Recent years have seen the widespread application of Natural Computing algorithms (broadly defined in this context as computer algorithms whose design draws inspiration from phenomena in the natural world) for the purposes of financial modelling and optimisation. A related stream of work has also seen the application of learning mechanisms drawn from Natural Computing algorithms for the purposes of agent-based modelling in finance and economics. In this book we have collected a series of chapters which illustrate these two faces of Natural Computing. The first part of the book illustrates how algorithms inspired by the natural world can be used as problem solvers to uncover and optimise financial models. The second part of the book examines a number agent-based simulations of financial systems.
This book follows on from Natural Computing in Computational Finance (Volume 100 in Springer’s Studies in Computational Intelligence series) which in turn arose from the success of EvoFIN 2007, the very first European Workshop on Evolutionary Computation in Finance & Economics held in Valencia, Spain in April 2007.
Author(s): Anthony Brabazon, Michael O’Neill (auth.), Anthony Brabazon, Michael O’Neill (eds.)
Series: Studies in Computational Intelligence 185
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2009
Language: English
Commentary: 55783
Pages: 250
Tags: Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics); Economics general
Front Matter....Pages -
Natural Computing in Computational Finance (Volume 2): Introduction....Pages 1-5
Front Matter....Pages 7-7
Statistical Arbitrage with Genetic Programming....Pages 9-29
Finding Relevant Variables in a Financial Distress Prediction Problem Using Genetic Programming and Self-organizing Maps....Pages 31-49
Ant Colony Optimization for Option Pricing....Pages 51-73
A Neuro-Evolutionary Approach for Interest Rate Modelling....Pages 75-93
Who’s Smart and Who’s Lucky? Inferring Trading Strategy, Learning and Adaptation in Financial Markets through Data Mining....Pages 95-114
Front Matter....Pages 115-115
Financial Bubbles: A Learning Effect Modelling Approach....Pages 117-135
Evolutionary Computation and Artificial Financial Markets....Pages 137-179
Classical and Agent-Based Evolutionary Algorithms for Investment Strategies Generation....Pages 181-205
Income Distribution and Lottery Expenditures in Taiwan: An Analysis Based on Agent-Based Simulation....Pages 207-223
The Emergence of a Market: What Efforts Can Entrepreneurs Make?....Pages 225-243
Back Matter....Pages -
