Genetic algorithms and genetic programming in computational finance

著者

    • International Conference of the Society for Computational Economics on Computing in Economics and Finance

書誌事項

Genetic algorithms and genetic programming in computational finance

edited by Shu-Heng Chen

Kluwer Academic Publishers, c2002

大学図書館所蔵 件 / 全10件

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注記

"Ten chapters of the volume are based on a selection of papers presented at the 6th International Conference of the Society for Computational Economics on Computing in Economics and Finance (SCE'2000), which was held at Universitat Pompeu Fabra, Barcelona, Catalonia, Spain on July 6-8, 2000"--Pref

Includes bibliographical references and index

内容説明・目次

内容説明

After a decade of development, genetic algorithms and genetic programming have become a widely accepted toolkit for computational finance. Genetic Algorithms and Genetic Programming in Computational Finance is a pioneering volume devoted entirely to a systematic and comprehensive review of this subject. Chapters cover various areas of computational finance, including financial forecasting, trading strategies development, cash flow management, option pricing, portfolio management, volatility modeling, arbitraging, and agent-based simulations of artificial stock markets. Two tutorial chapters are also included to help readers quickly grasp the essence of these tools. Finally, a menu-driven software program, Simple GP, accompanies the volume, which will enable readers without a strong programming background to gain hands-on experience in dealing with much of the technical material introduced in this work.

目次

  • List of Figures. List of Tables. Preface. 1. An Overview
  • S.-H. Chen. Part I: Introduction. 2. Genetic Algorithms in Economics and Finance
  • A.E. Drake, R.E. Marks. 3. Genetic Programming: A Tutorial
  • S.-H. Chen, et al. Part II: Forecasting. 4. GP and the Predictive Power of Internet Message Traffic
  • J.D. Thomas, K. Sycara. 5. Genetic Programming of Polynomial Models for Financial Forecasting
  • N.Y. Nikolaev, H. Iba. 6. NXCS: Hybrid Approach to Stock Indexes Forecasting
  • G. Armano, et al. Part III: Trading. 7. EDDIE for Financial Forecasting
  • E.P.K. Tsang, J. Li. 8. Forecasting Market Indices Using Evolutionary Automatic Programming
  • O'Neil, et al. 9.Genetic Fuzzy Expert Trading System for NASDAQ Stock Market Timing
  • S.S. Lam, et al. Part IV: Miscellaneous Applications Domains. 10. Portfolio Selection and Management
  • J.G.L. Lazo, et al. 11. Intelligent Cash Flow: Planning and Optimization Using GA
  • M.A.C. Pacheco, et al. 12. The Self-Evolving Logic of Financial Claim Prices
  • T.H. Noe, J. Wang. 13. using GP to Predict Exchange Rate Volatility
  • C.J. Neely, P.A. Weller. 14. EDDIE for Stock Index Options and Futures Arbitrage
  • S. Markose, et al. Part V: Agent-Based Computational Finance. 15. A Model of Boundedly Rational Consumer Choice
  • T. Riechmann. 16. Price Discovery in Agent-Based Computational Modeling of the Artificial Stock Market
  • S.-H. Chen, C.-C. Liao. 17. Individual Rationality as a Partial Impediment to Market Efficiency
  • S.-H. Chen, et al. 18. A Numerical Study on the Evolution of Portfolio Rules
  • G. Caldarelli, et al. 19. Adaptive Portfolio Managers in Stock Markets
  • K.Y. Szeto. 20. Learning and Convergence to Pareto Optimality
  • C.R. Birchenhall, J.-S. Lin. Part VI: Retrospect and Prospect. 21. The New Evolutionary Computational Paradigm
  • S.M. Markose. Index.

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