Predicting Corporate Risk by Jointly Modeling Company Networks and Dialogues in Earnings Conference Calls

Earnings conference calls are attracting an increasing number of researchers due to their free form and rich information. Existing studies, however, do not take speaker role information into account. Furthermore, current research does not fully account for the impact of inter-company relationships on company risk. The only study that integrates company networks and earnings conference calls constructs an undirected graph for companies holding earnings conference calls at different dates, failing to meet the requirement of no temporal information leakage for prediction tasks. To address the aforementioned issues, we propose a new model called Temporal Virtual Graph Neural Network (TVGNN), which incorporates earnings conference calls and company networks to predict company risk. For the first time, our model incorporates participant role information in dialogue modeling. Moreover, we develop a new approach to construct company networks that ensures no temporal information leakage in the graph. In experiments, our proposed model outperforms all baselines. The supplementary analyses demonstrate the model's effectiveness and interpretability.