Utilizing K-Means Clustering for Constructing Black-Litterman Portfolio Models

Authors

  • Fitri Amanah Program Studi Statistika, Universitas Islam Bandung
  • Fauziah Roshafara Program Studi Statistika, Universitas Islam Bandung
  • Puri Indah Lestari Program Studi Statistika, Universitas Islam Bandung
  • Salwa Salsabila Program Studi Statistika, Universitas Islam Bandung
  • Renita Maharani Program Studi Statistika, Universitas Islam Bandung

DOI:

https://doi.org/10.20956/j.v20i3.34165

Keywords:

K-Means, Black-Litterman, Portfolio, Investment

Abstract

A portfolio in finance is a collection of investment assets that aims to reduce risk by spreading investment across various assets. In building a portfolio, cluster analysis is used to select assets. K-Means cluster is often used because it is considered efficient for handling large data. In addition, the Black-Litterman Model is used because it can combine investor knowledge into asset allocation efficiently, so that the portfolio becomes more diverse, stable and adaptive to economic conditions, and reflects the investment manager's views. The research results show that k-means cluster analysis can be applied in forming the Black-Litterman model portfolio. Two clusters were obtained, namely cluster I consisting of ADRO, AKRA, BRMS, MIKA, TLKM, UNVR shares, and cluster II consisting of INDF, INKP, SMGR, UNTR. The two clusters were then formed into portfolios I and II. The calculation of expected return and portfolio risk shows that portfolio II produces profits (expected return portfolio) that are greater than portfolio I, namely 0.04445 or IDR 4.445.344,00, and the risk level of portfolio II is also smaller than portfolio I, namely 0.02104 or IDR 2.104.400,00

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Published

2024-05-15

How to Cite

Amanah, F. ., Roshafara, F. ., Lestari, P. I. ., Salsabila, S. ., & Maharani, R. . (2024). Utilizing K-Means Clustering for Constructing Black-Litterman Portfolio Models. Jurnal Matematika, Statistika Dan Komputasi, 20(3), 670-679. https://doi.org/10.20956/j.v20i3.34165

Issue

Vol. 20 No. 3 (2024): May 2024

Section

Research Articles

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

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