Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature
Thierry Warin and
Aleksandar Stojkov ()
JRFM, 2021, vol. 14, issue 7, 1-31
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.
Keywords: efficient market hypothesis; machine learning; network analysis; sentiment analysis (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:14:y:2021:i:7:p:302-:d:587602
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