Biologically Inspired Algorithms for Financial Modelling / Edition 1

Biologically Inspired Algorithms for Financial Modelling / Edition 1

by Anthony Brabazon, Michael O'Neill
     
 

ISBN-10: 3540262520

ISBN-13: 9783540262527

Pub. Date: 02/10/2006

Publisher: Springer Berlin Heidelberg

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

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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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Product Details

ISBN-13:
9783540262527
Publisher:
Springer Berlin Heidelberg
Publication date:
02/10/2006
Series:
Natural Computing Series
Edition description:
2006
Pages:
277
Product dimensions:
6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

1Introduction1
2Neural network methodologies15
3Evolutionary methodologies37
4Grammatical evolution73
5The particle swarm model89
6Ant colony models99
7Artificial immune systems107
8Model development process121
9Technical analysis143
10Overview of case studies159
11Index prediction using MLPs161
12Index prediction using a MLP-GA hybrid175
13Index trading using grammatical evolution183
14Adaptive trading using grammatical evolution193
15Intra-day trading using grammatical evolution203
16Automatic generation of foreign exchange trading rules211
17Corporate failure prediction using grammatical evolution219
18Corporate failure prediction using an ant model229
19Bond rating using grammatical evolution239
20Bond rating using AIS249
21Wrap-up255

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