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An Ensemble of Neural Networks for Stock Trading Decision Making

  • Pei-Chann Chang
  • Chen-Hao Liu
  • Chin-Yuan Fan
  • Jun-Lin Lin
  • Chih-Ming Lai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5755)

Abstract

Stock turning signals detection are very interesting subject arising in numerous financial and economic planning problems. In this paper, Ensemble Neural Network system with Intelligent Piecewise Linear Representation for stock turning points detection is presented. The Intelligent piecewise linear representation method is able to generate numerous stocks turning signals from the historic data base, then Ensemble Neural Network system will be applied to train the pattern and retrieve similar stock price patterns from historic data for training. These turning signals represent short-term and long-term trading signals for selling or buying stocks from the market which are applied to forecast the future turning points from the set of test data. Experimental results demonstrate that the hybrid system can make a significant and constant amount of profit when compared with other approaches using stock data available in the market.

Keywords

Stock turning signals Ensemble neural network PLR method Financial time series data 

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References

  1. 1.
    Kovalerchuk, B., Vityaev, E.: Data Mining in Finance. Kluwer Academic Publisher, USA (2000)zbMATHGoogle Scholar
  2. 2.
    Abu-Mostafa, Y.S., Atiya, A.F.: Introduction to financial forecasting. Applied Intelligence 6, 205–213 (1996)CrossRefGoogle Scholar
  3. 3.
    Aiken, M., Bsat, M.: Forecasting Market Trends with Neural Networks. Information Systems Management 16(4), 42–48 (1994)Google Scholar
  4. 4.
    Baba, N., Inoue, N., Asakawa, H.: Utilization of Neural Networks & s for Constructing Reliable Decision Support Systems to Deal Stocks. In: IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000), vol. 5, pp. 5111–5116 (2000)Google Scholar
  5. 5.
    Brownstone, D.: Using Percentage Accuracy to Measure Neural Network Predictions in Stock Market Movements. Neurocomputing 10, 237–250 (1996)CrossRefGoogle Scholar
  6. 6.
    Chang, P.C., Liao, T.W.: Combing SOM and Fuzzy Rule Base for Flow Time Prediction in Semiconductor Manufacturing Factory. Applied Soft Computing 6(2), 198–206 (2006a)CrossRefGoogle Scholar
  7. 7.
    Chang, P.C., Wang, Y.W.: Fuzzy Delphi and Back-Propagation Model for sales forecasting in PCB Industry. Expert Systems with Applications 30(4), 715–726 (2006b)CrossRefGoogle Scholar
  8. 8.
    Chang, P.C., Fan, C.Y., Liu, C.H.: Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points Prediction. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews (December 2008)Google Scholar
  9. 9.
    Chang, P.C., Wang, Y.W., Yang, W.N.: An Investigation of the Hybrid Forecasting Models for Stock Price Variation in Taiwan. Journal of the Chinese Institute of Industrial Engineering 21(4), 358–368 (2004)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Chen, A.S., Leung, M.T., Daouk, H.: Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index. Computers and Operations Research 30, 901–923 (2003)zbMATHCrossRefGoogle Scholar
  11. 11.
    West, D., Dellana, S., Qian, J.: Neural network ensemble strategies for financial decision applications. Computers and Operations Research 32(10), 2543–2559 (2005)zbMATHCrossRefGoogle Scholar
  12. 12.
    Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 993–1001 (1990)CrossRefGoogle Scholar
  13. 13.
    Pendharkar, P.C.: An empirical study of design and testing of hybrid evolutionary-neural approach for classiffcation. Omega-International Journal of Management Science 29, 361–374 (2001)CrossRefGoogle Scholar
  14. 14.
    Schapire, R.E.: The strength of weak learnability. Machine Learning 1990.5, 197–227 (19905)Google Scholar
  15. 15.
    Henley, W.E., Hand, D.J.: A k-nearest neighbor classi&er for assessing consumer credit risk. Statistician 996.44, 77–95 (1990)Google Scholar
  16. 16.
    Jensen, H.L.: Using neural networks for credit scoring. Managerial Finance 18, 15–26 (1992)CrossRefGoogle Scholar
  17. 17.
    Zhou, M., Wei, H.: Face Verification Using GaborWavelets and AdaBoost. In: The Eighteenth International Conference on Pattern Recognition, Hong Kong, pp. 404–407 (2006)Google Scholar
  18. 18.
    Sun, Y., Wang, Y., Wong, A.K.C.: Boosting an associative classifier. IEEE Trans. Knowledge and Data Engineering 18, 988–992 (2006)CrossRefGoogle Scholar
  19. 19.
    Jangmin, O., Lee, J.W., Park, S.B., Zhang, B.T.: Stock Trading by Modelling Price Trend with Dynamic Bayesian Networks. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 794–799. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  20. 20.
    Mallick, D., Lee, V.C.S., Ong, Y.S.: An empirical study of genetic programming generated trading rules in computerized stock trading service system. In: 5th International Conference Service Systems and Service Management - Exploring Service Dynamics with Science and Innovative Technology, ICSSSM 2008 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Pei-Chann Chang
    • 1
  • Chen-Hao Liu
    • 2
  • Chin-Yuan Fan
    • 3
  • Jun-Lin Lin
    • 1
  • Chih-Ming Lai
    • 1
  1. 1.Department of Information ManagementYuan Ze UniversityTaoyuanTaiwan
  2. 2.Department of Information ManagementKainan UniversityTaoyuanTaiwan
  3. 3.Department of Business Innovation and DevelopmentMing Dao UniversityChanghuaTaiwan

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