A study on behavioral finance and its implications for financial modeling and optimization in the Indian context

Abstract

Being informed by psychology, behavioral finance has grown to be vital in modern financial modeling. The research looks at how people’s emotions, the performance of their portfolios, and ongoing market changes influence financial optimization. Both econometric approaches and secondary data analyses extracted from peer-reviewed journals were part of the mixed-method study. Overconfidence bias occurs when investment confidence increases in up markets and decreases during difficult financial times, as shown by the Chi-Square Test for Independence. Multiple correlation studies prove that biases like overconfidence, anchoring, representativeness, and loss aversion have a significant impact on investing, whereas herding habits do not. From the portfolio optimization efficiency study, financial services and information technology sectors achieve better results, with lower-risk portfolios getting higher Sharpe ratios. The strength study shows that there are strong links between conservatism, herding, self-efficacy, and financial behavior. Research indicates that financial modeling could be more accurate by teaching individuals, using AI to predict, and making rules consistent. To help improve prediction financial models, future studies should investigate long-term patterns in behaviors by using big data.