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Biologically Inspired Algorithms for Financial Modelling: Natural Computing Series

Autor Anthony Brabazon, Michael O'Neill
en Limba Engleză Paperback – 12 feb 2010
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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Specificații

ISBN-13: 9783642065736
ISBN-10: 3642065732
Pagini: 296
Ilustrații: XV, 277 p.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.45 kg
Ediția:Softcover reprint of hardcover 1st edition 2006
Editura: Springer
Colecția Natural Computing Series
Seria Natural Computing Series

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

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.

Recenzii

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)

Caracteristici

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

Notă biografică

Prof. Anthony Brabazon is currently Associate Dean of the Smurfit Graduate School of Business, University College Dublin (UCD) and Professor of Accountancy; previous positions include Vice-Principal of Research and Innovation for the College of Business and Law, Head of Research for the School of Business and Programme Director for the Master of Accounting Degree. 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 co director of the Natural Computing Research and Applications Group at UCD, among the most successful research groups dedicated to this subject. He has a bachelor's degree in commerce and a diploma in accounting, he is a qualified professional accountant andhe has postgraduate qualifications in statistics and operations research. Prof. Michael O'Neill holds the ICON Chair of Business Analytics, is Vice-Principal for Research, Innovation & Impact in the UCD College of Business, and is a founding Director of the UCD Natural Computing Research and Applications Group, among the most successful international research groups dedicated to this subject. He is one of the inventors of Grammatical Evolution and is independently ranked as one of the top 5 researchers in Genetic Programming, with over 250 peer-reviewed publications, over 4000 citations and a H-index of 29. He has held senior positions in the key academic conference committees, journal boards and review committees in this field and he has supervised many Ph.D. and research M.Sc. projects in evolutionary computing. He has a bachelor's degree in biology and a Ph.D. in computer science. Dr. Seán McGarraghy is the Director of the UCD Smurfit Graduate School of Business M.Sc. 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.