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The Synthesis of Fuzzy Logic and DST in Stock Trading Decision Support Systems

  • Ludmila Dymowa
Part of the Intelligent Systems Reference Library book series (ISRL, volume 6)

Abstract

Modern computerized stock trading systems (mechanical trading systems) are based on the simulation of the decision making process and generate advice for traders to buy or sell stocks or other financial tools taking into account the price history, technical analysis indicators, accepted rules of trading and so on. There are many approaches to building stock trading systems proposed in the literature. The applications of the methods of soft computing in this field of researches are analysed in Chapter 2. It is noted that the source of many failures when building really profitable stock trading systems is the ignoring of human factor. It was recognized in [32], after obtaining a negative result that “The trading system loses money and gets a negative Sharpe Ratio. We believe that if expert’s experience is available, it will generate more promising results”

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References

  1. 1.
    Achelis, S.B.: Technical Analysis from A to Z: Covers Every Trading Tool-From the Absolute Breadth Index to the Zig Zag. Probus Publisher, Chicago (1995)Google Scholar
  2. 2.
    Baba, N., Kozaki, M.: An intelligent forecasting system of stock price using neural networks. In: Proceedings of IJCNN 1992, pp. 317–377 (1992)Google Scholar
  3. 3.
    Bayes, T.: An assay toward solving a problem in the doctrine of chances. Phil. Trans. Roy. Soc. (London) 53, 370–418 (1763)Google Scholar
  4. 4.
    Binaghi, E., Madella, P.: Fuzzy Dempster- Shafer reasoning for rule-based classifiers. Intelligent Syst. 14, 559–583 (1999)CrossRefMATHGoogle Scholar
  5. 5.
    Binaghi, E., Gallo, I., Madella, P.: A neural model for fuzzy Dempster-Shafer classifiers. International Journal of Approximate Reasoning 25, 89–121 (2000)CrossRefMathSciNetMATHGoogle Scholar
  6. 6.
    Brock, W., Lakonishok, J., LeBaron, B.: Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance 47, 1731–1764 (1992)CrossRefGoogle Scholar
  7. 7.
    Dacorogna, M.M., Muller, U.A., Jost, C., Pictet, O.V., Olsen, R.B., Ward, J.R.: Heterogeneous real-time trading strategies in the foreign exchange market. The European Journal of Finance 1, 383–403 (1995)CrossRefGoogle Scholar
  8. 8.
    Dimova, L., Sevastianov, P., Sevastianov, D.: MCDM in a fuzzy setting: investment projects assessment application. International Journal of Production Economics 100, 10–29 (2006)CrossRefGoogle Scholar
  9. 9.
    Dourra, H., Siy, P.: Investment using technical analysis and fuzzy logic. Fuzzy Sets and Systems 127, 221–240 (2002)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Dymova, L., Sevastianov, P., Bartosiewicz, P.: A new approach to the rule-base evidential reasoning: stock trading expert system application. Expert Systems with Applications 37, 5564–5576 (2010)CrossRefGoogle Scholar
  11. 11.
    Fodor, J.C.: On fuzzy implications operators. Fuzzy Sets and Systems 42, 293–300 (1991)CrossRefMathSciNetMATHGoogle Scholar
  12. 12.
    Gorzalczany, M.B., Piasta, Z.: Neuro-fuzzy approach versus rough-set inspired methodology for intelligent decision support. Information Sciences 120, 45–68 (1999)CrossRefGoogle Scholar
  13. 13.
    Haefke, C., Helmenstein, C.: Predicting stock market averages to enhance profitable trading strategies. In: Proceedings of the Third International Conference on Neural Networks in the Capital Markets, London, pp. 378–389 (2000)Google Scholar
