Natural Computing in Computational Finance

Volume 3
1. Auflage. 2010
ISBN: 978-3-642-13949-9
Verlag: Springer

Buch, Englisch, Band 293, 241 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 554 g

Reihe: Studies in Computational Intelligence

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 computational
finance 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.

Zielgruppe

Research

Autoren/Hrsg.

Brabazon, Anthony

Maringer, Dietmar G.

O'Neill, Michael

Fachgebiete

• Mathematik | Informatik Mathematik Mathematik Interdisziplinär Finanz- und Versicherungsmathematik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Technik Allgemein Bionik, Biomimetik

Inhaltsverzeichnis

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.