A Yield Prediction Model for Fintech Platforms Based on Investor Behavior Characteristics and Its Application in Capital Allocation Decisions

A Yield Prediction Model for Fintech Platforms Based on Investor Behavior Characteristics and Its Application in Capital Allocation Decisions | 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 A Yield Prediction Model for Fintech Platforms Based on Investor Behavior Characteristics and Its Application in Capital Allocation Decisions Xiaoyi Meng, Shaochun Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9217736/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 Investment decisions and capital allocation on fintech platforms often rely on experiential judgments, lacking systematic quantitative analysis of investor behavior changes. To reveal the impact of investor behavior on return formation mechanisms, this study constructs a "behavioral characteristics-return performance" prediction model based on 240,000 investor behavior data points from eight consecutive quarters on a fintech platform. This model evaluates the indirect effects of different capital allocation strategies on investment returns.The study first converts investor behavior into 12 reproducible feature variables, including position depth, trading frequency, cross-cycle revisit rate, and risk exposure adjustment behavior. It then compares the predictive performance of Lasso, Random Forest, CatBoost, and XGBoost models.Results indicate that incorporating behavioral features significantly enhances model interpretability, with the R² for return prediction increasing from 0.41 to 0.68. Among these features, "deep investment behavior" and "cross-cycle trading stability" contributed the most.Further scenario simulations reveal that increasing investment budgets alone does not directly boost returns, but significantly promotes deeper investor behavior. A near-linear relationship exists between depth behavior and return performance. This study uncovers behavioral transmission pathways through which capital allocation influences investment returns, providing a quantitative analytical framework for fintech platforms to make scientific investment and budget decisions. Artificial Intelligence and Machine Learning investor behavior return prediction capital allocation fintech CatBoost scenario simulation 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-9217736","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611718284,"identity":"54b54af7-a9fd-4cff-bfe1-a5876017e6c9","order_by":0,"name":"Xiaoyi Meng","email":"data:image/png;base64,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","orcid":"","institution":"Department of University of California,University of California,Irvine, US","correspondingAuthor":true,"prefix":"","firstName":"Xiaoyi","middleName":"","lastName":"Meng","suffix":""},{"id":611718285,"identity":"019a0acc-35b9-47f1-8770-67a2c0809585","order_by":1,"name":"Shaochun Liu","email":"","orcid":"","institution":"Beijing JoinQuant Investment Management Co., Ltd,Beijing, China","correspondingAuthor":false,"prefix":"","firstName":"Shaochun","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-03-25 03:50:52","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9217736/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9217736/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105747857,"identity":"a1933ee8-208f-4673-ab97-a35f0a4b6bcd","added_by":"auto","created_at":"2026-03-30 14:43:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":757885,"visible":true,"origin":"","legend":"","description":"","filename":"2IEEEICAIF2026.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9217736/v1_covered_d5001816-11b5-4b63-908d-2bf41014cedf.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eA Yield Prediction Model for Fintech Platforms Based on Investor Behavior Characteristics and Its Application in Capital Allocation Decisions\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"investor behavior, return prediction, capital allocation, fintech, CatBoost, scenario simulation","lastPublishedDoi":"10.21203/rs.3.rs-9217736/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9217736/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInvestment decisions and capital allocation on fintech platforms often rely on experiential judgments, lacking systematic quantitative analysis of investor behavior changes. To reveal the impact of investor behavior on return formation mechanisms, this study constructs a \"behavioral characteristics-return performance\" prediction model based on 240,000 investor behavior data points from eight consecutive quarters on a fintech platform. This model evaluates the indirect effects of different capital allocation strategies on investment returns.The study first converts investor behavior into 12 reproducible feature variables, including position depth, trading frequency, cross-cycle revisit rate, and risk exposure adjustment behavior. It then compares the predictive performance of Lasso, Random Forest, CatBoost, and XGBoost models.Results indicate that incorporating behavioral features significantly enhances model interpretability, with the R² for return prediction increasing from 0.41 to 0.68. 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