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.

Inhalt

Optimisation.- Natural Computing in Computational Finance: An Introduction.- Constrained Index Tracking under Loss Aversion Using Differential Evolution.- An Evolutionary Approach to Asset Allocation in Defined Contribution Pension Schemes.- Evolutionary Strategies for Building Risk-Optimal Portfolios.- Evolutionary Stochastic Portfolio Optimization.- Non-linear Principal Component Analysis of the Implied Volatility Smile using a Quantum-inspired Evolutionary Algorithm.- Estimation of an EGARCH Volatility Option Pricing Model using a Bacteria Foraging Optimisation Algorithm.- Model Induction.- Fuzzy-Evolutionary Modeling for Single-Position Day Trading.- Strong Typing, Variable Reduction and Bloat Control for Solving the Bankruptcy Prediction Problem Using Genetic Programming.- Using Kalman-filtered Radial Basis Function Networks for Index Arbitrage in the Financial Markets.- On Predictability and Profitability: Would GP Induced Trading Rules be Sensitive to the Observed Entropy of Time Series?.- Hybrid Neural Systems in Exchange Rate Prediction.- Agent-based Modelling.- Evolutionary Learning of the Optimal Pricing Strategy in an Artificial Payment Card Market.- Can Trend Followers Survive in the Long-Run% Insights from Agent-Based Modeling.- Co-Evolutionary Multi-Agent System for Portfolio Optimization.