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A Review of Stock Market Prediction Using Computational Methods

  • I. E. Diakoulakis
  • D. E. Koulouriotis
  • D. M. Emiris
Part of the Applied Optimization book series (APOP, volume 74)

Abstract

This study constitutes a review of the domain of stock price forecasting, which in the last decade, has drawn particular attention, due to the intellectual challenge and the economic usefulness it presents. Approximately forty of the most important studies in this research area are herein selected and analyzed according to diverse criteria, such as the applied modeling technique, the quantitative and qualitative factors regarded as inputs in each implemented method, the basic features and parameters of the developed systems and the horizon of the prediction, to name a few. The conducted analysis outlines the methodological framework on which the development of the various systems is based upon, compares the performance of the existing systems-whenever this is possible-, and traces future research directions according to the overall results and conclusions.

Keywords

Review Stock Market Forecasting Computational Intelligence Hybrid and Qualitative Methods 

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References

  1. [BCC96]
    P.L. Belcaro, E. Canestrelli, and M. Corazza. Artificial Neural Networks Forecasting Models: an Application to the Italian Stock Market. Badania Operacyjne i Decyzjepages 30–48, 1996.Google Scholar
  2. [BM91]
    E.A. Boehm and G.H. Moore. Financial Market Forecasts and Rates of Return Based on Leading Index Signals. International Journal of Forecasting7:357–374, 1991.Google Scholar
  3. [Cha94]
    A.J. Chapman. Stock Market Trading Systems through Neural Networks: Developing a Model. International Journal of Applied Experts Systems2:88–100, 1994.Google Scholar
  4. [CYCL96]
    S.T. Chou, C. Yang, C. Chen, and F. Lai. A Rule-Based Neural Stock Trading Decision Support System. In IEEEIIAFE Conference on Computational Intelligence for Financial Engineering, pages 148–154, 1996.Google Scholar
  5. [Deb94]
    G. Deboeck, editor. Trading on the Edge, Neural, Genetic and Fuzzy Systems for Chaotic Financial Markets. John Wiley and Sons, New York, NY, 2nd edition, 1994.Google Scholar
  6. [DK96]
    R.G. Donaldson and M. Kamstra. Forecast Combining with Neural Networks. Journal of Forecasting, 15: 49–61, 1996.CrossRefGoogle Scholar
  7. [DK99]
    R.G. Donaldson and M. Kamstra. Neural Network Forecast Combining with Interaction Effects. Journal of the Franklin Institute, 336: 227–236, 1999.CrossRefGoogle Scholar
  8. [HH95]
    Y. Hiemstra and C. Haefke. Predicting Quarterly Excess Returns: Two Multilayer Perceptron Training Strategies. In 3rd International Conference on Artificial Intelligence Applications on Wall Street, pages 212–217, 1995.Google Scholar
  9. [HH96]
    Y. Hiemstra and C. Haefke. Two Multilayer Perceptron Training Strategies for Low Frequency SandP 500 Prediction. In R.R. Trippi and E. Turban, editors, Neural Networks in Finance and Investing, pages 511–523. IRWIN Professional Company, Chicago, 1996.Google Scholar
  10. [Hie93]
    Y. Hiemstra. A Neural Net to Predict Quarterly Stock Market Excess Returns Using Business Cycle Turning Points. In 1st International Workshop on Neural Networks in the Capital Markets, pages 1–14, 1993.Google Scholar
  11. [Hie96]
    Y. Hiemstra. Linear Regression versus Back-Propagation Networks to Predict Quarterly Stock Market Excess Returns. Computational Economics, 1996.Google Scholar
  12. [JL93]
