Comparative Case Study on Portfolio Optimization: Modified Continuous Rank Length vs. Stochastic Dominance

This paper conducts a case study comparing the effectiveness of a modified Continuous Rank Length (CRL) metric with the Stochastic Dominance approach in portfolio optimization.The modified CRL method, designed to assess portfolio performance across the entire time interval, provides a holistic evaluation of rank stability, while Stochastic Dominance offers a robust framework for comparing return distributions.The analysis of simulated portfolios demonstrates that SD consistently identifies portfolios with the highest returns and lowest risks, particularly in early periods of the dataset.Conversely, CRL excels at selecting portfolios with enhanced long-term stability but is less efficient due to computational demands and broader stock inclusion.These findings provide actionable insights into the trade-offs between maximizing returns, ensuring stability, and managing computational resources in portfolio selection.