Author(s): Emma Clarke
Portfolio optimization is a cornerstone of modern financial management, enabling investors to balance risk and return effectively. Under conditions of uncertainty, traditional portfolio models may fail to capture the dynamic nature of markets, necessitating advanced tools and quantitative approaches. This paper explores various models and applications for portfolio optimization under uncertainty, including mean-variance analysis, stochastic programming, robust optimization, and AI-based techniques. The study also examines practical applications in asset allocation, risk management, and investment strategy. Emphasis is placed on integrating uncertainty into decision-making to enhance portfolio performance and resilience.