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Earnings conference calls are significant information events for volatilityforecasting, which is essential for financial risk management and assetpricing. Although some recent volatility forecasting models have utilized thetextual content of conference calls, the dialogue structures of conferencecalls and company relationships are almost ignored in extant literature. Tobridge this gap, we propose a new model called Temporal Virtual Graph NeuralNetwork (TVGNN) for volatility forecasting by jointly modeling conference calldialogues and company networks. Our model differs from existing models inseveral important ways. First, we propose to exploit more dialogue structuresby encoding position, utterance, speaker role, and Q\&A segments. Second, wepropose to encode the market states for volatility forecasting by extending theGated Recurrent Units (GRU). Third, we propose a new method for constructingtemporal company networks in which the messages can only flow from temporallypreceding to successive nodes, and extend the Graph Attention Networks (GAT)for modeling company relationships. We collect conference call transcripts ofS\&P500 companies from 2008 to 2019, and construct a dataset of conference calldialogues with additional information on dialogue structures and companynetworks. Empirical results on our dataset demonstrate the superiority of ourmodel over competitive baselines for volatility forecasting. We also conductsupplementary analyses to examine the effectiveness of our model's keycomponents and interpretability.

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