APPLICATION OF RADIAL BASIS FUNCTION NEURAL NETWORK TO PREDICT EXCHANGE RATE WITH FINANCIAL TIME SERIES

1.Chen, S., Cowan, C.F.N., Grant, P.M., 1991. Orthogonal least squares learning algorithm for radial basis function neural networks. IEEE Trans. Neural Netw.2,302-309.10.1109/72.80341 Search in Google Scholar

2.Desai, Vijay S., Bharati, Rakesh, 1998. The efficacy of neural networks in predicting returns on stock and bond indices. Decis. Sci. 29, 405-423.10.1111/j.1540-5915.1998.tb01582.x Search in Google Scholar

3.Kashaninejad, M., Dehghani, A., Kashiri, M., 2009. Modeling of wheat soaking using two artificial neural networks (MLP and RBF). J. Food Eng. 91, 602-607.10.1016/j.jfoodeng.2008.10.012 Search in Google Scholar

4.Park, J.-W., Venayagamoorthy, G.K., Harley, R.G., 2005. MLP/RBF neural-networksbased online global model identification of synchronous generator. Ind. Electron, IEEE Trans. 52, 1685-1695.10.1109/TIE.2005.858703 Search in Google Scholar

5.Wasserman, Philip, 1993. Neural Computing. Van Nostrand Reinhold, New York . Search in Google Scholar

6.BINNER, J. M., BISSOONDEEAL, R. K., ELGER, T., GAZELY, A. M. and MULLINEUX, A. W. (2005). A comparison of linear forecasting models and neural networks; an application to euroinflation and euroDivisia. Applied Economics, 37, pp. 665–80.10.1080/0003684052000343679 Search in Google Scholar

7.BISSOONDEEAL, R. K., BINNER, J. M., BHURUTH, M., GAZELY, A. and MOOTANAH, V. (2008).Forecasting exchange rates with linear and nonlinear models. Global Business and Economic Review, 10, pp. 414–29.10.1504/GBER.2008.020593 Search in Google Scholar

8.BISSOONDEEAL, R. K., BINNER, J. M. and ELGER, T. (2009). Monetary models of exchange rates and sweep Programs. Applied Financial Economics, 19, pp. 1117–29.10.1080/09603100802375501 Search in Google Scholar

9.ENGEL, C. M. and HAMILTON, J. D. (1990). Long swings in the exchange rate: are they in the data and do the markets know it? American Economic Review, 80, pp. 689–713. Search in Google Scholar

10.FRANSES, P. H. and GRIENSVEN, K. V. (1998). Forecasting exchange rates using neural networks for technical trading rules. Studies in Nonlinear Dynamics and Econometrics, 2,pp. 109–14. Search in Google Scholar

11.MEESE, R. A. and ROGOFF, K. (1983). Empirical exchange rate models of the seventies: Do they fit out of sample? Journal of International Economics, 14, pp. 3–24. Search in Google Scholar

12.NAG, A. K. and MITRA, A. (2002). Forecasting daily foreign exchange rates using genetically optimized neural networks. Journal of Forecasting, 21, pp. 501-11.10.1002/for.838 Search in Google Scholar

13.OLDEN, J. D. and JACKSON, D. A. (2002). Illuminating the ‘‘backbox’’: a randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling, 154, pp. 135–50.10.1016/S0304-3800(02)00064-9 Search in Google Scholar

14.PLASMANS, J., VERKOOIJEN, W. and DANIELS, H. (1998). Estimating structural exchange rate models by artificial neural networks.Applied Financial Economics, 8, pp. 541-51.10.1080/096031098332844 Search in Google Scholar

15.QI, M. and WU, Y. (2003). Nonlinear prediction of exchange rates with monetary fundamentals. Journal of Empirical Finance, 10, pp. 623–40.10.1016/S0927-5398(03)00008-2 Search in Google Scholar

16.QIU, F. and JENSEN, J. R. (2004). Opening the black box of neural networks for remote sensing image classification. International Journal of Remote Sensing, 25, pp. 1749-68.10.1080/01431160310001618798 Search in Google Scholar

17.SETIONO, R., LEOW, W.-K. and THONG, J. (2000). Opening the neural network black box: an algorithm for extracting rules from function approximating artificial neural networks.ICIS 2000 Proceedings.Paper 17. Search in Google Scholar

18.TAYLOR, M. P. and PEEL, D. A. (2000). Nonlinear adjustment, long-run equilibrium and exchange rate fundamentals. Journal of International Finance, 19, pp. 33–53.10.1016/S0261-5606(99)00044-3 Search in Google Scholar

19.ZHANG, G., PATUWO, B. E. and HU, Y. H. (1998). Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting, 14, pp. 17–34.10.1016/S0169-2070(97)00044-7 Search in Google Scholar