Performance Evaluation of Shariah-Compliant Portfolios Using Grey Relational Clustering and Markowiz Model

Stock performance serves as a critical indicator for investors to assist them in the decision-making process. The main objective of this study is to utilize Grey Relational Analysis and Agglomerative Hierarchical Clustering methods to conduct a comparative performance of Shariah-compliant portfolios...

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Bibliographic Details
Published in:2024 5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024 - Proceedings
Main Author: Mohd Amin F.A.; Udin M.N.; Binti Zulkefli H.N.; Binti Che Halim I.N.; Ahmad Shafie S.N.B.; Zainol Abidin S.N.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209678071&doi=10.1109%2fAiDAS63860.2024.10730113&partnerID=40&md5=2e8479fc6a4d2b414f8d2d878814caab
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Summary:Stock performance serves as a critical indicator for investors to assist them in the decision-making process. The main objective of this study is to utilize Grey Relational Analysis and Agglomerative Hierarchical Clustering methods to conduct a comparative performance of Shariah-compliant portfolios across three main sectors in Bursa Malaysia: Industrial products and services, Consumer products and services and Property. The Grey Relational Clustering method is employed to categorize similar stocks within each sector from 2013 to 2022 based on financial ratios and minimum distance measurements. Meanwhile the Markowitz model is used to construct an optimal portfolio. Results indicate that diversification within the same sector enhances portfolio performance. Furthermore, the Markowitz model highlighted that the Consumer Products and Services sector was the best performing sector. Future research could extend this methodology to additional sectors to provide broader insights into optimal investment strategies within the Shariah-compliant market. © 2024 IEEE.
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DOI:10.1109/AiDAS63860.2024.10730113