A Review on Machine Learning for Asset Management

Pedro M. Mirete-Ferrer, Alberto Garcia-Garcia, Juan Samuel Baixauli-Soler and Maria Prats
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Pedro M. Mirete-Ferrer: Escuela Internacional de Doctorado Universidad de Murcia, Interuniversity Doctorate in Economics (DEcIDE), 30100 Murcia, Spain
Alberto Garcia-Garcia: Departamento de Tecnología Informática y Computación, Universidad de Alicante, 03690 Alicante, Spain
Juan Samuel Baixauli-Soler: Facultad de Economía y Empresa, Universidad de Murcia, 30100 Murcia, Spain

Risks, 2022, vol. 10, issue 4, 1-46

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.

Keywords: finance; machine learning; asset management; portfolio management; factor investing (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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