+612 9045 4394
 
CHECKOUT
Foreign-Exchange-Rate Forecasting with Artificial Neural Networks : International Series in Operations Research & Management Science - Lean Yu

Foreign-Exchange-Rate Forecasting with Artificial Neural Networks

International Series in Operations Research & Management Science

Hardcover Published: 1st August 2007
ISBN: 9780387717197
Number Of Pages: 316

Share This Book:

Hardcover

RRP $480.99
$332.75
31%
OFF
or 4 easy payments of $83.19 with Learn more
Ships in 7 to 10 business days

Other Available Editions (Hide)

  • Paperback View Product Published: 25th November 2010
    $213.24

The book focuses on forecasting foreign exchange rates via artificial neural networks. It creates and applies the highly useful computational techniques of Artificial Neural Networks (ANNs) to foreign-exchange-rate forecasting. The result is an up-to-date review of the most recent research developments in forecasting foreign exchange rates coupled with a highly useful methodological approach to predicting rate changes in foreign currency exchanges. Foreign Exchange Rate Forecasting with Artificial Neural Networks is targeted at both the academic and practitioner audiences. Managers, analysts and technical practitioners in financial institutions across the world will have considerable interest in the book, and scholars and graduate students studying financial markets and business forecast will also have considerable interest in the book.

The book discusses the most important advances in foreign-exchange-rate forecasting and then systematically develops a number of new, innovative, and creatively crafted neural network models that reduce the volatility and speculative risk in the forecasting of foreign exchange rates. The book discusses and illustrates three general types of ANN models. Each of these model types reflect the following innovative and effective characteristics: (1) The first model type is a three-layer, feed-forward neural network with instantaneous learning rates and adaptive momentum factors that produce learning algorithms (both online and offline algorithms) to predict foreign exchange rates. (2) The second model type is the three innovative hybrid learning algorithms that have been created by combining ANNs with exponential smoothing, generalized linear auto-regression, and genetic algorithms. Each of these three hybrid algorithms has been crafted to forecast various aspects synergetic performance. (3) The third model type is the three innovative ensemble learning algorithms that combining multiple neural networks into an ensemble output. Empirical results reveal that these creative models can produce better performance with high accuracy or high efficiency.

From the reviews:

"This monograph consisting of six parts focuses on forecasting exchange rates via artificial neural networks (ANNs) and it is based on the fruit of a very pleasant scientific cooperation between three genuine academic researchers. ...The academic researchers together with the business practitioners interested in the recent developments concerning the forecasting foreign exchange rates with ANNs will find in this book an excellent reference." (Vasile Postolica, Zentralblatt MATH, Vol. 1125 (2), 2008)

