V ol TAGE : Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls (2020)

Abstract: Natural language processing has recently made stock movement forecasting and volatility forecasting advances, leading to improved financial forecasting. Transcripts of companies’ earnings calls are well studied for risk modeling, offering unique investment insight into stock performance. However, vocal cues in the speech of company executives present an underexplored rich source of natural language data for estimating financial risk. Additionally, most existing approaches ignore the correlations between stocks. Building on existing work, we introduce a neural model for stock volatility prediction that accounts for stock interdependence via graph convolutions while fusing verbal, vocal, and financial features in a semi-supervised multi-task risk forecasting formulation. Our proposed model, VolTAGE, outperforms existing methods demonstrating the effectiveness of multimodal learning for volatility prediction.

## introduction motivation financial risk modeling is of great interest to capital market participants for making sound investment decisions. stock volatility is a vital indicator of a company's risk profile @xcite . the stock market presents various opportunities that increasingly attract investors, who utilize the market's potential to generate profits, wherein stock volatility is a vital risk modeling factor. one underexplored, yet crucial event that leads to significant fluctuations in stock volatility, is the earnings conference call. these calls are held periodically by publicly traded companies' executives to summarize and prognosticate company's performance @xcite . harnessing the interplay between the multimodal verbal and vocal cues in earnings calls can help better analyze the impact these calls may have on financial markets and forecast stock volatility @xcite . challenges while stock trading presents unparalleled investment opportunities, accurately predicting the rise and fall of stock prices has numerous challenges @xcite . conventional research in finance revolves around using historical stock data to develop statistical models and recurrent neural networks (rnns) capable of forecasting price trends @xcite . they are influenced by many factors ranging from public opinion to the movements of other related stocks @xcite . recent advances in deep learning present a promising prospect in multimodal stock forecasting by analyzing online news @xcite , and social media @xcite to learn latent patterns affecting stock prices @xcite . however, the challenging aspect in stock forecasting is that most existing work treats stock movements to be independent of each other, contrary to true market function @xcite . additionally, existing research has not leveraged the rich audio signals in company executives' speech, which could indicate the emotional and affective state of the speakers, and provide insights into company performance. more recently, the use of audio processing for earnings calls has gained an interest in both financial and linguistic research @xcite . multimodal approaches can extract complementary information from multiple modalities to improve financial modeling, mdrm @xcite , and html @xcite validate the premise of such approaches for volatility forecasting. additionally, advances in graph-based deep learning @xcite have led to the rise of graph neural networks (gnns) that can model the relationships between related stocks @xcite . publicly available online company information can be used to identify connections between stocks that might influence each other, such as those having the same ceo or belonging to the same industry. financial tasks are often correlated, thus making multi-task learning a promising choice for financial forecasting. contributions building on advances in the intersection of financial research, graph neural networks, and natural language processing, we present voltage: volatility forecasting via text-audio fusion with graph convolution networks for earnings calls. voltage comprises a set of neural components to capture cross-modal signals from earnings calls transcripts, ceo speech, inter stock dependence graphs, and numerical financial features. first, voltage combines the verbal-vocal coherence between earnings calls transcripts and speech via an inter-modal multi-utterance attention mechanism. the fused features are then fed to a graph convolution network (gcn) to simultaneously solve two homogeneous stock volatility tasks -average volatility (main task) and singleday volatility prediction (auxiliary task), in a semisupervised fashion. through a set of comparative, qualitative, and ablation experiments on real-world s&p 500 index data, we show voltage's utility of augmenting vocal and verbal cues with graphbased features in a multi-task setup. ## background extensive studies have shown the utility of employing historical financial data @xcite for volatility prediction, yet financial forecasting using multiple modalities remains an underexplored avenue. while newer work focuses on data across multiple modalities, there exist drawbacks and understudied approaches to improve current methods, which we describe next. volatility forecasting forecasting stock volatility is a