Title: Predicting stock market trends using hybrid ant-colony-based data mining algorithms: an empirical validation on the Bombay Stock Exchange

Authors: Binoy B. Nair; V.P. Mohandas; N.R. Sakthivel

Addresses: Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, P.O. Amrita Nagar, Ettimadai, Coimbatore, Tamil Nadu, 641112, India. ' Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, P.O. Amrita Nagar, Ettimadai, Coimbatore, Tamil Nadu, 641112, India. ' Department of Mechanical Engineering, Amrita Vishwa Vidyapeetham, P.O. Amrita Nagar, Ettimadai, Coimbatore, Tamil Nadu, 641112, India

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.

Keywords: ACO; ant colony optimisation; data mining; SVM; support vector machines; Ant-Miner; Ant-Miner2; cAnt-Miner; stock market trends; technical indicators; stock markets; stock market predictions; selling securities; buying securities; Bayes; artificial neural networks; ANNs.

DOI: 10.1504/IJBIDM.2011.044976

International Journal of Business Intelligence and Data Mining, 2011 Vol.6 No.4, pp.362 - 381

Accepted: 01 Aug 2011
Published online: 22 Apr 2015 *

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