Natural Computing in Computational Finance

The chapters in this book illustrate the application of a range of cutting-edge natural computing and agent-based methodologies in computational finance and economics. The eleven chapters were selected following a rigorous, peer-reviewed, selection process.

This book consists of eleven chapters each of which was selected following a rigorous, peer-reviewed, selection process. The chapters illustrate the application of a range of cutting-edge natural computing and agent-basedmethodologies in computational finance and economics. While describing cutting edge applications, the chapters are written so that they are accessible to a wide audience. Hence, they should be of interest to academics, students and practitioners in the fields of computationalfinance and economics. The inspiration for this book was due in part to the success of EvoFIN 2009, the 3rd European Workshop on Evolutionary Computation in Finance and Economics. This book follows on from Natural Computing in Computational Finance Volumes I and II.

Reports recent research results Computation Intelligence in Finance Written by leading experts in this field Inspired by EvoFIN 2009, the 3rd European Workshop on Evolutionary Computation in Finance and Economics

Autorentext
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 leading evolutionary computation (EC) journals, namely IEEE Trans. on Evolutionary Computation, MIT Press's Evolutionary Computation, and Springer's Genetic Programming and Evolvable Hardware journal. 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.

Inhalt
Natural Computing in Computational Finance (Volume 3): Introduction.- Natural Computing in Computational Finance (Volume 3): Introduction.- I: Financial and Agent-Based Models.- Robust Regression with Optimisation Heuristics.- Evolutionary Estimation of a Coupled Markov Chain Credit Risk Model.- Evolutionary Computation and Trade Execution.- Agent-Based Co-operative Co-evolutionary Algorithms for Multi-objective Portfolio Optimization.- Inferring Trader's Behavior from Prices.- II: Dynamic Strategies and Algorithmic Trading.- Index Mutual Fund Replication.- Frequent Knowledge Patterns in Evolutionary Decision Support Systems for Financial Time Series Analysis.- Modeling Turning Points in Financial Markets with Soft Computing Techniques.- Evolutionary Money Management.- Interday and Intraday Stock Trading Using Probabilistic Adaptive Mapping Developmental Genetic Programming and Linear Genetic Programming.