crucial pillar across multiple financial domains and has focused on numerous academic studies. volatility is a key indicator of uncertainty and is a decisive variable to many investment decisions and portfolio creations. previous work in this domain has mainly relied on numerical features @xcite , such as macroeconomic indicators @xcite . this includes discrete (garch @xcite , rolling regression @xcite ), continuous @xcite , and neural approaches @xcite . this comprehensive work illustrates the significance of volatility in investment, security valuation, and risk management. ## forecasting stock volatility following @xcite and @xcite we define stock volatility as a regression task. for a given stock, with a close price of p i on trading day i, we calculate the average log volatility over n days following the earnings call as: problem statement given an earning call e, comprising of an audio a, and aligned text t , and stock prices p [0,n] , we aim to learn a predictive regression function ## voltage: architecture and learning below, we describe both the individual components and joint optimization of voltage, and present an overview of the architecture in figure 1 . ## qualitative analysis we analyze the q3-2017 earnings call for dg (dollar general), an american variety store company. the stock's price became highly volatile for a few days following the earnings call. figure 4a shows the audio-aware text attention heatmap for the duration of the earning call. the heatmap represents cross-modal attention weights assigned to textual utterances using corresponding vocal cues. here each cell (i, j) represents the weight of j th vocal utterance on the i th textual utterance. it is observed that the highest attention is towards the middle of the call, suggesting that the verbal cues of this portion have the highest impact on the text contextual embeddings for most of the sentences in the call. earning calls of companies are often structured such that the beginning of the call involves introductory disclaimer and greetings, while the ceo starts presenting financial results for the reporting quarter along with future goals of the company towards the middle of the call, which indicates why we see such influential utterances in this portion. figure 4b shows the disparity between ceo's vocal and verbal cues around the utterance. while textual content seems positive, a sudden spike in shimmer features in ceo's voice while speaking this sentence suggests disharmony between verbal and vocal cues. past research in acoustics @xcite suggests an elevated shimmer could be indicative of underlying stress in speech. after the earning call, it was noted that the gross margin of the company slipped by 0.4%, due to the increased transportation costs due to hurricane irma in 2017. on analyzing the graph, we observe that dg has edge connections with wmt (wallmart) and tgt (target corp.), both of which are retail variety stores, like dg. analysts had estimated a negative impact of about $2.8 billion on the retail sector due to the hurricane irma. this examination is also reflected in the high volatilities recorded for wmt and tgt during the same quarter. a unimodal model may miss these subtle disparities between text and audio. therefore, voltage, by leveraging cross-modal attention fusion and correlation graphs, accurately forecasts the volatility of dg, three days post the earnings call. ## conclusion and future work volatility, measured as a deviation in returns, is a reliable indicator of market risk linked with a stock. a rich source of company information is earnings calls that provide high risk-reward opportunities given their uniqueness and critical information disclosure. although evidence shows that enriching models with speech and inter-stock correlations can improve volatility forecasting, this area is underexplored. we propose voltage, a neural architecture that jointly exploits coherence over speech, text, and inter-stock correlations for volatility forecasting following earnings calls. through experiments on s&p 500 index data, we show the merit of crossmodal gated attention fusion, graph-based learning, and multi-task prediction for volatility forecasting. there are several promising directions of future work that we wish to explore. first, we want to improve upon the audio feature extraction. to model the speech of ceos in earnings calls, using semitones rather than raw frequency for pitch-related features. experimenting with other sets of commonly used acoustic features such as mfcc coefficients, opensmile features and audeep features for representing audio utterances also form a future direction for audio feature extraction. second, we want to expand the analysis presented in this paper beyond the s&p 500 index and us-based companies. existing research @xcite and this work at the intersection of natural language processing and earnings calls are limited to a small set of companies and earnings calls. analyzing the demographic, cultural, and gender bias in research pertaining to financial disclosures, particularly earnings calls, forms a future direction of research. we would also want to work on studying a wider set of earnings calls and companies spanning multiple languages, demographics, speakers and gender.