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Abstract
This study addresses the construction of an optimal stock portfolio under uncertainty in risk and return. It aims to optimize a portfolio from the IDXBUMN20 index using K-Medoids clustering for stock selection and the Mean-Variance Efficient Portfolio method with Ledoit–Wolf shrinkage to improve risk estimation stability. The data consist of daily closing prices of IDXBUMN20 stocks from August 2025 to February 2026. K-Medoids clustering is applied to 20 stocks based on financial performance indicators, including Return on Assets, Net Profit Margin, and Earnings per Share, to select representative stocks with the highest expected return from each cluster. Four representative stocks are obtained, and the optimal portfolio is constructed using the Mean-Variance approach with a Ledoit–Wolf covariance estimator, resulting in portfolio weights of BBTN (20.00%), TINS (19.68%), BBNI (40.63%), and PGEO (19.69%). Risk is measured using Historical Simulation Value at Risk at a 95% confidence level, indicating a controlled potential loss of 0.0269. Portfolio evaluation using the Sharpe Ratio yields a value of 0.1220, indicating that the combined approach produces an optimal portfolio.
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