Abstract
Financial risk, defined as the chance to deviate from
return expectations, is most commonly measured
with volatility. Due to its value for investment
decision making, volatility prediction is probably
among the most important tasks in finance and risk
management. Although evidence exists that enriching purely financial models with natural language
information can improve predictions of volatility,
this task is still comparably underexplored. We introduce PRoFET, the first neural model for volatility prediction jointly exploiting both semantic language representations and a comprehensive set of
financial features. As language data, we use transcripts from quarterly recurring events, so-called
earnings calls; in these calls, the performance of
publicly traded companies is summarized and prognosticated by their management. We show that our
proposed architecture, which models verbal context
with an attention mechanism, significantly outperforms the previous state-of-the-art and other strong
baselines. Finally, we visualize this attention mechanism on the token-level, thus aiding interpretability and providing a use case of PRoFET as a tool
for investment decision support