Computational Intelligence in Economics and Finance

Volume II

By Paul P. Wang , Tzu-Wen Kuo

Computational Intelligence in Economics and Finance Cover Image

  • ISBN13: 978-3-5407-2820-7
  • 242 Pages
  • User Level: Science
  • Publication Date: July 11, 2007
  • Available eBook Formats: PDF
  • eBook Price: $115.00
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Full Description
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.
Table of Contents

Table of Contents

  1. Computational Intelligence in Economics and Finance: Shifting the Research Frontier.
  2. An Overview of Insurance Uses of Fuzzy Logic.
  3. Forecasting Agricultural Commodity Prices using Hybrid Neural Networks.
  4. Nonlinear Principal Component Analysis for Withdrawal from the Employment Time Guarantee Fund.
  5. Estimating Female Labor Force Participation through Statistical and Machine Learning Methods.
  6. An Application of Kohonen’s SOFM to the Management of Benchmarking Policies.
  7. Trading Strategies Based on K
  8. means Clustering and Regression Models.
  9. Comparison of Instance
  10. Based Techniques for Learning to Predict Changes in Stock Prices.
  11. Application of an Instance Based Learning Algorithm for Predicting the Stock Market Index.
  12. Evaluating the Efficiency of Index Fund Selections Over the Fund’s Future Period.
  13. Failure of Genetic
  14. Programming Induced Trading Strategies: Distinguishing between Efficient Markets and Inefficient Algorithms.
  15. Nonlinear Goal
  16. Directed CPPI Strategy.
  17. Hybrid
  18. Agent Organization Modeling: A Logical
  19. Heuristic Approach.
  20. Index.
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