in the online lending context, and the authentic negative reviews in the short term work as a significant signal for fraud risk.

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Notes

  1. To explore the potential relationship between fake positive comments and business performance of P2P lending platforms, we conduct linear regressions of the number of manipulated reviews / average rating on transaction volume. The results suggest that average daily transaction volume of a platform is positively and significantly related with the number and percentage of manipulated positive reviews (b = 0.591, p < 0.01 and b = 0.009, p < 0.001).

  2. We can get more details in the robustness checks section about different manipulation detection rules.

  3. https://xw.qq.com/cmsid/20200614A04GM400

  4. To check the relationship between reviews and platform quality, we run a single model including the interaction between manipulated positive review and Baidu index. The results indicate that there is a positive yet not significant interaction effect between platform survival time (platform duration) and the manipulated positive review (coefficient = − 0.357, p-value > 0.1). This finding suggests that manipulated positive reviews have lower effect on fraud risk prediction for the platform with longer survival time, which is consistent with prior literatures that discovered a negative effect of firm reputation on strategical review (Luca and Zervas [36]; Hollenbeck et al. [19]).

  5. http://irm.cninfo.com.cn/

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Acknowledgements

This study is supported by Natural Science Foundation of China (72071160, 71771159), and the Chinese Ministry of Education Humanities and Social Sciences Fund (18YJAZH142).

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Authors and Affiliations

  1. School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China

    Liting Li

  2. Financial Intelligence and Financial Engineering Key Laboratory of Sichuan Province, Southwestern University of Finance and Economics, Chengdu, China

    Haichao Zheng

  3. Dongwu Business School, SooChow University, Suzhou, China

    Dongyu Chen

  4. College of Business, Oregon State University, Corvallis, OR, USA

    Bin Zhu

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  1. Liting Li
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  4. Bin Zhu
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Correspondence to Haichao Zheng.

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Appendix

Appendix

To illustrate examples of manipulated reviews, we extracted several typical manipulated positive reviews on a specific platform (platform code = 628). Two reviewers who are denoted as Reviewer1 and Reviewer2, wrote reviews almost at the same time on the platform, and the text-similarity is relatively high. For example, the text from Reviewer1 on 2018-07-06 is almost the same as the text from Reviewer2 on 2018-04-13, which indicates that the two reviewers may share the same instructions to praise the platform (Tables

Table 13 Detection rules for manipulated reviews
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13,

Table 14 Examples of fake reviews in the data set
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14).

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Li, L., Zheng, H., Chen, D. et al. Whose reviews are most valuable for predicting the default risk of peer-to-peer lending platforms? Evidence from China. Electron Commer Res 24, 1619–1658 (2024). https://doi.org/10.1007/s10660-022-09571-7

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