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Computational Intelligence and Neuroscience
Volume 2014 (2014), Article ID 318524, 6 pages
http://dx.doi.org/10.1155/2014/318524
Research Article

Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions

School of Engineering and Computer Science, Independent University, Dhaka 1229, Bangladesh

Received 7 May 2013; Revised 17 December 2013; Accepted 9 January 2014; Published 19 February 2014

Academic Editor: Simone Fiori

Copyright © 2014 Shipra Banik et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.