Financial decision-making is a complex process influenced by psychological, behavioral, and quantitative factors. Traditional financial theories assume that investors behave rationally and make decisions based on logical analysis and complete market information. However, modern behavioral finance studies reveal that investor decisions are often affected by emotions, cognitive biases, and psychological tendencies. The study also evaluates the predictive effectiveness of behavioral mathematical models in comparison with traditional financial models.

The research adopts a quantitative and analytical methodology based on both primary and secondary data sources. Primary data were collected through a structured questionnaire from 400 respondents including investors, salaried employees, finance professionals, businesspersons, and students associated with financial activities. Statistical tools such as reliability analysis, regression analysis, ANOVA, correlation analysis, and paired sample t-tests were used to analyze the collected data and test the hypotheses.

The findings of the study indicate that behavioral biases significantly influence financial decision-making and investment outcomes. Psychological factors such as confidence, risk perception, emotional responses, and decision-making tendencies were found to have a strong relationship with financial performance. The results also revealed that behavioral mathematical models provide greater predictive accuracy than traditional financial models because they incorporate human behavior and psychological variables along with quantitative data. Additionally, mathematical skills and analytical abilities were positively associated with improved financial decision quality. The study concludes that financial decision-making cannot be understood solely through traditional rational models, as investor behavior is strongly affected by psychological and emotional influences. The integration of behavioral finance concepts with mathematical modeling provides a more realistic and effective framework for understanding financial markets and investor behavior. The findings may help investors, policymakers, financial analysts, and researchers develop improved investment strategies and financial models that better reflect real-world decision-making patterns.