Foreign-Exchange-Rate Forecasting with Artificial Neural Networks [electronic resource] /
Yu, Lean.author.
Wang, Shouyang.author.
Lai, Kin Keung.author.
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 industrial communities. In this monograph, the authors try to apply artificial neural networks (ANNs) to exchange rates forecasting. Selection of the ANN approach for exchange 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 functional approximator that can capture any type of complex relationships.
Description based upon print version of record.
Includes bibliographical references and index.
Preface -- Are foreign exchange rates predictable? An anatomy of a survey from artificial neural networks perspective -- Basic principles of ANN algorithms -- Data preparation in neural network data analysis -- Forecasting foreign exchange rates using an adaptive back-propagation algorithm with optimal learning rate and momentum factor -- An online learning algorithm with adaptive forgetting factors for BP neural network in foreign exchange rate forecasting -- An improved BP algorithm with adaptive smoothing momentum terms for foreign exchange rate prediction -- Hybridizing BPNN and exponential smoothing for foreign exchange rate prediction -- A nonlinear combined model integrating ANN and GLAR for exchange rate forecasting -- A hybrid GA-based SVM model for foreign exchange market trends exploration -- Forecasting foreign exchange rates with a multistage neural network ensemble model -- Foreign exchange rate ensemble forecasting with neural network meta-learning -- A confidence-based neural network ensemble model for predicting foreign exchange market movement direction -- Foreign exchange rates forecasting with multiple candidate models: selecting or combining? -- Developing an intelligent Forex rolling forecasting and trading decision support system I: conceptual framework, modeling techniques and system implementation: developing an intelligent Forex rolling forecasting and trading decision support system II-An empirical and comprehensive assessment -- References -- Subject index -- Author index.
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 industrial communities. In this monograph, the authors try to apply artificial neural networks (ANNs) to exchange rates forecasting. Selection of the ANN approach for exchange 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 functional approximator that can capture any type of complex relationships.
Finance.
Macroeconomics.
Artificial intelligence
Economics, Mathematical .
Operations research.
Decision making.
Computer mathematics.
Finance, general.
Macroeconomics/Monetary Economics//Financial Economics.
Artificial Intelligence.
Quantitative Finance.
Operations Research/Decision Theory.
Computational Mathematics and Numerical Analysis.

International Series in Operations Research & Management Science