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dc.contributor.authorMcAvoy, John
dc.contributor.authorMurphy, Conor
dc.contributor.authorMushtaq, Laila
dc.contributor.authorO'Donnell, James
dc.contributor.authorBrennan, Attracta
dc.contributor.authorDempsey, Mary
dc.contributor.authorKiely, Gaye
dc.date.accessioned2022-10-24T20:45:30Z
dc.date.available2022-10-24T20:45:30Z
dc.date.issued2022-10
dc.identifier.urihttps://escholarshare.drake.edu/handle/2092/2262
dc.description21 pagesen_US
dc.description.abstractMachine learning provides many benefits to Portfolio Managers in analysing data and has the potential to provide much more. A concern with the approach to Machine Learning in Portfolio Management is that is caught between two domains: finance and information systems. In reality, to ensure its success, having these two separate and distinct domains are problematic. What is required is a holistic view, facilitating discussions, with data being the unifying concept and the one that is key to success. The data value map is a lens that allows all involved, in the use or adoption of Machine Learning in Portfolio Management, to form a shared understanding of the lifecycle of the data involved. Rather than being seen as a financial concept or a technical concept, this view of the data lifecycle provides a platform for all involved to determine what is required, and to identify and deal with any potential pitfalls along the way. A holistic view, and shared understanding, are required for the success of Machine Learning in Portfolio Management. Research on the intersection between Machine Learning and Portfolio Management is currently lacking. A focus on the different parts of the data lifecycle provides an opportunity for further research.en_US
dc.language.isoen_USen_US
dc.publisherDrake Management Reviewen_US
dc.relation.ispartofseriesDrake Management Review;Volume 12, Issue 1/2, October 2022
dc.subjectFinanceen_US
dc.subjectInformation Systemsen_US
dc.titlePortfolio Management: The Holistic Data Lifecycleen_US
dc.typeArticleen_US


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