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Biologically Inspired Algorithms for Financial Modelling

By Anthony Brabazon , Michael O'Neill

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  • ISBN13: 978-3-5402-6252-7
  • 296 Pages
  • User Level: Science
  • Publication Date: March 28, 2006
  • Available eBook Formats: PDF
Full Description
Predicting the future for financial gain is a difficult, sometimes profitable activity. The focus of this book is the application of biologically inspired algorithms (BIAs) to financial modelling. In a detailed introduction, the authors explain computer trading on financial markets and the difficulties faced in financial market modelling. Then Part I provides a thorough guide to the various bioinspired methodologies – neural networks, evolutionary computing (particularly genetic algorithms and grammatical evolution), particle swarm and ant colony optimization, and immune systems. Part II brings the reader through the development of market trading systems. Finally, Part III examines real-world case studies where BIA methodologies are employed to construct trading systems in equity and foreign exchange markets, and for the prediction of corporate bond ratings and corporate failures. The book was written for those in the finance community who want to apply BIAs in financial modelling, and for computer scientists who want an introduction to this growing application domain.
Table of Contents

Table of Contents

  1. Introduction: Introduction.
  2. Part 1 Methodologies: Introduction to Modelling.
  3. Neural Network Methodologies.
  4. Evolutionary Methodologies.
  5. Grammatical Evolution.
  6. The Particle Swarm Model.
  7. Ant Colony Systems.
  8. Artificial Immune Systems.
  9. Part 2 Model Development: Model Development Process.
  10. Technical Analysis.
  11. Overview of Case Studies.
  12. Index Prediction Using MLPs.
  13. Part 3 Case Studies: Index Prediction Using a Hybrid MLP
  14. GA.
  15. Index Trading Using Grammatical Evolution.
  16. Intra
  17. day Trading Using Grammatical Evolution.
  18. Automatic Generation of Foreign Exchange Trading Rules.
  19. Corporate Failure Prediction Using GE.
  20. Corporate Failure Prediction Using an Ant
  21. Clustering Model.
  22. Bond Rating Using GE.
  23. Bond Rating Using AIS.
  24. Wrap
  25. up.
  26. References.

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