  Earnings conference calls are significant information events for volatility
forecasting, which is essential for financial risk management and asset
pricing. Although some recent volatility forecasting models have utilized the
textual content of conference calls, the dialogue structures of conference
calls and company relationships are almost ignored in extant literature. To
bridge this gap, we propose a new model called Temporal Virtual Graph Neural
Network (TVGNN) for volatility forecasting by jointly modeling conference call
dialogues and company networks. Our model differs from existing models in
several important ways. First, we propose to exploit more dialogue structures
by encoding position, utterance, speaker role, and Q\&A segments. Second, we
propose to encode the market states for volatility forecasting by extending the
Gated Recurrent Units (GRU). Third, we propose a new method for constructing
temporal company networks in which the messages can only flow from temporally
preceding to successive nodes, and extend the Graph Attention Networks (GAT)
for modeling company relationships. We collect conference call transcripts of
S\&P500 companies from 2008 to 2019, and construct a dataset of conference call
dialogues with additional information on dialogue structures and company
networks. Empirical results on our dataset demonstrate the superiority of our
model over competitive baselines for volatility forecasting. We also conduct
supplementary analyses to examine the effectiveness of our model's key
components and interpretability.
