Evaluation on Rapid Profiling With Clustering Algorithms for Plantation Stocks on Bursa Malaysia

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.