The prediction of Taiwan 10-year government bond yield

WSEAS Transactions on Systems

Volumn 8, Issue 9, 2009, Pages 1051-1060

  Chen, Kuentai ^a   Lin, Hong Yu ^a   Huang, Tz Chen ^b  

^a MING CHI UNIVERSITY OF TECHNOLOGY   (Taiwan)

^b NONE

Author keywords

Artificial neural network; Forecasting; Government bond; Neural networks; Radial Basis Function Neural Network; Yield

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; ARTIFICIAL NEURAL NETWORK; BACKPROPAGATION NETWORK; DIFFERENT STRUCTURE; FORECASTING PERFORMANCE; GOVERNMENT BOND; GOVERNMENT BONDS; HIDDEN LAYERS; IN-DEPTH STUDY; INPUT NODE; LOCAL MINIMUMS; POTENTIAL PROBLEMS; PROCESSING TIME; RADIAL BASIS FUNCTION NEURAL NETWORKS; RESILIENT PROPAGATION; SUPPORT VECTOR REGRESSIONS; TRAINING SAMPLE; TRAINING SPEED;

ATTITUDE CONTROL; BACKPROPAGATION; FORECASTING; FUZZY INFERENCE; FUZZY SYSTEMS; RADIAL BASIS FUNCTION NETWORKS;

NEURAL NETWORKS;

EID: 73849140052     PISSN: 11092777     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (28)

1. Awad, M., Pomares, H., Rojas, I., Herrera, L.J., Guillen, A., Valenzuela, O. (2006) "Hierarchical Structure for function approximation using Radial Basis Function" WSEAS Trans. on Mathematics, Issue 1 Vol 5, January 2006 pp75
2. Bergerson, K. and D. C. Wunsch (1991). "A Commodity Trading Model Based on a Neural Network-Expert System Hybrid." IJCNN-91, Vol.I, pp.289-293.
3. Cheng W, Wanger L, Lin CH. Forecasting the 30-year US treasury bond witha system of neural networks. Journal of Computational Intelligence in Finance 1996;4:10-6.
4. Fama, E. F. (1965). "Mandelbrot and the Stable Paretian Hypothesis." Journal of Business, Vol.38, pp.34-105.
5. Grudnitski, G. and L. Osburn (1993). "Forecasting S&P and Gold Future Prices: An Application of Neural Networks." The Journal of Futures Markets, Vol.13, No.6, pp.631-643.
6. Jang, J. S. R. (1993). "ANFIS: Adaptive Network-based Fuzzy Inference System." IEEE Transactions on Systems, Man, and Cybernetics, Vol.23, No.3, pp.665-685.
7. Kimoto, T. and K. Asakawa (1990). "Stock Market Predication System with Modular Networks." IJCNN-90-Wash, Vol.I, pp.1-6.
8. Klinger, Roman and Rudolph, Gunter (2006), "Evolutionary Composition of Music with Learned Melody Evaluation", Proceedings of the 5th wseas int. conf. on Computational intelligence, Man-machine systems and Cybernetics, Venice, Italy, November 20-22, 2006 pp234
9. Kosaka, M., Mizuno, H., T. Sasaki, R. Someya, and N. Hamada (1991). "Applications of Fuzzy logic/Neural Network to Securities Trading Decision Support System." IEEE, pp.1913-1919.
10. Kuan, C. and H. White (1994). "Artificial Neural Networks: An Econometric Perspective." Econometric Reviews, Vol.13, No.1, pp.1-91.
11. Lai, C.C., Hsu, Y. and R. Tsaih (1998). ISSN: 1109-2777 "Forecasting S&P 500 stock index futures with a hybrid AI system." Decision Support Systems, Vol.23, pp.161-174.
12. Lapedes, A. and R. Farber (1987). "Nonlinear Signal Processing Using Neural Networks: Prediction and System Modeling." Los Alamos National Laboratory Report.
13. Lin, C. T. and C. S. G. Lee (1996). Neural Fuzzy System. New Jersey: Prentice-Hall Inc.
14. Lucas, R. E. (1976). "Econometric Policy Evaluation: A Critique." Carnegie-Rochester Conference Series on Public Policy, Vol.1, pp.19-46.
15. Mandelbrot, B. (1963). "The Variation of Certain Speculative Prices." Journal of Business, Vol.36, pp.394-419.
16. Michael, P., Nobay, A. R. and D. A. Peel (1997). "Transactions Costs and Nonlinear Adjustment in Real Exchange Rates: An Empirical Investigation." Journal of Political Economy, Vol.105, No.4, pp.862-879.
17. Moody, J. and C. Darken (1988). "Learning with Localized Receptive Fields." Proceedings of the 1988 Connectionist Models Summer School, Carnegie Mellon University, Pittsburgh, PA. San Mateo, CA Morgan Kaufmann Publishers.
18. Refenes, Apostolos-Paul and A. Zaidi (1995). "Managing Exchange Rate Prediction Strategies with Neural Networks.", Neural Networks in the Capital Markets, Chichester, England, John Wiley and Sons, pp.213-219.
19. Riedmiller, M. and H. Braun (1993). "A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm." In Proceedings of the IEEE International Conference on Neural Networks, pp.586-591.
20. Rumelhart, D. E., Hinton, G. E. and R. J. Williams (1986). "Learning Representations by Back-Propagating Errors." Nature, Vol.323, pp.533-536.
21. Tsoukalas, L. H. and R. E. Uhrig (1997). Fuzzy and Neural Approaches in Engineering. New York: Wiley and Sons.
22. Vapnik. (1995) The Nature of Statistical Learning Theory. Springer, N.Y., 1995.
23. Vapnik, V. Golowich, S. and Smola. A., (1997) "Support vector method for function approximation, regression estimation, and signal processing". Advances in Neural Information Processing Systems 9, pages 281-287, Cambridge, MA, 1997. MIT Press.
24. Vanstone, B. and Finnie, G. (2009) "An empirical methodology for developing stockmarket trading systems using artificial neural networks", Expert Systems with Applications, Vol. 36, Issue 3, Part 2, April 2009, Pages 6668-6680
25. Vasudevan, M., Arumugam, R., Venkatesan, G., Paramasivam, S. (2003) "Estimation of stator resistance of induction motor for direct torque control scheme using adaptive neuro-fuzzy inference systems" WSEAS Trans. on Circuits and Systems, Issue 2, Vol 2, April 2003 pp327
26. Wu, B. (1995). "Model-free Forecasting for Nonlinear Time Series: with Application in Exchange Rates." Computational Statistics and Date Analysis, Vol.19, No.4, pp.433-459.
27. Xiao, H., ZHANG, X., Du, Yumei (2008) "A Comparison of Neural Network, Rough Sets and Support Vector Machine on Remote Sensing Image Classification", 7th WSEAS Int. Conf. on Applied computer & Applied computational science (ACACOS '08), Hangzhou, China, April 6-8, 2008 pp597
28. Zhang, G. and M. Y. Hu (1998). "Neural Network Forecasting of the British Pound/US Dollar Exchange Rate." Journal of Management Science, Vol.26, No.4, pp.495-506