A Machine Learning Model for Algorithmic Optimization of Superannuation Schemes

A Machine Learning Model for Algorithmic Optimization of Superannuation Schemes | 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 Machine Learning Model for Algorithmic Optimization of Superannuation Schemes Winfred Katile MUKUNZI, Brian Wesley MUGANDA, Bernard Shibwabo KASAMANI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5280421/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 This paper sought to address the challenge of designing a superannuation pension scheme by developing a machine learning-based recommendation model for optimal asset portfolio selection and allocation. Pension schemes face challenges in dealing with the uncertainties associated with financial markets, especially in selecting an appropriate assets portfolio that can optimize the Return-on-Investment. This study used various machine learning algorithms to build optimal portfolios, which were evaluated based on the portfolio’s return and Sharpe ratio. Data used was obtained from the annual financial reports on pension assets’ cost and market value as well as asset income for the period of July 2013 to June 2023. The Genetic Algorithm, Particle Swarm Optimization, K-means clustering, and Mean-Variance Optimization techniques were employed to construct optimal portfolios. Evaluation based on portfolio return and Sharpe ratio revealed the K-means cluster focused on government securities as a high-performing, low-risk option with 11.16% return and 7.11 Sharpe ratio. Conversely, the genetic algorithm and particle swarm optimized portfolio demonstrated a more diversified conservative asset allocation, leading to a mean return of 7.63% with a Sharpe ratio of 3.91, and a mean return of 6.61% with a 3.16 Sharpe ratio respectively. Comparing these constructed portfolios with OECD (2022) which reported that Kenya achieved a real investment return of 2.9% in 2021, signifies that 7/10 of the constructed optimal superannuation portfolios would have resulted in better performance. Machine Learning Algorithmic Optimization Portfolio Optimization Genetic Algorithms Particle Swarm Optimization Superannuation Pension Scheme 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. 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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-5280421","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":368097696,"identity":"26686b78-b18c-4dcf-9d12-373435c078a8","order_by":0,"name":"Winfred Katile MUKUNZI","email":"","orcid":"","institution":"Retirement Benefits Authority","correspondingAuthor":false,"prefix":"","firstName":"Winfred","middleName":"Katile","lastName":"MUKUNZI","suffix":""},{"id":368097697,"identity":"81d87161-e55d-48a1-a0ba-d75ff954a804","order_by":1,"name":"Brian Wesley MUGANDA","email":"","orcid":"","institution":"Strathmore University","correspondingAuthor":false,"prefix":"","firstName":"Brian","middleName":"Wesley","lastName":"MUGANDA","suffix":""},{"id":368097698,"identity":"61644b33-6523-440e-a9b9-1bd6b7ce0a82","order_by":2,"name":"Bernard Shibwabo KASAMANI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYBACAwYehsNAOoGBvQEskACVYCZCC88BRqAmA+K0MIO1SCQQqcWc/+zBw4Vtdnn8ks+fP66o+ZOn29778AFDhXViAw4tljPyEg7PbEsulpydY9h45phBsdmZ48YGDGfScWoxuMFjcJh3G3Pihts5jI0NbAaJ226ksUkwth3GreX8GZCW+sQNN48/bGz4B9bC/oPxHx4tB3JAWg4nbrjBYNjY2AaxhYGxAbcWyxlALTP/HS+W7MkxnNnYZ5y47cwxZomEY+nGuLSY858x/lxwpjqPn/34g48N3+QStx1vY/zwocZaFpcWHCCBNOWjYBSMglEwCtAAAFHVZO3wFt+AAAAAAElFTkSuQmCC","orcid":"","institution":"Strathmore University","correspondingAuthor":true,"prefix":"","firstName":"Bernard","middleName":"Shibwabo","lastName":"KASAMANI","suffix":""}],"badges":[],"createdAt":"2024-10-17 07:08:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5280421/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5280421/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76212596,"identity":"3015a4b7-826f-4670-a20a-4436f9100b78","added_by":"auto","created_at":"2025-02-13 13:47:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":623761,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptMachineLearningSuperannuationReviewedandUpdatedSubmitted.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5280421/v1_covered_178b9f80-162b-49ab-94f7-303245ab97cc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Machine Learning Model for Algorithmic Optimization of Superannuation Schemes","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":"Machine Learning, Algorithmic Optimization, Portfolio Optimization, Genetic Algorithms, Particle Swarm Optimization, Superannuation Pension Scheme","lastPublishedDoi":"10.21203/rs.3.rs-5280421/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5280421/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper sought to address the challenge of designing a superannuation pension scheme by developing a machine learning-based recommendation model for optimal asset portfolio selection and allocation. Pension schemes face challenges in dealing with the uncertainties associated with financial markets, especially in selecting an appropriate assets portfolio that can optimize the Return-on-Investment. This study used various machine learning algorithms to build optimal portfolios, which were evaluated based on the portfolio\u0026rsquo;s return and Sharpe ratio. Data used was obtained from the annual financial reports on pension assets\u0026rsquo; cost and market value as well as asset income for the period of July 2013 to June 2023. The Genetic Algorithm, Particle Swarm Optimization, K-means clustering, and Mean-Variance Optimization techniques were employed to construct optimal portfolios. Evaluation based on portfolio return and Sharpe ratio revealed the K-means cluster focused on government securities as a high-performing, low-risk option with 11.16% return and 7.11 Sharpe ratio. Conversely, the genetic algorithm and particle swarm optimized portfolio demonstrated a more diversified conservative asset allocation, leading to a mean return of 7.63% with a Sharpe ratio of 3.91, and a mean return of 6.61% with a 3.16 Sharpe ratio respectively. Comparing these constructed portfolios with OECD (2022) which reported that Kenya achieved a real investment return of 2.9% in 2021, signifies that 7/10 of the constructed optimal superannuation portfolios would have resulted in better performance.\u003c/p\u003e","manuscriptTitle":"A Machine Learning Model for Algorithmic Optimization of Superannuation Schemes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-21 11:30:57","doi":"10.21203/rs.3.rs-5280421/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"9dc5b648-7a17-41ee-ba6b-8fb88d2e347a","owner":[],"postedDate":"October 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-13T13:39:03+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-21 11:30:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5280421","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5280421","identity":"rs-5280421","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}