JMGT_V11_N1_RP3 Madhavi Latha K. Siva Nageswara Rao Journal on Management 2230 – 715X 11 1 20 27 Bombay Stock Exchange, Dynamic Rough Set Theory, Investment, Prediction, Support Vector Machine The main goal of this study is to investigate the profitable index among the bulk of indices from Bombay Stock Exchange (BSE), and to obtain a Support Vector Machine (SVM) classifier function to classify the indices on the basis of adherence level. The Dynamic Rough Set Theory (DRS) is used to identify the most important attributes and induce decision making with the support of the profitable indices from 302 samples of BSE. To increase the efficiency of the classification process, “Dynamic Rough Set using Machine Learning Algorithm” (DRSMLA), is introduced and implemented to discritize the data, then rough set reduction technique is applied to find all reduced sets of the data which contains the minimal subset of attributes, that are associated with class label for classification. Finally, the generated class labels are used to predict best indices for long term, medium term and short term investors by the implementation of SVM classification technique. A comparison between the obtained results using DRSMLA with ID3 decision tree, discriminant analysis, neural networks and rough set theory classifier algorithms has been made. DRSMLA method shows a higher overall accuracy rates and lower execution time consumption. June - August 2016 Copyright © 2016 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=8071