Skip to main content
Log in

Fuzzy Inference-Enhanced VC-DRSA Model for Technical Analysis: Investment Decision Aid

International Journal of Fuzzy Systems Aims and scope Submit manuscript

Cite this article

Abstract

To support investment decision based on technical analysis (TA), this study aims to retrieve the knowledge or rules of various indicators by a hybrid soft computing model. Although the validity of TA has been examined extensively by various statistical methods in literature, previous studies mainly explored the effectiveness of each technical indicator separately; therefore, a practical approach that may consider the inconsistency of various technical indicators simultaneously and the down-side risk of an investment decision is still underexplored. Thus, a hybrid model—by constructing a variable consistency dominance-based rough set approach (VC-DRSA) information system with the fuzzy inference-enhanced discretization of signals—is proposed, to retrieve the imprecise patterns from commonly adopted technical indicators. At the first stage, the trading signals (i.e., buy, neutral, or sell) are preprocessed in two groups: straight-forward signals and complicated signals. The straight-forward technical indicators (i.e., for signals that are decided by precise rules) are suggested by domain experts, and the buy-in signals are simulated by several trading strategies to examine the outcomes of each indicator. As for those complicated signals (i.e., for signals that require imprecise judgments with perceived feeling of domain experts to identify patterns), a fuzzy inference technique is incorporated to enhance the discretization of signals; those signals are also simulated by the aforementioned trading strategies to obtain the corresponding results. At the second stage, the trading signals generated by each technical indicator and their pertinent results from the previous stage are combined for VC-DRSA modeling to gain decision rules. To illustrate the proposed model, the weighted average index of the Taiwan stock market was examined from mid/2002 to mid/2014, and a set of decision rules with nearly 80 % classification accuracy (both in the training and the testing sets) were obtained in this empirical case. The findings suggest that several technical indicators should be considered simultaneously, and the retrieved rules (knowledge) have practical implications for investors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Lo, A.W., Mamaysky, H., Wang, J.: Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. J. Financ. 55(4), 1705–1770 (2000)

    Article  Google Scholar 

  2. Menkhoff, L.: The use of technical analysis by fund managers: international evidence. J. Bank. Financ. 34(11), 2573–2586 (2010)

    Article  Google Scholar 

  3. Shiller, R.J.: From efficient market theory to behavioral finance. J. Econ. Perspect. 17(1), 19–33 (2003)

    Article  Google Scholar 

  4. Froot, K.A., Scharfstein, D.S., Stein, J.C.: Herd on the street: informational inefficiencies in a market with short-term speculation. J. Financ. 47(4), 1461–1484 (1992)

    Article  Google Scholar 

  5. Park, C.H., Irwin, S.H.: What do we know about the profitability of technical analysis? J. Econ. Surv. 21(4), 786–826 (2007)

    Article  Google Scholar 

  6. Wei, L.Y., Chen, T.L., Ho, T.H.: A hybrid model based on adaptive-network-based fuzzy inference system to forecast Taiwan stock market. Expert Syst. Appl. 38(11), 13625–13631 (2011)

    Google Scholar 

  7. Enke, D., Thawornwong, S.: The use of data mining and neural networks for forecasting stock market returns. Expert Syst. Appl. 29(4), 927–940 (2005)

    Article  Google Scholar 

  8. Ticknor, J.L.: A Bayesian regularized artificial neural network for stock market forecasting. Expert Syst. Appl. 40(4), 5501–5506 (2013)

    Article  Google Scholar 

  9. Brown, M.S., Dynamic-radius species-conserving genetic algorithm for the financial forecasting of Dow Jones index stocks. Mach. Learn. Data Min. Pattern Recognit. Lecture Notes in Computer Science, vol. 7988, pp. 27–41 (2013)

  10. Rosillo, R., Giner, J., Fuente, D.D.: Stock market simulation using support vector machines. J. Forecast. 33(6), 488–500 (2014)

