Machine learning in finance: A metadata-based systematic review of the literature - Thierry Warin; Aleksandar Stojkov (Journal of Risk and Financial Management, 2021)
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: 330; efficient market hypothesis; machine learning; network analysis; sentiment analysis