Portfolio Adjusting Model with Newly Listed Stocks Using Uncertainty Theory

The financial market is a dynamic and unpblackictable system, requiring portfolio managers to continuously adjust investment strategies in response to market fluctuations. This study addresses the portfolio adjustment problem by incorporating newly added stocks into an existing portfolio while considering transaction costs. We propose a novel mean-semi absolute deviation-skewness model for portfolio optimization in an uncertain framework, effectively capturing both risk and asymmetry in returns. Unlike traditional models, our approach explicitly treats the returns of newly added stocks as uncertain variables, estimated based on expert judgment. The proposed model is formulated as a constrained nonlinear optimization problem and solved using the “fmincon” function in MATLAB R2018a. A numerical example demonstrates the practical applicability of our model, and a comparative analysis highlights its advantages over existing portfolio optimization methods. The results show that our approach provides a more flexible and realistic framework for portfolio adjustments, particularly in uncertain financial environments.