    G.S. Jang and F. Lai. Intelligent Stock Market Prediction System using Dual Adaptive-Structure Neural Networks. In 2nd International Conference on Artificial Intelligence Applications on Wall Street, pages 88–93, 1993.Google Scholar
  13. [JL94]
    G.S. Jang and E Lai. Intelligent Trading of an Emerging Market. In G. Deboeck, editor, Trading on the Edge, pages 80–101. John Wiley and Sons, New York, NY, 1994.Google Scholar
  14. [JLJ+91]
    G.S. Jang, F. Lai, B.W. Jiang, C.C. Pan, and L.H. Chien. An Intelligent Stock Portfolio Management System Based on Short-Term Trend Prediction using Dual-Module Neural Networks. In International Conference on Artificial Neural Networks, pages 447–452, 1991.Google Scholar
  15. [JLJC91]
    G.S. Jang, F. Lai, B.W. Jiang, and L.H. Chien. An Intelligent Trend Prediction and Reversal Recognition System using Dual- Module Neural Networks. In 1st International Conference on Artificial Intelligence Applications on Wall Street, pages 42–51, 1991.CrossRefGoogle Scholar
  16. [KAYT90]
    T. Kimoto, K. Asakawa, M. Yoda, and M. Takeoka. Stock Market Prediction System with Modular Neural Networks. In Proceedings of the IEEE International Joint Conference in Neural Networks, pages 1–6, 1990.Google Scholar
  17. [KC98]
    S.H. Kim and S.H. Chun. Graded Forecasting using an Array if Bipolar Predictions: Application of Probabilistic Neural Networks to a Stock Market Index. International Journal of Forecasting, 14: 323–337, 1998.CrossRefGoogle Scholar
  18. [KEDZ01]
    D.E. Koulouriotis, D.M. Emiris, I.E. Diakoulakis, and C. Zopounides. Comparative Analysis and Evaluation of Intelligent Methodologies for Short-Term Stock Price Forecasting. Fuzzy Economic Review,2001. (to appear).Google Scholar
  19. [KGW96]
    L. Kryzanowski, M. Galler, and D.W. Wright. Using Artificial Neural Networks to Pick Stocks. In R.R. Trippi and E. Turban, editors, Neural Networks in Finance and Investing; pages 525541. IRWIN Professional Company, Chicago, 1996.Google Scholar
  20. [KIFN97]
    K. Kohara, T. Ishikawa, Y. Fukuhara, and Y. Nakamura. Stock Price Prediction using Prior Knowledge and Neural Networks. International Journal of Intelligent Systems in Accounting, Finance and Management, 6: 11–22, 1997.Google Scholar
  21. [KLL96]
    R.J. Kuo, L.C. Lee, and C.F. Lee. Intelligent Stock Market Forecasting System Through ANN and Fuzzy Delphi. In World Congress on Neural Networks, pages 886–889, 1996.Google Scholar
  22. [KW971.
    F. Kai and X. Wenhua. Training Neural Network with Genetic Algorithms for Forecasting the Stock Price Index. In IEEE In- ternational Conference on Intelligent Processing Systems, pages 401–403, China, 1997.Google Scholar
  23. [KWL94]
    W. Kreesuradej, D. Wunsch, and M. Lane Time-Delay Neural Network for Small Time Series Data Sets. In World Congress on Neural Networks, June 1994.Google Scholar
  24. [LDC00]
    M.T. Leung, H. Daouk, and A.S. Chen. Forecasting Stock Indices: a Comparison of Classification and Level Estimation Models. International Journal of Forecasting, 16: 173–190, 2000.CrossRefGoogle Scholar
  25. [LL97]
    N.K. Liu and K.K. Lee. An Intelligent Business Advisor System for Stock Investment. Expert Systems, 14: 129–139, 1997.CrossRefGoogle Scholar
  26. [MBB94]
    G.H. Moore, E.A. Boehm, and A. Banerji. Using Economic Indicators to Reduce Risk in Stock Market Investments. International Journal of Forecasting, 10: 405–417, 1994.CrossRefGoogle Scholar
  27. [MW00]
