Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations
•We introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model.•In the RG-SVR, a GA is applied for optimal parameter selection and feature subset combination.•We introduce a GA fitness function for financial forecasting purposes.•The RG-SVR model is benchmarked against GA-...
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| Veröffentlicht in: | European journal of operational research Jg. 247; H. 3; S. 831 - 846 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Amsterdam
Elsevier B.V
16.12.2015
Elsevier Sequoia S.A |
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| ISSN: | 0377-2217, 1872-6860 |
| Online-Zugang: | Volltext |
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| Abstract | •We introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model.•In the RG-SVR, a GA is applied for optimal parameter selection and feature subset combination.•We introduce a GA fitness function for financial forecasting purposes.•The RG-SVR model is benchmarked against GA-SVR and simple SVR algorithms.
The motivation of this paper is to introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model for optimal parameter selection and feature subset combination. The algorithm is applied to the task of forecasting and trading the EUR/USD, EUR/GBP and EUR/JPY exchange rates. The proposed methodology genetically searches over a feature space (pool of individual forecasts) and then combines the optimal feature subsets (SVR forecast combinations) for each exchange rate. This is achieved by applying a fitness function specialized for financial purposes and adopting a sliding window approach. The individual forecasts are derived from several linear and non-linear models. RG-SVR is benchmarked against genetically and non-genetically optimized SVRs and SVMs models that are dominating the relevant literature, along with the robust ARBF-PSO neural network. The statistical and trading performance of all models is investigated during the period of 1999–2012. As it turns out, RG-SVR presents the best performance in terms of statistical accuracy and trading efficiency for all the exchange rates under study. This superiority confirms the success of the implemented fitness function and training procedure, while it validates the benefits of the proposed algorithm. |
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| AbstractList | The motivation of this paper is to introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model for optimal parameter selection and feature subset combination. The algorithm is applied to the task of forecasting and trading the EUR/USD, EUR/GBP and EUR/JPY exchange rates. The proposed methodology genetically searches over a feature space (pool of individual forecasts) and then combines the optimal feature subsets (SVR forecast combinations) for each exchange rate. This is achieved by applying a fitness function specialized for financial purposes and adopting a sliding window approach. The individual forecasts are derived from several linear and non-linear models. RG-SVR is benchmarked against genetically and non-genetically optimized SVRs and SVMs models that are dominating the relevant literature, along with the robust ARBF-PSO neural network. The statistical and trading performance of all models is investigated during the period of 1999-2012. As it turns out, RG-SVR presents the best performance in terms of statistical accuracy and trading efficiency for all the exchange rates under study. This superiority confirms the success of the implemented fitness function and training procedure, while it validates the benefits of the proposed algorithm. •We introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model.•In the RG-SVR, a GA is applied for optimal parameter selection and feature subset combination.•We introduce a GA fitness function for financial forecasting purposes.•The RG-SVR model is benchmarked against GA-SVR and simple SVR algorithms. The motivation of this paper is to introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model for optimal parameter selection and feature subset combination. The algorithm is applied to the task of forecasting and trading the EUR/USD, EUR/GBP and EUR/JPY exchange rates. The proposed methodology genetically searches over a feature space (pool of individual forecasts) and then combines the optimal feature subsets (SVR forecast combinations) for each exchange rate. This is achieved by applying a fitness function specialized for financial purposes and adopting a sliding window approach. The individual forecasts are derived from several linear and non-linear models. RG-SVR is benchmarked against genetically and non-genetically optimized SVRs and SVMs models that are dominating the relevant literature, along with the robust ARBF-PSO neural network. The statistical and trading performance of all models is investigated during the period of 1999–2012. As it turns out, RG-SVR presents the best performance in terms of statistical accuracy and trading efficiency for all the exchange rates under study. This superiority confirms the success of the implemented fitness function and training procedure, while it validates the benefits of the proposed algorithm. |
| Author | Theofilatos, Konstantinos Sermpinis, Georgios Stasinakis, Charalampos Karathanasopoulos, Andreas |
| Author_xml | – sequence: 1 givenname: Georgios surname: Sermpinis fullname: Sermpinis, Georgios email: georgios.sermpinis@glasgow.ac.uk, sermpinis@hotmail.com organization: University of Glasgow Business School, University of Glasgow, Adam Smith Building, Glasgow G12 8QQ, UK – sequence: 2 givenname: Charalampos surname: Stasinakis fullname: Stasinakis, Charalampos email: charalampos.stasinakis@glasgow.ac.uk organization: University of Glasgow Business School, University of Glasgow, Adam Smith Building, Glasgow G12 8QQ, UK – sequence: 3 givenname: Konstantinos surname: Theofilatos fullname: Theofilatos, Konstantinos email: theofilk@ceid.upatras.gr organization: Pattern Recognition Laboratory, Department of Computer Engineering & Informatics, University of Patras, 26500 Patras, Greece – sequence: 4 givenname: Andreas surname: Karathanasopoulos fullname: Karathanasopoulos, Andreas email: a.karathanasopoulos@uel.ac.uk organization: Andreas Karathanasopoulos Royal Docklands Business School, University of East London, UK |
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| Keywords | Exchange rates Support vector regression Forecast combinations Feature subset Genetic algorithms |
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| Snippet | •We introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model.•In the RG-SVR, a GA is applied for optimal parameter selection and... The motivation of this paper is to introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model for optimal parameter selection and... |
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| SubjectTerms | Currency transactions Decision making models Exchange rates Feature subset Forecast combinations Forecasting techniques Foreign exchange rates Genetic algorithms Regression analysis Studies Support vector regression |
| Title | Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations |
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