Exploring Hypergraph of Earnings Call for Risk Prediction (Student Abstract)

In financial economics, studies have shown that the textual content in the earnings conference call transcript has predictive power for a firm's future risk. However, the conference call transcript is very long and contains diverse non-relevant content, which poses challenges for the text-based risk forecast. This study investigates the structural dependency within a conference call transcript by explicitly modeling the dialogue between managers and analysts. Specifically, we utilize TextRank to extract information and exploit the semantic correlation within a discussion using hypergraph learning. This novel design can improve the transcript representation performance and reduce the risk of forecast errors. Experimental results on a large-scale dataset show that our approach can significantly improve prediction performance compared to state-of-the-art text-based models.