Formation of Mean-Semivariance Portfolio with Stock Selection Using Fuzzy C-Means Clustering on the LQ45 Stock Index
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2025
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| _version_ | 1866901567939018752 |
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| author | Viny Debora Sandri Suparti Rita Rahmawati |
| author_facet | Viny Debora Sandri Suparti Rita Rahmawati |
| contents | <p>Forming an optimal portfolio with a focus on minimizing risk can use the Mean-Semivariance method, which is accompanied by stock selection through clustering analysis with Fuzzy C-Means. The Fuzzy C-Means aims to group stocks based on certain characteristics. The data used are financial ratio data, namely Earning per Share (EPS), Price to Earning Ratio (PER), and Return on Equity (ROE) as criteria in the process of selecting stock securities with cluster analysis, and daily return data used for the formation of optimal stocks within the LQ45 Index. The results showed that the best number of clusters was 3 and representative stock securities of each cluster are selected based on the highest expected return value, namely UNTR and BRIS. The optimal portfolio obtained by the Mean-Semivariance method produces a weight value of 63.91% in UNTR stock securities and 36.09% in BRIS stock securities. Portfolio performance with the Sharpe index is 0.088966, which shows good portfolio performance results, and Value at Risk (VaR) calculated by Historical Simulation shows at a 95% confidence level for holding periods of 1, 7, and 30 days, respectively, it is 0.021543, 0.056998, and 0.117996.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17150094 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Formation of Mean-Semivariance Portfolio with Stock Selection Using Fuzzy C-Means Clustering on the LQ45 Stock Index Viny Debora Sandri Suparti Rita Rahmawati Portofolio Optimal, Fuzzy C-Means, Silhouette Coefficient, Mean-Semivariance, Indeks Sharpe, VaR <p>Forming an optimal portfolio with a focus on minimizing risk can use the Mean-Semivariance method, which is accompanied by stock selection through clustering analysis with Fuzzy C-Means. The Fuzzy C-Means aims to group stocks based on certain characteristics. The data used are financial ratio data, namely Earning per Share (EPS), Price to Earning Ratio (PER), and Return on Equity (ROE) as criteria in the process of selecting stock securities with cluster analysis, and daily return data used for the formation of optimal stocks within the LQ45 Index. The results showed that the best number of clusters was 3 and representative stock securities of each cluster are selected based on the highest expected return value, namely UNTR and BRIS. The optimal portfolio obtained by the Mean-Semivariance method produces a weight value of 63.91% in UNTR stock securities and 36.09% in BRIS stock securities. Portfolio performance with the Sharpe index is 0.088966, which shows good portfolio performance results, and Value at Risk (VaR) calculated by Historical Simulation shows at a 95% confidence level for holding periods of 1, 7, and 30 days, respectively, it is 0.021543, 0.056998, and 0.117996.</p> |
| title | Formation of Mean-Semivariance Portfolio with Stock Selection Using Fuzzy C-Means Clustering on the LQ45 Stock Index |
| topic | Portofolio Optimal, Fuzzy C-Means, Silhouette Coefficient, Mean-Semivariance, Indeks Sharpe, VaR |
| url | https://doi.org/10.5281/zenodo.17150094 |