Natural Computing in Computational Finance: Volume 3

by Anthony Brabazon (Editor), Michael O'Neill (Editor), Dietmar G. Maringer (Editor)

Overview

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.

Product Details

ISBN-13:	9783642263699
Publisher:	Springer Berlin Heidelberg
Publication date:	09/05/2012
Series:	Studies in Computational Intelligence , #293
Edition description:	2010
Pages:	241
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

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 Sk Trading Using Probabilistic Adaptive Mapping Developmental Genetic Programming and Linear Genetic Programming.