Online reviews of one firm may come from diverse sources including real customers, competitors, and the firm itself. Review manipulation by posting fake negative reviews on competitors and injecting fake positive oneself has great impacts on product sales and firm reputation. This study aims at answering the question - whose reviews are most valuable for predicting firm’s default risk? To uncover the value of manipulated and authentic reviews in firm default risk prediction, we conduct an empirical analysis using a unique weekly panel data from a third-party portal of online peer-topeer lending platforms in China. The results indicate that firm default probability increases with the number of manipulated positive reviews in the short-term, and decreases with the number of manipulated positive reviews emerged in the long-term. To our surprise, the authentic positive reviews are positively associated with default due to the overconfidence effect in the online lending context.
Li, Liting; Zheng, Haichao; Chen, Dongyu; and Zhu, Bin, "Whose Reviews Are Most Valuable for Predicting Default Risk of Peer-to-peer Lending Platforms? Evidence from China" (2020). PACIS 2020 Proceedings. 95.
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