A stock portfolio plays an important role in managing risk and achieving optimal returns in volatile markets. This study proposes an integrated framework that combines K-Means Clustering, Mean Absolute Deviation (MAD), and the Sortino Ratio. The main contribution lies in linking clustering-based asset selection with downside risk optimization and evaluation, enabling portfolio construction that accounts for asset similarity, risk measurement, and investor-oriented performance assessment. This approach addresses the limitation of previous studies that apply these methods separately by providing a more structured basis for downside risk-adjusted portfolio selection. Using daily IDX30 stock data from June 2024 to June 2025, samples were selected based on index consistency. The results indicate that a portfolio of ANTM and INDF achieved the highest Sortino Ratio of 0.2179. These findings suggest that combining high-return stocks with moderate volatility, supported by clustering and downside risk optimization, can improve downside risk-adjusted performance, providing practical guidance for investors.
Rifki Pebriyandi, E. Sulistianingsih, Hendra Perdana et al.· ZERO Jurnal Sains Matematika...· 0 citations
This research aims to construct an optimal stock portfolio from the Kompas100 index using stock performance indicators, fundamental indicators, K-Means, TOPSIS, and portfolio optimization. Of the 100 stocks, only 22 were suitable as candidates for portfolio formation. From these 22 stocks, 4 portfolio candidates were identified through K-means analysis and 7 through TOPSIS analysis. The next step was to determine the investment proportion for each stock in the portfolio using MVEP and MAD. Performance evaluation results show that Portfolio 4, consisting of PTRO and WIFI stocks, consistently yields the highest Sharpe Ratio under both weighting methods: 0.19 using MVEP and 0.21 using MAD. Portfolio 4’s performance was then re-evaluated using data from April through December 2025, resulting in a higher Sharpe ratio for both the MAD and MVEP. Overall, this study demonstrates that the combination of the K-Means Clustering, TOPSIS, MVEP, and MAD methods can be used to assist in the stock selection process and the formation of an optimal portfolio that is more efficient than investing in a single stock because it provides a better balance between return and risk through investment diversification, while remaining stable for the next nine months.
Wirda Andani, Shantika Martha, E. Sulistianingsih et al.· Inferensi· 0 citations
The capital market plays an important role in the economy by providing investment instruments for investors and financing sources for companies. A capital market portfolio consists of a collection of financial assets, such as stocks, constructed to achieve an optimal return while reducing investment risk. Mean-variance portfolio construction is highly sensitive to parameter estimation errors. Therefore, a robust estimation approach is employed to obtain more stable parameter estimates by minimizing the influence of outliers. This study aims to construct an optimal stock portfolio through diversification, determine stock weights using a two-constraint mean-variance model with robust S-estimation, calculate the expected return and risk, and evaluate portfolio performance. The analysis was conducted using the closing prices of stocks included in the IDX30 Index from October 2024 to September 2025. The results identified nine stocks with positive expected returns from five different sectors. Based on the stock selection criteria, two optimal portfolios were constructed. Portfolio 1 consists of ASII, BRPT, INDF, PGAS, and TLKM, whereas Portfolio 2 consists of ASII, ANTM, INDF, PGAS, and TLKM. Portfolio 1 generates an expected return of 0.137% with a risk of 2.226%, while Portfolio 2 generates an expected return of 0.097% with a risk of 1.319%. Based on the Sharpe and Treynor ratios, Portfolio 1 demonstrates relatively better performance than Portfolio 2.
Anis Faiqo Tuzzainiyah, E. Sulistianingsih, Nurfitri Imro'ah· Jambura Journal of Mathemati...· 0 citations
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