Prefacep. xi
Biographies of Three Authors of the Bookp. xv
List of Figuresp. xvii
List of Tablesp. xxi
Forecasting Foreign Exchange Rates with Artificial Neural Networks: An Analytical Surveyp. 1
Are Foreign Exchange Rates Predictable? - A Literature Review from Artificial Neural Networks Perspectivep. 3
Introductionp. 3
Literature Collectionp. 5
Analytical Results and Factor Investigationp. 7
Basic Classifications and Factors Summarizationp. 7
Factor Analysisp. 8
Implications and Research Topicsp. 21
Conclusionsp. 23
Basic Learning Principles of Artificial Neural Networks and Data Preparationp. 25
Basic Learning Principles of Artificial Neural Networksp. 27
Introductionp. 27
Basic Structure of the BPNN Modelp. 28
Learning Process of the BPNN Algorithmp. 30
Weight Update Formulae of the BPNN Algorithmp. 31
Conclusionsp. 37
Data Preparation in Neural Network Data Analysisp. 39
Introductionp. 39
Neural Network for Data Analysisp. 42
An Integrated Data Preparation Schemep. 44
Integrated Data Preparation Scheme for Neural Network Data Analysisp. 44
Data Pre-Analysis Phasep. 46
Data Preprocessing Phasep. 51
Data Post-Analysis Phasep. 56
Costs-Benefits Analysis of the Integrated Schemep. 59
Conclusionsp. 61
Individual Neural Network Models with Optimal Learning Rates and Adaptive Momentum Factors for Foreign Exchange Rates Predictionp. 63
Forecasting Foreign Exchange Rates Using an Adaptive Back-Propagation Algorithm with Optimal Learning Rates and Momentum Factorsp. 65
Introductionp. 65
BP Algorithm with Optimal Learning Rates and Momentum Factorsp. 68
Optimal Learning Rates Determinationp. 68
Determination of Optimal Momentum Factorsp. 76
Experiment Studyp. 78
Data Description and Experiment Designp. 78
Experimental Resultsp. 80
Concluding Remarksp. 84
An Online BP Learning Algorithm with Adaptive Forgetting Factors for Foreign Exchange Rates Forecastingp. 87
Introductionp. 87
An Online BP Learning Algorithm with Adaptive Forgetting Factorsp. 88
Experimental Analysisp. 94
Data Description and Experiment Designp. 94
Experimental Resultsp. 6
Conclusionsp. 99
An Improved BP Algorithm with Adaptive Smoothing Momentum Terms for Foreign Exchange Rates Predictionp. 101
Introductionp. 101
Formulation of the Improved BP Algorithmp. 103
Determination of Adaptive Smoothing Momentump. 103
Formulation of the Improved BPNN Algorithmp. 106
Empirical Studyp. 108
Data Description and Experiment Designp. 109
Forecasting Results and Comparisonsp. 109
Comparisons of Different Learning Ratesp. 112
Comparisons with Different Momentum Factorsp. 113
Comparisons with Different Error Propagation Methodsp. 114
Comparisons with Different Numbers of Hidden Neuronsp. 115
Comparisons with Different Hidden Activation Functionsp. 116
Comparisons of Three Single Neural Network Modelsp. 117
Conclusionsp. 117
Hybridizing ANN with Other Forecasting Techniques for Foreign Exchange Rates Forecastingp. 119
Hybridizing BPNN and Exponential Smoothing for Foreign Exchange Rate Predictionp. 121
Introductionp. 121
Basic Backgroundsp. 123
Exponential Smoothing Forecasting Modelp. 123
Network Forecasting Modelp. 125
A Hybrid Model Integrating BPNN and Exponential Smoothingp. 127
Experimentsp. 129
Conclusionsp. 130
A Nonlinear Combined Model Hybridizing ANN and GLAR for Exchange Rates Forecastingp. 133
Introductionp. 133
Model Building Processesp. 136
Generalized Linear Auto-Regression (GLAR) Modelp. 136
Artificial Neural Network (ANN) Modelp. 138
A Hybrid Model Integrating GLAR with ANNp. 139
Combined Forecasting Modelsp. 141
Nonlinear Combined (NC) Forecasting Modelp. 142
Evaluation Criteriap. 145
Empirical Analysisp. 148
Data Descriptionp. 148
Empirical Resultsp. 148
Conclusionsp. 153
A Hybrid GA-Based SVM Model for Foreign Exchange Market Tendency Explorationp. 155
Introductionp. 155
Formulation of the Hybrid GA-SVM Modelp. 158
Basic Theory of SVMp. 158
Feature Selection with GA for SVM Modelingp. 160
