Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature
Machine learning in finance has been on the rise in the past decade. The applications of machine learning have become a promising methodological advancement. The paper’s central goal is to use a metadata-based systematic literature review to map the current state of neural networks and machine learn...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Journal of Risk and Financial Management |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1911-8074/14/7/302 |
| Summary: | Machine learning in finance has been on the rise in the past decade. The applications of machine learning have become a promising methodological advancement. The paper’s central goal is to use a metadata-based systematic literature review to map the current state of neural networks and machine learning in the finance field. After collecting a large dataset comprised of 5053 documents, we conducted a computational systematic review of the academic finance literature intersected with neural network methodologies, with a limited focus on the documents’ metadata. The output is a meta-analysis of the two-decade evolution and the current state of academic inquiries into financial concepts. Researchers will benefit from a mapping resulting from computational-based methods such as graph theory and natural language processing. |
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| ISSN: | 1911-8066 1911-8074 |