Information Technology of Stock Indexes Forecasting on the Base of Fuzzy Neural Networks

• Autorzy: Tryus Y. , Antipova N. , Zhuravel K. , Zaspa G.
• Języki publikacji: EN

Abstrakty

EN

In this research the information technology for stock indexes forecast on the base of fuzzy neural networks was created. The possibility of its use for multi-parameter short-time stock indexes forecasts, in particular S&P500, DJ, NASDAC was checked. The created 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.

Słowa kluczowe

EN

neural networks; fuzzy neural networks; forecasting; stock indexes

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2017
• Tom: Vol. 13, no 1
• Strony: 29--40
• Opis fizyczny: Bibliogr. 12 poz., fig., tab.

Twórcy

autor

Tryus Y.

• Computer Science and Information Technology Department, Cherkasy State Technological University,460 Shevchenko Blvd, 18006, Cherkasy, Ukraine

autor

Antipova N.

• Computer Science and Information Technology Department, Cherkasy State Technological University,460 Shevchenko Blvd, 18006, Cherkasy, Ukraine

autor

Zhuravel K.

• Computer Science and Information Technology Department, Cherkasy State Technological University,460 Shevchenko Blvd, 18006, Cherkasy, Ukraine

autor

Zaspa G.

• Computer Science and Information Technology Department, Cherkasy State Technological University,460 Shevchenko Blvd, 18006, Cherkasy, Ukraine

Bibliografia

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• 5. 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.
• 6. 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.
• 7. 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. doi:10.1016/j.eswa.2013.05.029
• 8. Toolbox fuzzy-logic Matlab. (n.d.). Retrieved September 28, 2016, from MathWorks website, http://www.mathworks.com/products/fuzzy-logic
• 9. Wang, L.-X. (1994). Adaptive fuzzy systems and control: design and stability analysis. Upper Saddle River, NJ: Prentice Hall.
• 10. Wang, Y. M., & Elhag, T. (2008). An Adaptive Neuro-fuzzy Inference System for Bridge Risk Assessment. Expert Systems with Applications, 34(4), 3099–3106. doi:10.1016/j.eswa.2007.06.026
• 11. YahooFinance – BusinessFinance, StockMarket, Quotes, News. (n.d.). Retrieved September 21, 2016, from YahooFinance website, http://finance.yahoo.com
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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)