When Expert Screening and Crowd Validation Disagree: Signal Discordance and Venture Quality in Life-Science Equity Crowdfunding

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Abstract Specialized equity-crowdfunding platforms generate three non-equivalent signals about each venture: expert screening before launch, visible early traction during the campaign, and venture outcomes observed years later. These signals are often studied separately, even though platforms and investors must interpret them jointly, especially when they disagree. Using ninety-nine rounds closed on Capital Cell, a life-science equity-crowdfunding platform, between 2016 and 2023, we study this problem as one of signal discordance. We build block-level measures that compare expert evaluations, crowd traction, and lead-investor dominance, and we relate their agreement or disagreement to post-campaign venture quality, measured through venture status and valuation multiples. To analyse a small, noisy, and block-structured dataset under epistemic uncertainty, we use a soft quantum kernel whose softness is calibrated from the dispersion of expert scores. The method is used here as a tool for mixed-signal settings rather than as a claim of general quantum superiority, and we benchmark it against classical alternatives and a hardware implementation on the Quantum Blue QPU at the Barcelona Supercomputing Center. The results show that expert screening and crowd traction are distinct informational objects, that their disagreement is especially informative about long-run quality, and that specialized platforms add value when expert signals remain informative where early market validation does not.
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When Expert Screening and Crowd Validation Disagree: Signal Discordance and Venture Quality in Life-Science Equity Crowdfunding | 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 When Expert Screening and Crowd Validation Disagree: Signal Discordance and Venture Quality in Life-Science Equity Crowdfunding Laura Sáez-Ortuño, Marti Sagarra, Santiago Forgas-Coll, Valeria Isgrò, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9509733/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 Specialized equity-crowdfunding platforms generate three non-equivalent signals about each venture: expert screening before launch, visible early traction during the campaign, and venture outcomes observed years later. These signals are often studied separately, even though platforms and investors must interpret them jointly, especially when they disagree. Using ninety-nine rounds closed on Capital Cell, a life-science equity-crowdfunding platform, between 2016 and 2023, we study this problem as one of signal discordance. We build block-level measures that compare expert evaluations, crowd traction, and lead-investor dominance, and we relate their agreement or disagreement to post-campaign venture quality, measured through venture status and valuation multiples. To analyse a small, noisy, and block-structured dataset under epistemic uncertainty, we use a soft quantum kernel whose softness is calibrated from the dispersion of expert scores. The method is used here as a tool for mixed-signal settings rather than as a claim of general quantum superiority, and we benchmark it against classical alternatives and a hardware implementation on the Quantum Blue QPU at the Barcelona Supercomputing Center. The results show that expert screening and crowd traction are distinct informational objects, that their disagreement is especially informative about long-run quality, and that specialized platforms add value when expert signals remain informative where early market validation does not. Equity crowdfunding Expert screening Signal discordance Crowd dynamics Venture quality Entrepreneurial finance Full Text Additional Declarations No competing interests reported. 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-9509733","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630313402,"identity":"907140bb-455a-4851-a3e8-c1d9bcb2f05b","order_by":0,"name":"Laura Sáez-Ortuño","email":"","orcid":"","institution":"University of Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Sáez-Ortuño","suffix":""},{"id":630313403,"identity":"0dc349d4-9d35-4d66-ad58-4a97c00da3be","order_by":1,"name":"Marti Sagarra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYLCCBBjjA8MBZC5OwNgAU8M4g2gtMBYzDzFadNvPPn/w4I+dXf/ssw8/27bdYeBnzzHAq8XsTLphQ2JbcvKMc+nG0rltzxgke94Q0HIgjbEhsYE5meEMGwNQy2EGgxuEbDn/DOj9P/XJ8mfYmH9bArXYE9RyA2hLAtthO4MzbGzSjCBbJAhqecY4I7HteIIhUItlz7lnPBJnnhUQcFgaw8cff6rt5YAOu/Gj7I4cf3vyBrxaYCCxAcrgIUo5CNgTrXIUjIJRMApGHgAAm1RKTo6vzBwAAAAASUVORK5CYII=","orcid":"","institution":"University of Barcelona","correspondingAuthor":true,"prefix":"","firstName":"Marti","middleName":"","lastName":"Sagarra","suffix":""},{"id":630313404,"identity":"f717cf8b-70c4-4b2d-8b5e-d4107837b8be","order_by":2,"name":"Santiago Forgas-Coll","email":"","orcid":"","institution":"University of Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Santiago","middleName":"","lastName":"Forgas-Coll","suffix":""},{"id":630313405,"identity":"d91d1919-6b1d-40bd-9682-640f3a2017e1","order_by":3,"name":"Valeria Isgrò","email":"","orcid":"","institution":"University of Reggio Calabria","correspondingAuthor":false,"prefix":"","firstName":"Valeria","middleName":"","lastName":"Isgrò","suffix":""},{"id":630313406,"identity":"cde4621c-dd27-497b-af9b-e1b179bd9b8c","order_by":4,"name":"Massimiliano Ferrara","email":"","orcid":"","institution":"University of Reggio Calabria","correspondingAuthor":false,"prefix":"","firstName":"Massimiliano","middleName":"","lastName":"Ferrara","suffix":""}],"badges":[],"createdAt":"2026-04-23 18:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9509733/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9509733/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108182902,"identity":"c6cd64ba-4cb4-486a-9ce7-bec65db29b9e","added_by":"auto","created_at":"2026-04-30 08:59:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":283799,"visible":true,"origin":"","legend":"","description":"","filename":"sbeblindedtexfigurestables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9509733/v1_covered_7845f3dc-788a-4eaf-a658-2b1e81d1eb53.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eWhen Expert Screening and Crowd Validation Disagree: Signal Discordance and Venture Quality in Life-Science Equity Crowdfunding\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Equity crowdfunding, Expert screening, Signal discordance, Crowd dynamics, Venture quality, Entrepreneurial finance","lastPublishedDoi":"10.21203/rs.3.rs-9509733/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9509733/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSpecialized equity-crowdfunding platforms generate three non-equivalent signals about each venture: expert screening before launch, visible early traction during the campaign, and venture outcomes observed years later. 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