    Article  MathSciNet  Google Scholar 

  11. Kim, K.J.: Financial time series forecasting using support vector machines. Neurocomputing 55(1–2), 307–319 (2003)

    Article  Google Scholar 

  12. Tay, F.E.H., Cao, L.: Application of support vector machines in financial time series forecasting. Omega 29(4), 309–317 (2001)

    Article  Google Scholar 

  13. Atsalakis, G.S., Valavanis, K.P.: Surveying stock market forecasting techniques—Part II: soft computing methods. Expert Syst. Appl. 36(3 (part 2)), 5932–5941 (2009)

    Article  Google Scholar 

  14. Shen, K.Y.: Implementing value investing strategy by artificial neural network. Int. J. Bus. Inf. Technol. 1(1), 12–22 (2011)

    Google Scholar 

  15. Dourra, H., Siy, P.: Investment using technical analysis and fuzzy logic. Fuzzy Sets Syst. 127(2), 221–240 (2002)

    Article  MathSciNet  Google Scholar 

  16. Zhou, X.S., Dong, M.: Can fuzzy logic make technical analysis 20/20? Financ. Anal. J. 60(4), 54–75 (2004)

    Article  Google Scholar 

  17. Wang, J.L., Chan, S.H.: Stock market trading rule discovery using two-layer bias decision tree. Expert Syst. Appl. 30(4), 605–611 (2006)

    Article  Google Scholar 

  18. Boyacioglu, M.A., Avci, D.: An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange. Expert Syst. Appl. 37(12), 7908–7912 (2010)

    Article  Google Scholar 

  19. Cheng, C.H., Chen, T.L., Wei, L.Y.: A hybrid model based on rough set theory and genetic algorithms for stock price forecasting. Inf. Sci. 18(9), 1610–1629 (2010)

    Article  Google Scholar 

  20. Gradojevic, N., Gencay, R.: Fuzzy logic, trading uncertainty and technical trading. J. Bank. Financ 37(2), 578–586 (2013)

    Article  Google Scholar 

  21. Taylor, N.: The rise and fall of technical trading rule success. J. Bank. Finance 40, 286–302 (2014)

    Article  Google Scholar 

  22. Menkhoff, L., Taylor, M.P.: The obstinate passion of foreign exchange professionals: technical analysis. J. Econ. Lit. 45(4), 936–972 (2007)

    Article  Google Scholar 

  23. Lam, M.: Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decis. Support Syst. 37(4), 567–581 (2004)

    Article  Google Scholar 

  24. Fernando, F.R., Christian, G.M., Simon, S.R.: On the profitability of technical trading rules based on artificial neural networks: evidence from the Madrid stock market. Econ. Lett. 69(1), 89–94 (2000)

    Article  Google Scholar 

  25. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  26. Precup, R.E., Hellendoom, H.: A survey on industrial applications of fuzzy control. Comput. Ind. 62(3), 213–226 (2011)

    Article  Google Scholar 

  27. Turskis, Z., Zavadskas, E.K.: Multiple criteria decision making (MCDM) methods in economics: an overview. Technol. Econ. Dev. Econ. 2, 397–427 (2011)

    Google Scholar 

  28. Liou, J.H., Tzeng, G.H.: Comments on “Multiple criteria decision making (MCDM) methods in economics: an overview”. Technol. Econ. Dev. Econ. 18(4), 672–695 (2012)

    Article  Google Scholar 

  29. Tzeng, G.H., Huang, J.J.: Multiple attribute decision making: methods and applications. CRC Press, Boca Raton (2011)

    Google Scholar 

  30. Tzeng, G.H., Huang, J.J.: Fuzzy multiple objective decision making. CRC Press, Boca Raton (2013)

    Book  Google Scholar 

  31. Peng, K.H., Tzeng, G.H.: A hybrid dynamic MADM model for problems-improvement in economics and business. Technol. Econ. Dev. Econ. 19(4), 638–660 (2013)

    Article  Google Scholar 

  32. Liou, J.H., Chuang, Y.C., Tzeng, G.H.: A fuzzy integral-based model for supplier evaluation and improvement. Inf. Sci. 266(10), 199–217 (2014)

