Portfolio Optimization Under Uncertainty: A Risk- Based Approach

Abstract

Portfolio optimization plays an important role in financial decision making because investors must determine asset allocations that maximize expected return while controlling risk. Traditional portfolio models commonly rely on the mean variance framework, which evaluates the relationship between risk and return to identify efficient portfolios. However, these models often assume deterministic market parameters, while real financial markets are characterized by uncertainty, volatility, and incomplete information. This study investigates portfolio optimization under uncertainty using a risk based perspective through a systematic literature review. The review analyzes peer reviewed studies that discuss optimization methods, uncertainty modeling, and risk measurement in portfolio management. The findings indicate that modern research increasingly integrates robust optimization, stochastic modeling, and alternative risk measures to address uncertainty in financial markets. In addition, multi objective optimization approaches enable investors to evaluate trade offs between return, risk, and diversification simultaneously. Overall, incorporating uncertainty and risk based strategies can enhance portfolio stability and support more reliable investment decisions in dynamic financial environments.