Biologically Inspired Algorithms for Financial Modelling / Edition 1

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Overview

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.

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Editorial Reviews

From the Publisher
From the reviews:

"Anthony Brabazon and Michael O’Neill … have just published an interesting book that introduces a wide range of biologically inspired algorithms and their applications in financial modelling. … This book is a well-written, easy to read, brief introduction to the state-of-the-art biologically inspired algorithms." (Mak Kaboudan, Genetic Programming and Evolvable Machines, Vol. 7, 2006)

“The objective of this book is to provide an introduction to biologically inspired algorithms and some tightly scoped practical examples in finance. … provides some new insights and alternative tools for the financial modelling toolbox. … The goal and objective of the book is to provide practical examples using these evolutionary algorithms and it does that decently … . Overall I found the book very enlightening … and it has provided ideas and alternative ways to think about solutions.” (Brad G. Kyer, SIGACT News, Vol. 40 (4), 2009)

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Product Details

  • ISBN-13: 9783540262527
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 2/10/2006
  • Series: Natural Computing Series
  • Edition description: 2006
  • Edition number: 1
  • Pages: 275
  • Product dimensions: 9.21 (w) x 6.14 (h) x 0.69 (d)

Table of Contents

1 Introduction 1
2 Neural network methodologies 15
3 Evolutionary methodologies 37
4 Grammatical evolution 73
5 The particle swarm model 89
6 Ant colony models 99
7 Artificial immune systems 107
8 Model development process 121
9 Technical analysis 143
10 Overview of case studies 159
11 Index prediction using MLPs 161
12 Index prediction using a MLP-GA hybrid 175
13 Index trading using grammatical evolution 183
14 Adaptive trading using grammatical evolution 193
15 Intra-day trading using grammatical evolution 203
16 Automatic generation of foreign exchange trading rules 211
17 Corporate failure prediction using grammatical evolution 219
18 Corporate failure prediction using an ant model 229
19 Bond rating using grammatical evolution 239
20 Bond rating using AIS 249
21 Wrap-up 255
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