Clustering of Banking Sector Stocks using Integration of Fourier Transform, Spectral Clustering, and Fuzzy C-Means as a Basis for Mean-Variance Portfolio Optimization - Ricardo, Dimitri Salsabila Fakhriyah; Gusriani, Nurul; Napitupulu, Herlina (Department of Matematics, Universitas Padjadjaran, 2026)
The Indonesian capital market has experienced significant growth accompanied by high volatility, particularly in the banking sector whichholds a substantial contribution to market capitalization. Extremevolatility during the 2019-2024 period triggered by the impact of theCOVID-19 pandemic, economic recovery phases, and global macroeconomic challenges has created complexities in investment decisionmaking and portfolio optimization, which often produces unstable solutions under uncertain market conditions. Various studies have applied frequency domain analysis to uncover hidden patterns in stockprice movements or clustering to group stocks based on their characteristics to support optimal investment decision-making, however theintegration of these two approaches remains limited in its application togenerate robust portfolio optimization solutions in the Indonesian capital market. This study aims to generate clustering of banking sectorstocks through the integration of Fourier Transform, spectral clustering, and Fuzzy C-Means and to construct an optimal portfolio using theMean-Variance method based on the clustering results. This study usesclosing price data of 41 banking sector stocks on the Indonesia StockExchange for the 2019-2024 period through an integrated approach ofFourier Transform to extract frequency patterns, spectral clustering asa basis for grouping, Fuzzy C-Means to generate cluster membership degrees, and Mean-Variance for portfolio optimization. The results showthat the integration of these methods produces four optimal stock clusters consisting of nine stocks with a medium risk-low return profile,six stocks with a high risk-high return profile, fifteen stocks with a lowrisk-low return profile, and eleven stocks with a medium risk-high return profile. Based on the clustering results, four representative stockswere selected from each cluster for portfolio optimization, resulting inan optimal portfolio at a risk aversion value of ρ = 6.83 with a portfolio ratio of 3.4128877. This optimal portfolio is constructed from fourrepresentative stocks with weight allocations of 11.48% BMAS, 11.76%ARTO, 72.08% BNGA, and 4.68% BBHI, with an expected return valueof 0.0263613 and a portfolio variance of 0.0077241.