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All Journal International Journal of Quantitative Research and Modeling
Deva Putra Setyawan
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Physics

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Mean-VaR Portfolio Diversification Based on K-Medoids Clustering Deva Putra Setyawan; Alim Jaizul Wahid; Riza Andrian Ibrahim
International Journal of Quantitative Research and Modeling Vol. 7 No. 2 (2026): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i2.1330

Abstract

This study develops a diversified stock portfolio by integrating the Mean-Value at Risk (Mean-VaR) model with K-Medoids clustering. The approach groups stocks according to similar risk-return characteristics before the portfolio optimization stage. The data consist of daily closing prices of LQ45 index constituents from 3 February to 31 July 2025, obtained from the Indonesia Stock Exchange and Yahoo Finance. Of the 45 LQ45 stocks, 18 stocks satisfied the criteria of data completeness, liquidity, market capitalization stability, and sector representation. Clustering was performed using expected return and 95% Value at Risk (VaR) as input variables. The best clustering structure was obtained for two clusters, with a Silhouette Index of 0.6882. The first cluster represents aggressive stocks with relatively high expected returns and higher downside risk, including ANTM, BRPT, AMMN, and MDKA. The second cluster represents defensive stocks with lower risk and more stable returns, including INDF, ASII, ICBP, BBCA, and TLKM. The optimal Mean-VaR portfolio was constructed with minimum inter-cluster allocation constraints of 30% for the aggressive cluster and 70% for the defensive cluster. The resulting portfolio produced a daily expected return of 0.003272 and a 95% VaR of -0.029053. These results indicate that K-Medoids clustering can support portfolio diversification by identifying distinct risk-return groups and improving risk control in investment allocation.
Co-Authors Alim Jaizul Wahid Riza Andrian Ibrahim