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Deep neural-network prediction for study of informational efficiency

  • University of Bedfordshire
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

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.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 460-467 (8 pages)

Publication milestones

  • Published - 03/08/2021

Publication status

Published - 03/08/2021

Volume

295

Publisher

Springer, Japan, India, Australia, Germany, United States, United Arab Emirates, Austria, Switzerland, Italy, China, United Kingdom, Netherlands, Brazil, France, Singapore

Publication series

  • Publication series name: Lecture Notes in Networks and Systems
    ISSN (Print): 2367-3370
    ISSN (Electronic): 2367-3389
    Volume: 295

ISBN (Print)

9783030821951

ISBN (Electronic)

9783030821968

External Publication IDs

  • handle.net: 10547/625115
  • Scopus: 85113430637

Host publication title

Intelligent Systems and Applications - Proceedings of the 2021 Intelligent Systems Conference IntelliSys

Host publication editors

  • Kohei Arai

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