Foreign-Exchange-Rate Forecasting with Artificial Neural Networks

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149,79 

International Series in Operations Research & Management Science 107

ISBN: 1441944044
ISBN 13: 9781441944047
Autor: Yu, Lean/Wang, Shouyang/Lai, Kin Keung
Verlag: Springer Verlag GmbH
Umfang: xxiii, 316 S.
Erscheinungsdatum: 25.11.2010
Auflage: 1/2007
Produktform: Kartoniert
Einband: KT

The book’s modeling framework is multi-level enabling agent of an intelligent foreign-exchange-rate-forecasting methodology. Adding to the methodology is a decision-support system, which can be delivered by both a client/server model and widely-used web technologiesBecause of the highly useful computational techniques of Artificial Neural Networks (ANNs) to foreign-exchange-rate forecasting, managers, analysts and technical practitioners in financial institutions across the world will have considerable interest in the book, as well as scholars and graduate students studying financial markets and business forecastIncludes supplementary material: sn.pub/extras

Artikelnummer: 1522234 Kategorie:

Beschreibung

InhaltsangabePreface.- 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.

Inhaltsverzeichnis

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.

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