Modeling Investment Decisions Through Decision Tree Regression—A Behavioral Finance Theory Approach

This study examines the key factors influencing investment decisions through decision tree regression, grounded in behavioral finance theory. By analyzing a comprehensive dataset incorporating behavioral, demographic, and financial variables—including investment attitudes, decision-making behaviors, financial education, age, income, and education—this study identifies significant predictors of investment outcomes. While the model shows moderate predictive performance (R2 = 0.185; MAPE = 172.96%), it identifies hierarchical relationships among behavioral, cognitive, and demographic predictors. These results highlight the complexity of investment decisions and the need for integrative, behavioral-driven approaches in predictive modeling. Investment attitudes (25.88%), decision-making behaviors (19.53%), and financial education (16.68%) emerge as the most influential variables, while traditional demographic factors such as income and age have a lower impact. The hierarchical structure of the decision tree highlights critical decision-making patterns, particularly regarding speculative behaviors and investment attitudes. These findings challenge classical models of rationality by emphasizing the dominant role of behavioral factors in investment decision making. This study contributes to bridging computational modeling with financial economics, demonstrating the utility of decision tree regression in uncovering complex investor behavior. Practical implications include enhancing personalized financial advisory services and designing targeted financial literacy programs to improve decision-making efficiency. These insights, while exploratory, can guide future research and decision-support systems in behavioral finance.