Abstract
Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. This paper proposes a Flexible Neural Tree (FNT) model for forecasting three major international currency exchange rates. Based on the pre-defined instruction sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using the Extended Compact Genetic Programming and the free parameters embedded in the neural tree are optimized by particle swarm optimization algorithm. Empirical results indicate that the proposed method is better than the conventional neural network forecasting models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Theodossiou, P.: The Stochastic Properties of Major Canadian Exchange Rates. The Financial Review 29(2), 193–221 (1994)
So, M.K.P., Lam, K., Li, W.K.: Forecasting Exchange Rate volatility using Autoregressive Random Variance Model. Appl. Finan. Economics 9, 583–591 (1999)
Hsieh, D.A.: Modeling Heteroscedasticity in Daily Foreign-Exchange Rates. J. of Business and Economic Statistics 7, 307–317 (1989)
Chappel, D., Padmore, J., Mistry, P., Ellis, C.: A Threshold Model for French Franc/Deutsch Mark Exchange Rate. J. of Forecasting 15, 155–164 (1996)
Refenes, A.N.: Constructive Learning and Its Application to Currency Exchange Rate Forecasting. In: Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real-World Performance, pp. 777–805. Probus Publishing Company, Chicago (1993)
Refenes, A.N., Azema-Barac, M., Chen, L., Karoussos, S.A.: Currency Exchange Rate Prediction and Neural Network Design Strategies. Neural Computing and Application 1, 46–58 (1993)
Yu, L., Wang, S., Lai, K.-K.: Adaptive Smoothing Neural Networks in Foreign Exchange Rate Forecasting. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3516, pp. 523–530. Springer, Heidelberg (2005)
Yu, L., Wang, S. and Lai, K.-K.: A Novel Nonlinear Ensemble Forecasting Model Incorporating GLAR and ANN for Foreign Exchange Rates. Computers & Operations Research 32(2005) 2523 - 2541
Wang, W., Lai, K.-K., Nakamori, Y., Wang, S.: Forecasting Foreign Exchange Rates with Artificial Neural Networks: A Review. Int. J. of Information Technology & Decision Making 3(1), 145–165 (2004)
Chen, Y., Yang, Y., Dong, J.: Nonlinear System Modeling via Optimal Design of Neural Trees. Int. J. of Neural Systems 14(2), 125–137 (2004)
Chen, Y., Yang, B., Dong, J., Abraham, A.: Time-Series Forecasting using Flexible Neural Tree Model. Information Science 174(3-4), 219–235 (2005)
Yao, J.T., Tan, C.L.: A Case Study on Using Neural Networks to Perform Technical Forecasting of Forex. Neurocomputing 34, 79–98 (2000)
Sastry, K., Goldberg, D.E.: Probabilistic Model Building and Competent Genetic Programming. In: Riolo, R.L., Worzel, B. (eds.) Genetic Programming Theory and Practise, Ch.13, pp. 205–220 (2003)
Yu, L., Wang, S., Lai, K.-K.: Adaptive Smoothing Neural Networks in Foreign Exchange Rate Forecasting. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3516, pp. 523–530. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, Y., Peng, L., Abraham, A. (2006). Exchange Rate Forecasting Using Flexible Neural Trees. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_76
Download citation
DOI: https://doi.org/10.1007/11760191_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
eBook Packages: Computer ScienceComputer Science (R0)