FINANCIAL PORTFOLIO ATTRIBUTE ANALYSIS UNDER BEHAVIORAL CONSTRAINTS: A DATA-DRIVEN OPTIMIZATION FRAMEWORK

Abstract

This study combines behavioural finance and traditional models of portfolio performance to explore the effects of investors emotional , psychological and cognitive errors (like risk perception , anchoring bias , herd behaviour , overconfidence, loss aversion, and portfolio behaviour) and investors’ financial charac teristics (age, gender, income, and education) on the structure and performance of a portfolio. Unlike traditional finnacial and economics models that assume investors rational decision-making while mak ing investment , investment behavior is fairly governed by psychological , emotional , cognitive errors. The research was quantitative in nature, and fundamental metrics were collected through a structured questionnaire framework from investors, and secondary data was used to calculate performance metrics such as return, risk, and the Sharpe ratio. The results show the negative influence of psychological and cognitive errors on the performance of a portfolio. The research found risk perception, anchoring bias, and portfolio behavior to be the main causes of risk-taking, lower returns, and poor investment decisions. Based on these results, a portfolio optimization model with behavioral bias adjustment was developed. The model improves the accuracy of portfolio performance by using psychological and finan cial attributes. provides a more realistic approach to an effective investor decision-making framework. The research shows that the incorporation of behavioral financial factors in traditional model theory can lead to more accurate, resilient, and real-world-applicable portfolio optimization and performance.