Foreign-Exchange-Rate Forecasting with Artificial Neural Networks

Pirmais vāks
Springer Science & Business Media, 2007. gada 2. aug. - 313 lappuses
1 Atsauksme
Atsauksmes netiek pārbaudītas, taču Google meklē viltus saturu un noņem to, ja tāds tiek identificēts.
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

Lietotāju komentāri - Rakstīt atsauksmi

Ierastajās vietās neesam atraduši nevienu atsauksmi.

Atlasītās lappuses

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
References
291
Subject Index
311
Autortiesības

Citi izdevumi - Skatīt visu

Bieži izmantoti vārdi un frāzes

Bibliogrāfiskā informācija