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    Deep neural-network prediction for study of informational efficiency

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    Authors
    Sulaiman, Rejwan Bin
    Schetinin, Vitaly
    Affiliation
    University of Bedfordshire
    Issue Date
    2021-08-03
    Subjects
    autoregressive modelling
    group method of data handling
    deep learning
    machine learning
    time series
    Subject Categories::G760 Machine Learning
    
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    Other Titles
    Proceedings of the 2021 Intelligent Systems Conference (IntelliSys) Volume 2
    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.
    Citation
    Sulaiman RB, Schetinin V (2022) 'Deep neural-network prediction for study of informational efficiency', IntelliSys 2021: Intelligent Systems and Applications - Online, Springer .
    Publisher
    Springer
    URI
    http://hdl.handle.net/10547/625115
    DOI
    10.1007/978-3-030-82196-8_34
    Additional Links
    https://link.springer.com/chapter/10.1007%2F978-3-030-82196-8_34
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISBN
    9783030821951
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-82196-8_34
    Scopus Count
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    Computing

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