Neural Computing & Applications

, Volume 13, Issue 3, pp 255–260 | Cite as

Stock market prediction using artificial neural networks with optimal feature transformation

  • Kyoung-jae KimEmail author
  • Won Boo Lee
Original Article


This paper compares a feature transformation method using a genetic algorithm (GA) with two conventional methods for artificial neural networks (ANNs). In this study, the GA is incorporated to improve the learning and generalizability of ANNs for stock market prediction. Daily predictions are conducted and prediction accuracy is measured. In this study, three feature transformation methods for ANNs are compared. Comparison of the results achieved by a feature transformation method using the GA to the other two feature transformation methods shows that the performance of the proposed model is better. Experimental results show that the proposed approach reduces the dimensionality of the feature space and decreases irrelevant factors for stock market prediction.


Feature transformation Genetic algorithms Fuzzification Artificial neural networks Stock market prediction 



This work is supported by the Dongguk University Research Fund.


  1. 1.
    Bigus JP (1996) Data mining with neural networks: solving business problems from application development to decision support. McGraw-Hill, New YorkGoogle Scholar
  2. 2.
    Liu H, Motoda H (1998) Feature transformation and subset selection. IEEE Intell Syst Appl 13(2):26–28CrossRefGoogle Scholar
  3. 3.
    Kimoto T, Asakawa K, Yoda M, Takeoka M (1990) Stock market prediction system with modular neural network. In: Proceedings of the international joint conference on neural networks (IJCNN), Washington, DC, June 1990, pp 1–6Google Scholar
  4. 4.
    Kamijo K, Tanigawa T (1990) Stock price pattern recognition: a recurrent neural network approach. In: Proceedings of the international joint conference on neural networks (IJCNN), Washington, DC, June 1990, pp 215–221Google Scholar
  5. 5.
    Ahmadi H (1990) Testability of the arbitrage pricing theory by neural networks. In: Proceedings of the international joint conference on neural networks (IJCNN), Washington, DC, June 1990, pp 385–393Google Scholar
  6. 6.
    Yoon Y, Swales G (1991) Predicting stock price performance: a neural network approach. In: Proceedings of the 24th annual Hawaii international conference on system sciences (HICSS-24), Kauai, Hawaii, January 1991, pp 156–162Google Scholar
  7. 7.
    Trippi RR, DeSieno D (1992) Trading equity index futures with a neural network. J Portfolio Manag 19:27–33Google Scholar
  8. 8.
    Choi JH, Lee MK, Rhee MW (1995) Trading S&P 500 stock index futures using a neural network. In: Freedman RS (ed) Proceedings of the 3rd annual international conference on artificial intelligence applications on Wall Street, New York, June 1995, pp 63–72Google Scholar
  9. 9.
    Tsaih R, Hsu Y, Lai CC (1998) Forecasting S&P 500 stock index futures with a hybrid AI system. Decis Support Syst 23(2):161–174CrossRefGoogle Scholar
  10. 10.
    Kohara K, Ishikawa T, Fukuhara Y, Nakamura Y (1997) Stock price prediction using prior knowledge and neural networks. Int J Intell Syst Accounting Finance Manage 6(1):11–22CrossRefGoogle Scholar
  11. 11.
    Lawrence S, Tsoi AC, Giles CL (1996) Noisy time series prediction using symbolic representation and recurrent neural network grammatical inference. Technical report UMIACS-TR-96–27 and CS-TR-3625, Institute for Advanced Computer Studies, University of MarylandGoogle Scholar
  12. 12.
    Sexton RS, Alidaee B, Dorsey RE, Johnson JD (1998) Global optimization for artificial neural networks: a tabu search application. Eur J Operational Res 106(2–3):570–584Google Scholar
