A Systematic Literature Review on Mean-CVaR Based Financial Asset Portfolio Weight Allocation Using K-Means Clustering

This study aims to identify and analyze the application of the Mean-Conditional Value-at-Risk (Mean-CVaR) model in the allocation of financial asset portfolio weights combined with the K-Means Clustering algorithm. The Systematic Literature Review (SLR) method is used with the PRISMA protocol through the stages of identification, screening, eligibility, and inclusion. Data is obtained from Scopus, ScienceDirect, and Dimensions databases, then selected up to six relevant primary articles. The results of the study indicate that CVaR is the dominant risk measure in portfolio optimization, while K-Means Clustering serves as a method of grouping assets to increase diversification. The optimization methods used include Genetic Algorithm, Particle Swarm Optimization, Teaching Learning-Based Optimization, and Stochastic Programming. However, direct integration between Mean-CVaR and K-Means within a portfolio weight allocation framework is still rare. This research emphasizes the need to develop a hybrid model that combines both approaches in an integrated manner, applied to a multi-asset portfolio, and validated under various market conditions to produce an optimal, adaptive, and resilient investment strategy against extreme risks.