Analisis Cluster Saham LQ45 Berdasarkan Return, Volatilitas, dan Volume Perdagangan
DOI:
https://doi.org/10.59001/pjeb.v5i2.901Keywords:
hierarchical clustering, return, saham LQ45, volatilitas, volume perdaganganAbstract
The stocks included in the LQ45 Index exhibit diverse characteristics in terms of return, volatility, and trading activity, making stock clustering important in investment analysis. This article aims to determine the optimal cluster structure of LQ45 stocks based on return, volatility, and trading volume using the hierarchical clustering method. The similarity among stocks is measured using Ward Linkage and Euclidean Distance, while data preprocessing involves logarithmic transformation and Z-score standardization. The optimal number of clusters is evaluated using the Silhouette Coefficient, Davies-Bouldin Index (DBI), and the SW/SB ratio. The results indicate that LQ45 stocks exhibit heterogeneous characteristics and can be optimally grouped into seven clusters. The resulting clusters represent distinct stock profiles, ranging from relatively stable and liquid stocks to stocks characterized by higher levels of return, volatility, and investment risk. These findings demonstrate that hierarchical clustering is effective in identifying stock characteristics based on data similarity and can provide useful information for investment analysis and decision-making in the Indonesian capital market.
Saham-saham yang tergabung dalam indeks LQ45 memiliki karakteristik yang beragam dari sisi return, volatilitas, dan aktivitas perdagangan sehingga pengelompokan saham menjadi penting dalam analisis investasi. Artikel ini bertujuan untuk menentukan struktur cluster optimal pada saham LQ45 berdasarkan variabel return, volatilitas, dan volume perdagangan menggunakan metode hierarchical clustering. Pengukuran tingkat kemiripan antar saham dilakukan menggunakan Ward Linkage dan Euclidean Distance, sedangkan tahap preprocessing data meliputi transformasi logaritma dan standarisasi Z-score. Penentuan jumlah cluster optimal dievaluasi menggunakan Silhouette Coefficient, Davies-Bouldin Index (DBI), dan rasio SW/SB. Hasil analisis menunjukkan bahwa saham-saham LQ45 memiliki karakteristik yang heterogen dan dapat dikelompokkan ke dalam tujuh cluster optimal. Cluster yang terbentuk menunjukkan profil saham yang berbeda, mulai dari saham yang relatif stabil dan likuid hingga saham dengan tingkat return, volatilitas, dan risiko investasi yang tinggi. Temuan ini menunjukkan bahwa metode hierarchical clustering efektif dalam mengidentifikasi karakteristik saham berdasarkan tingkat kemiripan data serta dapat memberikan informasi yang bermanfaat dalam analisis investasi dan pengambilan keputusan di pasar modal Indonesia.
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