Computational Intelligence in Economics and Finance
By: edited by Shu-Heng Chen, Paul P. Wang
Contributors: Wang, Paul P.
Series: Advanced Information Processing; Advanced Information Processing
Publisher: Springer Berlin Heidelberg
Published: 2004
Place of publication: Berlin, Heidelberg
Language: eng
Format: [electronic resource] /
Number of pages: 480
ISBN-13: 9783642079023; 9783662063736
Subjects: Mathematics; Information systems; Computer science; Artificial intelligence; Finance
Description: 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.
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Table of contents: title: Part I Introduction: Computational Intelligence in Economics and Finance; title: Part II Fuzzy Logic and Rough Sets: Intelligent System to Support Judgemental Business Forecasting: The Case of Estimating Hotel Room Demand; Fuzzy Investment Analysis Using Capital Budgeting and Dynamic Programming Techniques; Rough Sets Theory and Multivariate Data Analysis in Classification Problems; title: Part III Artificial Neural Networks and Support Vector Machines: Foreasting the Opening Cash Price Index in Integrating Grey Forecasting and Neural Networks; A Support Vector Machine Model for Currency Crises Discrimination; Saliency Analysis of Support Vector Machines for Feature Selection in Financial Time Series Forecasting; title: Part IV Self-Organizing Maps and Wavelets: Searching Financial Patterns with Self-Organizing Maps; Effective Position of European Firms in the Face of Monetary Integration Using Kohonen's SOFM; Financial Applications of Wavelets and Self-Organizing Maps; title: Part V Sequence Matching and Feature-Based Time Series Models: Pattern Matching in Multidimensional Time Series; Structural Pattern Discovery in Time Series Databases; Are Efficient Markets Really Efficient?; Can Financial Econometric Tests Convince Machine-Learning People?; Nearest-Neighbour Predictions in Foreign Exchange Markets; title: Part VI - Evolutionary Computation, Swarm Intelligence and Simulated Annealing: Discovering Hidden Patterns with Genetic Programming; Numerical Solutions to Stochastic Growth Model Based on the Evolution of Radial Basis Network; Evolutionary Strategies versus Neural Networks: An Inflation Forecasting Experiment; Business Failure Prediction Using Modified Ants Algorithm; Towards Automated Optimal Equity Portfolios Discovery in a Financial Knowledge Management System; title: Part VII State Space Modeling of Time Series: White Noise Tests and Synthesis of APT Electronic Factors Using TFA; Learning and Monetary Policy in a Spectral Analysis Representation; International Transmission Business Cycles: A Self-Organizing Markov-Switching State-Space Model; title: Part VIII Agent-Based Models: How Information Technology Creates Business Value in the Past and in the Current EC Area.