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.

Applies biologically inspired algorithms (BIAs) to financial modeling Shows how financial modeling benefits from techniques developed for biological studies: neural networks, evolutionary computing, particle swarm and ant colony optimization, and immune systems The authors are unusually well qualified to explain BIA methodologies to financial trading specialists, and financial trading models to computer scientists This approach has been refined in postgraduate classes in both disciplines Includes supplementary material: sn.pub/extras

Autorentext

Prof. Anthony Brabazon is currently Dean of the UCD College of Business. Previous positions held in UCD include Associate Dean and Director of the Smurfit Graduate School of Business, Vice-Principal of Research and Innovation for the College of Business and Law, and Head of Research for the School of Business. His primary research interests concern the development of natural computing theory and the application of related algorithms to real-world problems, particularly in the domain of business and finance, and he has pioneered multidisciplinary collaborations with industry in areas such as financial mathematics, financial economics, and computer science. He is cofounder and codirector of the Natural Computing Research and Applications Group at UCD, among the most successful research groups dedicated to this subject. Among his publications are the successful coauthored books 'Natural Computing Algorithms', 'Foundations in Grammatical Evolution for DynamicEnvironments', and 'Biologically Inspired Algorithms for Financial Modelling'. Dr. Seán McGarraghy is the director of the UCD Centre for Business Analytics, he was formerly director of the UCD Smurfit Graduate School of Business MSc in Business Analytics. He has qualifications in electronics, mathematics and management and his teaching and academic publications cover many aspects of business analytics and operations research. Particular topics of interests include combinatorial enumeration and optimization, network algorithms, supply chain management, quadratic forms and K-theory. Among his publications are the successful coauthored book 'Natural Computing Algorithms'.

Inhalt
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.