The Role of Social Media in Financial Risk Prediction: Evidence from China^*

Abstract

In this paper, we develop an intelligent approach to detect default risk of FinTech lending platforms. Using China's peer‐to‐peer (P2P) lending market as an empirical application, we assemble a unique dataset of matched default and non‐default platforms. We apply state‐of‐art techniques to extract sentiment and topic features from several stakeholders' social media data, which are used as supportive soft information. Our approach exhibits better predictive abilities than those with hard information only, where the value of dynamic soft information is demonstrated. Our approach serves as a proof of concept to complement traditional methods of financial risk prediction.