Abstract
PurposeTo enhance portfolio decision-making using a capital asset pricing model-based clustering analysis.Design/methodology/approachCapital asset pricing model (CAPM); K-means clustering; agglomerative clustering.FindingsEmploying clustering along with CAPM to identify varying levels of risk appetite among customers enables the customization of security recommendations, enhancing client satisfaction and portfolio performance.Originality/valueBy employing multi-factor models as the foundation for clustering, thereby integrating additional dimensions of risk and return.
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