The collapse of Chinese peer-to-peer lending platforms in 2018 has caused severe financial loss to investors and a massive shakeout to the p2p lending industry. However, as a considerable financial risk, platform collapse risk currently has not been carefully investigated, perhaps because of lacking relevant methods. To address this research gap, we examine whether Marketer-Generated Content in social media can be mined for indicators of platform collapse risk. Following the Signaling Theory, we use text mining techniques to extract linguistic cues in content posted by platforms on social media. Empirical results show that platforms adopting social media are less likely to collapse while linguistic cues in posted content also offer significant signals of platform collapse risk. Our study contributes to the literature by identifying relevant factors for assessing the stability of financial intermediaries. Furthermore, our work lays a theoretical foundation for platform selection by investors and for government regulation in internet finance.
HUANG, Wenjie and Zhao, J. Leon, "Mining Marketer-Generated Content for Platform Risk Signals in P2P Lending" (2020). PACIS 2020 Proceedings. 89.
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