Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network
This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neur...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2016/3460293 |
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| _version_ | 1832554406362480640 |
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| author | Lukas Falat Dusan Marcek Maria Durisova |
| author_facet | Lukas Falat Dusan Marcek Maria Durisova |
| author_sort | Lukas Falat |
| collection | DOAJ |
| description | This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. |
| format | Article |
| id | doaj-art-12ede72169b947f8a20b7d9671217884 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-12ede72169b947f8a20b7d96712178842025-02-03T05:51:27ZengWileyThe Scientific World Journal2356-61401537-744X2016-01-01201610.1155/2016/34602933460293Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural NetworkLukas Falat0Dusan Marcek1Maria Durisova2Faculty of Management Science and Informatics, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, SlovakiaFaculty of Economics, VSB-Technical University of Ostrava, Sokolska Trida 33, 701 21 Ostrava 1, Czech RepublicFaculty of Management Science and Informatics, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, SlovakiaThis paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.http://dx.doi.org/10.1155/2016/3460293 |
| spellingShingle | Lukas Falat Dusan Marcek Maria Durisova Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network The Scientific World Journal |
| title | Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network |
| title_full | Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network |
| title_fullStr | Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network |
| title_full_unstemmed | Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network |
| title_short | Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network |
| title_sort | intelligent soft computing on forex exchange rates forecasting with hybrid radial basis neural network |
| url | http://dx.doi.org/10.1155/2016/3460293 |
| work_keys_str_mv | AT lukasfalat intelligentsoftcomputingonforexexchangeratesforecastingwithhybridradialbasisneuralnetwork AT dusanmarcek intelligentsoftcomputingonforexexchangeratesforecastingwithhybridradialbasisneuralnetwork AT mariadurisova intelligentsoftcomputingonforexexchangeratesforecastingwithhybridradialbasisneuralnetwork |