Natural Computing in Computational Finance - Studies in Computational Intelligence 2008 edition

Marc Notes: Includes bibliographical references and index. Jacket Description/Back: Natural Computing in Computational Finance is a innovative volume containing fifteen chapters which illustrate cutting-edge applications of natural computing or agent-based modeling in modern computational finance. Following an introductory chapter the book is organized into three sections. The first section deals with optimization applications of natural computing demonstrating the application of a broad range of algorithms including, genetic algorithms, differential evolution, evolution strategies, quantum-inspired evolutionary algorithms and bacterial foraging algorithms to multiple financial applications including portfolio optimization, fund allocation and asset pricing. The second section explores the use of natural computing methodologies such as genetic programming, neural network hybrids and fuzzy-evolutionary hybrids for model induction in order to construct market trading, credit scoring and market prediction systems. The final section illustrates a range of agent-based applications including the modeling of payment card and financial markets. Each chapter provides an introduction to the relevant natural computing methodology as well as providing a clear description of the financial application addressed. The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and students, in the fields of both natural computing and finance. Table of Contents: 1. Natural Computing in Computational Finance: An Introduction / Anthony Brabazon, Michael O'Neill -- Part I. Optimisation -- 2. Constrained Index Tracking under Loss Aversion Using Differential Evolution / Dietmar Maringer -- 3. An Evolutionary Approach to Asset Allocation in Defined Contribution Pension Schemes / Kerem Senel, A. Bulent Pamukcu, Serhat Yanik -- 4. Evolutionary Strategies for Building Risk-Optimal Portfolios / Piotr Lipinski -- 5. Evolutionary Stochastic Portfolio Optimization / Ronald Hochreiter -- 6. Non-linear Principal Component Analysis of the Implied Volatility Smile using a Quantum-inspired Evolutionary Algorithm / Kai Fan, Conall O'Sullivan, Anthony Brabazon, Michael O'Neill -- 7. Estimation of an EGARCH Volatility Option Pricing Model using a Bacteria Foraging Optimisation Algorithm / Jing Dang, Anthony Brabazon, Michael O'Neill, David Edelman -- Part II. Model Induction -- 8. Fuzzy-Evolutionary Modeling for Single-Position Day Trading / Celia da Costa Pereira, Andrea G. B. Tettamanzi -- 9. Strong Typing, Variable Reduction and Bloat Control for Solving the Bankruptcy Prediction Problem Using Genetic Programming / Eva Alfaro-Cid, Alberto Cuesta-Canada, Ken Sharman, Anna I. Esparcia-Alcazar -- 10. Using Kalman-filtered Radial Basis Function Networks for Index Arbitrage in the Financial Markets / David Edelman -- 11. On Predictability and Profitability: Would GP Induced Trading Rules be Sensitive to the Observed Entropy of Time Series? / Nicolas Navet, Shu-Heng Chen -- 12. Hybrid Neural Systems in Exchange Rate Prediction / Andrzej Bielecki, Pawel Hajto, Robert Schaefer -- Part III. Agent-based Modelling -- 13. Evolutionary Learning of the Optimal Pricing Strategy in an Artificial Payment Card Market / Biliana Alexandrova-Kabadjova, Edward Tsang, Andreas Krause -- 14. Can Trend Followers Survive in the Long-Run? Insights from Agent-Based Modeling / Xue-Zhong He, Philip Hamill, Youwei Li -- 15. Co-Evolutionary Multi-Agent System for Portfolio Optimization / Rafal Drezewski, Leszek Siwik -- Index.

Contributor Bio:  Brabazon, Anthony 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 leadingevolutionary computation (Ee journals, namely IEEE Trans. on Evolutionary Computation, MIT Press's Evolutionary Computation, and Springer's Genetic Programming and Evolvable Hardware journal. Contributor Bio:  O'Neill, Michael MICHAEL O'NEILL is Founder and Director of the Institute for Nonprofit Organization Management and Professor in the Department of Public Management at the University of San Francisco.