An Evaluation Framework for Machine Learning and Data Science-Based Financial Strategies: A Case Study-Driven Decision Model

Basit Öğe Kaydı
dc.authorid0000-0002-2381-773X
dc.authorid0000-0002-5233-2141
dc.authorid0000-0002-9668-3584
dc.authorid0000-0003-1432-8958
dc.contributor.authorSaadatmand, Mohammadsaleh
dc.contributor.authorDaim, Tugrul
dc.contributor.authorMena, Carlos
dc.contributor.authorYalcin, Haydar
dc.contributor.authorBolatan, Gulin
dc.contributor.authorChatterjee, Manali
dc.date.accessioned2026-01-24T12:29:01Z
dc.date.available2026-01-24T12:29:01Z
dc.date.issued2025
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractBig data and computational technologies are increasingly important worldwide in asset and investment management. Many investment management firms are adopting these data science (DS) methods and technologies to improve performance across all investment processes. A good question is whether we can make better decisions in developing quantitative strategies. Therefore, the main objective of this research was to develop a multicriteria assessment framework and scoring decision support system to evaluate quantitative investment strategies that apply machine learning (ML) and DS techniques in their research and development. Subject matter experts will assess all framework perspectives from a systematic literature review to approve their reliability. The perspectives consist of economic and financial foundations, data perspective, features perspective, modeling perspective, and performance perspective. The research methodology applied was the hierarchical decision model (HDM) to provide a 360 degrees view of the quantitative investment strategy and improve and generalize the concept to other asset classes and regions. This study accomplished a rigorous integration of an extensive literature review connecting DS, ML, and investment decision-making in developing quantitative investment strategies. As a result, the major contribution of this study is the comprehensive examination, which included identifying and quantifying perspectives and criteria. The results, while limited indicated significant gaps in strategies examined and therefore generated critical knowledge to improve ML/DS-driven investment strategies, which are valuable for financial companies and policymakers.
dc.identifier.doi10.1109/TEM.2024.3522313
dc.identifier.endpage362
dc.identifier.issn0018-9391
dc.identifier.issn1558-0040
dc.identifier.scopus2-s2.0-85213458491
dc.identifier.scopusqualityQ1
dc.identifier.startpage349
dc.identifier.urihttps://doi.org/10.1109/TEM.2024.3522313
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5074
dc.identifier.volume72
dc.identifier.wosWOS:001398078900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Transactions on Engineering Management
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260121
dc.subjectArtificial intelligence
dc.subjectInvestment
dc.subjectMachine learning
dc.subjectData science
dc.subjectTechnological innovation
dc.subjectIndustries
dc.subjectCompanies
dc.subjectSystematics
dc.subjectEconometrics
dc.subjectPricing
dc.subjectArtificial intelligence (AI)
dc.subjectFintech
dc.subjecthierarchical decision model (HDM)
dc.subjectinvestment management
dc.subjectmachine learning (ML)
dc.titleAn Evaluation Framework for Machine Learning and Data Science-Based Financial Strategies: A Case Study-Driven Decision Model
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu