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

Author

Listed:
  • 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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    References listed on IDEAS

    as
    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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    Keywords

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    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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