Advertisement

Improving Trading Systems Using the RSI Financial Indicator and Neural Networks

  • Alejandro Rodríguez-González
  • Fernando Guldrís-Iglesias
  • Ricardo Colomo-Palacios
  • Juan Miguel Gomez-Berbis
  • Enrique Jimenez-Domingo
  • Giner Alor-Hernandez
  • Rubén Posada-Gomez
  • Guillermo Cortes-Robles
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6232)

Abstract

Trading and Stock Behavioral Analysis Systems require efficient Artificial Intelligence techniques for analyzing Large Financial Datasets (LFD) and have become in the current economic landscape a significant challenge for multi-disciplinary research. Particularly, Trading-oriented Decision Support Systems based on the Chartist or Technical Analysis Relative Strength Indicator (RSI) have been published and used worldwide. However, its combination with Neural Networks as a branch of computational intelligence which can outperform previous results remain a relevant approach which has not deserved enough attention. In this paper, we present the Chartist Analysis Platform for Trading (CAST, in short) platform, a proof-of-concept architecture and implementation of a Trading Decision Support System based on the RSI and Feed-Forward Neural Networks (FFNN). CAST provides a set of relatively more accurate financial decisions yielded by the combination of Artificial Intelligence techniques to the RSI calculation and a more precise and improved upshot obtained from feed-forward algorithms application to stock value datasets.

Keywords

Neural Networks RSI Financial Indicator 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wen, Q., Yang, Z., Song, Y., Jia, P.: Automatic stock decision support system based on box theory and SVM algorithm. Expert Systems with Applications 37(2), 1015–1022 (2010)CrossRefGoogle Scholar
  2. 2.
    Wang, Y.F.: Mining stock prices using fuzzy rough set system. Expert System with Applications 24(1), 13–23 (2003)CrossRefGoogle Scholar
  3. 3.
    Chiu, D.Y., Chen, P.J.: Dynamically exploring internal mechanism of stock market by fuzzy-based support vector machines with high dimension input space and genetic algorithm. Expert Systems with Applications 36(4), 1240–1248 (2009)CrossRefGoogle Scholar
  4. 4.
    Hiemstra, C., Jones, D.: Testing for Linear and Nonlinear Granger Causality in the Stock Price-volume Relation. Journal of Finance 49(5), 1639–1664 (1994)CrossRefGoogle Scholar
  5. 5.
    Black, A.J., Mcmillan, D.G.: Non-linear predictability of value and growth stocks and economic activity. Journal of Business Finance and Accounting 31(3/4), 439–474 (2004)CrossRefGoogle Scholar
  6. 6.
    Bao, D., Yang, Z.: Intelligent stock trading system by turning point confirming and probabilistic reasoning. Expert Systems with Applications 34(1), 620–627 (2008)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Chang, P.C., Liu, C.H.: A TSK type fuzzy rule based system for stock price prediction. Expert Systems with Applications 34(1), 135–144 (2008)CrossRefGoogle Scholar
  8. 8.
    Leigh, W., Modani, N., Purvis, R., Roberts, T.: Stock market trading rule discovery using technical charting heuristics. Expert Systems with Applications 23(2), 155–159 (2002)CrossRefGoogle Scholar
  9. 9.
    Edwards, R., Magee, J.: Technical analysis of stock trends, 7th edn. Amacom, New York (1997)Google Scholar
  10. 10.
    Malkiel, B.G.: A random walk down wall street. Norton & Co., New York (1995)Google Scholar
  11. 11.
    Wang, J.L., Chan, S.H.: Stock market trading rule discovery using two-layer bias decision tree. Expert Systems with Applications 30(4), 605–611 (2006)CrossRefGoogle Scholar
  12. 12.
    Kovalerchuk, B., Vityaev, E.: Data mining in finance: advances in relational and hybrid methods. Kluwer Academic, Dordrecht (2000)zbMATHGoogle Scholar
  13. 13.
    White, H.: Economic prediction using neural networks: The case of IBM daily stock returns. In: Proceedings of the 2nd Annual IEEE Conference on Neural Networks, II, pp. 451–458 (1988)Google Scholar
  14. 14.
    Kimoto, T., Asakawa, K., Yoda, M., Takeoka, M.: Stock market prediction system with modular neural network. In: Proceedings of the International Joint Conference on Neural Networks, San Diego, CA, pp. 1–6 (1990)Google Scholar
  15. 15.
    Trippi, R.R., DeSieno, D.: Trading equity index futures with a neural network. Journal of Portfolio Management 19(1), 27–33 (1992)CrossRefGoogle Scholar
  16. 16.
    Aiken, M., Bsat, M.: Forecasting market trends with neural networks. Information Systems Management 6(4), 42–48 (1994)Google Scholar
  17. 17.
    Wang, L.P., Fu, X.J.: Data Mining with Computational Intelligence. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  18. 18.
    Yao, J., Herbert, J.P.: Financial time-series analysis with rough sets. Applied Soft Computing 9(3), 1000–1007 (2009)CrossRefGoogle Scholar
  19. 19.
    Wilder Jr., J.W.: New Concepts in Technical Trading Systems. Hunter Publishing Company, Greensboro (1978)Google Scholar
  20. 20.
    Arulampalam, G., Bouzerdoum, A.: A generalized feedforward neural network architecture for classification and regression. Neural Networks 16(5-6), 561–568 (2003)CrossRefGoogle Scholar
  21. 21.
    Fernández-Rodríguez, F., González-Martel, C., Sosvilla-Rivero, S.: On the profitability of technical trading rules based on artificial neural networks: Evidence from the Madrid stock market. Economics Letters 69(1), 89–94 (2000)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alejandro Rodríguez-González
    • 1
  • Fernando Guldrís-Iglesias
    • 1
  • Ricardo Colomo-Palacios
    • 1
  • Juan Miguel Gomez-Berbis
    • 1
  • Enrique Jimenez-Domingo
    • 1
  • Giner Alor-Hernandez
    • 2
  • Rubén Posada-Gomez
    • 2
  • Guillermo Cortes-Robles
    • 2
  1. 1.Universidad Carlos III de MadridMadridSpain
  2. 2.Division of Research and Postgraduate StudiesInstituto Tecnológico de OrizabaMéxico

Personalised recommendations