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Proceedings of SAI Intelligent Systems Conference

IntelliSys 2021: Intelligent Systems and Applications pp 460–467Cite as

Deep Neural-Network Prediction for Study of Informational Efficiency

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 295)

Abstract

In this paper, we attempt to verify a hypothesis of informational efficiency of financial markets, known as “random walk” introduced by Fama. Such hypotheses could be considered in relation to financial crises. In our study the hypothesis is tested on data taken from Warsaw Stock Exchange in 2007–2009 years. The hypothesis is tested by predictive modelling based on Machine Learning (ML). We compare conventional ML techniques and the proposed “deep” neural-network structures grown by Group Method of Data Handling (GMDH). In our experiments a GMDH-type neural-network model has outperformed the conventional ML techniques, which is important for achieving the reliable results of predictive modelling and testing the hypothesis. GMDH-type modelling does not require the knowledge of network structure, as a desired network of near-optimal connectivity is learnt from the data. The experimental results compared in terms of prediction error show that the GMDH-type prediction model has a significantly smaller error than the conventional autoregressive and neural-network models.

Keywords

  • Machine Learning
  • Autoregressive modelling
  • Group Method of Data Handling
  • Deep learning
  • Time series

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Authors and Affiliations

  1. School of Computer Science and Technology, University of Bedfordshire, Luton, LU1 3JU, UK

    Rejwan Bin Sulaiman & Vitaly Schetinin

Authors
  1. Rejwan Bin Sulaiman
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  2. Vitaly Schetinin
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Correspondence to Vitaly Schetinin .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Prof. Dr. Kohei Arai

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Sulaiman, R.B., Schetinin, V. (2022). Deep Neural-Network Prediction for Study of Informational Efficiency. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-82196-8_34

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