Brabazon / O'Neill Biologically Inspired Algorithms for Financial Modelling

1. Auflage 2006
ISBN: 978-3-540-31307-6
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 277 Seiten, eBook

Reihe: Natural Computing Series

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.

Zielgruppe

Research

Autoren/Hrsg.

Brabazon, Anthony

O'Neill, Michael

Inhaltsverzeichnis

Methodologies.- Neural Network Methodologies.- Evolutionary Methodologies.- Grammatical Evolution.- The Particle Swarm Model.- Ant Colony Models.- Artificial Immune Systems.- Model Development.- Model Development Process.- Technical Analysis.- Case Studies.- Overview of Case Studies.- Index Prediction Using MLPs.- Index Prediction Using a MLP-GA Hybrid.- Index Trading Using Grammatical Evolution.- Adaptive Trading Using Grammatical Evolution.- Intra-day Trading Using Grammatical Evolution.- Automatic Generation of Foreign Exchange Trading Rules.- Corporate Failure Prediction Using Grammatical Evolution.- Corporate Failure Prediction Using an Ant Model.- Bond Rating Using Grammatical Evolution.- Bond Rating Using AIS.- Wrap-up.