DECISION SYSTEM FOR STOCK DATA FORECASTING BASED ON HOPFIELD ARTIFICIAL NEURAL NETWORK

Michał Paluch

mpaluch@kis.p.lodz.pl
Lodz University of Technology, Institute of Applied Computer Science (Poland)

Lidia Jackowska-Strumiłło


Lodz University of Technology, Institute of Applied Computer Science (Poland)

Abstract

The paper describes a new method using Hopfield artificial neural network combined with technical analysis fractal analysis and feed-forward artificial neural networks for predicting share prices for a next day on a Stock Exchange. The developed method and networks are implemented in an Expert System, which is proposed as a valuable comprehensive, analytical tool. A new algorithm for artificial neural networks training and testing is also presented. It automatically chooses the best network structure, and the most important input parameters.


Keywords:

Hybrid intelligent system, Hopfield artificial neural network

Bensignor R.: New Concepts in Technical Analysis. Wig-Press, Warszawa 2004.
  Google Scholar

Box G.E.P., Jenkins G.M.: Time Series Analysis. Forecasting and control. Holden-Day Inc., San Francisco, USA, 1976.
  Google Scholar

Brdyś M. A., Borowa A., Idźkowiak P., Brdyś M. T.: Adaptive Prediction of Stock Exchange Indices by State Space Wavelet Networks. Int. J. Appl. Math. Comput. Sci., 19(2)/2009, 337–348. [DOI: 10.1.1.390.8001].
  Google Scholar

Bulkowski T.N.: Formation Analysis on Stock Charts. Linia, Warszawa 2011.
  Google Scholar

Dębski W.: Financial Market and it mechanisms. PWN, Warszawa 2010.
  Google Scholar

Drabik E.: Applications of game theory to invest in securities. University of Bialystok, Bialystok 2000.
  Google Scholar

Ehlers J.: Fractal Adaptive Moving Average. Technical Analysis of Stock & Commodities, 2005.
  Google Scholar

Ehlers J.: Cybernetics Analysis For Stocks And Futures. John Wiley & Sons, New York 2004.
  Google Scholar

Ehlers J. Using the Fisher Transform. Technical Analysis of Stocks & Commodities, 2002.
  Google Scholar

Gately E.: Neural Networks for Financial Forecasting. New York, Wiley 1995.
  Google Scholar

Güresen E., Kayakutlu G.: Forecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models. In IFIP International Federation for Information Processing, 288/2008, 129–137.
  Google Scholar

Güresen E., Kayakutlu G., Daim T.U.: Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38/2011, 10389–10397. [DOI: 10.1016/j.eswa.2011.02.068].
  Google Scholar

Jackowska-Strumiłło L.: Hybrid Analytical and ANN-based Modelling of Temperature Sensors Nonlinear Dynamic Properties. Lecture Notes in Artificial Intelligence Part I, Springer-Verlag, 2011, 356–363. [DOI: 10.1007/978-3-642-21219-2_45].
  Google Scholar

Jackowska-Strumiłło L., Jackowski T., Chylewska B., Cyniak D.: Application of hybrid neural model to determination of selected yarn parameters. Fibres & Textiles in Eastern Europe, 6(4)/1998, 27–32.
  Google Scholar

Khashei M., Bijari M.: An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with Applications, 37(1)/2010, 479–489. [DOI: 10.1016/j.eswa.2009.05.044].
  Google Scholar

Majhi R., Panda G., Sahoo G.: Efficient prediction of exchange rates with low complexity artificial neural network models. Expert Systems with Applications, 36/2009, 181–189. [DOI: 10.1016/j.eswa.2007.09.005].
  Google Scholar

Murphy J.J.: Technical Analysis of Financial Markets. Wig-Press, Warszawa 2008.
  Google Scholar

Paluch M., Jackowska-Strumiłło L.: Intelligent Information System For Stock Exchange Data Processing And Presentation. 8th International Conference on Human System Interactions, 2015.
  Google Scholar

Paluch M., Jackowska-Strumiłło L.: Prediction of closing prices on the Stock Exchange with the use of artificial neural networks. Image Processing & Communication, 17(4)/2012, 275–282.
  Google Scholar

Paluch M., Jackowska-Strumiłło L.: The influence of using fractal analysis in hybrid MLP model for short-term forecast of close prices on Warsaw Stock Exchange. Proc. Federated Conference on Computer Science and Information Systems 2014, FedCSIS 2014, 7–10 Sep. 2014, Warsaw, Poland, 111–118.
  Google Scholar

Paluch M., Jackowska-Strumiłło L.: Intelligent Information System For Stock Exchange Data Processing And Presentation. 8th International Conference on Human System Interactions, IEEExplore, 2015.
  Google Scholar

Rutkowski L.: Methods and Techniques of Artificial Intelligence. PWN, Warszawa 2009.
  Google Scholar

Sutheebanjard P., Premchaiswadi W.: Stock Exchange of Thailand Index Prediction Using Back Propagation Neural Networks. Proc. of the Second International Conference on Computer and Network Technology (ICCNT), 2010, Bangkok, 377–380. [DOI: 10.1109/ICCNT.2010.21].
  Google Scholar

Tadeusiewicz R.: Discovering Neural Networks, Kraków 2007.
  Google Scholar

Tilakaratne C.D., Morris S.A., Mammadov M.A., Hurst C.P.: Predicting Stock Market Index Trading Signals Using Neural Networks. Proc. of the 14th Annual Global Finance Conference (GFC 2007), Melbourne, Australia, 2007, 171–179.
  Google Scholar

Walls C.: Spring in Action, Helion, Gliwice.
  Google Scholar

Witkowska D., Marcinkiewicz E.: Construction and Evaluation of Trading Systems: Warsaw Index Futures. International Advances in Economic Research, 11/2005, 83–92. [DOI: 10.1007/s11294-004-7496-7].
  Google Scholar

Zieliński J.: Intelligent management systems – theory and practice. Warszawa 2000.
  Google Scholar

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Published
2016-05-10

Cited by

Paluch, M. ., & Jackowska-Strumiłło, L. (2016). DECISION SYSTEM FOR STOCK DATA FORECASTING BASED ON HOPFIELD ARTIFICIAL NEURAL NETWORK. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 6(2), 28–33. https://doi.org/10.5604/20830157.1201313

Authors

Michał Paluch 
mpaluch@kis.p.lodz.pl
Lodz University of Technology, Institute of Applied Computer Science Poland

Authors

Lidia Jackowska-Strumiłło 

Lodz University of Technology, Institute of Applied Computer Science Poland

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