    Article  MathSciNet  Google Scholar 

  33. Shen, K.Y.: Implementing value investing strategy through an integrated fuzzy-ANN model. J. Theor. Appl. Inf. Technol. 51(1), 150–157 (2013)

    Google Scholar 

  34. Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

    Article  Google Scholar 

  35. Shen, K.Y., Tzeng, G.H.: DRSA-based neuro-fuzzy inference systems for the financial performance prediction of commercial banks. Int. J. Fuzzy Syst. 16(2), 173–183 (2014)

    Google Scholar 

  36. XQ global winners: SysJust Co. Ltd. http://www.sysjust.com.tw (2014). Accessed June 2014

  37. Achelis, S.B.: Technical Analysis from A to Z. McGraw Hill, New York (2001)

    Google Scholar 

  38. Mamdani, E.H., Assilian, S.: An experiment in linguistic synhesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7(1), 1–13 (1975)

    Article  MATH  Google Scholar 

  39. Kandel, A., Li, L., Cao, Z.: Fuzzy inference and its applicability to control systems. Fuzzy Sets Syst. 48(1), 99–111 (2006)

    Article  MathSciNet  Google Scholar 

  40. Fernandez, A., Herrera, F.: Linguistic fuzzy rules in data mining: follow-up Mamdani fuzzy modeling principle. Comb. Exp. Theory (Stud. Fuzziness Soft Comput.) 271, 103–122 (2012)

    Article  Google Scholar 

  41. Opricovic, S., Tzeng, G.H.: Defuzzification within a multicriteria decision model. Int. J. Uncertain. Fuzziness Knowl-Based Syst. 11(5), 635–652 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  42. Greco, S., Matarazzo, B., Slowinski, R.: Rough sets theory for multicriteria decision analysis. Eur. J. Oper. Res. 129(1), 1–47 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  43. Greco, S., Matarazzo, B., Slowinski, R.: Rough approximation by dominance relations. Int. J. Intell. Syst. 17(2), 153–171 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  44. Greco, S., Matarazzo, B., Slowinski, R.: Rough sets methodology for sorting problems in presence of multiple attributes and criteria. Eur. J. Oper. Res. 138(2), 247–259 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  45. Błaszczyński, J., Słowiński, R., Szeląg, M.: Sequential covering rule induction algorithm for variable consistency rough set approaches. Inf. Sci. 181(5), 987–1002 (2011)

    Article  Google Scholar 

  46. Błaszczyński, J., Greco, S., Słowiński, R., Szeląg, M.: Monotonic variable consistency rough set approaches. In: Yao, J., Lingras, P., Wu, W.Z., Szczuka, M., Cercone, N., Slezak, D. (eds.) Rough Sets and Knowledge Technology, pp. 126–133. Springer, Berlin Heidelberg (2007)

  47. Błaszczyński, J., Greco, S., Matarazzo, B., Słowiński, R., Szeląg, M.: jMAF dominance-based rough set data analysis framework. In: Skowron, A., Suraj, Z. (eds.) Rough Sets and Intelligent Systems-Professor Zdzisław Pawlak in Memoriam, pp. 185–209. Springer, Berlin Heidelberg (2013)

    Google Scholar 

Download references

Acknowledgments

Advices and opinions provided by the professionals from the Foreign Investment Department of Treasury of a financial-holding company are deeply appreciated, and the technical supports from the manager of SysJust also helped us a lot. Authors appreciate those valuable assistances; also, we are grateful for the funding support from the project of Ministry of Science and Technology (101-2410-H-424-009-MY3).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gwo-Hshiung Tzeng.

Appendix 1

Appendix 1

See Table 10.

Table 10 Crisp MA signals for comparison

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shen, KY., Tzeng, GH. Fuzzy Inference-Enhanced VC-DRSA Model for Technical Analysis: Investment Decision Aid. Int. J. Fuzzy Syst. 17, 375–389 (2015). https://doi.org/10.1007/s40815-015-0058-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-015-0058-8

Keywords

Navigation