Machine Learning in Behavioral Finance: A Systematic Literature Review

Abstract

This article endeavors to investigate the application of machine learning in behavioral economics and behavioral finance to represent a profile of studies conducted in this field. To accomplish this task, 90 scientific studies were systematically extracted between 2000 and June 1, 2020. Utilizing the text analysis techniques and related statistical methods, the abstracts of the extracted studies were reviewed and analyzed. First, it was found that attention to this field has developed in recent years with an accelerating trend. Second, it was demonstrated that specialized journals have also bestowed more curiosity in these studies than in the past by publishing more relevant studies. Third, results revealed that machine learning has been applied in areas such as investor sentiment, decision making, consumer behavior, trading strategies, game theory, and other areas in the field of behavioral economics and behavioral finance. In this regard, the application of machine learning has included techniques such as support vector machine, regression, neural networks, random forest, and so on. Despite the expanding consideration adjusted to this field by researchers and specialized journals, there are still many research gaps in this field. Accordingly, there is a relatively significant distance until fully unleashing the superior powers of machine learning, like prediction and classification in behavioral economics and behavioral finance. Finally, this research completed its mission by suggesting implications for the future of this field based on the acquired outcomes.