  14. 14.
    Hodges, J., Bridges, S., Sparrow, C., Wooley, B., Tang, B., Jun, C.: The development of an expert system for the characterization of containers of contaminated waste. Expert Systems with Applications 17, 167–181 (1999)CrossRefGoogle Scholar
  15. 15.
    Ishizuka, M., Fu, K.S., Yao, J.: Inference procedure and uncertainty for the problem reduction method. Inform. Sci. 28, 179–206 (1982)CrossRefMathSciNetMATHGoogle Scholar
  16. 16.
    Kaufmann, A., Gupta, M.: Introduction to Fuzzy Arithmetic-Theory and Applications. Van Nostrand Reinhold, New York (1985)MATHGoogle Scholar
  17. 17.
    Kendall, S.M., Ord, K.: Time series, 3rd edn. Oxford University Press, New York (1990)MATHGoogle Scholar
  18. 18.
    Kim, K.J., Han, I.: Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications 19, 125–132 (2000)CrossRefGoogle Scholar
  19. 19.
    Kuo, R.J., Chen, C.H., Hwang, Y.C.: An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets and Systems 118, 21–45 (2001)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Mahfoud, S., Mani, G.: Financial forecasting using genetic algorithms. Applications of Artificial Intelligence 10, 543–566 (1996)CrossRefGoogle Scholar
  21. 21.
    Mamdani, E., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Mach. Studies 1, 1–13 (1975)CrossRefGoogle Scholar
  22. 22.
    Mehta, K., Bhattacharyy, S.: Adequacy of training data for evolutionary mining of trading rules. Decision Support Systems 37, 461–474 (2004)CrossRefGoogle Scholar
  23. 23.
    Parson, S.: Current approaches to handling imperfect information in data and knowledge bases. IEEE Transactions on Knowledge and Date Engineering 8, 353–372 (1996)CrossRefGoogle Scholar
  24. 24.
    Pawlak, Z.: Rough Sets. International Journal of Information and Computer Science 11, 145–172 (1982)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Pictet, O.V., Docorogna, M.M., Chopard, B., Shirru, M., Tomassini, M.: Using genetic algorithms for robust optimization in financial applications. Neural Network World 5, 573–587 (1995)Google Scholar
  26. 26.
    Rutkowska, D.: Neuro-fuzzy architectures and hybrid learning. Physica-Verlag, Heidelberg (2002)MATHGoogle Scholar
  27. 27.
    Rutkowski, L.: Flexible Neuro-Fuzzy Systems: Structures, Learning and Performance Evaluation. Kluwer, Boston (2004)MATHGoogle Scholar
  28. 28.
    Rutkowski, L., Cpałka, K.: Designing and Learning of Adjustable Quasi-Triangular Norms With Applications to Neuro-Fuzzy Systems. IEEE Transaction on Fuzzy Systems 13, 140–151 (2005)CrossRefGoogle Scholar
  29. 29.
    Santiprabhob, P., Nguyen, H.T., Pedrycz, W., Kreinovich, V.: Logic-Motivated Choice of Fuzzy Logic Operators. In: FUZZ-IEEE, pp. 646–649 (2001)Google Scholar
  30. 30.
    Sevastianov, P., Dymova, L.: Synthesis of fuzzy logic and Dempster-Shafer Theory for the simulation of the decision-making process in stock trading systems. Mathematics and Computers in Simulation 80, 506–521 (2009)CrossRefMathSciNetMATHGoogle Scholar
  31. 31.
    Sevastjanov, P., Figat, P.: Aggregation of aggregating modes in MCDM, Synthesis of Type 2 and Level 2 fuzzy sets. Omega 35, 505–523 (2007)CrossRefGoogle Scholar
  32. 32.
    Shen, L., Loh, H.T.: Applying rough sets to market timing decisions. Decision Support Systems 37, 583–597 (2004)CrossRefGoogle Scholar
  33. 33.
    Straszecka, E.: Combining uncertainty and imprecision in models of medical diagnosis. Information Sciences 176, 3026–3059 (2006)CrossRefMathSciNetGoogle Scholar
  34. 34.
