Physics-informed modeling of persistent predictive penalty from vocal affect in markets

Physics-informed modeling of persistent predictive penalty from vocal affect in markets | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Physics-informed modeling of persistent predictive penalty from vocal affect in markets Xiaoliang Chen, Le Chang, Xin Yu, Yunhe Huang, Teng Jing, Jiashuai He, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7655247/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Non-verbal emotion shapes collective decisions, yet its predictive value at scale remains unclear. Using a physics-informed multimodal model that isolates clear vocal emotion from 1,795 dynamic and noisy earnings calls, we show a persistent, counterintuitive effect we term the Predictive Penalty : adding vocal affect to standard predictors makes post-call volatility less predictable. This penalty is small on day one, intensifies over the next two weeks, and persists for about a month, proving strongest during the unscripted Q&A. Vocal emotion does not forecast returns, pointing to sentiment-driven risk rather than new information. The pattern holds across industries, time periods, and volatility regimes and is robust against extensive checks. We recast executive emotion as a quantitative gauge of communication-induced risk and present a reproducible approach. Our work provides a blueprint for quantifying how the non-verbal dimension of communication injects measurable risk into high-stakes environments, from trading floors to treaty rooms. Physical sciences/Mathematics and computing/Computer science Scientific community and society/Social sciences/Economics Physical sciences/Physics/Applied physics/Acoustics Scientific community and society/Social sciences/Psychology Predictive penalty Vocal Affect Physics-informed multimodal model High-Stakes Communication Affective acoustics Behavioural finance Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.pdf Supplementary Data: A Compendium of 15 Tables, 4 Figures, and Calculation Outputs Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7655247","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":519073121,"identity":"67f2c5a8-0490-464f-ac97-7a68bbf9232c","order_by":0,"name":"Xiaoliang 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