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Autor
Paluch Michał (Lodz University of Technology, Poland), Jackowska-Strumiłło Lidia (Lodz University of Technology, Poland)
Tytuł
The influence of using fractal analysis in hybrid MLP model for short-term forecast of closing prices on Warsaw Stock Exchange
Źródło
Annals of Computer Science and Information Systems, 2014, vol. 2, s. 111-118, rys., tab., bibliogr. 33 poz.
Słowa kluczowe
Sztuczne sieci neuronowe (SSN), Ceny akcji, Giełda
Artificial neural networks (ANN), Shares prices, Stock exchange
Uwagi
summ.
Firma/Organizacja
Giełda Papierów Wartościowych w Warszawie
Warsaw Stock Exchange
Abstrakt
The paper describes a new method of combining Artificial Neural Networks (ANN), technical analysis and fractal analysis for predicting share prices on the Warsaw Stock Exchange. The proposed hybrid model consists of two consecutive modules. In the first step share prices are pre-processed and calculated into moving averages and oscillators. Then, in the next step, they are given to the ANN inputs, which provides the closing values of the asset for the next day. ANN of Multi-Layer Perceptron (MLP) type, and fractal analysis are applied. The hybrid model combining ANN with technical and fractal analysis is compared with hybrid model combining ANN with technical analysis. The obtained results indicate that hybrid model combined with fractal analysis is more accurate and stable in the long run than the hybrid model.(original abstract)
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Bibliografia
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  1. Bensignor R.: New Concepts in Technical Analysis. Wig-Press, Warsaw 2004 (in Polish).
  2. Box G. E. P., & Jenkins G. M. (1976). Time Series Analysis. Forecasting and control. Holden-Day Inc., San Francisco, California, USA.
  3. 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., 2009 , Vol. 19 , No. 2 , 337-348. DOI: 10.1.1.390.8001
  4. Bulkowski Thomas N., Formation Analysis on Stock Charts. Linia, Warsaw 2011 (in Polish)
  5. Dębski W.: Financial Market and it mechanisms. PWN, Warsaw 2010 (in Polish)
  6. Dourraa H., Siyb P. (2002). Investment using technical analysis and fuzzy logic. Fuzzy Sets and Systems, 127, 221-240.
  7. Drabik E.: Applications of game theory to invest in securities, Wydawnictwo Uniwersytetu w Białymstoku, Bialystok 2000. (in Polish)
  8. Ehlers J.: Fractal Adaptive Moving Average", Technical Analysis of Stock & Commodities" October 2005.
  9. Ehlers J.: "Cybernetics Analysis For Stocks And Futures", John Wiley & Sons, New York 2004.
  10. Gately E. (1995). Neural Networks for Financial Forecasting, New York: Wiley.
  11. Ghiassi M., Saidane H., Zimbra D. K.: Dynamic artificial neural network model for forecasting time series events. International Journal of Forecasting, 2005, Vol. 21, pp. 341-362. DOI: 10.1016/j.ijforecast.2004.10.008
  12. Güresen E., Kayakutlu G.: Forecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models. In IFIP International Federation for Information Processing, 2008, Volume 288; Intelligent Information Processing IV; Zhongzhi Shi, E. Mercier-Laurent, D. Leake; (Boston: Springer), pp. 129-137.
  13. Güresen E., Kayakutlu G., Daim T. U.: Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 2011, Vol. 38, pp. 10389-10397. DOI: 10.1016/j.eswa.2011.02.068
  14. Hajto P. (2012). A Neural Economic Time Series Prediction with the Use of a Wavelet Analysis. Schedae Informaticae, 11, 115-132.
  15. Hamzacebi, C., Akay, D., & Kutay, F. (2009). Comparison of direct and iterative artificial neural network forecast approaches in multiperiodic time series forecasting. Expert Systems with Applications, 36, 3839-3844. DOI: 10.1016/j.eswa.2008.02.042
  16. Jackowska-Strumiłło L.: Hybrid Analytical and ANN-based Modelling of Temperature Sensors Nonlinear Dynamic Properties, The 6th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2011, Wroclaw, Poland, 23-25 May, Lecture Notes in Artificial Intelligence, LNAI 6678, 2011, Springer-Verlag, Part I, pp. 356-363. DOI: 10.1007/978-3-642-21219-2_45
  17. 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, ISSN 1230-3666, 1998, Vol. 6, Nr 4 (23), pp. 27-32.
  18. Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with Applications, 37(1), 479-489. DOI: 10.1016/j.eswa.2009.05.044
  19. Majhi, R., Panda, G., & Sahoo, G. (2009). Efficient prediction of exchange rates with low complexity artificial neural network models. Expert Systems with Applications, 36, 181-189. DOI: 10.1016/j.eswa.2007.09.005
  20. Murphy J. J.: Technical Analysis of Financial Markets. Wig-Press, Warsaw, 2008 (in Polish).
  21. Narendra K. S., Parthasarathy K.: Identification and control of dynamics systems using neural networks, IEEE Transactions on Neural Networks, 1990, vol.1, no. 1, pp. 4-27 DOI: 10.4236/ica.2011.23021
  22. Paluch M., Jackowska-Strumiłło L.: Prediction of closing prices on the Stock Exchange with the use of artificial neural networks. Image Processing & Communication, 2012, Vol. 17, No. 4, pp. 275-282.
  23. Rutkowski L.,: Methods and Techniques of Artificial Intelligence. PWN, Warsaw 2009 (in Polish)
  24. Sutheebanjard, P., Premchaiswadi, W.: Stock Exchange of Thailand Index Prediction Using Back Propagation Neural Networks. In: Proc. of the Second International Conference on Computer and Network Technology (ICCNT), 2010, Bangkok, pp. 377-380. DOI: 10.1109/ICCNT.2010.21
  25. Tadeusiewicz R.: Artificial Neural Networks. Warsaw 1993 (in Polish).
  26. Tadeusiewicz R.: Discovering Neural Networks. Cracow 2007 (in Polish).
  27. Tilakaratne C. D., Morris S. A., Mammadov M. A., Hurst C. P. (2007). 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)
  28. Witkowska D.: Artificial Neural Networks and statistical methods. Selected financial issues, C. H. Beck, Warsaw 2002, (in Polish)
  29. Witkowska D., & Marcinkiewicz E. (2005). Construction and Evaluation of Trading Systems: Warsaw Index Futures. International Advances in Economic Research, 11, 83-92. DOI: 10.1007/s11294-004-7496-7
  30. Zaremba A.: Stock Exachange, 2010 (in Polish)
  31. Zhou X. S., Dong M. (2004). Can fuzzy logic make technical analysis 20/20? Financial Analyst Journal, 60, 54-75. DOI: 10.2469/faj.v60.n4.2637
  32. Zieliński J.: Intelligent management systems - theory and practice. Warsaw 2000 (in Polish).
  33. Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175
Cytowane przez
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ISSN
2300-5963
Język
eng
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