TY - GEN
T1 - Deep neural-network prediction for study of informational efficiency
AU - Sulaiman, Rejwan Bin
AU - Schetinin, Vitaly
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021/8/3
Y1 - 2021/8/3
N2 - 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.
AB - 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.
KW - Group Method of Data Handling
KW - autoregressive modelling
KW - Deep learning
KW - Time series
KW - machine learning
KW - Autoregressive modelling
KW - Machine Learning
UR - https://www.scopus.com/pages/publications/85113430637
U2 - 10.1007/978-3-030-82196-8_34
DO - 10.1007/978-3-030-82196-8_34
M3 - Conference contribution
SN - 9783030821951
VL - 295
T3 - Lecture Notes in Networks and Systems
SP - 460
EP - 467
BT - Intelligent Systems and Applications - Proceedings of the 2021 Intelligent Systems Conference IntelliSys
A2 - Arai, Kohei
PB - Springer
T2 - IntelliSys 2021: Intelligent Systems and Applications
Y2 - 2 September 2021 through 3 September 2021
ER -