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Prediction of Closing Prices on the Stock Exchange with the Use of Artificial Neural Networks

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EN
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EN
Article describes, the use of Artificial Neural Networks (ANN) for predicting values of Stock Exchange shares. Rules of Stock Exchange functioning, principles of technical analysis and the most important stock market indices are described, which support investors, who plan to make transactions. ANN of Multi-Layer Perceptron (MLP) type, and a moving window method are applied. A hybrid method is also proposed, in which time series of CLOSE values as a function of the following trading days are used to stock market indices calculation, such as moving averages and oscillators, which are applied to ANN inputs. Research was conducted for 80 companies, selected from the 1218 companies functioning on Stock Exchange. The achieved maximum error in one day ahead CLOSE value prediction is 1,31%.
Twórcy
autor
  • Computer Engineering Department, Technical University of Lodz, Lodz, Poland
  • Computer Engineering Department, Technical University of Lodz, Lodz, Poland
Bibliografia
  • [1] R. Bensignor, New Concepts in Technical Analysis, Wig-Press, (in Polish) Warsaw 2004
  • [2] M.A. Brdyś, A. Borowa, P. Idźkowiak, M.T. Brdyś, Adaptive Prediction of Stock Exchange Indices by State Space Wavelet Networks, Int. J. Appl. Math. Comput. Sci., Vol. 19 , No. 2, pp. 337-348, 2009
  • [3] W. Dębski, Financial Market and it mechanisms, (in Polish), PWN, Warsaw 2010
  • [4] Ed. Gately, Neural Networks - Financial forecasting and transactional systems design, (in Polish), Wig- Press, Warsaw 1999
  • [5] J.J. Murphy, Technical Analysis of Financial Markets, (in Polish), Wig-Press, Warsaw, 2008
  • [6] K.S. Narendra, K. Parthasarathy, Identification and control of dynamics systems using neural networks, IEEE Transactions on Neural Networks, Vol. 1, No. 1, pp. 4-27, 1990
  • [7] P. Sutheebanjard, W. Premchaiswadi, Stock Exchange of Thailand Index Prediction Using Back Propagation Neural Networks, In Proc. of the Second International Conference on Computer and Network Technology (ICCNT), Bangkok, pp. 377-380, 2010
  • [8] R. Tadeusiewicz, Artificial Neural Networks, (in Polish), Warsaw 1993
  • [9] R. Tadeusiewicz, Discovering Neural Networks, (in Polish), Cracow 2007
  • [10] C.D. Tilakaratne, S.A. Morris, M.A. Mammadov, C.P. Hurst, Predicting Stock Market Index Trading Signals Using Neural Networks, In Proc. of the 14th Annual Global Finance Conference (GFC 2007), Melbourne, Australia, pp. 171-179, Sep. 2007
  • [11] D. Witkowska, Artificial Neural Networks and statistical methods, Selected financial issues, (in Polish), C. H. Beck, Warsaw 2002
  • [12] A. Zaremba, Stock Exachange, (in Polish), 2010
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  • [14] www.mbank.pl/inwestycje/centruminwestora/fundusze/narzedzia/ryzykoinwestycyjne.html
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d173edd9-c199-4784-924e-d59650cf9cf3
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