Computational Intelligence in Economics and Finance: Volume II, 2. sējums

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Paul P. Wang, Tzu-Wen Kuo
Springer Science & Business Media, 2007. gada 11. jūl. - 228 lappuses

Computational intelligence (CI), as an alternative to statistical and econometric approaches, has been applied to a wide range of economics and finance problems in recent years, for example to price forecasting and market efficiency.

This book contains research ranging from applications in financial markets and business administration to various economics problems. Not only are empirical studies utilizing various CI algorithms presented, but so also are theoretical models based on computational methods. In addition to direct applications of computational intelligence, readers can also observe how these methods are combined with conventional analytical methods such as statistical and econometric models to yield preferred results.

Chen, Wang, and Kuo have grouped the 12 contributions following their introductory chapter into applications of fuzzy logic, neural networks (including self-organizing maps and support vector machines), and evolutionary computation. All chapters were selected either by invitation or based on a careful selection and extension of best papers from the International Workshop on Computational Intelligence in Economics and Finance in 2005. Overall, the book offers researchers an excellent overview of current advances and applications of computational intelligence techniques to economics and finance problems.

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Shifting the Research Frontier
An Overview of Insurance Uses of Fuzzy Logic
Forecasting Agricultural Commodity Prices using Hybrid Neural Networks
Nonlinear Principal Component Analysis for Withdrawal from the Employment Time Guarantee Fund
A Comparison
An Application of Kohonens SOFM to the Management of Benchmarking Policies
Trading Strategies Based on Kmeans Clustering and Regression Models
Comparison of InstanceBased Techniques for Learning to Predict Changes in Stock Prices
Application of an Instance Based Learning Algorithm for Predicting the Stock Market Index
Evaluating the Efficiency of Index Fund Selections Over the Funds Future Period
Distinguishing between Efficient Markets and Inefficient Algorithms
Nonlinear GoalDirected CPPI Strategy
A LogicalHeuristic Approach

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49. lappuse - Assume that we are given a fuzzy goal G and a fuzzy constraint C in a space of alternatives X. Then, G and C combine to form a decision, D, which is a fuzzy set resulting from intersection of G and C. In symbols...
17. lappuse - Weka is a collection of machine learning algorithms for solving real world data mining problems. It Is written in Java and runs on almost any platform.
6. lappuse - San Diego Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation).
60. lappuse - An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network", Fuzzy Sets and Systems 118, 21-45 (2001).
60. lappuse - Fuzzy modelling and analytic hierarchy processing to quantify risk levels associated with occupational injuries - Part I: The development of fuzzy-linguistic risk levels, IEEE Transactions on Fuzzy Systems, Vol.4, No.2, pp.
29. lappuse - FL in the evaluation of seismic intensity and damage forecasting, and for the development of models to estimate earthquake insurance premium rates and insurance strategies.
1. lappuse - Department of Electrical and Computer Engineering Duke University, Durham, NC 27708, USA...
155. lappuse - Neural Networks for Technical Analysis: A Study on KLCI," International Journal of Theoretical, and Applied Finance, 2(2), 221-241. 160. Zhang, P. (1998), "Exotic Options," World Scientific Advanced FX Exotic Option Seminar Series.
28. lappuse - A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one.

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Par autoru (2007)

Prof. Dr Shu-Heng Chen is a professor in the Department of Economics of the National Chengchi University. He serves as the Director of the AI-ECON Research Center, National Chengchi University. Dr. Chen holds a M.A. degree in mathematics and a Ph. D. in Economics from the University of California at Los Angeles. He has more than 150 publications in international journals, edited volumes and conference proceedings.

Prof. Dr. Paul P. Wang, has published extensively in the fields of mathematical systems modeling, fuzzy logic, pattern recognition,intelligent ystems,managements of economical systems, and the computational biology and bioinformatics. He has been a co-founder of several corporations including Intelligent Machines Inc. He has served as an EiC of the Information Sciences Journal for two decades and he is the managing editor of the New Mathematics & Natural Computing at present. In addition,he is the founder of JCIS, Inc. and Society for Mathematics of Uncertainty in 2006.

Prof. Dr. Tzu-Wen Kuo is an assistant professor in Department of Finance and Banking of Aletheia University in Taiwan. She is also a fellow of AI-ECON research center. Her research interest is Genetic Programming in Economics and Finance.

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