OPTIMASI PORTOFOLIO MEAN-VARIANCE DENGAN ANALISIS KLASTER FUZZY C-MEANS - Gubu, La; Cahyono, Edi; Arman, Arman; Budiman, Herdi; Djafar, Muh. Kabil (Jurnal Gaussian, 2023)
Many studies have been carried out to solve and develop the Markowitz portfolio model. This was done to correct existing models in response to the changes in financial market dynamics and the needs of capital market practitioners. In this study, we provide Mean-Variance (MV) portfolio selection via cluster analysis. Fuzzy C-Means clustering is used to separate stocks into different categories. As a comparison, stocks categories were also carried out using K-Mean clustering. Based on the Sharpe ratio, a stock from each cluster is chosen as a cluster representative. The stocks chosen for each cluster have the greatest Sharpe ratio. The MV portfolio model is used to determine the best portfolio. For the empirical analysis, we examined the fundamental data and the daily return data of stocks that were included in the LQ-45 index from August 2022 to January 2023. The fundamental data of stocks are used to form clusters and the daily return of stocks are used to construct the best portfolio. The results of this study reveal that, for all given risk aversion values, portfolio performance created by Fuzzy C-Means clustering outperformed portfolio performance produced by K-Means clustering.