References

Boonpeng, S. and P. Jeatrakul, 2016. Decision support system for investing in stock market by using OAA-neural network. Proceedings of the 8th International Conference on Advanced Computational Intelligence (ICACI), February 14-16, 2016, IEEE, Chiang Mai, Thailand, ISBN:978-1-4673-7780-5, pp: 1-6.

Chavan, P.S. and S.T. Patil, 2013. Parameters for stock market prediction. Intl. J. Comput. Technol. Appl., 4: 337-340.

Chen, X. and Z.J. He, 2015. Prediction of stock trading signal based on support vector machine. Proceedings of the 8th International Conference on Intelligent Computation Technology and Automation (ICICTA), June 14-15, 2015, IEEE, Nanchang, China, ISBN:978-1-4673-7644-0, pp: 651-654.

Dempster, M.A.H. and C.M. Jones, 1998. The profitability of intra-day FX trading using technical indicators. Master Thesis, University of Cambridge, Cambridge, England.

Edwards, R.D., W.H.C. Bassetti and J. Magee, 2001. Technical Analysis of Stock Trends. 8th Edn., CRC Press, Boca Raton, Florida, USA.,.

Fernandez-Blanco, P., D.J. Bodas-Sagi, F.J. Soltero and J.I. Hidalgo, 2008. Technical market indicators optimization using evolutionary algorithms. Proceedings of the 10th Annual Conference Companion on Genetic and Evolutionary Computation, July 12-16, 2008, ACM, Atlanta, Georgia, USA., ISBN:978-1-60558-131-6, pp: 1851-1858.

Han, J., M. Kamber and J. Pei, 2011. Data Mining: Concepts and Techniques. 3rd Edn., Morgan Kaufmann Publishers, USA., ISBN-13: 9780123814791, Pages: 744.

Micheline, K., J. Han and J. Pei, 2012. Data Mining: Concepts and Techniques. 3rd Edn., Elsevier, Amsterdam, Netherlands, ISBN:978-0-12-381479-1, Pages: 673.

Mingyue, Q., L. Cheng and S. Yu, 2016. Application of the artifical neural network in predicting the direction of stock market index. Proceedings of the 10th International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), July 6-8, 2016, IEEE, Fukuoka, Japan, ISBN:978-1-5090-0988-6, pp: 219-223.

Murphy, J.J., 1998. Study Guide for Technical Analysis of the Financial Markets: A comprehensive Guide to Trading Methods and Applications. 2nd Edn., Institute of Finance, New York, USA., ISBN-10: 0735200653, Pages: 160.

Navale, G.S., N. Dudhwala, K. Jadhav, P. Gabda and B. Kishor, 2016. Prediction of stock market using data mining and artificial intelligence. Intl. J. Comput. Appl., 134: 9-11.

Panigrahi, S.S. and J.K. Mantri, 2015. A text based decision tree model for stock market forecasting. Proceedings of the International Conference on Green Computing and Internet of Things (ICGCIoT), October 8-10, 2015, IEEE, Noida, India, ISBN:978-1-4673-7911-3, pp: 405-411.

Tan, P.N., M. Steinbach and V. Kumar, 2005. Introduction to Data Mining. 1st Edn., Pearson Addison Wesley, New York, ISBN-13: 978-0321321367, Pages: 769.

Teeples, A.W., 2010. An evolutionary approach to optimization of compound stock trading indicators used to confirm buy signals. BA Thesis, Utah State University, Logan, Utah.

Yefimochkin, O., 2011. Fundamental: Using macroeconomic indicators and genetic algorithms in stock market forecasting. Master Thesis, Instituto Superior Tecnico, Lisbon, Portugal.

Zaamout, K. and J.Z. Zhang, 2012. Improving neural networks classification through chaining. Proceedings of the 22nd International Conference on Artificial Neural Networks, September 11-14, 2012, Springer, Lausanne, Switzerland, pp: 288-295.