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Biologically Inspired Algorithms for Financial Modelling

By: Anthony Brabazon, Michael O'Neill

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Hardcover | 16 December 2005

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Hardcover
296 Pages

Dimensions(cm)
23.5 x 15.88 x 2.54

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$169.00

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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.

Industry Reviews

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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Paperback

Published: 12th February 2010

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Introduction	p. 1
Biologically Inspired Algorithms	p. 2
Artificial Neural Networks	p. 2
Evolutionary Computation	p. 2
Social Systems	p. 4
Artificial Immune Systems	p. 4
Computer Trading on Financial Markets	p. 4
Challenges in the Modelling of Financial Markets	p. 5
Do Prices Follow a Random Walk?	p. 6
Attack of the Anomalies	p. 7
Linear Models	p. 8
Structure of the Book	p. 10
Methodologies
Neural Network Methodologies	p. 15
A Taxonomy of NNs	p. 15
The Multi Layer Perceptron	p. 16
Training an MLP	p. 20
Practical Issues in Training MLPs	p. 23
Recurrent Networks	p. 28
Radial Basis Function Networks	p. 29
Self-organising Maps	p. 32
Implementing a SOM	p. 33
Summary	p. 35
Evolutionary Methodologies	p. 37
Genetic Algorithm	p. 37
Canonical GA	p. 40
Example of the GA	p. 41
Extending the Canonical GA	p. 43
Schema and Building Blocks	p. 48
Differential Evolution	p. 49
DE Algorithm	p. 49
Genetic Programming	p. 54
More Complex GP Architectures	p. 58
Combining EA and MLP Methodologies	p. 63
Applying EAs to Evolve Trading Rules	p. 68
Recent Developments in Evolutionary Computation	p. 70
Summary	p. 71
Grammatical Evolution	p. 73
Grammatical Evolution	p. 73
Biological Analogy	p. 74
Mapping Process	p. 76
Mapping Example	p. 79
Mutation and Crossover in GE	p. 82
Recent Developments in GE	p. 84
Search Engine	p. 84
Meta-grammars	p. 85
[pi]GE	p. 87
Applications and Alternative Grammars	p. 87
Summary	p. 88
The Particle Swarm Model	p. 89
PSO Algorithm	p. 89
Constriction Coefficient Version of PSO	p. 92
Parameter Settings for PSO	p. 93
Discrete PSO	p. 94
Comparing PSO and the GA	p. 94
MLP-Swarm Hybrids	p. 95
Grammatical Swarm	p. 95
Example of a Financial Application of PSO	p. 96
Recent Developments in PSO	p. 96
Summary	p. 97
Ant Colony Models	p. 99
Ant-Foraging Models	p. 99
Ant-Foraging Algorithm	p. 100
A Financial Application of ACO	p. 104
Ant-Inspired Classification Algorithms	p. 105
Hybrid Ant Models	p. 105
Summary	p. 106
Artificial Immune Systems	p. 107
Overview of Natural Immune Systems	p. 108
Innate vs Adaptive Immunity	p. 108
Components of the Immune System	p. 108
Designing Artificial Immune Algorithms	p. 113
Negative Selection Algorithm	p. 113
Clonal Expansion and Selection Algorithm	p. 114
Financial Application of the Negative Selection Algorithm	p. 116
Summary	p. 118
Model Development
Model Development Process	p. 121
Project Goals	p. 121
What to Forecast?	p. 121
What Performance Measure Is Appropriate?	p. 123
Data Collection	p. 124
Trading Philosophy	p. 124
How Much Data Is Enough?	p. 128
Selecting and Preprocessing the Data	p. 130
Selection	p. 130
Preprocessing	p. 130
Postprocessing the Output	p. 134
Entry Strategy	p. 134
Exit Strategy	p. 134
Money Management	p. 135
Validating the System	p. 135
Implementation and Maintenance	p. 140
Summary	p. 142
Technical Analysis	p. 143
Technical Indicators	p. 144
Moving Average	p. 146
Momentum	p. 148
Breakout	p. 149
Stochastic Oscillators	p. 150
Volume Data	p. 152
Other Indicators	p. 153
Using Technical Indicators in a Trading System	p. 154
Summary	p. 155
Case Studies
Overview of Case Studies	p. 159
Index Prediction Using MLPs	p. 161
Methodology	p. 162
Model Selection	p. 166
Model Stacking	p. 167
Results	p. 169
RMSE and Correlation	p. 169
Trading System	p. 171
Discussion	p. 172
Index Prediction Using a MLP-GA Hybrid	p. 175
Methodology	p. 175
Model Construction	p. 176
Results	p. 178
MLP-GA	p. 179
Analysis of Weight Vectors	p. 180
Discussion	p. 182
Index Trading Using Grammatical Evolution	p. 183
Methodology	p. 183
GE System Setup	p. 188
Results	p. 189
Discussion	p. 190
Adaptive Trading Using Grammatical Evolution	p. 193
Introduction	p. 193
Methodology	p. 193
Moving Window	p. 194
Variable Position Trading	p. 194
Return Calculation	p. 195
Results	p. 196
Training Returns	p. 197
Out-of-Sample Returns	p. 199
Discussion	p. 201
Intra-day Trading Using Grammatical Evolution	p. 203
Background	p. 203
Methodology	p. 204
Trading System	p. 206
GE System Setup	p. 207
Results	p. 208
Discussion	p. 210
Automatic Generation of Foreign Exchange Trading Rules	p. 211
Background	p. 211
Methodology	p. 212
Results	p. 214
US-STG	p. 216
US-Yen	p. 217
US-DM	p. 217
Discussion	p. 218
Corporate Failure Prediction Using Grammatical Evolution	p. 219
Background	p. 220
Definition of Corporate Failure	p. 220
Explanatory Variables	p. 221
Methodology	p. 222
GE System Setup	p. 223
LDA Method	p. 224
Results	p. 224
Form of the Evolved Classifiers	p. 225
Discussion	p. 226
Corporate Failure Prediction Using an Ant Model	p. 229
Background	p. 229
Methodology	p. 230
Ant System	p. 231
Results	p. 235
Discussion	p. 238
Bond Rating Using Grammatical Evolution	p. 239
Background	p. 240
Rating Process	p. 240
Methodology	p. 241
Results	p. 243
Discussion	p. 247
Bond Rating Using AIS	p. 249
Methodology	p. 249
Results	p. 252
Discussion	p. 252
Wrap-up	p. 255
References	p. 257
Index	p. 271
Table of Contents provided by Ingram. All Rights Reserved.

Biologically Inspired Algorithms for Financial Modelling

At a Glance

Hardcover

Industry Reviews

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