Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258406 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 14 [Issue:] 7 [Article No.:] 302 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-31
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
efficient market hypothesis
machine learning
network analysis
sentiment analysis
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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