Machine learning for trading in the stock market: A bibliometric review

Abstract

Machine learning (ML) methods applied in stock market trading have attracted a lot of attention lately. This Bibliometric study seeks to investigate the increasing corpus of research on ML application in financial markets, especially in the field of stock trading. This study aims to spot important trends, approaches, prominent papers, and field research gaps by means of analysis of the pertinent publications released throughout the previous two decades. We get understanding from a large collection of scholarly works via Bibliometric analysis. The most often used machine learning models in stock market prediction are highlighted in this study together with the importance of feature selection and the developing patterns at the junction of artificial intelligence and finance. The last section of the study addresses possible directions for future research and its consequences for practitioners.