Biologically Inspired Algorithms for Financial Modelling

  • Anthony Brabazon
  • Michael O’Neill

Part of the Natural Computing Series book series (NCS)

Table of contents

  1. Front Matter
    Pages I-XV
  2. Introduction

    1. Pages 1-11
  3. Methodologies

    1. Front Matter
      Pages 13-13
    2. Pages 73-88
    3. Pages 99-106
  4. Model Development

    1. Front Matter
      Pages 119-119
    2. Pages 143-155
  5. Case Studies

  6. Back Matter
    Pages 257-276

About this book


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.


Finance Rating algorithms ant colony systems biologically inspired algorithms (BIAs) calculus computer trading evolutionary methodologies financial trading genetic algorithms (GAs) grammatical evolution (GE) linear optimization model modeling optimization

Authors and affiliations

  • Anthony Brabazon
    • 1
  • Michael O’Neill
    • 1
  1. 1.University College DublinBelfield, Dublin 4Ireland

Bibliographic information