Disclosed Human-Capital Disruption and Firm-Specific Risk

[2608.14859] Disclosed Human-Capital Disruption and Firm-Specific Risk Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantitative Finance > Statistical Finance arXiv:2608.14859 (q-fin) [Submitted on 14 Aug 2026] Title: Disclosed Human-Capital Disruption and Firm-Specific Risk Authors: Ang Zhang View a PDF of the paper titled Disclosed Human-Capital Disruption and Firm-Specific Risk, by Ang Zhang View PDF HTML (experimental) Abstract: Human capital is a central organizational input, but standard financial data reveal little about firm-specific disruptions to workforce availability, cost, skills, and continuity. I construct a measure of disclosed human-capital disruption from earnings calls using author-defined coding criteria and a contextual language model. Within firms, a one-standard-deviation increase in the annual measure is associated with 0.55 percentage points higher idiosyncratic volatility, 0.58 percentage points higher downside deviation, and a 0.46 percentage point lower worst monthly return, with no corresponding relation to market beta. The results are stable across seven broader and narrower classification rules and remain after removing explicit labor-shortage passages and controlling for a recently published labor-shortage measure and transcript-wide negative and uncertain language. At the call level, human-capital disruption predicts approximately 0.50% higher idiosyncratic volatility over the following 42 trading days after conditioning on pre-call risk. Risk is elevated before the call as well, and the score predicts the continuation of that firm-specific risk state over the following 42 trading days. After telegraphing and succession passages are removed, the measure also predicts subsequent named-executive roster exits and the incumbent CEO's effective departure from office. Earnings calls therefore reveal disturbances to a key organizational input that are broader than labor shortages and informative about the distribution of firm outcomes. Comments: 50 pages Subjects: Statistical Finance (q-fin.ST) Cite as: arXiv:2608.14859 [q-fin.ST] (or arXiv:2608.14859v1 [q-fin.ST] for this version) https://doi.org/10.48550/arXiv.2608.14859 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ang Zhang [ view email ] [v1] Fri, 14 Aug 2026 19:54:33 UTC (91 KB) Full-text links: Access Paper: View a PDF of the paper titled Disclosed Human-Capital Disruption and Firm-Specific Risk, by Ang Zhang View PDF HTML (experimental) TeX Source view license Current browse context: q-fin.ST < prev | next > new | recent | 2026-08 Change to browse by: q-fin References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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