Embedding technical analysis into neural network based trading systems

Applied Artificial Intelligence

Volumn 10, Issue 6, 1996, Pages 523-542

  Chenoweth, Tim ^a   Obradovic, Zoran ^a   Lee, Sauchi Stephen ^b  

^a Washington State University   (United States)

^b UNIVERSITY OF IDAHO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMBINATORIAL CIRCUITS; FINANCIAL DATA PROCESSING; FORECASTING; LOGIC PROGRAMMING; NEURAL NETWORKS;

ANNUAL RATE OF RETURN (ARR); DATA FILTERING; PREDICTION INTEGRATION;

EXPERT SYSTEMS;

EID: 0030287051     PISSN: 08839514     EISSN: 10876545     Source Type: Journal    
DOI: 10.1080/088395196118416     Document Type: Article

References (17)

1. Black, F., and M. Scholes. 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81(May-June).
2. Chenoweth, T., and Z. Obradovi. 1995a. A multi-component approach to stock market predictions. In Proc. third int. conf. on artificial intelligence applications on Wall Street. 74-79.
3. Chenoweth, T., and Z. Obradovid. 1995b. An explicit feature selection strategy for predictive models of the S&P 500 index. NeuroveSt Journal 3(6): 14-21.
4. Chenoweth, T., and Z. Obradovid. 1996. A multi-component nonlinear prediction system for the S&P 500 index. Neurocomputing Journal
5. Cybenko, G. 1989. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems 2: 303-314.
6. Elder, A. 1993. Trading for a living. Traders Press, Inc.
7. Fletcher, J., and Z. Obradovit 1993. Combining prior symbolic knowledge and constructive neural networks. Connection Science: Journal of Neural Computing. Artificial Intelligence and Cognitive Research 5(3 & 4): 365-375.
8. Fletcher, J., and Z. Obradovio. In press. A discrete approach to constructive neural network learning. Neural, Parallel and Scientific Computations 3(3): 307-320.
9. Haykin, S. 1995. Neural networks: A comprehensive foundation. New York: MacMillan.
10. Hutchinson, J. 1993. A radial basis function approach to financial time series analysis. Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, Mass.
11. LeBeau, C., and D. Lucas. 1992. Computer analysis of the futures market. Traders Press, Inc.
12. Mahfoud, S., and G. Mani. 1995. Genetic algorithms for predicting individual stock performance. In Proc. third int. conf. on artificial intelligence applications on Wall Street. 174-181.
13. Romero, P., and Z. Obradovid. 1995. Comparison of symbolic and connectionist approaches to local experts integration. In Proceedings of the IEEE technical applications conference. Portland, Oreg.: Nortcon: 105-110.
14. Rumelhart, D. E., etal. 1986. Parallel distributed processing: Explorations in the microstructure of cognition, vols. 1 and 2. Cambridge, Mass.: MIT Press.
15. Weiss, S. M., and C. A. Kulikowski. 1991. Computer systems that learnMorgan Kaufmann. San Mateo, Calif.
16. Werbos, P. 1974. Beyond regression: New tools for prediction and analysis in the behavioral sciences, Ph.D. thesis, Harvard University, Cambridge, Mass. Reprinted as P. Werbos, The Roots ofbackpropagation: From ordered derivatives to neural networks and political forecasting. New York: John Wiley & Sons. 1994.
17. White, H. 1988. Economic prediction using neural networks: The case of IBM daily stock returns. In Proc. IEEE int. conf. on neural networks, vol. 2, 451-458.