    Sun, R.: Robust reasoning: integrating rule-based and similarity based reasoning. Artificial Intelligence 75, 241–295 (1995)CrossRefGoogle Scholar
  35. 35.
    Torn, A., Zilinskas, A.: Global optimization. Springer, Berlin (1989)Google Scholar
  36. 36.
    Tsumoto, S.: Automated extraction of hierarchical decision rules from clinical databases using rough set model. Expert Systems with Applications 24, 189–197 (2003)CrossRefGoogle Scholar
  37. 37.
    Turksen, I.B., Kreinovich, V., Yager, R.R.: A new class of fuzzy implications (axioms of fuzzy implication revisited). Fuzzy Sets and Systems 100, 267–272 (1998)CrossRefMathSciNetGoogle Scholar
  38. 38.
    Wadman, D., Schneider, M., Schnaider, E.: On the use of interval mathematics in fuzzy expert system. International Journal of Intelligent Systems 9, 241–259 (1994)CrossRefGoogle Scholar
  39. 39.
    Wang, Y.-F.: Mining stock price using fuzzy rough set system. Expert Systems with Applications 24, 13–23 (2003)CrossRefGoogle Scholar
  40. 40.
    Warren, A.W.: Data Filtering For Trend Channel Analysis. Stocks and Commodities 11, 103–111 (1993)Google Scholar
  41. 41.
    Xu, D.-L., Liu, J., Yang, J.-B., Liu, G.-P., Wang, J., Jenkinson, I., Ren, J.: Inference and learning methodology of belief-rule-based expert system for pipeline leak detection. Expert Systems with Applications 32, 103–113 (2007)CrossRefGoogle Scholar
  42. 42.
    Yager, R.R.: Multiple objective decision-making using fuzzy sets. International Journal of Man-Machine Studies 9, 375–382 (1979)CrossRefGoogle Scholar
  43. 43.
    Yager, R.R.: Generalized probabilities of fuzzy events from belief structures. Inform. Sci. 28, 45–62 (1982)CrossRefMathSciNetMATHGoogle Scholar
  44. 44.
    Yager, R.R., Filev, D.P.: Including probabilistic uncertainty in fuzzy logic controller modeling using Dempster-Shafer theory. IEEE Trans. Syst., Man Cybernet. 25, 1221–1230 (1995)CrossRefGoogle Scholar
  45. 45.
    Yang, J.B.: Rule and utility based evidential reasoning approach for multi-attribute decision analysis under uncertainties. European Journal of Operational Research 131, 31–61 (2001)CrossRefMathSciNetMATHGoogle Scholar
  46. 46.
    Yang, J.B., Liu, J., Wang, J., Sii, H.S., Wang, H.: Belief rule-base inference methodology using the evidential reasoning approach - RIMER. IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans 36, 266–285 (2006)CrossRefGoogle Scholar
  47. 47.
    Yang, J.B., Liu, J., Xu, D.L., Wang, J., Wang, H.: Optimization Models for Training Belief-Rule-Based Systems. IEEE Transactions on Systems, Man and Cybernetics, Part A-Systems and Humans 37, 569–585 (2007)CrossRefGoogle Scholar
  48. 48.
    Yang, J.B., Xu, D.L.: On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 32, 289–304 (2002)CrossRefGoogle Scholar
  49. 49.
    Yang, J.B., Xu, D.L.: Nonlinear information aggregation via evidential reasoning in multi-attribute decision analysis under uncertainty. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 32, 376–393 (2002)CrossRefGoogle Scholar
  50. 50.
    Yen, J.: Generalizing the Dempster-Shafer theory to fuzzy sets. IEEE Trans. Syst., Man Cybernet. 20, 559–570 (1990)CrossRefMathSciNetMATHGoogle Scholar
  51. 51.
    Zadeh, L.A.: Fuzzy Sets. Information and Control 8, 338–353 (1965)CrossRefMathSciNetMATHGoogle Scholar
  52. 52.
    Zimmerman, H.J., Zysno, P.: Latent connectives in human decision making. Fuzzy Sets and Systems 4, 37–51 (1980)CrossRefGoogle Scholar

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  • Ludmila Dymowa

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