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

International Journal of Fuzzy Systems

Volumn 17, Issue 3, 2015, Pages 375-389

  Shen, Kao Yi ^a   Tzeng, Gwo Hshiung ^b  

^a CHINESE CULTURE UNIVERSITY   (Taiwan)

^b NATIONAL TAIPEI UNIVERSITY   (Taiwan)

Author keywords

Fuzzy inference system (FIS); Investment decision; Knowledge retrieval; Rough set theory (RST); Soft computing; Technical analysis (TA); Variable consistency dominance based rough set approach (VC DRSA)

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMMERCE; DECISION MAKING; DECISION SUPPORT SYSTEMS; DECISION THEORY; FINANCIAL MARKETS; FUZZY SYSTEMS; INVESTMENTS; ROUGH SET THEORY; SOFT COMPUTING; STATISTICAL METHODS;

DOMINANCE-BASED ROUGH SETS; FUZZY INFERENCE SYSTEM; FUZZY INFERENCE SYSTEMS; INVESTMENT DECISIONS; KNOWLEDGE RETRIEVAL; ROUGH SET THEORY; SETS APPROACH; SOFT-COMPUTING; TECHNICAL ANALYSE; TECHNICAL ANALYSIS; VARIABLE CONSISTENCY DOMINANCE-BASED ROUGH SET APPROACH;

FUZZY INFERENCE;

EID: 84940904131     PISSN: 15622479     EISSN: 21993211     Source Type: Journal    
DOI: 10.1007/s40815-015-0058-8     Document Type: Article

References (47)

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)
2. Menkhoff, L.: The use of technical analysis by fund managers: international evidence. J. Bank. Financ. 34(11), 2573-2586 (2010)
3. Shiller, R.J.: From efficient market theory to behavioral finance. J. Econ. Perspect. 17(1), 19-33 (2003)
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)
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)
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)
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)
8. Ticknor, J.L.: A Bayesian regularized artificial neural network for stock market forecasting. Expert Syst. Appl. 40(4), 5501-5506 (2013)
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)
11. Kim, K.J.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307-319 (2003)
12. Tay, F.E.H., Cao, L.: Application of support vector machines in financial time series forecasting. Omega 29(4), 309-317 (2001)
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)
14. Shen, K.Y.: Implementing value investing strategy by artificial neural network. Int. J. Bus. Inf. Technol. 1(1), 12-22 (2011)
15. Dourra, H., Siy, P.: Investment using technical analysis and fuzzy logic. Fuzzy Sets Syst. 127(2), 221-240 (2002)
16. Zhou, X.S., Dong, M.: Can fuzzy logic make technical analysis 20/20? Financ. Anal. J. 60(4), 54-75 (2004)
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)
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)
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)
20. Gradojevic, N., Gencay, R.: Fuzzy logic, trading uncertainty and technical trading. J. Bank. Financ 37(2), 578-586 (2013)
21. Taylor, N.: The rise and fall of technical trading rule success. J. Bank. Finance 40, 286-302 (2014)
22. Menkhoff, L., Taylor, M.P.: The obstinate passion of foreign exchange professionals: technical analysis. J. Econ. Lit. 45(4), 936-972 (2007)
23. Lam, M.: Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decis. Support Syst. 37(4), 567-581 (2004)
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)
25. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338-353 (1965)
26. Precup, R.E., Hellendoom, H.: A survey on industrial applications of fuzzy control. Comput. Ind. 62(3), 213-226 (2011)
27. Turskis, Z., Zavadskas, E.K.: Multiple criteria decision making (MCDM) methods in economics: an overview. Technol. Econ. Dev. Econ. 2, 397-427 (2011)
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)
29. Tzeng, G.H., Huang, J.J.: Multiple attribute decision making: methods and applications. CRC Press, Boca Raton (2011)
30. Tzeng, G.H., Huang, J.J.: Fuzzy multiple objective decision making. CRC Press, Boca Raton (2013)
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)
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)
33. Shen, K.Y.: Implementing value investing strategy through an integrated fuzzy-ANN model. J. Theor. Appl. Inf. Technol. 51(1), 150-157 (2013)
34. Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665-685 (1993)
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)
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)
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)
39. Kandel, A., Li, L., Cao, Z.: Fuzzy inference and its applicability to control systems. Fuzzy Sets Syst. 48(1), 99-111 (2006)
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)
41. Opricovic, S., Tzeng, G.H.: Defuzzification within a multicriteria decision model. Int. J. Uncertain. Fuzziness Knowl-Based Syst. 11(5), 635-652 (2003)
42. Greco, S., Matarazzo, B., Slowinski, R.: Rough sets theory for multicriteria decision analysis. Eur. J. Oper. Res. 129(1), 1-47 (2001)
43. Greco, S., Matarazzo, B., Slowinski, R.: Rough approximation by dominance relations. Int. J. Intell. Syst. 17(2), 153-171 (2002)
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)
45. Błaszczyński, J., Słowiński, R., Szelag, M.: Sequential covering rule induction algorithm for variable consistency rough set approaches. Inf. Sci. 181(5), 987-1002 (2011)
46. Błaszczyński, J., Greco, S., Słowiński, R., Szelag, 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., Szelag, 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)