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A Review on Machine Learning for Asset Management

Author

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  • Pedro M. Mirete-Ferrer

    (Escuela Internacional de Doctorado Universidad de Murcia, Interuniversity Doctorate in Economics (DEcIDE), 30100 Murcia, Spain
    Faculty of Economics and Business Administration (ICADE), Universidad Pontificia Comillas, 28015 Madrid, Spain
    These authors contributed equally to this work.)

  • Alberto Garcia-Garcia

    (Departamento de Tecnología Informática y Computación, Universidad de Alicante, 03690 Alicante, Spain
    These authors contributed equally to this work.)

  • Juan Samuel Baixauli-Soler

    (Facultad de Economía y Empresa, Universidad de Murcia, 30100 Murcia, Spain)

  • Maria A. Prats

    (Facultad de Economía y Empresa, Universidad de Murcia, 30100 Murcia, Spain)

Abstract

This paper provides a review on machine learning methods applied to the asset management discipline. Firstly, we describe the theoretical background of both machine learning and finance that will be needed to understand the reviewed methods. Next, the main datasets and sources of data are exposed to help researchers decide which are the best ones to suit their targets. After that, the existing methods are reviewed, highlighting their contribution and significance in the analyzed financial disciplines. Furthermore, we also describe the most common performance criteria that are applied to compare such methods quantitatively. Finally, we carry out a critical analysis to discuss the current state-of-the-art and lay down a set of future research directions.

Suggested Citation

  • Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:4:p:84-:d:793303
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    References listed on IDEAS

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    3. Francisco Peñaranda & Enrique Sentana, 2024. "Portfolio management with big data," Working Papers wp2024_2411, CEMFI.

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