Optimization of technical trading strategies and the profitability in security markets

Economics Letters

Volumn 59, Issue 2, 1998, Pages 249-254

  Gençay, Ramazan ^a  

^a UNIVERSITY OF WINDSOR   (Canada)

Author keywords

G10; G14; Neural network models; Security markets; Technical trading

Indexed keywords

EID: 0032219508     PISSN: 01651765     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0165-1765(98)00051-2     Document Type: Article

References (19)

1. Brock W.A., Lakonishok J., LeBaron B. Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance. 47:1992;1731-1764.
2. Campbell, J.Y., A.W. Lo, A.C. MacKinlay, 1997. The Econometrics of Financial Markets. Priceton University Press, Princeton NJ.
3. Cybenko G. Approximation by superposition of a sigmoidal function. Mathematics of Control, Signals and Systems. 2:1989;303-314.
4. Engle R.F., Lilien D.M., Robins R.P. Estimating time varying risk premia in the term structure: The ARCH-M model. Econometrica. 55:1987;391-408.
5. Funahashi K.-I. On the approximate realization of continuous mappings by neural networks. Neural Networks. 2:1989;183-192.
6. Gallant, A.R., White, H., 1988. There exists a neural network that does not make avoidable mistakes. Proceedings of the Second Annual IEEE Conference on Neural Networks, San Diego, CA. IEEE Press, New York, pp. I.657-I.664.
7. Gallant A.R., White H. On learning the derivatives of an unknown mapping with multilayer feedforward networks. Neural Networks. 5:1992;129-138.
8. Gençay, R., 1997a. Linear, nonlinear and essential foreign exchange prediction. Journal of International Economics, forthcoming.
9. Gençay, R., 1997b. The predictability of security returns with technical trading rules. Journal of Empirical Finance, forthcoming.
10. Hecht-Nielsen, R., 1989. Theory of the backpropagation neural networks. Proceedings of the international joint conference on neural networks, Washington DC. IEEE Press, New York, pp. I.593-I.605.
11. Henriksson R.D., Merton R.C. On the market timing and investment performance II: Statistical procedures for evaluating forecasting skills. Journal of Business. 54:1981;513-533.
12. Hornik K., Stinchcombe M., White H. Multilayer feedforward networks are universal approximators. Neural Networks. 2:1989;359-366.
13. Hornik K., Stinchcombe M., White H. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Networks. 3:1990;551-560.
14. Kuan C.-M., White H. Artificial neural networks: An econometric perspective. Econometric Reviews. 13:1994;1-91.
15. Lo A., MacKinlay A.C. Data snooping biases in tests of financial asset pricing models. Review of Financial Studies. 3:1990;431-468.
16. Nelson D.B. Conditional heteroskedasticity in asset returns: A new approach. Econometrica. 59:1991;347-370.
17. Pesaran M.H., Timmermann A. A simple nonparametric test of predictive performance. Journal of Business and Economic Statistics. 10:1992;461-465.
18. Pesaran M.H., Timmermann A. Forecasting stock returns, An examination of stock market trading in the presence of transaction costs. Journal of Forecasting. 13:1994;335-367.
19. Pesaran M.H., Timmermann A. Predictability of stock returns: Robustness and economic significance. Journal of Finance. 50:1995;1201-1228.