Natural Computing in Computational Finance: An Introduction

Natural computing can be broadly defined as the development of computer programs and computational algorithms using metaphorical inspiration from systems and phenomena that occur in the natural world. The inspiration for natural computing methodologies typically stem from real-world phenomena which exist in high-dimensional, noisy and uncertain, dynamic environments. These are characteristics which fit well with the nature of financial markets. Prima facie, this makes natural computing methods interesting for financial modelling applications. Another feature of natural environments is the phenomenon of emergence, or the activities of multiple individual agents combining to create their own environment.

This book contains fourteen chapters which illustrate the cutting-edge of natural computing and agent-based modelling in modern computational finance. A range of methods are employed including, Differential Evolution, Genetic Algorithms, Evolution Strategies, Quantum-Inspired Evolutionary Algorithms, Bacterial Foraging Algorithms, Genetic Programming, Agent-based Modelling and hybrid approaches including Fuzzy-Evolutionary Algorithms, Radial-Basis Function Networks with Kalman Filters, and a Multi-Layer Perceptron-Wavelet hybrid. A complementary range of applications are addressed including Fund Allocation, Asset Pricing, Market Prediction, Market Trading, Bankruptcy Prediction, and the agent based modelling of payment card and financial markets.

The book is divided into three sections each corresponding to a distinct grouping of chapters. The first section deals with optimisation applications of natural computing in finance, the second section explores the use of natural computing methodologies for model induction and the final section illustrates a range of agent-based applications in finance.