Keng, Hoong Ng and Kok, Chin Khor (2016) Evaluation on rapid profiling with clustering algorithms for plantation stocks on Bursa Malaysia. Journal of Information and Communication Technology, 15 (2). pp. 63-84. ISSN 2180-3862

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Abstract

Building a stock portfolio often requires extensive financial knowledge and Herculean efforts looking at the amount of financial data to analyse. In this study, we utilized Expectation Maximization (EM), K-Means (KM), and Hierarchical Clustering (HC) algorithms to cluster the 38 plantation stocks listed on Bursa Malaysia using 14 financial ratios derived from the fundamental analysis.The clustering allows investors to profile each resulted cluster statistically and assists them in selecting stocks for their stock portfolios rapidly.The performance of each cluster was then assessed using 1-year stock price movement.The result showed that a cluster resulted from EM had a better profile and obtained a higher average capital gain as compared with the other clusters.

Item Type:	Article
Uncontrolled Keywords:	Stock profiling, stock portfolio, financial ratios, expectation maximization, K-means, hierarchical clustering.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	29 Apr 2018 01:43
Last Modified:	29 Apr 2018 01:43
URI:	https://repo.uum.edu.my/id/eprint/24069

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