Currency Exchange Rate Forecasting Using Artificial Neural Networks Backpropagation Method
Pages 14 - 33
Abstract
Since 1997, the rupiah currency has a tendency to change at any time since the economic crisis that hit Indonesia. One of the most widely traded currencies in the international exchange market is the U.S. dollar. This paper forecasts the exchange rate by using back propagation neural networks. Variables that affecting currency exchange rates is inflation, gross national product and interest rates. After performing data processing with the help of software VB.net forecasting results and forecasting program, it is displayed online by using PHP to construct the webpage.
References
[1]
Ade, K. S., & Agustiawan, H. 2005. Stock price prediction using neural networks Unpublished master's thesis. Gunadarma University, West Java, Indonesia.
[2]
Alvarez-Diaz, M., & Alvarez, A. 2007. Forecasting exchange rates using an evolutionary neural network Applied Financial . Economics Letters, 31, 5-9.
[3]
Ashena, R., & Moghasi, J. 2011. Bottom hole pressure estimation using evolved neural networks by real coded ant colony optimization and genetic algorithm. Journal of Petroleum Science Engineering, 77, 375-385.
[4]
Box, G. E. P., & Jenkins, G. M. 1994. Forecasting and control. Upper Saddle River, NJ: Prentice Hall.
[5]
Chakradhara, P., & Narasimhan, V. 2007. Forecasting exchange rate better with artificial neural network. Journal of Policy Modeling, 29, 227-236.
[6]
Clements, K. W., & Lan, Y. 2010. A new approach to forecasting exchange rates. Journal of International Money and Finance, 29, 1424-1437.
[7]
Dadashzadeh, M. 1991. Microcomputer-based decision support for facilities planning and management. Organizational and End User Computing, 34, 22-31.
[8]
Decherchi, S., Parodi, M., & Ridella, S. 2012. Learning the mean: A neural network approach. Neurocomputing, 77, 129-143.
[9]
Emam, A., & Min, H. 2009. The artificial neural network for forecasting foreign exchange rates. International Journal of Services and Operations Management, 56, 740-757.
[10]
Global Rate. n.d. 1 month U.S. dollar LIBOR interest rate. Retrieved September 17, 2001, from http://www.global-rate.com/Produk_domestik_bruto
[11]
Hajizadeh, E., Seifi, A., Fazel Zarandi, M. H., & Turksen, I. B. 2012. A hybrid modeling approach for forecasting the volatility of S&P 500 index return. Expert Systems with Applications, 391, 431-436.
[12]
Hill, T., Marquez, L., O'Connor, M., & William, R. 1994. Artificial neural network models for forecasting and decision making. International Journal of Forecasting, 10, 5-15.
[13]
Hsu, C. C. 2011. Factors affecting webpage's visual interface design and style. Procedia Computer Science, 3, 1315-1320.
[14]
Kadir, A. 2001. Dynamic web programming basics using PHP. Yogyakarta, Indonesia: Andi Offset.
[15]
Kamruzzaman, J., & Sarker, R. A. 2003. Forecasting of currency exchange rates using ANN: A case study. In Proceedings of the International Conference on Neural Networks and Signal Processing Vol. 1, pp. 793-797.
[16]
Khashei, M., & Bijari, M. 2010. An artificial neural network p, d, q model for timeseries forecasting. Expert Systems with Applications, 37, 479-489.
[17]
Kohara, K. 2002. Neural networks for economic forecasting problems. Expert Systems: International Journal of Knowledge Engineering and Neural Networks, 4, 1175-1197.
[18]
Muhammad, M. 2010. Comparison of backpropagation artificial neural network and method of Arima Box-Jenkins as a method of forecasting currency rupiah against the U.S. dollar. North Sumatra, Indonesia: University of North Sumatra.
[19]
Mulyono, S. 2000, Forecasting stock prices and exchange rates: Box-Jenkins techniques. Economics and Finance Indonesia, 472.
[20]
Office of Management and Budget. 2011. Historical budget of U.S. Government. Washington, DC: U.S. Government Printing Office.
[21]
Robert, S. 2008. Neural classification approach for short term forecast of exchange rate movement with point and figure charts. In Proceedings of the International Joint Conference on Neural Networks pp. 2841-2848.
[22]
Sexton, R. S. 1999. Beyond backpropagation: Using simulated annealing for training neural network. International Journal of Organizational and End User Computing, 113, 3-10.
[23]
Siang, J. J. 2005. Artificial neural networks using matlab and pemogramannya. Yogyakarta, Indonesia: Andi Offset.
[24]
Spyros, M., Wheelwright, S. C., & McGee, V. E. 1999. Forecasting methods and applications. Jakarta, Indonesia: Binarupa.
[25]
Subiyanto. 2010. Application of artificial neural networks as an alternative method of short-term load forecast. Retrieved December 10, 2010, from http://www.elektroindonesia.com/elektro/ener29.html-42k-
[26]
Tkacz, G. 2001. Neural network forecasting of Canadian GDP growth. International Journal of Forecasting, 17, 57-69.
[27]
Wibowo, T. A. 2005. Factors affecting exchange rate euro. Journal of Economic Studies and Financial.
[28]
Yu, L., Shougang, W., Lai, K. K., & Wen, F. 2010. A multiscale neural network learning paradigm for financial crisis forecasting. Neurocomputing, 73, 716-725.
[29]
Zhang, G., Pattuwo, B., & Hu, M. Y. 1997. Forecasting with artificial neural networks. The State of the Art: Elsevier International Journal of Forecasting, 141, 35-62.
- Currency Exchange Rate Forecasting Using Artificial Neural Networks Backpropagation Method
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Published: 01 July 2012
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