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Computational Intelligence in Economics and Finance

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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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Contents
Computational Intelligence in Economics and Finance: Shifting the Research Frontier ShuHeng Chen, Paul P. Wang, TzuWen Kuo. . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
An Overview of Insurance Uses of Fuzzy Logic Arnold F. Shapiro. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25
Forecasting Agricultural Commodity Prices using Hybrid Neural Networks Tamer Shahwan, Martin Odening. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .63
Nonlinear Principal Component Analysis for Withdrawal from the Employment Time Guarantee Fund LiWeigang,AiporeRodriguesdeMoraes,ShiLihua,RaulYukihiro Matsushita. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75
Estimating Female Labor Force Participation through Statistical and Machine Learning Methods: A Comparison Omar Zambrano, Claudio M. Rocco S, Marco Muselli. . . . . . . . . . . . . . . . . . . .93
An Application of Kohonen’s SOFM to the Management of Benchmarking Policies Raquel FlorezLopez. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .107
Trading Strategies Based on Kmeans Clustering and Regression Models Hongxing He, Jie Chen, Huidong Jin, ShuHeng Chen. . . . . . . . . . . . . . . . . . . .123
Comparison of InstanceBased Techniques for Learning to Predict Changes in Stock Prices David B. LeRoux. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .135
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Contents
Application of an Instance Based Learning Algorithm for Predicting the Stock Market Index Ruppa K. Thulasiram, Adenike Y. Bamgbade. . . . . . . . . . . . . . . . . . . . . . . . . . . .145
Evaluating the Efficiency of Index Fund Selections Over the Fund’s Future Period Yukiko Orito, Manabu Takeda, Kiyoaki Iimura, Genji Yamazaki. . . . . . . . . . . . .157
Failure of GeneticProgramming Induced Trading Strategies: Distinguishing between Efficient Markets and Inefficient Algorithms ShuHeng Chen, Nicolas Navet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .169
Nonlinear GoalDirected CPPI Strategy JiahShing Chen, Benjamin Penyang Liao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .183
HybridAgent Organization Modeling: A LogicalHeuristic Approach Ana Marostica, Cesar A. Briano, Ernesto Chinkes. . . . . . . . . . . . . . . . . . . . . . .209
Index225. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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