HTML: Hierarchical Transformer-based Multi-task Learning for Volatility Prediction

Thevolatility forecastingtask refers to predicting the amount ofvariability in the price of a financial asset over a certain period.It is an important mechanism for evaluating the risk associatedwith an asset and, as such, is of significant theoretical and practicalimportance in financial analysis. While classical approaches haveframed this task as a time-series prediction one – using historicalpricing as a guide to future risk forecasting – recent advances innatural language processing have seen researchers turn to com-plementary sources of data, such as analyst reports, social media,and even the audio data from earnings calls. This paper proposes anovel hierarchical, transformer, multi-task architecture designedto harness the text and audio data from quarterly earnings confer-ence calls to predict future price volatility in the short and longterm. This includes a comprehensive comparison to a variety ofbaselines, which demonstrates very significant improvements inprediction accuracy, in the range 17% - 49% compared to the currentstate-of-the-art. In addition, we describe the results of an ablationstudy to evaluate the relative contributions of each component ofour approach and the relative contributions of text and audio datawith respect to prediction accuracy.