Kok, Yit-Pong and Shamsuddin, Siti Mariyam and Alwi, Razana and Sallehuddin, Roselina and Ahmad, Norbahiah (2004) Rough sets for predicting the Kuala Lumpur Stock Exchange Composite Index returns. In: Knowledge Management International Conference and Exhibition 2004 (KMICE 2004), 14-15 February 2004, Evergreen Laurel Hotel, Penang.

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Abstract

This study aims to prove the usability of Rough Set approach in capturing the relationship between the technical indicators and the level of Kuala Lumpur Stock Exchange Composite Index (KLCI) over time.Stock markets are affected by many interrelated economic, political, and even psychological factors.Therefore, it is generally very difficult to predict its movements. There are extensive literatures available describing attempts to use artificial intelligence techniques; in particular neural networks and genetic algorithm for analyzing stock market variations.However, drawbacks are found where neural networks have great complexity in interpreting the results; genetic algorithms create large data redundancies.A relatively new approach, the rough sets are suggested for its simple knowledge representation, ability to deal with uncertainties and lowering data redundancies.In this study, a few different discretization algorithms were used at data preprocessing. From the simulations and result produced, the rough sets approach can be a promising alternative to the existing methods for stock market prediction.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	ISBN 983-2865-90-5 Organized by: Faculty of Information Technology, UUM
Uncontrolled Keywords:	Financial Analysis, Index Fund, Continuous Attributes, Data Mining, Discretization
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	College of Arts and Sciences
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	13 Apr 2015 08:52
Last Modified:	13 Apr 2015 08:52
URI:	https://repo.uum.edu.my/id/eprint/13840

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