Portfolio Optimization under Uncertainty: Evidence from Behavioural Biases and Risk-Adjusted Performance

Abstract

Portfolio optimization under uncertainty remains a critical challenge in modern finance, particularly when cognitive biases and behavioral factors distort rational decision-making. Traditional mean-variance optimization models assume investor rationality, yet empirical evidence demonstrates that loss aversion, herding behavior, and probability weighting significantly influence asset allocation choices. This study examines how behavioral biases interact with uncertainty to shape portfolio construction and performance. Drawing on prospect theory and behavioral portfolio theory, we investigate three key dimensions: (1) the relationship between loss aversion and portfolio risk tolerance, (2) the effect of herding behavior on diversification patterns, and (3) how probability distortions impact optimal asset weighting under market volatility. Using correlation and regression analysis on data from 450 retail and institutional investors, findings reveal that loss aversion (r = 0.51) and herding (r = 0.48) are the most significant behavioral predictors of suboptimal portfolio composition, while biases collectively account for 58% of variance in risk-adjusted returns. These insights challenge classical financial assumptions and provide practical guidance for behavioral-adjusted portfolio management strategies that enhance performance under uncertainty. This research bridges behavioral finance theory with practical portfolio optimization, offering evidence-based recommendations for investors and asset managers navigating complex financial environments.