Foreign-Exchange-Rate Forecasting with Artificial Neural NetworksSpringer Science & Business Media, 2007. gada 2. aug. - 313 lappuses 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. |
No grāmatas satura
1.–5. rezultāts no 25.
xi. lappuse
Atvainojiet, šīs lappuses saturs ir ierobežots..
Atvainojiet, šīs lappuses saturs ir ierobežots..
27. lappuse
Atvainojiet, šīs lappuses saturs ir ierobežots..
Atvainojiet, šīs lappuses saturs ir ierobežots..
30. lappuse
Atvainojiet, šīs lappuses saturs ir ierobežots..
Atvainojiet, šīs lappuses saturs ir ierobežots..
44. lappuse
Atvainojiet, šīs lappuses saturs ir ierobežots..
Atvainojiet, šīs lappuses saturs ir ierobežots..
46. lappuse
Atvainojiet, šīs lappuses saturs ir ierobežots..
Atvainojiet, šīs lappuses saturs ir ierobežots..
Saturs
Are Foreign Exchange Rates Predictable? A Literature Review from Artificial Neural Networks Perspective | 3 |
12 Literature Collection | 5 |
13 Analytical Results and Factor Investigation | 7 |
132 Factor Analysis | 8 |
14 Implications and Research Topics | 21 |
15 Conclusions | 23 |
Basic Learning Principles of Artificial Neural Networks and Data Preparation | 25 |
Basic Learning Principles of Artificial Neural Networks | 27 |
922 Feature Selection with GA for SVM Modeling | 160 |
923 A Hybrid GASVM Model | 164 |
93 Empirical Study | 165 |
932 Descriptions of Other Comparable Forecasting Models | 167 |
933 Experiment Results | 168 |
94 Comparisons of Three Hybrid Neural Network Models | 172 |
95 Conclusions | 173 |
Neural Network Ensemble for Foreign Exchange Rates Forecasting | 175 |
22 Basic Structure of the BPNN Model | 28 |
23 Learning Process of the BPNN Algorithm | 30 |
24 Weight Update Formulae of the BPNN Algorithm | 31 |
25 Conclusions | 37 |
Data Preparation in Neural Network Data Analysis | 39 |
32 Neural Network for Data Analysis | 42 |
33 An Integrated Data Preparation Scheme | 44 |
332 Data PreAnalysis Phase | 46 |
333 Data Preprocessing Phase | 51 |
334 Data PostAnalysis Phase | 56 |
34 CostsBenefits Analysis of the Integrated Scheme | 59 |
35 Conclusions | 61 |
Individual Neural Network Models with Optimal Learning Rates and Adaptive Momentum Factors for Foreign Exchange Rates Prediction | 63 |
Forecasting Foreign Exchange Rates Using an Adaptive BackPropagation Algorithm with Optimal Learning Rates and Momentum Factors | 65 |
42 BP Algorithm with Optimal Learning Rates and Momentum Factors | 68 |
422 Determination of Optimal Momentum Factors | 76 |
43 Experiment Study | 78 |
432 Experimental Results | 80 |
44 Concluding Remarks | 84 |
An Online BP Learning Algorithm with Adaptive Forgetting Factors for Foreign Exchange Rates Forecasting | 87 |
52 An Online BP Learning Algorithm with Adaptive Forgetting Factors | 88 |
53 Experimental Analysis | 94 |
532 Experimental Results | 96 |
54 Conclusions | 99 |
An Improved BP Algorithm with Adaptive Smoothing Momentum Terms for Foreign Exchange Rates Prediction | 101 |
62 Formulation of the Improved BP Algorithm | 103 |
622 Formulation of the Improved BPNN Algorithm | 106 |
63 Empirical Study | 108 |
631 Data Description and Experiment Design | 109 |
633 Comparisons of Different Learning Rates | 112 |
634 Comparisons with Different Momentum Factors | 113 |
635 Comparisons with Different Error Propagation Methods | 114 |
636 Comparisons with Different Numbers of Hidden Neurons | 115 |
637 Comparisons with Different Hidden Activation Functions | 116 |
64 Comparisons of Three Single Neural Network Models | 117 |
Hybridizing ANN with Other Forecasting Techniques for Foreign Exchange Rates Forecasting | 119 |
Hybridizing BPNN and Exponential Smoothing for Foreign Exchange Rate Prediction | 121 |
72 Basic Backgrounds | 123 |
722 Neural Network Forecasting Model | 125 |
73 A Hybrid Model Integrating BPNN and Exponential Smoothing | 127 |
74 Experiments | 129 |
75 Conclusions | 130 |
A Nonlinear Combined Model Hybridizing ANN and GLAR for Exchange Rates Forecasting | 133 |
82 Model Building Processes | 136 |
822 Artificial Neural Network ANN Model | 138 |
823 A Hybrid Model Integrating GLAR with ANN | 139 |
824 Combined Forecasting Models | 141 |
825 A Nonlinear Combined NC Forecasting Model | 142 |
