This paper systematically reviews the literature on behavioral biases affecting individual investors' decision-making, with a particular focus on methodological approaches, including sampling techniques and data analysis methods. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study analyzes 63 peer-reviewed studies published between 2015 and 2024, identifying key trends in research methodology, discussing their limitations, and exploring strategies to enhance methodological rigor. Findings indicate that most studies focus on emerging markets and predominantly employ survey-based research using non-probability sampling techniques, particularly convenience and snowball sampling. Additionally, there has been a notable shift from traditional regression analysis to Structural Equation Modeling (SEM) in recent years. However, survey-based research, such as a questionnaire with a cross-sectional design, raises concerns, for example, the inability to prove temporal relationships and self-report bias. Therefore, the paper highlights the need for future studies to address methodological shortcomings through advanced techniques such as AI, deep learning, and big data analytics. This paper will be useful in guiding potential researchers in selecting suitable methodologies for their studies on behavioral biases that affect individual investors' decision-making.
Xin et al. (Mon,) studied this question.