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A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets

Title
A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets
Authors
Kim H.-j.Shin K.-s.
Ewha Authors
신경식
SCOPUS Author ID
신경식scopus
Issue Date
2007
Journal Title
Applied Soft Computing Journal
ISSN
1568-4946JCR Link
Citation
vol. 7, no. 2, pp. 569 - 576
Indexed
SCIE; SCOPUS WOS scopus
Abstract
This study investigates the effectiveness of a hybrid approach based on the artificial neural networks (ANNs) for time series properties, such as the adaptive time delay neural networks (ATNNs) and the time delay neural networks (TDNNs), with the genetic algorithms (GAs) in detecting temporal patterns for stock market prediction tasks. Since ATNN and TDNN use time-delayed links of the network into a multi-layer feed-forward network, the topology of which grows by on layer at every time step, it has one more estimate of the number of time delays in addition to several control variables of the ANN design. To estimate these many aspects of the ATNN and TDNN design, a general method based on trial and error along with various heuristics or statistical techniques is proposed. However, for the reason that determining the number of time delays or network architectural factors in a stand-alone mode does not guarantee the illuminating improvement of the performance for building the ATNN and TDNN model, we apply GAs to support optimization of the number of time delays and network architectural factors simultaneously for the ATNN and TDNN model. The results show that the accuracy of the integrated approach proposed for this study is higher than that of the standard ATNN, TDNN and the recurrent neural network (RNN). © 2006 Elsevier B.V. All rights reserved.
DOI
10.1016/j.asoc.2006.03.004
Appears in Collections:
경영대학 > 경영학전공 > Journal papers
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