An Epsilon constraint method for selecting Indicators for use in Neural Networks for stock market forecasting

Abstract : Forecasting future moves of stock markets has been and always will be of great interest to researchers and practitioners. This paper proposes a multi-objective programming methodology to select the optimum technical indicators to be used as input in a Neural Network (NN) in order to predict stock market prices. A new mathematical model will be proposed which involves objective functions and constraints to filter out the noisy signals and maximize the prediction power. The 0-1 multi-objective model aims to select the indicators maximizing the covariance of the indicators with the output of the NN while minimizing the covariance among the indicators themselves. The Multi-objective model is transformed via the Epsilon Constraint technique. Many efficient configurations of indicators for different values of epsilon are evaluated and their resulting errors are presented. Our approach provides a systematic methodology in order to choose the variables that significantly affect price movements. The methodology is applied on the NIKKEI225 stock market index.
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INFOR : Information Systems and Operational Research, the Canadian Operational Research Society, University of Toronto Press, 2014, Vol. 52 (n° 3), pp 116-125. 〈10.3138/infor.52.3.114〉
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Soumis le : lundi 2 novembre 2015 - 16:34:37
Dernière modification le : mardi 3 novembre 2015 - 01:04:04

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Fouad Ben Abdelaziz, Mohamed Amer, Hazim El-Baz. An Epsilon constraint method for selecting Indicators for use in Neural Networks for stock market forecasting. INFOR : Information Systems and Operational Research, the Canadian Operational Research Society, University of Toronto Press, 2014, Vol. 52 (n° 3), pp 116-125. 〈10.3138/infor.52.3.114〉. 〈hal-01223453〉

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