Enhancing Portfolio Efficiency with Mean-Shift Clustering

DOI

10.2991/978-94-6463-413-6_13How to use a DOI?

Keywords

Mean Shift Clustering; Optimal Portofolio; Mean-Variance Method

Abstract

Mean-Shift Clustering, an unsupervised learning algorithm, facilitates the grouping of objects sharing similar characteristics without assuming a predefined number of clusters during the calculation. In the context of portfolio formation, investors seek groups of stocks across diverse sectors to construct a well-diversified portfolio, aiming to minimize risk. The aims of this paper are to provide an overview of how Mean-Shift Clustering can be applied to portfolio construction and highlight the advantages and benefits of using Mean-Shift in comparison to traditional methods This research employs the Mean-Shift Clustering algorithm to cluster IDX80 stocks based on expected return variance and average sales volume. The Mean-Shift Clustering algorithm yields five clusters for IDX80, of which four clusters are identified as optimal portfolios using mean variance. Evaluating the portfolios based on the Sharpe index value, portfolio 3, comprising BRIS, DMMX, ENRG, ESSA, HRUM, MDKA, and SMDR, emerges as the most optimal among the considered portfolios.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ni Made Sri Kumala Dewi Oka
AU  - Komang Dharmawan
AU  - I G. N. Lanang Wijaya Kusuma
PY  - 2024
DA  - 2024/05/13
TI  - Enhancing Portfolio Efficiency with Mean-Shift Clustering
BT  - Proceedings of the First International Conference on Applied Mathematics, Statistics, and Computing (ICAMSAC 2023)
PB  - Atlantis Press
SP  - 130
EP  - 140
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-413-6_13
DO  - 10.2991/978-94-6463-413-6_13
ID  - Oka2024
ER  -