    L. Motiwalla and M. Wahab. Predictable Variation and Profitable Trading of US Equities: a Trading Simulation using Neural Net- works. Computers and Operations Research, 27: 1111–1129, 2000.CrossRefGoogle Scholar
  28. [PG90]
    L. Pau and G. Gianotti. Technical Analysis for Securities Trading. Economic and Financial KB Processing, 1990.CrossRefGoogle Scholar
  29. [Phi96]
    H. Philips. Adaptive Forecasting. In World Congress on Neural Networks, pages 504–507, 1996.Google Scholar
  30. [Pod98]
    T. Podding. Developing Forecasting Models for Integrated Financial Markets using Artificial Neural Networks. Neural Network World, pages 65–80, January 1998.Google Scholar
  31. [QS99]
    T.S. Quah and B. Srinivasan. Improving Returns on Stock Investment through Neural Network Selection. Expert Systems with Applications, 17: 295–301, 1999.CrossRefGoogle Scholar
  32. [RZF94]
    N.A. Refenes, A. Zapranis, and G. Francis. Stock Performance Modeling using Neural Networks: A Comparative Study with Regression Models. Neural Networks, 7: 375–388, 1994.CrossRefGoogle Scholar
  33. [SD96]
    K. Schierholt and C.H. Dagli. Stock Market Prediction using Different Neural Network Classification Architectures. In IEEEIIAFE Conference on Computational Intelligent for Financial Engineering, pages 72–78, 1996.Google Scholar
  34. [SDK+92]
    K. Siriopoulos, G. Doukidis, G. Karakoulas, T. Liakopoulou, and E. Skevofilax. An Intelligent Advisor System for Stock Market Investments. AM-Bulletin of Greeks Banks Association, 3: 7883, 1992. (in Greek).Google Scholar
  35. [SPW96]
    E. Saad, D. Prokhorov, and D. Wunsch. Advanced Neural Network Training Methods for Low False Alarm Stock Trend Prediction. In International Conference in Neural Networks, volume 9, pages 2021–2026, 1996.Google Scholar
  36. [SPW98]
    E. Saad, D. Prokhorov, and D. Wunsch. Comparative Study of Stock Trend Prediction using Time Delay, Recurrent and Probabilistic Neural Networks. IEEE Transactions on Neural Networks, 9: 1456–1469, 1998.CrossRefGoogle Scholar
  37. [SZ]
    K. Siriopoulos and D. Zaharakis. Use and Effectiveness of Box and Jenkins Methodology in Athens Stock Market Prediction. (in Greek).Google Scholar
  38. [TPW95]
    H. Tan, D. Prokhorov, and D. Wunsch. Probabilistic and Time-Delay Neural-Network Techniques for Conservative Short-Term Stock Trend Prediction. In World Congress on Neural Networks1995.Google Scholar
  39. [WWGQ92]
    F.S. Wong, P.Z. Wang, T.H. Goh, and B.K. Quek. Fuzzy Neural Systems for Stock Selection. Financial Analysts Journal, pages 47–52, Jan-Feb 1992.Google Scholar
  40. [YS95]
    Y. Yoon and G. Swales. Predicting Stock Price Performance: A Neural Network Approach. In IEEE 24thAIC of Systems Sciencespages 156–162, 1995.Google Scholar
  41. [Zem99]
    S. Zemke. Nonlinear Index Prediction. Physica, 269: 177–183, 1999.Google Scholar
  42. [ZL93]
    Y. Ye Zhongxing and G. Liting. A Hybrid Cognition System: Application to Stock Market Analysis. In International Joint Conference on Neural Networkspages 3000–3003, 1993.Google Scholar
  43. [ZM82]
    V. Zarnowitz and G.H. Moore. Sequential Signals of Recession and Recovery. Journal of Business55:57–85, 1982.Google Scholar
  44. [ZXM97]
    P. Zuohong, L. Xiaodi, and O. Mejabi. A Neural Fuzzy System for Forecasting. In 30th Hawaiian International Conference on System Sciences, pages 549–5583, 1997.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • I. E. Diakoulakis
    • 1
  • D. E. Koulouriotis
    • 1
  • D. M. Emiris
    • 1
  1. 1.Department of Production Engineering and ManagementTechnical University of CreteChaniaGreece

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