A Review on Machine Learning for Asset Management

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...

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Bibliographic Details
Published in:Risks (Basel) 2022-04, Vol.10 (4), p.84
Main Authors: Mirete-Ferrer, Pedro M., Garcia-Garcia, Alberto, Baixauli-Soler, Juan Samuel, Prats, Maria A.
Format: Article
Language:English
Subjects:
Asset management
Bank technology
Deep learning
Economics
factor investing
finance
Forecasting
Forecasting techniques
Fraud prevention
Investments
Machine learning
Neural networks
Portfolio management
Program trading
Rates of return
Regulation of financial institutions
Risk assessment
Securities markets
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Summary: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.
ISSN:2227-9091
2227-9091
DOI:10.3390/risks10040084