Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature

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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.

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