Neural networks performance in exchange rate prediction

Exploration of ANNs for the economic purposes is described and empirically examined with the foreign exchange market data. For the experiments, panel data of the exchange rates (USD/EUR, JPN/USD, USD/GBP) are examined and optimized to be used for time-series predictions with neural networks. In this stage the input selection, in which the processing steps to prepare the raw data to a suitable input for the models are investigated. The best neural network is found with the best forecasting abilities, based on a certain performance measure. A visual graphs on the experiments data set is presented after processing steps, to illustrate that particular results. The out-of-sample results are compared with training ones.

[1]  H. White,et al.  Reply to comments on “artificial neural networks: an econometric perspective“ , 1994 .

[2]  M. Álvarez‐Díaz Exchange rates forecasting: local or global methods? , 2008 .

[3]  G. Maddala,et al.  Economic factors and the stock market: a new perspective , 1999 .

[4]  Monica Lam,et al.  Neural network techniques for financial performance prediction: integrating fundamental and technical analysis , 2004, Decis. Support Syst..

[5]  Lucio Grandinetti,et al.  Technique of learning rate estimation for efficient training of MLP , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[6]  Hannah Thinyane,et al.  An Investigation into the Use of Intelligent Systems for Currency Trading , 2011 .

[7]  Filiz Özkan,et al.  A Comparison of the Monetary Model and Artificial Neural Networks in Exchange Rate Forecasting , 2012 .

[8]  Halbert White,et al.  Artificial neural networks: an econometric perspective ∗ , 1994 .

[9]  Muddun Bhuruth,et al.  Forecasting exchange rates with linear and nonlinear models , 2008 .

[10]  Norman R. Swanson,et al.  Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models , 1997 .

[11]  Alicia M. Gazely,et al.  Forecasting the UK/US Exchange Rate with Divisia Monetary Models and Neural Networks , 2011 .

[12]  Kenneth Rogoff,et al.  Empirical exchange rate models of the seventies , 1983 .

[13]  Tugrul U. Daim,et al.  Using artificial neural network models in stock market index prediction , 2011, Expert Syst. Appl..

[14]  Ramazan Gençay,et al.  Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules , 1999 .

[15]  Kenneth S. Rogoff,et al.  Exchange rate models of the seventies. Do they fit out of sample , 1983 .

[16]  Christian L. Dunis,et al.  Forecasting and Trading Currency Volatility: An Application of Recurrent Neural Regression and Model Combination , 2002 .

[17]  Leandro dos Santos Coelho,et al.  Computational intelligence approaches and linear models in case studies of forecasting exchange rates , 2007, Expert Syst. Appl..

[18]  Martin Stepnicka,et al.  Forecasting seasonal time series with computational intelligence: On recent methods and the potential of their combinations , 2013, Expert Syst. Appl..

[19]  Yanqing Zhang,et al.  Statistical fuzzy interval neural networks for currency exchange rate time series prediction , 2007, Appl. Soft Comput..

[20]  Emrah Önder,et al.  Forecasting Macroeconomic Variables Using Artificial Neural Network and Traditional Smoothing Techniques , 2013 .