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Forecasting Stock Price using Wavelet Neural Network Optimized by Directed Artificial Bee Colony Algorithm

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Stock prediction with data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. This study proposes an integrated approach where Haar wavelet transform and Artificial Neural Network optimized by Directed Artificial Bee Colony algorithm are combined for the stock price prediction. The proposed approach was tested on the historical price data collected from Yahoo Finance with different companies. Furthermore, the prediction result was found satisfactorily enough as a guide for traders and investors in making qualitative decisions.
Rocznik
Tom
Strony
43--52
Opis fizyczny
Bibliogr. 23 poz., rys., tab.
Twórcy
autor
  • The University of Danang, University of Science and Technology, 54 Nguyen Luong Bang, Lien Chieu, Danang, Vietnam
autor
  • The University of Danang, University of Science and Technology, 54 Nguyen Luong Bang, Lien Chieu, Danang, Vietnam
autor
  • The University of Danang, University of Science and Technology, 54 Nguyen Luong Bang, Lien Chieu, Danang, Vietnam
autor
  • The University of Danang, University of Science and Technology, 54 Nguyen Luong Bang, Lien Chieu, Danang, Vietnam
Bibliografia
  • [1] G. Marketos, K. Pediaditakis, Y. Theodoridis, and B. Theodoulidis, “Intelligent stock market assistant using temporal data mining", in Proc. 10th Panhellenics Conf. Inform. PCI05, Volos, Greece, 2005.
  • [2] T. H. Roh, “Forecasting the volatility of stock price index", Expert Syst. with Appl., vol. 33, no. 4, pp. 916-922, 2007.
  • [3] Q. Yang and Y. Wu, “10 challenging problems in data mining research", Int. J. Inform. Technol. & Decision Making, vol. 5, no. 4, pp. 597-604, 2006.
  • [4] A. Adebiyi, C. Ayo, M. O. Adebiyi, and S. Otokiti, “Stock Price Prediction using Neural Network with Hybridized Market Indicators", J. Emerg. Trends in Comput. & Inform. Sci., vol. 3, no. 1, pp. 1-9, 2012.
  • [5] I. El-Henawy, A. Kamal, H. Abdelbary, and A. Abas, “Predicting stock index using neural network combined with evolutionary computation methods", in Proc. 7th Int. Conf. Inform. & Syst INFOS 2010, Cairo, Egypt, 2010.
  • [6] T. J. Hsieh, H. F. Hsiao, and W. C. Yeh, “Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm", Appli. Soft Comput., vol. 11, no. 2, pp. 2510-2525, 2011.
  • [7] E. Nourani, A. M. Rahmani, and A. H. Navin, “Forecasting stock prices using a hybrid artificial bee colony based neural network", in Proc. Int. Conf. Innov. Manag. & Technol. Res. ICIMTR 2012, Malacca, Malaysia, 2012, pp. 486-490.
  • [8] M. S. Kiran and O. Findik, “A directed artificial bee colony algorithm", Appl. Soft Comput., vol. 26, pp. 454-462, 2015.
  • [9] J. B. Ramsey, „The contribution of wavelets to the analysis of economic and financial data", Philosoph. Trans. Royal Soc. of London A: Mathem., Phys. & Engin. Sci., vol. 357, no. 1760, pp. 2593-2606, 1999.
  • [10] D. Karaboga and B. Basturk, „A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm", J. Global Optimiz., vol. 39, no. 3, pp. 459-471, 2007.
  • [11] S. H. Kim and S. H. Chun, „Graded forecasting using an array of bipolar predictions: application of probabilistic neural networks to a stock market index", Int. J. Forecast., vol. 14, no. 3, pp. 323-337, 1998.
  • [12] H. N. Hao, „Notice of Retraction Short-term forecasting of stock price based on genetic-neural network", in Proc. 6th Int. Conf. Nat. Comput. ICNC 2010, Yantai, China, 2010, pp. 1838-1841.
  • [13] H. Huang, M. Pasquier, and C. Quek, „Financial market trading system with a hierarchical coevolutionary fuzzy predictive model", IEEE Trans. Evolut. Comput., vol. 13, no. 1, pp. 56-70, 2009.
  • [14] M. E. Abdual-Salam, H. M. Abdul-Kader, and W. F. Abdel-Wahed, „Comparative study between Differential Evolution and Particle Swarm Optimization algorithms in training of feed-forward neural network for stock price prediction", in Proc. 7th Int. Conf. Inform. & Syst. INFOS 2010, Cairo, Egypt, 2010.
  • [15] K. J. Kim and I. Han, „Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index", Expert Syst. with Appl., vol. 19, no. 2, pp. 125-132, 2000.
  • [16] J. Kishikawa and S. Tokinaga, „Realization of feature descriptive systems for clusters by using rule generations based on the genetic programming and its applications", IEICE Trans. Fundament. Of Electron., Commun. & Comp. Sci., vol. 89, no. 12, pp. 2627-2635, 2000.
  • [17] G. Cybenko, „Approximation by superpositions of a sigmoidal function", Mathem. of Control, Sig. & Syst., vol. 2, no. 4, pp. 303-314, 1989.
  • [18] S. Shen, H. Jiang, and T. Zhang, „Stock market forecasting using machine learning algorithms", Tech. Rep., Department of Electrical Engineering Stanford University, Stanford, CA, USA, 2012.
  • [19] G. Orr, N. Schraudolph, and F. Cummins, „Overfitting and regularization" [Online]. Available: http://www.willamette.edu/~gorr/classes/cs449/overfitting.html (accessed Oct. 11, 2015).
  • [20] K. Hornik, M. Stinchcombe, and H. White, „Multilayer feed-forward networks are universal approximators", Neur. Netw., vol. 2, no. 5, pp. 359-366, 1989.
  • [21] H. Takaho, T. Arai, T. Otake, and M. Tanaka, „Prediction of the next stock price using neural network for data mining", in Proc. Int. Symp. Non-Linear Theory & its Appl. NOLTA 2002, Xi'an, China, 2002, pp. 411-414.
  • [22] A. Omidi, E. Nourani, and M. Jalili, „Forecasting stock prices using financial data mining and Neural Network", in Proc. 3rd Int. Conf. Comp. Res. & Develop. ICCRD 2011, Shanghai, China, 2011, pp. 242-246.
  • [23] Yahoo Finance [Online]. Available: http://finance.yahoo.com/ (accessed Oct. 20, 2015).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d6787548-b33d-4bee-89be-009b69cba77d
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