Publication Type:

Journal Article

Source:

International Journal of Business Intelligence and Data Mining, Volume 6, Number 4, p.362-381 (2011)

URL:

http://www.inderscienceonline.com/doi/abs/10.1504/IJBIDM.2011.044976

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.

Notes:

cited By (since 1996)0

Cite this Research Publication

B.B. Nair, Mohandas, V. P., and Sakthivel, N. R., “Predicting stock market trends using hybrid ant-colony-based data mining algorithms: an empirical validation on the Bombay Stock Exchange”, International Journal of Business Intelligence and Data Mining, vol. 6, pp. 362-381, 2011.