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Foreign-Exchange-Rate Forecasting with Artificial Neural Networks

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Cover of 'Foreign-Exchange-Rate Forecasting with Artificial Neural Networks'

Table of Contents

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    Book Overview
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    Chapter 1 Are Foreign Exchange Rates Predictable? — A Literature Review from Artificial Neural Networks Perspective
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    Chapter 2 Basic Learning Principles of Artificial Neural Networks
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    Chapter 3 Data Preparation in Neural Network Data Analysis
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    Chapter 4 Forecasting Foreign Exchange Rates Using an Adaptive Back-Propagation Algorithm with Optimal Learning Rates and Momentum Factors
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    Chapter 5 An Online BP Learning Algorithm with Adaptive Forgetting Factors for Foreign Exchange Rates Forecasting
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    Chapter 6 An Improved BP Algorithm with Adaptive Smoothing Momentum Terms for Foreign Exchange Rates Prediction
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    Chapter 7 Hybridizing BPNN and Exponential Smoothing for Foreign Exchange Rate Prediction
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    Chapter 8 A Nonlinear Combined Model Hybridizing ANN and GLAR for Exchange Rates Forecasting
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    Chapter 9 A Hybrid GA-Based SVM Model for Foreign Exchange Market Tendency Exploration
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    Chapter 10 Forecasting Foreign Exchange Rates with a Multistage Neural Network Ensemble Model
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    Chapter 11 Neural Networks Meta-Learning for Foreign Exchange Rate Ensemble Forecasting
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    Chapter 12 Predicting Foreign Exchange Market Movement Direction Using a Confidence-Based Neural Network Ensemble Model
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    Chapter 13 Foreign Exchange Rates Forecasting with Multiple Candidate Models: Selecting or Combining? A Further Discussion
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    Chapter 14 Developing an Intelligent Forex Rolling Forecasting and Trading Decision Support System I: Conceptual Framework, Modeling Techniques and System Implementations
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    Chapter 15 Developing an Intelligent Forex Rolling Forecasting and Trading Decision Support System II: An Empirical and Comprehensive Assessment
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Title
Foreign-Exchange-Rate Forecasting with Artificial Neural Networks
Published by
Springer Science & Business Media, February 2010
DOI 10.1007/978-0-387-71720-3
ISBNs
978-0-387-71720-3, 978-0-387-71719-7, 978-1-4419-4404-7
Authors

Yu, Lean, Wang, Shouyang, Lai, Kin Keung, Lean Yu, Shouyang Wang, Kin Keung Lai

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 1 3%
Japan 1 3%
Unknown 35 95%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 32%
Student > Ph. D. Student 9 24%
Researcher 4 11%
Unspecified 3 8%
Student > Bachelor 3 8%
Other 5 14%
Unknown 1 3%
Readers by discipline Count As %
Computer Science 10 27%
Economics, Econometrics and Finance 6 16%
Engineering 4 11%
Business, Management and Accounting 3 8%
Mathematics 3 8%
Other 10 27%
Unknown 1 3%