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O Hegazy
OS Soliman 
AA Toony

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O Hegazy 
OS Soliman
AA Toony

Issues in Business Management and Economics
Vol.2 (6), pp. 094-102, June 2014
ISSN 2350-157X
Article ID BM/014/053/09 pages
Copyright © 2014 Author(s) retain the copyright of this article. Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 3.0 International License



Original Research Article

Hybrid of neuro-fuzzy inference system and quantum genetic algorithm for prediction in stock market

O Hegazy., O.S., Soliman and AA. Toony

Faculty of Computers and Information – Cairo University, Egypt

*Corresponding Author Email: ahmed.a.eltoony(at)gmail.com
Tel.:+201223334656



date Received:     date Accepted: June 9, 2014     date Published:


 Abstract

Many classical soft computing approaches have successfully applied in the prediction of stock price and showed a good performance. This paper investigates the power of Quantum Genetic Algorithm in a neuro-fuzzy system composed of an Adaptive Neuro Fuzzy Inference System (ANFIS) controller used in prediction of stock market, identified using an optimization technique based on a double chains quantum genetic algorithm. In this paper the ANFIS controller and the stock market process model inputs are chosen based on a comparative study of fifty different combinations of past stock prices to determine the stock market process model inputs that return the best stock trend prediction for the next day in terms of the minimum Mean Square Error (MSE). The proposed model are tested with  actual financial  data  and  show  the weak form of compared approach by demonstrating much improved and better predictions by finding optimal value for optimization variable in ANFIS using a double chains quantum genetic algorithm.


Key words: Fuzzy logic, neural network, stock market prediction, neuro-fuzzy, quantum genetic algorithm


Hegazy et al