Foreign-Exchange-Rate Forecasting with Artificial Neural NetworksSpringer Science & Business Media, 2010. gada 26. febr. - 316 lappuses The foreign exchange market is one of the most complex dynamic markets with the characteristics of high volatility, nonlinearity and irregularity. Since the Bretton Woods System collapsed in 1970s, the fluctuations in the foreign exchange market are more volatile than ever. Furthermore, some important factors, such as economic growth, trade development, interest rates and inflation rates, have significant impacts on the exchange rate fluctuation. Meantime, these characteristics also make it extremely difficult to predict foreign exchange rates. Therefore, exchange rates forecasting has become a very important and challenge research issue for both academic and ind- trial communities. In this monograph, the authors try to apply artificial neural networks (ANNs) to exchange rates forecasting. Selection of the ANN approach for - change rates forecasting is because of ANNs’ unique features and powerful pattern recognition capability. Unlike most of the traditional model-based forecasting techniques, ANNs are a class of data-driven, self-adaptive, and nonlinear methods that do not require specific assumptions on the und- lying data generating process. These features are particularly appealing for practical forecasting situations where data are abundant or easily available, even though the theoretical model or the underlying relationship is - known. Furthermore, ANNs have been successfully applied to a wide range of forecasting problems in almost all areas of business, industry and engineering. In addition, ANNs have been proved to be a universal fu- tional approximator that can capture any type of complex relationships. |
Saturs
| 3 | |
Basic Learning Principles of Artificial Neural Networks 27 2 1 Introduction | 27 |
Individual Neural Network Models with Optimal | 63 |
An Online BP Learning Algorithm with Adaptive Forgetting | 86 |
An Improved BP Algorithm with Adaptive Smoothing | 101 |
Hybridizing ANN with Other Forecasting | 119 |
A Nonlinear Combined Model Hybridizing ANN and GLAR | 132 |
Rates Forecasting 175 10 Forecasting Foreign Exchange Rates with a Multistage Neural | 177 |
Neural Networks MetaLearning for Foreign Exchange Rate | 203 |
Predicting Foreign Exchange Market Movement Direction | 217 |
Foreign Exchange Rates Forecasting with Multiple Candidate | 232 |
Developing an Intelligent Foreign Exchange | 246 |
Developing an Intelligent Forex Rolling Forecasting | 275 |
References | 291 |
Subject Index | 311 |
Citi izdevumi - Skatīt visu
Foreign-Exchange-Rate Forecasting with Artificial Neural Networks Lean Yu,Shouyang Wang,Kin Keung Lai Ierobežota priekšskatīšana - 2007 |
Foreign-Exchange-Rate Forecasting with Artificial Neural Networks Lean Yu,Shouyang Wang,Kin Keung Lai Priekšskatījums nav pieejams - 2007 |
Foreign-Exchange-Rate Forecasting with Artificial Neural Networks Lean Yu,Shouyang Wang,Kin Keung Lai Priekšskatījums nav pieejams - 2010 |
Bieži izmantoti vārdi un frāzes
ANN model ARIMA artificial neural network back-propagation base models BP learning algorithm BPNN algorithm BPNN learning BPNN model BPNNFRFS chapter combined forecasting comparison computational data set Dstat ensemble forecasting ensemble members ensemble strategy Equation error function EUR/USD evaluation exchange rates forecasting exchange rates prediction exponential smoothing financial time series forecasting model forecasting results foreign exchange market foreign exchange rates forex forex forecasting FRFTDSS GASVM genetic algorithm gradient descent hidden layer individual neural Japanese yen JPY/USD linear mean square error meta-learning meta-model method network data analysis network ensemble model neural network data neural network ensemble neural network models neural predictors NMSE obtained online learning algorithm optimal learning rate overfitting parameters phase problem proposed online learning rate and momentum RMSE rolling forecasting sample series forecasting standard BPNN SVM model Table technique tion training data training set transfer function vector WFTDSS Zhang