INCORPORATING BEHAVIOURAL BIASES INTO FUZZY LINEAR PROGRAMMING FOR PORTFOLIO OPTIMIZATION: A COMPARATIVE ANALYSIS

Both fuzzy optimization and behavioral finance have made important contributions to the field of portfolio selection on their own. Although behavioral finance emphasizes how cognitive biases like herding, anchoring, and overconfidence affect investment choices, fuzzy linear programming (FLP) has gained popularity as a way to handle uncertainty and conflicting goals in portfolio optimization. Even though each field of study is expanding, little is known about how behavioral biases can be incorporated into fuzzy optimization frameworks. This study suggests a novel behavioral fuzzy linear programming (BFLP) model that uses fuzzy representations of return expectations, risk perceptions, and allocation preferences to incorporate important behavioral biases into the portfolio selection process. The study looks into whether taking these biases into consideration impacts how optimal portfolio choices are. This study evaluates the degree to which psychological distortions affect portfolio composition and performance under uncertainty by contrasting the outcomes of the BFLP model with a conventional bias-neutral FLP model.