K-Medoids Clustering dan Mean-Value at Risk untuk Optimasi Portofolio Saham Jakarta Islamic Index

Eka Sri Puspaningsih, Di Asih I Maruddani, Tarno Tarno

Abstract

The problem of the portfolio is how to choose stocks and determine their weights in order to generate maximum returns with minimal risk. Portfolios are formed by selecting stocks that have different characteristics. K-Medoids Clustering can be used to group data sets that contain outliers. Validate cluster results using the Davies Bouldin Index to determine the best number of clusters. Portfolio weighting is determined using the Mean-VaR method by taking into account the expected return value and minimizing the VaR risk value. Stocks are grouped based on Return on Assets, Return on Equity, Debt to Asset Ratio, and Debt to Equity Ratio. The results of cluster formation on the Jakarta Islamic Index stocks obtained six portfolio constituent stocks based on the highest expected return value from each cluster, consisting of PTBA, ADRO, AKRA, EXCL, PTPP, and UNVR. The results of calculating the weight of the optimal portfolio with Mean-VaR obtained a weight for PTBA of 0.46536; AKRA of 0.24018; EXCL of 0.25421; and UNVR of 0.25392. ADRO and PTPP stocks have a negative weight value of -0,07775 and -0,13593 this indicates the occurrence of short selling in the weighting. At the 95% confidence level, the VaR portfolio value is 5.06%.

Keywords: Clustering; K-Medoids; Daveis Bouldin Index; Portfolio; Mean-VaR

Full Text:

PDF

References

I.M. Andyana, Manajemen Investasi dan Portofolio. Jakarta: Lembaga Penerbitan Universitas Nasional. 2020.

BEI, Indeks Saham, 2022. https://www.idx.co.id/produk/indeks/. (accessed September 28, 2022).

L. Gubu, D. Rosadi, dan A. Abdurakhman, “Pembentukan Portofolio Saham Menggunakan Klastering Time Series K-Medoid dengan Ukuran Jarak Dynamic Time Warping”, Jurnal Aplikasi Statistika & Komputasi Statistik, vol. 13, no. 2, pp. 35-46, 2021.

C. A. Chairunnisa, H. Yozza, dan D. Devianto, “Pengukuran Nilai Risiko Portofolio Berdasarkan Mean-VaR”, Jurnal Matematika UNAND, vol. 7, no. 4, pp. 24-32, 2018.

F. P. P. Abi, Semakin Dekat Dengan Pasar Modal Indonesia. Yogyakarta: Deepublish Publisher, 2016.

A. P. Harahap dan D. Saraswati, Bank dan Lembaga Keuangan Lainnya. Surabaya: CV. Jakad Media Publishing, 2020.

T. R. Mayes dan T. M. Shank, Financial Analysis. South Western: Thomson.

S. Santoso, Statistik Multivariat. Jakarta: PT Alex Media Komputindo, 2010.

J. F. Hair, W. C. Black, B. J. Babin, dan R. E. Anderson, Multivariate Data Analysis Seventh Edition. New Jersey: Pearson Prentice Hall, 2010.

A. Struyf, M. Hubert, P. Rousseeuw, “Clustering in an Object-Oriented Environment”, Journal of Statistical Software, vol. 1, no. 4, pp. 1–30, 1997.

C. Vercellis, Business Intelligence: Data Mining and Optimization for Decision Making. Italy: Wiley, 2009.

A. Badruttamam, S. Sudarno, dan D. A. I. Maruddani, “Penerapan Analisis Klaster K-Modes dengan Validasi Davies Bouldin Index dalam Menentukan Karakteristik Kanal Youtube di Indonesia”, Jurnal Gaussian, vol. 9, no. 3, pp. 263-272, 2020.

D. A. I. C. Dewi dan D. A. K. Pramita, “Analisis Perbandingan Metode Elbow dan Sillhoutte pada Algoritma Clustering K-Medoids dalam Pengelompokan Produksi Kerajinan Bali”, Jurnal Matrix, vol. 9, no. 3, pp. 102-109, 2019.

M. N. Hilmi, Y. Wilandari, H. dan Yasin, “Pemetaan Referensi Mahasiswa Baru dalam Memilih Jurusan Menggunakan Artificial Neural Network (ANN) dengan Algoritma Self Organizing Maps (SOM)”, Jurnal Gaussian, vol. 4, no. 1, pp. 53-60, 2015.

D. A. I. Maruddani, Value at Risk Untuk Pengukuran Risiko Investasi Saham: Aplikasi dengan Program R. Ponorogo: Wade Group, 2019.

R. S. Tsay, Analysis of Financial Time Series. Canada: John Wiley and Sons, Inc., 2005.

D. A.I. Maruddani, dan A. Purbowati, “Pengukuran Value at Risk pada Aset Tunggal dan Portofolio dengan Simulasi Monte Carlo”, Media Statistika Vol. 2, No. 2, 93-104. 2009.

Sukono, P. Sidi, A. Talib, dan S. Supian, “Modeling of Mean-VaR Portofolio Optimization by Risk Tolerance When the Utility Function is Quadratic”. Statistics and Applications. AIP Conference Proceedings. AIP Publishing: 30 Maret 2017. 2017.

DOI: https://doi.org/10.13057/ijas.v6i1.79231

Refbacks

  • There are currently no refbacks.