Paralinguistic Alpha: Predictive Power of Vocal Bio Metrics

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Management routinely conveys previously material non-public information to the market during earnings calls, and these disclosures move stock prices. Although communication in this setting is inherently multi-modal, market participants overwhelmingly focus on the words spoken, treating transcripts as sufficient representations of managerial intent and conviction. This paper demonstrates that the voice itself carries a distinct and economically meaningful information channel. Using paralinguistic acoustic features extracted from CEO speech during the Q&A segments of earnings calls, we show that vocal delivery encodes managerial confidence, stress, and uncertainty in ways that are orthogonal to textual content. Employing an event-study framework, we find that these acoustic signals predict short- and medium-horizon excess returns, indicating that markets do not fully incorporate the information conveyed by voice at the time of disclosure. The results establish the voice channel as a material, under-recognized component of corporate communication and a source of alpha for systematic investors, independent of language-based analysis 

Asset managers increasingly explore the idea of extracting confidence, stress, or tone from earnings-call audio. Many already license the raw inputs: audio recordings and transcripts, and assume that with modern ML tools, building proprietary behavioral signals is a straightforward extension. It is not.

Access to audio and text is necessary, but it is nowhere near sufficient.

Across firms, internal build attempts repeatedly fail for the same fundamental reasons.