  13. 13.
    Sexton RS, Dorsey RE, Johnson JD (1998) Toward global optimization of neural networks: a comparison of the genetic algorithm and backpropagation. Decis Support Syst 22(2):171–185CrossRefGoogle Scholar
  14. 14.
    Adeli H, Hung S (1995) Machine learning: neural networks, genetic algorithms, and fuzzy systems. Wiley, New YorkGoogle Scholar
  15. 15.
    Bauer RJ (1994) Genetic algorithms and investment strategies. Wiley, New YorkGoogle Scholar
  16. 16.
    Davis L (1994) Genetic algorithms and financial applications. In: Deboeck GJ (ed) Trading on the edge. Wiley, New York, pp 133–147Google Scholar
  17. 17.
    Hertz A, Kobler D (2000) A framework for the description of evolutionary algorithms. Eur J Operational Res 126(1):1–12CrossRefzbMATHGoogle Scholar
  18. 18.
    Wong F, Tan C (1994) Hybrid neural, genetic, and fuzzy systems. In: Deboeck GJ (ed) Trading on the edge. Wiley, New York, pp 243–261Google Scholar
  19. 19.
    Liu H, Setiono R (1996) Dimensionality reduction via discretization. Knowl Based Syst 9(1):67–72CrossRefzbMATHGoogle Scholar
  20. 20.
    Dash M, Liu H (1997) Feature selection methods for classifications. Intell Data Anal Int J 1(3):131–156CrossRefzbMATHGoogle Scholar
  21. 21.
    Dougherty J, Kohavi R, Sahami M (1995) Supervised and unsupervised discretization of continuous features. In: Prieditis A, Russell S (eds) Proceedings of the 12th international conference on machine learning (ICML), Tahoe City, California, July 1995. Morgan Kaufmann, San Francisco, pp 194–202Google Scholar
  22. 22.
    Scott PD, Williams KM, Ho KM (1997) Forming categories in exploratory data analysis and data mining. In: Liu X, Cohen PR, Berthold MR (1997) Proceedings of the 2nd international symposium on advances in intelligent data analysis (IDA’97), London, UK, August 1997. Springer, Berlin Heidelberg New York, pp 235–246Google Scholar
  23. 23.
    Susmaga R (1997) Analyzing discretizations of continuous attributes given a monotonic discrimination function. Intell Data Anal Int J 1(3):157–179CrossRefGoogle Scholar
  24. 24.
    Raymer ML, Punch WF, Goodman ED, Kuhn LA, Jain AK (2000) Dimensionality reduction using genetic algorithms. IEEE Trans Evol Comput 4(2):164–171CrossRefGoogle Scholar
  25. 25.
    Chou ST, Hsu H, Yang C, Lai F (1997) A stock selection DSS combining AI and technical analysis. Ann Operations Res 75:335–353CrossRefzbMATHGoogle Scholar
  26. 26.
    Klimasauskas CC (1992) Hybrid fuzzy encodings for improved backpropagation performance. Advanced technology for developers, September 1992, pp 13–16Google Scholar
  27. 27.
    Liu H, Motoda H (1998) Feature selection for knowledge discovery and data mining. Kluwer, Norwell, MassachusettsGoogle Scholar
  28. 28.
    Achelis SB (1995) Technical analysis from A to Z. Probus Publishing, Chicago, IllinoisGoogle Scholar
  29. 29.
    Murphy JJ (1986) Technical analysis of the futures markets: a comprehensive guide to trading methods and applications. New York Institute of Finance, Prentice-Hall, New YorkGoogle Scholar
  30. 30.
    Choi J (1995) Technical indicator. Jinritamgu Publishing, Seoul, KoreaGoogle Scholar
  31. 31.
    Chang J, Jung Y, Yeon K, Jun J, Shin D, Kim H (1996) Technical indicators and analysis methods. Jinritamgu Publishing, Seoul, KoreaGoogle Scholar
  32. 32.
    Edwards RD, Magee J (1997) Technical analysis of stock trends, 7th edn. Amacom, New YorkGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2004

Authors and Affiliations

  1. 1.Department of Information SystemsDongguk UniversitySeoulKorea

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