Adapting Fuzzy Semi-Deviation Models for Public Markets Asset Allocation: A Comparative Analysis of FSMAD 1, FSMAD 2, and Mean-Variance Optimization

Abstract

Traditional Mean-Variance (MV) portfolio optimization faces significant challenges. It assumes normal return distributions, often underestimating risk during market stress due to financial returns exhibiting fat tails and skewness. The model is highly sensitive to input estimation errors, leading to unstable and extreme portfolio allocations. Furthermore, MV often results in corner solutions, where portfolios are concentrated in a few assets, undermining diversification. This study assess whether Fuzzy Semi-Mean Absolute Deviation (FSMAD) models can overcome these shortcomings.