Rough sets for predicting the Kuala Lumpur Stock Exchange Composite Index returns

• Main Authors: Kok, Yit-Pong, Shamsuddin, Siti Mariyam, Alwi, Razana, Sallehuddin, Roselina, Ahmad, Norbahiah
• Format: Conference or Workshop Item
• Language: English
• Published: 2004
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://repo.uum.edu.my/13840/1/KM108.pdf; http://repo.uum.edu.my/13840/; http://www.kmice.cms.net.my

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.