Stock market trend prediction model for the egyptian stock market using neural networks and fuzzy logic

Authors:
Maha Mahmoud Abd ElAal;Gamal Selim;Waleed Fakhr
Affiliations:
Computer Engineering, Arab Academy for Science, Technology and Maritime Transport, Cairo, Egypt;Computer Engineering, Arab Academy for Science, Technology and Maritime Transport, Cairo, Egypt;Computer Engineering, Arab Academy for Science, Technology and Maritime Transport, Cairo, Egypt
Venue:
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Year:
2011

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Abstract

This paper presents a study and implementation of a stock trend prediction system based on Artificial Neural Network (ANN) and fuzzy logic rules. Technical analysis tools such as technical indicators and Elliott's wave theory were deployed in the presented prediction system. In this approach the neural network functions as a classifier, where the technical analysis indicators are its input features. The multilayer perceptron (MLP), Support Vector Machine (SVM) and Radial Bases Function (RBF) are tested as classification tools. Also, a fuzzy rule based system based on the Elliott's wave theory is developed to predict the short term stock trend. Finally, integration between these two modules is established using neural network. The System was trained and tested with real data from the Egyptian stock market. The obtained results are encouraging.