A Hybrid GASVM Modelp. 164
Empirical Studyp. 165
Research Datap. 165
Descriptions of Other Comparable Forecasting Modelsp. 167
Experiment Resultsp. 168
Comparisons of Three Hybrid Neural Network Modelsp. 172
Conclusionsp. 173
Neural Network Ensemble for Foreign Exchange Rates Forecastingp. 175
Forecasting Foreign Exchange Rates with a Multistage Neural Network Ensemble Modelp. 177
Introductionp. 177
Motivations for Neural Network Ensemble Modelp. 179
Formulation of Neural Network Ensemble Modelp. 181
Framework of Multistage Neural Ensemble Modelp. 181
Preprocessing Original Datap. 182
Generating Individual Neural Predictorsp. 185
Selecting Appropriate Ensemble Membersp. 187
Ensembling the Selecting Membersp. 192
Empirical Analysisp. 196
Experimental Data and Evaluation Criterionp. 196
Experiment Designp. 196
Results and Comparisonsp. 198
Conclusionsp. 201
Neural Networks Meta-Learning for Foreign Exchange Rate Ensemble Forecastingp. 203
Introductionp. 203
Introduction of Neural Network Learning Paradigmp. 204
Neural Network Meta-Learning Process for Ensemblep. 206
Basic Background of Meta-Leamingp. 206
Data Samplingp. 207
Individual Neural Network Base Model Creationp. 209
Neural Network Base Model Pruningp. 210
Neural-Network-Based Meta-Model Generationp. 212
Empirical Studyp. 213
Research Data and Experiment Designp. 213
Experiment Resultsp. 215
Conclusionsp. 216
Predicting Foreign Exchange Market Movement Direction Using a Confidence-Based Neural Network Ensemble Modelp. 217
Introductionp. 217
Formulation of Neural Network Ensemble Modelp. 219
Partitioning Original Data Setp. 220
Creating Individual Neural Network Classifiersp. 221
BP Network Learning and Confidence Value Generationp. 222
Confidence Value Transformationp. 223
Integrating Multiple Classifiers into an Ensemble Outputp. 223
Empirical Studyp. 226
Comparisons of Three Ensemble Neural Networksp. 230
Conclusionsp. 230
Foreign Exchange Rates Forecasting with Multiple Candidate Models: Selecting or Combining? A Further Discussionp. 233
Introductionp. 233
Two Dilemmas and Their Solutionsp. 237
Empirical Analysisp. 242
Conclusions and Future Directionsp. 244
Developing an Intelligent Foreign Exchange Rates Forecasting and Trading Decision Support Systemp. 247
Developing an Intelligent Forex Rolling Forecasting and Trading Decision Support System I: Conceptual Framework, Modeling Techniques and System Implementationsp. 249
Introductionp. 249
System Framework and Main Functionsp. 250
Modeling Approach and Quantitative Measurementsp. 252
BPNN-Based Forex Rolling Forecasting Sub-Systemp. 253
Web-Based Forex Trading Decision Support Systemp. 263
Development and Implementation of FRFTDSSp. 269
Development of the FRFTDSSp. 269
Implementation of the FRFTDSSp. 270
Conclusionsp. 274
Developing an Intelligent Forex Rolling Forecasting and Trading Decision Support System II: An Empirical and Comprehensive Assessmentp. 275
Introductionp. 275
Empirical Assessment on Performance of FRFTDSSp. 276
Evaluation Methodsp. 276
Nonparametric Evaluation Methodsp. 278
Performance Comparisons with Classical Modelsp. 280
Selection for Comparable Classical Modelsp. 280
Performance Comparison Results with Classical Modelsp. 280
Comparisons with Other Systemsp. 281
Searching for Existing Forex Forecasting Systemsp. 281
Performance Comparisons with Other Existing Systemsp. 283
A Comprehensive Comparison Analysisp. 285
Discussions and Conclusionsp. 288
Referencesp. 291
Subject Indexp. 311
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9780387717197
ISBN-10: 0387717196
Series: International Series in Operations Research & Management Science
Audience: Tertiary; University or College
Format: Hardcover
Language: English
Number Of Pages: 316
Published: 1st August 2007
Publisher: Springer-Verlag New York Inc.
Country of Publication: US
Dimensions (cm): 23.5 x 15.5  x 2.54
Weight (kg): 0.68