Computational intelligence for evolving trading rules

IEEE Transactions on Evolutionary Computation

Volumn 13, Issue 1, 2009, Pages 71-86

  Ghandar, Adam ^a   Michalewicz, Zbigniew ^a,b,c   Schmidt, Martin ^c   To, Thuy Duong ^a   Zurbrugg, Ralf ^a  

^a UNIVERSITY OF ADELAIDE   (Australia)

^b INSTITUTE OF COMPUTER SCIENCE   (Poland)

^c SolvelT Software Pty Ltd   (Australia)

Author keywords

Evolutionary computation; Fuzzy systems; Portfolio management; Stock market; Trading systems

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMMERCE; FINANCIAL DATA PROCESSING; FUZZY SETS; FUZZY SYSTEMS; INTELLIGENT CONTROL; INVESTMENTS;

COMPREHENSIVE ANALYSIS; COMPUTATIONAL INTELLIGENCES; DYNAMIC MARKETS; EVOLUTIONARY COMPUTATION; EVOLUTIONARY PROCESS; FINANCIAL INDUSTRIES; FUZZY LOGIC RULES; PORTFOLIO EVALUATIONS; PORTFOLIO MANAGEMENT; STOCK MARKET; TRADING RULES; TRADING SYSTEMS;

FUZZY LOGIC;

EID: 59749106662     PISSN: 1089778X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TEVC.2008.915992     Document Type: Article

References (27)

1. F. Allen and R. Karjalainen, "Using genetic algorithms to find technical trading rules," J. Financial Econ., vol. 51, pp. 245-271, 1999.
2. P. C. Andreou, C. Charalambous, and S. H. Martzoukos, "Robust artificial neural networks for pricing of European options," Comput. Econ., vol. 27, no. 2-3, pp. 329-351, 2006.
3. A. Atiya, "Bankruptcy prediction for credit risk using neural networks: A survey and new results," IEEE Trans. Neural Netw., vol. 12, pp. 929-935, 2001.
4. A. Azzini and A. G. B. Tettamanzi, "A neural evolutionary approach to financial modeling," in Proc. 8th Ann. Conf. Genetic Evol. Comput. (GECCO'06), New York, NY, USA, 2006, pp. 1605-1612.
5. G. F. Barrett and S. G. Donald, "Consistent Tests for Stochastic Dominance," Econometrica, vol. 71, no. 1, pp. 71-104, 2003.
6. A. Bera and C. Jarque, "Efficient tests for normality, heteroscedasticity, and serial independence of regression residuals," Econ. Lett., vol. 6, pp. 255-259, 1980.
7. A. Brabazon and M. O'Neill, Biologically Inspired Algorithms for Financial Modelling, ser. Natural Computing Series. Berlin, Germany: Springer-Verlag, 2006.
8. W. Brock, J. Lakonishok, and B. LeBaron, "Simple technical trading rules and the stochastic properties of stock returns," J. Finance vol. 47, pp. 1731-1764, 1992.
9. R. Davidson and J.-Y. Duclos, "Statistical inference for stochastic dominance and for the measurement of poverty and inequality," Econometrica, vol. 68, no. 6, pp. 1435-1464, 2000.
10. E. F. Fama, "Components of investment performance," J. Finance, vol. 27, no. 3, pp. 551-567, 1972.
11. E. F. Fama and K. French, "Common risk factors in the returns of stocks and bonds," J. Financial Econ., vol. 33, no. 1, pp. 3-56, 1993.
12. C. Fyfe, J. Marney, and H. Tarbert, "Technical analysis versus market efficiency - A genetic programming approach," Appl. Financial Econ. vol. 9, pp. 183-191, 1999.
13. C. L. Giles, S. Lawrence, and A. C. Tsoi, "Noisy time series prediction using recurrent neural networks and grammatical inference," Mach. Learn., vol. 44, no. 1-2, pp. 161-183, 2001.
14. T. H. Goodwin, "The information ratio," Financial Analysts J., vol. 54, no. 4, pp. 34-43, 1998.
15. R. C. Grinold and R. N. Kahn, Active Portfolio Management, 2 ed. New York: McGraw-Hill, 2000.
16. P. Hsu and C. Kuan, "Reexamining the profitability of technical analysis with data snooping checks," J. Financial Econ., vol. 3, no. 4, pp. 606-628, 2005.
17. N. Jegadeesh and S. Titman, "Profitability of momentum strategies: An evaluation of alternative explanations," J. Finance, vol. 56, no. 2, pp. 699-720, 2001.
18. M. C. Jensen, "The performance of mutual funds in the period 1945-1964," J. Finance, vol. 23, no. 2, pp. 389-416, 1968.
19. H. Leland, "Beyond mean-variance: Performance Measurement in a nonsymmetrical world," Financial Analysts J., pp. 27-36, 1999.
20. A. W. Lo, H. Mamaysky, and J. Wang, "Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation," J. Finance, vol. 55, no. 4, pp. 1705-1765, 2000.
21. C. Neely, P. Weller, and R. Dittmar, "Is technical anlaysis in the foreign exchange market profitable? a genetic programming approach," J. Financial Quant. Analysis, vol. 32, no. 4, pp. 405-426, 1997.
22. O. Jangmin, J. Lee, J. W. Lee, and B.-T. Zhang, "Dynamic asset allocation for stock trading optimized by evolutionary computation," IEICE - Trans. Inf. Syst., vol. E88-D, no. 6, pp. 1217-1223, 2005.
23. J.-Y. Potvin, P. Soriano, and M. Valle, "Generating trading rules on the stock markets with genetic programming," Comput. Oper. Res., vol. 31, no. 7, pp. 1033-1047, 2004.
24. W. F. Sharpe, "Mutual fund performance," J. Business, vol. 39, no. 1, pp. 119-138, 1966.
25. H. Subramanian, S. Ramamoorthy, P. Stone, and B. J. Kuipers, "Designing safe, profitable automated stock trading agents using evolutionary algorithms," in Proc. 8th Ann. Conf. Genetic Evol. Comput. (GECCO 06), New York, 2006, pp. 1777-1784.
26. N. Svangard, S. Lloyd, P. Nordin, and C. Wihlborg, D. B. Fogel, M. A. El-Sharkawi, X. Yao, G. Greenwood, H. Iba, P. Marrow, and M. Shackleton, Eds., "Evolving short-term trading strategies using genetic programming," in Proc. 2002 Congr. Evol. Comput. (CEC2002), 2002, pp. 2006-2010.
27. S.-I. Wu and R.-P. Lu, "Combining artificial neural networks and statistics for stock-market forecasting," in Proc. 1993 ACM Conf. Comput. Sci. (CSC'93), New York, 1993, pp. 257-264.