Natural Computing in Computational Finance. Vol.4

This book follows on from Natural Computing in Computational Finance Volumes I, II and III. As in the previous volumes of this series, the book consists of a series of 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-based methodologies in computational finance and economics.
The applications explored include option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation. 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 computational finance and economics.

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-based methodologies in computational finance and economics.
The applications explored include option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation. 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 computational finance and economics.

The applications explored include option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation. 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 computational finance and economics.

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 computational finance and economics.

Table des matières

1 Natural Computing in Computational Finance (Volume 4): Introduction.- 2 Calibrating Option Pricing Models with Heuristics.- 3 A Comparison Between Nature-Inspired and Machine Learning Approaches to Detecting Trend Reversals in Financial Time Series.- 4 A soft computing approach to enhanced indexation.- 5 Parallel Evolutionary Algorithms for Stock Market Trading Rule Selection on Many-Core Graphics Processors.- 6 Regime-Switching Recurrent Reinforcement Learning in Automated Trading.- 7 An Evolutionary Algorithmic Investigation of US Corporate Payout Policy Determination.- 8 Tackling Overfitting in Evolutionary-driven Financial Model Induction.- 9 An Order-Driven Agent-Based Artificial Stock Market to Analyze Liquidity Costs of Market Orders in the Taiwan Stock Market.- 10 Market Microstructure: A Self-Organizing Map Approach to Investigate Behavior Dynamics under an Evolutionary Environment.

A propos de l'auteur

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 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.

Résumé

This book follows on from Natural Computing in Computational Finance  Volumes I, II and III.   As in the previous volumes of this series, the  book consists of a series of  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-based methodologies in computational finance and economics. The applications explored include  option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading,  corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation.  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 computational finance and  economics.  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-based methodologies in computational finance and economics. The applications explored include  option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation.  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 computational finance and  economics.  The applications explored include  option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading,  corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation.  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 computational finance and  economics.  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 computational finance and  economics.  

Détails du produit

• Collaboration: Anthony Brabazon (Editeur), Dietmar Maringer (Editeur), Dietmar G. Maringer (Editeur), Michae O'Neill (Editeur), Michael O'Neill (Editeur)
• Edition: Springer, Berlin
• Langues: Anglais
• Format d'édition: Livre Relié
• Sortie: 01.10.2011
• EAN: 9783642233357
• ISBN: 978-3-642-23335-7
• Pages: 202
• Poids: 486 g
• Illustrations: X, 202 p.
• Thèmes: Studies in Computational Intelligence; Studies in Computational Intelligence
• Catégories: Sciences naturelles, médecine, informatique, technique > Technique > Général, dictionnaires; B, Artificial Intelligence, engineering, IT in Business, Information Technology, Business mathematics & systems, Computational Intelligence, Business—Data processing, Business applications