What You Say and How You Say It Matters:
Predicting Financial Risk Using Verbal and Vocal Cues
Abstract
Predicting financial risk is an essential task in
financial market. Prior research has shown that
textual information in a firm’s financial statement can be used to predict its stock’s risk
level. Nowadays, firm CEOs communicate
information not only verbally through press
releases and financial reports, but also nonverbally through investor meetings and earnings conference calls. There are anecdotal
evidences that CEO’s vocal features, such as
emotions and voice tones, can reveal the firm’s
performance. However, how vocal features
can be used to predict risk levels, and to what
extent, is still unknown. To fill the gap, we obtain earnings call audio recordings and textual
transcripts for S&P 500 companies in recent
years. We propose a multimodal deep regression model (MDRM) that jointly model CEO’s
verbal (from text) and vocal (from audio) information in a conference call. Empirical results show that our model that jointly considers verbal and vocal features achieves significant and substantial prediction error reduction. We also discuss several interesting findings and the implications to financial markets.
The processed earnings conference calls data
(text and audio) are released for readers who
are interested in reproducing the results or designing trading strategy.