CLUSTER ANALYSIS OF K-MEANS AND WARD METHOD IN FORMING A ROBUST PORTFOLIO: AN EMPIRICAL STUDY OF JAKARTA ISLAMIC INDEX - Zain, Zuva Amalina; Mussafi, Noor Saif Muhammad; Supandi, Epha Diana (PATTIMURA UNIVERSITY, 2025)
Building a portfolio is one method of reducing investment risk. Cluster analysis can shorten the time required to choose companies for a portfolio because it makes it easy to put firms in the same category together. To maintain the best state of the portfolio cluster analysis in the case of data containing outliers, K-means, and ward cluster analysis are employed in conjunction with a robust portfolio strategy. K-means clustering is a popular method for grouping data by assigning observations to clusters based on proximity to the cluster’s center meanwhile the Ward method is based on the size of the distance between clusters by minimizing the number of squares. This study seeks to determine the robust portfolio performance comparison outcomes produced by K-Means and Ward clustering utilizing the Sharpe ratio criterion. The Sharpe ratio is one of the most widely used methods to evaluate a portfolio’s risk-adjusted performance. The greater a portfolio's Sharpe ratio, the better its risk-adjusted performance. Stocks included in the Jakarta Islamic Index 70 (JII70) are used in this research. The results of the formation of a robust portfolio on K-Means clustering produce a return rate of 0.01038627 and risk of 0.1066364, while in the Ward cluster, the portfolio profit rate is obtained at 0.01632749 and the risk is 0.1340073. Based on the Sharpe ratio criteria, in this case, the robust portfolio with the Ward cluster is superior to the K-Means cluster because it produces a higher Sharpe value.