Computational Intelligence in Economics and FinancePaul P. Wang Springer Science & Business Media, 2013. gada 9. marts - 480 lappuses Due to the ability to handle specific characteristics of economics and finance forecasting problems like e.g. non-linear relationships, behavioral changes, or knowledge-based domain segmentation, we have recently witnessed a phenomenal growth of the application of computational intelligence methodologies in this field. In this volume, Chen and Wang collected not just works on traditional computational intelligence approaches like fuzzy logic, neural networks, and genetic algorithms, but also examples for more recent technologies like e.g. rough sets, support vector machines, wavelets, or ant algorithms. After an introductory chapter with a structural description of all the methodologies, the subsequent parts describe novel applications of these to typical economics and finance problems like business forecasting, currency crisis discrimination, foreign exchange markets, or stock markets behavior. |
Saturs
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References | 51 |
Intelligent System to Support Judgmental | 57 |
References | 91 |
Cengiz Kahraman and Cafer Erhan Bozdağ | 93 |
References | 125 |
References | 145 |
Forecasting the Opening Cash Price Index in Integrating | 149 |
References | 261 |
Can Financial | 288 |
References | 296 |
Discovering Hidden Patterns with Genetic Programming | 327 |
Numerical Solutions to a Stochastic Growth Model | 348 |
References | 357 |
References | 368 |
Towards Automated Optimal Equity Portfolios | 387 |
References | 169 |
References | 180 |
Searching Financial Patterns with Selforganizing Maps | 201 |
References | 216 |
References | 232 |
Pattern Matching in Multidimensional Time Series | 251 |
tions | 400 |
Learning and Monetary Policy in a Spectral Analysis | 420 |
References | 446 |
How Information Technology Creates Business Value | 448 |
References | 465 |