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

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  • M. Hanias
  • P. Curtis
  • E. Thalassinos

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.

Suggested Citation

  • M. Hanias & P. Curtis & E. Thalassinos, 2012. "Time Series Prediction with Neural Networks for the Athens Stock Exchange Indicator," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 23-32.
  • Handle: RePEc:ers:journl:v:xv:y:2012:i:2:p:23-32
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    File URL: http://www.ersj.eu/repec/ers/papers/12_2_p2.pdf
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    References listed on IDEAS

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    1. Eleftherios Thalassinos & Pantelis E. Thalassinos, 2006. "Stock Markets' Integration Analysis," European Research Studies Journal, European Research Studies Journal, vol. 0(3-4), pages 3-14.
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    12. Jaydip SEN & Tamal DATTA CHAUDHURI, 2016. "An Alternative Framework for Time Series Decomposition and Forecastingand its Relevance for Portfolio Choice – A Comparative Study of the Indian Consumer Durable and Small Cap Sectors," Journal of Economics Library, KSP Journals, vol. 3(2), pages 303-326, June.
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    More about this item

    Keywords

    Time Series Forecasting; Neural Networks; Perceptions; Neuron;

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C69 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Other

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