INFORMATION TECHNOLOGY OF STOCK INDEXES FORECASTING ON THE BASE OF FUZZY NEURAL NETWORKS
Yuriy TRYUS
tryusyv@gmail.comComputer Science and Information Technology Department, Cherkasy State Technological University,460 Shevchenko Blvd, 18006, Cherkasy (Ukraine)
Nataliya ANTIPOVA
Computer Science and Information Technology Department, Cherkasy State Technological University,460 Shevchenko Blvd, 18006, Cherkasy, (Ukraine)
Kateryna ZHURAVEL
Computer Science and Information Technology Department, Cherkasy State Technological University,460 Shevchenko Blvd, 18006, Cherkasy, (Ukraine)
Grygoriy ZASPA
Software Department, Cherkasy State Technological University, 460 Shevchenko Blvd, 18006, Cherkasy (Ukraine)
Abstract
In this research the information technology for stock indexes forecast on the base of fuzzy neural networks was created. Thepossibility of its use for multi-parameter short-time stock indexes forecasts, in particular S&P500, DJ, NASDAC was checked. Thecreated information technology is used making several consequential steps. The stock indexes forecast numeral experiment based on real data for period of several years with use of the technology offered was made.
Keywords:
neural networks, fuzzy neural networks, forecasting, stock indexesReferences
Adaptive Neuro-Fuzzy Modeling. (n.d.). Retrieved September 26, 2016, from MathWorks website, https://www.mathworks.com/help/fuzzy/adaptive-neuro-fuzzy-inference-systems.html
Google Scholar
Adaptive neuro-fuzzy inference system. (n.d.). Retrieved September 26, 2016, from MathWorks website, https://www.mathworks.com/help/fuzzy/neuro-adaptive-learning-and-anfis.html
Google Scholar
Jang, J.-S. R., (1993). ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685.
DOI: https://doi.org/10.1109/21.256541
Google Scholar
Jang, J.-S. R., & Sun, C.-T. (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83(3), 378–406.
DOI: https://doi.org/10.1109/5.364486
Google Scholar
Jang, J.-S. R., & Sun, C.-T. (1997). Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Upper Saddle River, NJ: Prentice Hall.
DOI: https://doi.org/10.1109/TAC.1997.633847
Google Scholar
Mohaddes, S. A., & Fahimifard, S. M. (2015). Application of Adaptive Neuro-Fuzzy Inference System (ANFIS) in Forecasting Agricultural Products Export Revenues (Case of Iran’s Agriculture Sector). Journal of Agricultural Science and Technology, 17(1), 1–10.
Google Scholar
Svalina, I., Galzina, V., Lujić, R., & Šimunović, G. (2013). An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices. Expert Systems with Applications, 40(15), 6055-6063. https://doi.org/10.1016/j.eswa.2013.05.029
DOI: https://doi.org/10.1016/j.eswa.2013.05.029
Google Scholar
Toolbox fuzzy-logic Matlab. (n.d.). Retrieved September 28, 2016, from MathWorks website, http://www.mathworks.com/products/fuzzy-logic/
Google Scholar
Wang, L.-X. (1994). Adaptive fuzzy systems and control: design and stability analysis. Upper Saddle River, NJ: Prentice Hall.
Google Scholar
Wang, Y. M., & Elhag, T. (2008). An Adaptive Neuro-fuzzy Inference System for Bridge Risk Assessment. Expert Systems with Applications, 34(4), 3099–3106. https://doi.org/10.1016/j.eswa.2007.06.026
DOI: https://doi.org/10.1016/j.eswa.2007.06.026
Google Scholar
YahooFinance – BusinessFinance, StockMarket, Quotes, News. (n.d.). Retrieved September 21, 2016, from YahooFinance website, http://finance.yahoo.com
Google Scholar
Zhang, G., & Hu, M. Y. (1998). Neural Network Forecasting of the British Pound/US Dollar Exchange Rate. Omega The International Journal of Management Science, 26(4), 495–506. https://doi.org/10.1016/S0305-0483(98)00003-6
DOI: https://doi.org/10.1016/S0305-0483(98)00003-6
Google Scholar
Authors
Yuriy TRYUStryusyv@gmail.com
Computer Science and Information Technology Department, Cherkasy State Technological University,460 Shevchenko Blvd, 18006, Cherkasy Ukraine
Authors
Nataliya ANTIPOVAComputer Science and Information Technology Department, Cherkasy State Technological University,460 Shevchenko Blvd, 18006, Cherkasy, Ukraine
Authors
Kateryna ZHURAVELComputer Science and Information Technology Department, Cherkasy State Technological University,460 Shevchenko Blvd, 18006, Cherkasy, Ukraine
Authors
Grygoriy ZASPASoftware Department, Cherkasy State Technological University, 460 Shevchenko Blvd, 18006, Cherkasy Ukraine
Statistics
Abstract views: 92PDF downloads: 12
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Toufik GHRIB, Yacine KHALDI, Purnendu Shekhar PANDEY, Yusef Awad ABUSAL, ADVANCED FRAUD DETECTION IN CARD-BASED FINANCIAL SYSTEMS USING A BIDIRECTIONAL LSTM-GRU ENSEMBLE MODEL , Applied Computer Science: Vol. 20 No. 3 (2024)
You may also start an advanced similarity search for this article.