826 Forecasting Evaluation Criteria | 145 |
83 Empirical Analysis | 148 |
84 Conclusions | 153 |
A Hybrid GABased SVM Model for Foreign Exchange Market Tendency Exploration | 155 |
92 Formulation of the Hybrid GASVM Model | 158 |
Forecasting Foreign Exchange Rates with a Multistage Neural Network Ensemble Model | 177 |
102 Motivations for Neural Network Ensemble Model | 179 |
103 Formulation of Neural Network Ensemble Model | 181 |
1032 Preprocessing Original Data | 182 |
1033 Generating Individual Neural Predictors | 185 |
1034 Selecting Appropriate Ensemble Members | 187 |
1035 Ensembling the Selected Members | 192 |
104 Empirical Analysis | 196 |
1043 Experiment Results and Comparisons | 198 |
105 Conclusions | 201 |
Neural Networks MetaLearning for Foreign Exchange Rates Ensemble Forecasting | 203 |
112 Introduction of Neural Network Learning Paradigm | 204 |
113 Neural Network MetaLearning Process for Ensemble | 206 |
1132 Data Sampling | 207 |
1133 Individual Neural Network Base Model Creation | 209 |
1134 Neural Network Base Model Pruning | 210 |
1135 NeuralNetworkBased MetaModel Generation | 212 |
114 Empirical Study | 213 |
1142 Experiment Results | 215 |
115 Conclusions | 216 |
Predicting Foreign Exchange Market Movement Direction Using a ConfidenceBased Neural Network Ensemble Model | 217 |
122 Formulation of Neural Network Ensemble Model | 219 |
1221 Partitioning Original Data Set | 220 |
1222 Creating Individual Neural Network Classifiers | 221 |
1223 BP Network Learning and Confidence Value Generation | 222 |
1224 Confidence Value Transformation | 223 |
123 Empirical Study | 226 |
124 Comparisons of Three Ensemble Neural Networks | 230 |
Foreign Exchange Rates Forecasting with Multiple Candidate Models Selecting or Combining? A Further Discussion | 233 |
132 Two Dilemmas and Their Solutions | 237 |
133 Empirical Analysis | 242 |
134 Conclusions and Future Directions | 244 |
Developing an Intelligent Foreign Exchange Rates Forecasting and Trading Decision Support System | 247 |
Developing an Intelligent Forex Rolling Forecasting and Trading Decision Support System I Conceptual Framework Modeling Techniques and Syste... | 249 |
142 System Framework and Main Functions | 250 |
143 Modeling Approach and Quantitative Measurements | 252 |
1431 BPNNBased Forex Rolling Forecasting SubSystem | 253 |
1432 WebBased Forex Trading Decision Support System | 263 |
144 Development and Implementation of FRFTDSS | 269 |
1442 Implementation of the FRFTDSS | 270 |
145 Conclusions | 274 |
Developing an Intelligent Forex Rolling Forecasting and Trading Decision Support System II An Empirical and Comprehensive Assessment | 275 |
152 Empirical Assessment on Performance of FRFTDSS | 276 |
1522 Nonparametric Evaluation Methods | 278 |
153 Performance Comparisons with Classical Models | 280 |
154 Performance Comparisons with Other Systems | 281 |
1542 Performance Comparisons with Other Existing Systems | 283 |
1543 A Comprehensive Comparison Analysis | 285 |
155 Discussions and Conclusions | 288 |
291 | |
311 | |
Citi izdevumi - Skatīt visu
Foreign-Exchange-Rate Forecasting with Artificial Neural Networks Lean Yu,Shouyang Wang,Kin Keung Lai Ierobežota priekšskatīšana - 2010 |
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 combined forecasting computational data preparation scheme data set Dstat ensemble forecasting ensemble members ensemble strategy Equation error function EUR/USD evaluation exchange rates forecasting exchange rates prediction exponential smoothing forecasting model forecasting results foreign exchange market foreign exchange rates forex forecasting FRFTDSS GASVM genetic algorithm GLAR gradient descent hidden layer individual neural Japanese yen JPY/USD linear mean square error meta-learning meta-model method MLFNN network data analysis network ensemble model network learning 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 subsets SVM model Table technique tion training data training set transfer function vector WFTDSS Zhang
Atsauces uz šo grāmatu
Bio-Inspired Credit Risk Analysis: Computational Intelligence with Support ... Lean Yu,Shouyang Wang,Kin Keung Lai,Ligang Zhou Ierobežota priekšskatīšana - 2008 |