Biologically Inspired Algorithms for Financial Modelling

Introduction: Introduction.
- Part 1 Methodologies: Introduction to Modelling.
- Neural Network Methodologies.
- Evolutionary Methodologies.
- Grammatical Evolution.
- The Particle Swarm Model.
- Ant Colony Systems.
- Artificial Immune Systems.
- Part 2 Model Development: Model Development Process.
- Technical Analysis.
- Overview of Case Studies.
- Index Prediction Using MLPs.
- Part 3 Case Studies: Index Prediction Using a Hybrid MLP-GA.
- Index Trading Using Grammatical Evolution.
- Intra-day Trading Using Grammatical Evolution.
- Automatic Generation of Foreign Exchange Trading Rules.
- Corporate Failure Prediction Using GE.
- Corporate Failure Prediction Using an Ant-Clustering Model.
- Bond Rating Using GE.- Bond Rating Using AIS.
- Wrap-up.
- References.

Anthony Brabazon [B. Comm (UCD), DPA (UCD), Dip Stats (Dub), MS (Statistics) (Stanford), MS (Operations Research) (Stanford), MBA (Heriot-Watt), DBA (Kingston), FCA, ACMA] lectures at University College Dublin. His research interests include mathematical decision models, evolutionary computation, and the application of computational intelligence to the domain of finance. He has published in excess of 100 papers in journals, conferences and professional publications, and has been a member of the programme committee at both EuroGP and GECCO conferences, as well as acting as reviewer for several journals. He has also acted as consultant to a wide range of public and private companies in several countries. He currently serves as a member of the CCAB (Ireland) Consultative Committee on Accounting Standards, and is a former Secretary and Treasurer of the Irish Accounting and Finance Association. Prior to joining UCD, he worked in the banking sector, and for KPMG.
Michael O'Neill [BSc. (UCD), PhD (UL)] is a lecturer in the Department of Computer Science and Information Systems at the University of Limerick. He has over 70 publications on biologically inspired algorithms (BIAs). He coauthored the Springer title "Grammatical Evolution -- Evolutionary Automatic Programming in an Arbitrary Language", Genetic Programming Series, 2003, 160 pp., ISBN 1-4020-7444-1. He is one of the two original developers of the Grammatical Evolution algorithm, research that spawned an annual invited tutorial at the largest evolutionary computation conference and an international workshop, and is also on a number of relevant organising committees (e.g., GECCO 2005). Michael is a regular reviewer for the leading evolutionary computation (EC) journals, namely IEEE Trans. on Evolutionary Computation, MIT Press's Evolutionary Computation, and Springer's Genetic Programming and Evolvable Hardware journal.

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)m 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), 20