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

Authors

  • Yi He University of Electronic Science and Technology of China
  • Wenxin Tai University of Electronic Science and Technology of China
  • Fan Zhou University of Electronic Science and Technology of China Kashi Institute of Electronics and Information Industry
  • Yi Yang The Hong Kong University of Science and Technology

DOI:

https://doi.org/10.1609/aaai.v37i13.26973

Keywords:

Risk Prediction, Hypergraph, NLP

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.

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Published

2023-09-06

How to Cite

He, Y., Tai, W., Zhou, F., & Yang, Y. (2023). Exploring Hypergraph of Earnings Call for Risk Prediction (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16226-16227. https://doi.org/10.1609/aaai.v37i13.26973