An Agent Based Framework for Assessing Risk Actionability in SEC Filings

An Agent Based Framework for Assessing Risk Actionability in SEC Filings | 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 Research Article An Agent Based Framework for Assessing Risk Actionability in SEC Filings Weishen Chu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8463587/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 Risk factor disclosures in corporate filings are widely analyzed for their informational content, yet their usefulness for operational governance remains underexplored. This paper introduces risk actionability as a distinct concept from risk presence and proposes an interpretable, agent-based pipeline for assessing whether disclosed risks support intervention-oriented decisions. Operating at the paragraph level on SEC Form 10-K and 10-Q risk factors, the agent assigns risk attributes and maps disclosures to pass, review, or escalate states. We introduce the Intervention Load Index and Actionability Gap to quantify human oversight demand implied by disclosures. Empirical results show that disclosure severity does not reliably predict intervention needs, and that conservative disclosure styles can suppress actionable signals. A failure case highlights limitations arising from disclosure sparsity. Artificial Intelligence and Machine Learning Interpretable AI Rule-based agents Text-based risk assessment Human-in-the-loop systems Decision-centric modeling Full Text Additional Declarations The authors declare no competing interests. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8463587","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":566301071,"identity":"b6954a14-6e72-49e2-8b07-43d50e3c40bb","order_by":0,"name":"Weishen 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