 An Epsilon constraint method for selecting Indicators for use in Neural Networks for stock market forecastingFouad Ben Abdelaziz,
Mohamed Amer and
Hazim El-Baz
Post-Print from HAL 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. Keywords: multi-objective optimization; stock market; neural network; technical indicators (search for similar items in EconPapers) Published in INFOR: Information Systems and Operational Research , 2014, Vol. 52 (n° 3), pp 116-125. ⟨10.3138/infor.52.3.114⟩ There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01223453 Access Statistics for this paper More papers in Post-Print from HAL |
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