Predicting stock market trends using hybrid ant-colony-based data mining algorithms: an empirical validation on the Bombay Stock Exchange. (1st January 2011)

• Record Type: Journal Article
• Authors: Nair, Binoy B.; Mohandas, V.P.; Sakthivel, N.R.
• Is Part Of: International journal of business intelligence and data mining. Volume 6:Number 4(2011)
• Page Start: 362
• Page End: 381
• Publication Date: 2011-01-01
• Subjects: ACO -- ant colony optimisation -- data mining -- SVM -- support vector machine -- hybrid -- Ant-Miner -- Ant-Miner2 -- cAnt-Miner -- trend -- technical indicators -- stock market -- prediction; 006.312
• Languages: English
• ISSNs: 1743-8187

Abstract

Ant Colony Optimisation (ACO) algorithms use simple mutually cooperating agents (ants) to produce a robust and adaptive search system, which can be used for knowledge discovery. In this paper, a Support Vector Machine (SVM)-cAnt-Miner-based system for predicting the next-day's trend in stock markets is proposed. The trend predicted by the proposed system is then used to identify the appropriate time to buy and sell securities. Performance of the proposed system is evaluated against SVM-Ant-Miner, SVM-Ant-Miner2, Naïve-Bayes and an Artificial Neural Network (ANN)-based trend prediction system. The results indicate that the proposed system outperforms all the other techniques considered.