Biologically Inspired Algorithms for Financial Modelling

Biologically Inspired Algorithms for Financia ...
Anthony Brabazon, Michael O'Ne ...
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Last edited by ImportBot
December 25, 2021 | History

Biologically Inspired Algorithms for Financial Modelling

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.

Publish Date
Language
English

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Previews available in: English

Edition Availability
Cover of: Biologically Inspired Algorithms for Financial Modelling
Biologically Inspired Algorithms for Financial Modelling
2010, Springer Berlin / Heidelberg
in English
Cover of: Biologically Inspired Algorithms for Financial Modelling
Biologically Inspired Algorithms for Financial Modelling
2006, Springer London, Limited
in English
Cover of: Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
February 10, 2006, Springer
Hardcover in English - 1 edition

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Book Details


Classifications

Library of Congress
QA75.5-76.95

The Physical Object

Pagination
xv, 277
Weight
0.456

ID Numbers

Open Library
OL35767830M
ISBN 13
9783642065736

Source records

Better World Books record

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December 25, 2021 Created by ImportBot Imported from Better World Books record