Abstract
In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using neural network, TS fuzzy system and hierarchical TS fuzzy techniques. To demonstrate the different techniques, we considered Nasdaq–100 index of Nasdaq Stock MarketSM and the S&P CNX NIFTY stock index. We analyzed 7 year’s Nasdaq 100 main index values and 4 year’s NIFTY index values. The parameters of the different techniques are optimized by the particle swarm optimization algorithm. This paper briefly explains how the different learning paradigms could be formulated using various methods and then investigates whether they can provide the required level of performance, which are sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results reveal that all the models considered could represent the stock indices behavior very accurately.
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References
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Nasdaq Stock MarketSM: http://www.nasdaq.com
National Stock Exchange of India Limited: http://www.nse-india.com
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Chen, Y., Abraham, A., Yang, J., Yang, B. (2005). Hybrid Methods for Stock Index Modeling. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_137
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DOI: https://doi.org/10.1007/11540007_137
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
Online ISBN: 978-3-540-31828-6
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science
Keywords
- Mean Absolute Percentage Error
- Stock Index
- Stock Market Index
- National Stock Exchange
- Root Mean Square Error Result
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.