International Journal of Operations Research and Information Systems, 6(4), 1-18, October-December 2015 1 Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Keywords: Classifcation, Component Design, Data Mining, Decision Tree, Securities Account Holders ABSTRACT Popular decision tree (DT) algorithms such as ID3, C4.5, CART, CHAID and QUEST may have different results using same data set. They consist of components which have similar functionalities. These components implemented on different ways and they have different performance. The best way to get an optimal DT for a data set is one that use component-based design, which enables user to intelligently select in advance imple- mented components well suited to specifc data set. In this article the authors proposed component-based design of the optimal DT for classifcation of securities account holders. Research results showed that the optimal algorithm is not one of the original DT algorithms. This fact confrms that the component design provided algorithms with better performance than the original ones. Also, the authors found how the specifcities of the data infuence the DT components performance. Obtained results of classifcation can be useful to the future investors in the Montenegrin capital market. Component-Based Decision Trees: Empirical Testing on Data Sets of Account Holders in the Montenegrin Capital Market Ljiljana Kašćelan, Faculty of Economics, University of Montenegro, Podgorica, Montenegro Vladimir Kašćelan, Faculty of Economics, University of Montenegro, Podgorica, Montenegro 1. INTRODUCTION Popular DT algorithms are implemented with “black-box”-approach, which implies that the logic is hidden from users and there is no possibility of ad hoc changes. These algorithms are changed and improved incrementally, and that can take a long time. Component-based design implies “white-box”-construction of algorithms with help of stan- dardized reusable components (RCs) obtained from original DT algorithms (Delibasic, Jovanovic, Vukicevic, Suknovic & Obradovic, 2011; Suknovic, Delibasic, Jovanovic, Vukicevic, Becejski- Vujaklija & Obradovic, 2012). It enables combination of advantages of various algorithms and their comparison, as well as the testing of influence of certain components on performance of the algorithms. Combining these components we can improve performance and get optimal DT algorithm for a specific data set. DOI: 10.4018/IJORIS.2015100101