Natural Computing in Computational Finance

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Anthony Brabazon, Michael O'Neill
Springer Science & Business Media, May 9, 2008 - Mathematics - 303 pages
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Natural Computing in Computational Finance is a innovative volume containing fifteen chapters which illustrate cutting-edge applications of natural computing or agent-based modeling in modern computational finance. Following an introductory chapter the book is organized into three sections. The first section deals with optimization applications of natural computing demonstrating the application of a broad range of algorithms including, genetic algorithms, differential evolution, evolution strategies, quantum-inspired evolutionary algorithms and bacterial foraging algorithms to multiple financial applications including portfolio optimization, fund allocation and asset pricing. The second section explores the use of natural computing methodologies such as genetic programming, neural network hybrids and fuzzy-evolutionary hybrids for model induction in order to construct market trading, credit scoring and market prediction systems. The final section illustrates a range of agent-based applications including the modeling of payment card and financial markets. Each chapter provides an introduction to the relevant natural computing methodology as well as providing a clear description of the financial application addressed.

The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and students, in the fields of both natural computing and finance.

 

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Contents

Natural Computing in Computational Finance An Introduction
2
Optimisation
6
Constrained Index Tracking under Loss Aversion Using Differential Evolution
7
An Evolutionary Approach to Asset Allocation in Defined Contribution Pension Schemes
25
Evolutionary Strategies for Building RiskOptimal Portfolios
52
Evolutionary Stochastic Portfolio Optimization
67
Nonlinear Principal Component Analysis of the Implied Volatility Smile using a Quantuminspired Evolutionary Algorithm
88
Estimation of an EGARCH Volatility Option Pricing Model using a Bacteria Foraging Optimisation Algorithm
109
Strong Typing Variable Reduction and Bloat Control for Solving the Bankruptcy Prediction Problem Using Genetic Programming
160
Using Kalmanfiltered Radial Basis Function Networks for Index Arbitrage in the Financial Markets
187
On Predictability and Profitability Would GP Induced Trading Rules be Sensitive to the Observed Entropy of Time Series?
196
Hybrid Neural Systems in Exchange Rate Prediction
211
Agentbased Modelling
231
Evolutionary Learning of the Optimal Pricing Strategy in an Artificial Payment Card Market
233
Can Trend Followers Survive in the LongRun? Insights from AgentBased Modeling
252
CoEvolutionary MultiAgent System for Portfolio Optimization
271

Model Induction
128
FuzzyEvolutionary Modeling for SinglePosition Day Trading
131

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