IDX30 Stocks Clustering with K-Means Algorithm based on Expected Return and Value at Risk

Ahmad Fawaid Ridwan, Subiyanto Subiyanto, Sudradjat Supian

Abstract


Stocks are one of the investment instruments available in the capital market. Several indices show the characteristics of stocks listed on the Indonesia Stock Exchange. IDX30 is one of several indications that show the combined stocks are stocks with large market capitalization, high liquidity, and good fundamentals. The selection of assets to be allocated in the portfolio is an important factor in investing where the purpose of investing is to maximize returns and minimize risk. This study aims to classify stocks that have certain characteristics based on the expected return and value at risk of the stocks incorporated in IDX30 with a clustering algorithm. The clustering algorithm used is the K-Means algorithm. K-Means is a non-hierarchical clustering algorithm by groups each object based on its proximity to the cluster center. The method used in this research is a clustering simulation study using the K-Means algorithm on IDX30 stock data. By identifying the characteristics of the stock based on the characteristics of the cluster formed, it is hoped that it can be considered in choosing the assets to be used in the formation of an optimal portfolio.


Keywords


expected return, IDX30, K-Means, stock, value at risk

Full Text:

PDF

References


Bekhet, S., & Ahmed, A. (2020). Evaluation of Similarity Measures for Video Retrieval. Multimedia Tools and Applications, 79, 6265–6278. https://doi.org/https://doi.org/10.1007/s11042-019-08539-4

Cebeci, Z., & Yildiz, F. (2015). Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures. Journal of Agricultural Informatics, 6(3), 13–23. https://doi.org/10.17700/jai.2015.6.3.196

Deb, A. B., & Dey, L. (2017). Outlier Detection and Removal Algorithm in K-Means and Hierarchical Clustering. World Journal of Computer Application and Technology, 5(2), 24–29. https://doi.org/10.13189/wjcat.2017.050202

Gambrah, P., & Pirvu, T. (2014). Risk Measures and Portfolio Optimization. Journal of Risk and Financial Management, 7(3), 113–129. https://doi.org/10.3390/jrfm7030113

Ghosh, A., & Mahanti, A. (2014). Investment Portfolio Management : A Review from 2009 to 2014. Proceedings of 10th Global Business and Social Science Research Conference, 1(1), 1–21.

Gupta, M. K., & Chandra, P. (2020). An Empirical Evaluation of K-Means Clustering Algorithm Using Different Distance/Similarity Metrics. Proceedings of ICETIT 2019, 605, 884–892. https://doi.org/https://doi.org/10.1007/978-3-030-30577-2_79

Hassan, B. A., Rashid, T. A., & Hamarashid, H. K. (2021). A Novel Cluster Detection of COVID-19 Patients and Medical Disease Conditions using Improved Evolutionary Clustering Algorithm Star. Computers in Biology and Medicine, 138(July), 104866. https://doi.org/10.1016/j.compbiomed.2021.104866

Hidayati, A. N. (2017). Investasi : Analisis dan Relevansinya dengan Ekonomi Islam. Jurnal Ekonomi Islam, 8(2), 227–242.

IDX. (2021). IDX Stock Index Handbook V1.2. Indonesia Stock Exchange. https://www.idx.co.id/media/9816/idx-stock-index-handbook-v12-_-januari-2021.pdf

Im, S., Moseley, B., Sun, X., & Zhou, R. (2020). Fast Noise Removal for K-Means Clustering. ArXiv, 1–10.

Ismanto, H. (2016). Analisis Value at Risk dalam Pembentukan Portofolio Optimal (Studi Empiris pada Saham-saham). University Research Colloquium 2016, 3(1), 243–255.

Jiang, K., Li, D., Gao, J., & Yu, J. X. (2014). Factor Model Based Clustering Approach for Cardinality Constrained Portfolio Selection. IFAC Proceedings, 19(3), 10713–10718. https://doi.org/10.3182/20140824-6-za-1003.00663

León, D., Aragón, A., Sandoval, J., Hernández, G., Arévalo, A., & Niño, J. (2017). Clustering Algorithms for Risk-Adjusted Portfolio Construction. Procedia Computer Science, 108, 1334–1343. https://doi.org/10.1016/j.procs.2017.05.185

Naeem, S., & Wumaier, A. (2018). Study and Implementing K-mean Clustering Algorithm on English Text and Techniques to Find the Optimal Value of K. International Journal of Computer Applications, 182(31), 7–14. https://doi.org/10.5120/ijca2018918234

Putra, Y. E., Saepudin, D., & Aditsania, A. (2021). Portfolio Selection of KOMPAS-100 Stocks Index Using B-Spline Based Clustering. Procedia Computer Science, 179(2020), 375–382. https://doi.org/10.1016/j.procs.2021.01.019

Putri, D. M., & Hasibuan, L. H. (2020). Penerapan Gerak Brown Geometrik pada Data Saham PT. ANTM. Mathematics & Applications Journal, 1(1), 1–10.

Strassberger, M. (2006). Capital Requirement, Portfolio Risk Insurance, and Dynamic Risk Budgeting. Investment Management and Financial Innovations, 3(1), 78–88. https://doi.org/10.2139/ssrn.672302

Subekti, R., Kusumawati, R., & Sari, E. R. (2017). K-Means Clustering dan Average Linkage dalam Pembentukan Portfolio Saham. Seminar Matematika Dan Pendidikan Matematika UNY 2017, 219–224.

Sukono, Sidi, P., Bon, A. T. Bin, & Supian, S. (2017). Modeling of Mean-VaR Portfolio Optimization by Risk Tolerance when the Utility Function is Quadratic. AIP Conference Proceedings, 1827(020035). https://doi.org/10.1063/1.4979451

Utami, M. P., Saepudin, D., & Rohmawati, A. A. (2019). Pengaruh Teknik Clustering Harga Saham dalam Manajemen Portofolio. E-Proceeding of Engineering, 6(1), 2491–2509.

Widyadhana, D., Hastuti, R. B., Kharisudin, I., & Fauzi, F. (2021). Perbandingan Analisis Klaster K-Means dan Average Linkage untuk Pengklasteran Kemiskinan di Provinsi Jawa Tengah. PRISMA, Prosiding Seminar Nasional Matematika 4, 4, 584–594.




DOI: https://doi.org/10.46336/ijqrm.v2i4.157

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Published By: 

IJQRM: Jalan Riung Ampuh No. 3, Riung Bandung, Kota Bandung 40295, Jawa Barat, Indonesia

 

IJQRM Indexed By: 

width= width= width= width= width= width=

 


Lisensi Creative Commons Creation is distributed below Lisensi Creative Commons Atribusi 4.0 Internasional.


View My Stats