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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
References: Add references at CitEc
Citations: View citations in EconPapers (5)

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