Deep neural-network prediction for study of informational efficiency
Affiliation
University of BedfordshireIssue Date
2021-08-03Subjects
autoregressive modellinggroup method of data handling
deep learning
machine learning
time series
Subject Categories::G760 Machine Learning
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Proceedings of the 2021 Intelligent Systems Conference (IntelliSys) Volume 2Abstract
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
SpringerAdditional Links
https://link.springer.com/chapter/10.1007%2F978-3-030-82196-8_34Type
Conference papers, meetings and proceedingsLanguage
enISBN
9783030821951ae974a485f413a2113503eed53cd6c53
10.1007/978-3-030-82196-8_34