Time Series Prediction with Neural Networks for the Athens Stock Exchange Indicator

M. Hanias, P. Curtis and El Thalassinos ()

European Research Studies Journal, 2012, vol. XV, issue 2, 23-32

Abstract: The main aim of this study is to predict the daily stock exchange price index of the Athens Stock Exchange (ASE) using back propagation neural networks. We construct the neural network based on the minimum embedding dimension of the corresponding strange attractor. Multistep prediction for nine days ahead is achieved with this particular network indicating the increased possibility of this technique for immediate forecasts for very time-short data sets, mostly daily and weekly.

Keywords: Time Series Forecasting; Neural Networks; Perceptions; Neuron (search for similar items in EconPapers)
JEL-codes: C02 C22 C69 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (38)

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Persistent link: https://EconPapers.repec.org/RePEc:ers:journl:v:xv:y:2012:i:2:p:23-32

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