D ialogue GAT : A Graph Attention Network for Financial Risk Prediction by Modeling the Dialogues in Earnings Conference Calls (2022)

Abstract: Financial risk prediction is an essential task for risk management in capital markets. While traditional prediction models are built based on the hard information of numerical data, recent studies have shown that the soft information of verbal cues in earnings conference calls is significant for predicting market risk due to its less constrained fashion and direct interaction between managers and analysts. However, most existing models mainly focus on extracting useful semantic information from the textual conference call transcripts but ignore their subtle yet important information of dialogue structures. To bridge this gap, we develop a graph attention network called DialogueGAT for financial risk prediction by simultaneously modeling the speakers and their utterances in dialogues in conference calls. Different from previous studies, we propose a new method for constructing the graph of speakers and utterances in a dialogue, and design contextual attention at both speaker and utterance levels for disentangling their effects on the downstream prediction task. For model evaluation, we extend an existing dataset of conference call transcripts by adding the dialogue structure and speaker information. Empirical results on our dataset of S&P1500 companies demonstrate the superiority of our proposed model over competitive baselines from the extant literature.

## introduction financial risk prediction is an essential task for risk management in capital markets since risk is one of the most important variables for making investment decisions in tasks such as portfolio selection, asset pricing, and so on @xcite . due to its importance in risk assessment, the accurate prediction of financial risk is of great interest to academic and industry stakeholders in artificial intelligence, economics, and finance @xcite . ## related work this study mainly relates to two areas: (1) textbased financial risk prediction and (2) graph neural network for modeling dialogues. financial risk prediction is an essential task in capital markets. the early risk prediction models are built based on the hard information of numerical data, but recent studies have shown that the soft information of textual data is incrementally informative over the conventional numerical data for predicting corporate risks @xcite . two types of textual corporate disclosures are commonly used for financial risk prediction, including the annual reports @xcite and earnings conference call transcripts @xcite . compared with mandated corporate disclosure such as annual reports, voluntary conference calls have been recognized as significant information events to the market due to their less constrained fashion and direct interaction between managers and analysts @xcite . in this line of research, most existing models cast the risk prediction task as a standard text regression problem without considering the dialogue structures of conference calls @xcite @xcite . the only exception is a recent model called mrqa @xcite , which proposes a multi-round q&a attention network for considering the dialogue form. it is worth mentioning that this model ignores the speaker information when modeling the dialogue. we will use the mrqa model as a baseline in our experiment. our work is also related to recent studies using gnn (graph neural network) for modeling dialogues. gnn is a type of neural network which directly operates on the graph structure, and it has achieved great success in many natural language processing tasks @xcite . recently, some gnn models have been proposed for modeling dialogues, and the main challenge is how to construct a graph of utterances from dialogue in an effective manner @xcite . in this line of research, dia-loguegcn is a state-of-the-art model for dialoguebased emotion recognition without using external knowledge @xcite . this model is closely related to our model because we are one of the few dialogue-based gnn models that consider not only utterances but also speakers when constructing the dialogue graph. we will use the dialoguegcn model as a baseline in our experiment. ## methodology in this section, we formulate our problem of finance risk prediction and then present our proposed dia-loguegat model. ## experimentation in this section, we conduct empirical studies to evaluate model performance. we first describe our dataset and baselines and then present and analyze the experimental results. ## conclusion in this paper, we develop a model called dialogue-gat for financial risk prediction by jointly modeling the utterances and speakers in earnings conference calls. we make a methodological contribution by proposing a new method for constructing the dialogue graph of utterances and speakers and designing two contextual attention mechanisms for both utterance and speaker. our proposed method is general enough to be applied for other dialogue-based prediction tasks such as emotion recognition and sentiment analysis. we also add to the literature by introducing an extended dataset of conference call transcripts with dialogue structure and speaker information. empirical results on our extended dataset demonstrate the superiority of our proposed model over competitive baselines from the extant literature. we also conduct supplementary analysis for examining the effects of our model's key components, parameter sensitivity, and interpretability.