Forecasting Daily Foreign Exchange Rates Using Genetically Optimized Neural Networks
AbstractForecasting currency exchange rates is an important financial problem that has received much attention especially because of its intrinsic difficulty and practical applications. The statistical distribution of foreign exchange rates and their linear unpredictability are recurrent themes in the literature of international finance. Failure of various structural econometric models and models based on linear time series techniques to deliver superior forecasts to the simplest of all models, the simple random walk model, have prompted researchers to use various non-linear techniques. A number of non-linear time series models have been proposed in the recent past for obtaining accurate prediction results, in an attempt to ameliorate the performance of simple random walk models. In this paper, we use a hybrid artificial intelligence method, based on neural network and genetic algorithm for modelling daily foreign exchange rates. A detailed comparison of the proposed method with non-linear statistical models is also performed. The results indicate superior performance of the proposed method as compared to the traditional non-linear time series techniques and also fixed-geometry neural network models. Copyright © 2002 by John Wiley & Sons, Ltd.
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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.
Volume (Year): 21 (2002)
Issue (Month): 7 (November)
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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966
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- Shiyi Chen & Kiho Jeong & Wolfgang K. Härdle, 2008. "Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns," SFB 649 Discussion Papers SFB649DP2008-051, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Roberto Patuelli & Simonetta Longhi & Aura Reggiani & Peter Nijkamp, 2008.
"Neural networks and genetic algorithms as forecasting tools: a case study on German regions,"
Environment and Planning B: Planning and Design,
Pion Ltd, London, vol. 35(4), pages 701-722, July.
- Roberto Patuelli & Simonetta Longhi & Aura Reggiani & Peter Nijkamp, 2005. "Forecasting Regional Employment in Germany by Means of Neural Networks and Genetic Algorithms," Computational Economics 0511002, EconWPA.
- Sarantis, Nicholas, 2006. "On the short-term predictability of exchange rates: A BVAR time-varying parameters approach," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2257-2279, August.
- PREMINGER, Arie & FRANCK, Raphael, 2005.
"Forecasting exchange rates: a robust regression approach,"
CORE Discussion Papers
2005025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Preminger, Arie & Franck, Raphael, 2007. "Forecasting exchange rates: A robust regression approach," International Journal of Forecasting, Elsevier, vol. 23(1), pages 71-84.
- PREMINGER, Arie & FRANCK, Raphael, . "Forecasting exchange rates: a robust regression approach," CORE Discussion Papers RP -1917, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Farzan Aminian & E. Suarez & Mehran Aminian & Daniel Walz, 2006. "Forecasting Economic Data with Neural Networks," Computational Economics, Society for Computational Economics, vol. 28(1), pages 71-88, August.
- Angela He & Alan Wan, 2009. "Predicting daily highs and lows of exchange rates: a cointegration analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1191-1204.
- Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2005. "A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia," Applied Economics, Taylor & Francis Journals, vol. 37(6), pages 665-680.
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