Biologically Inspired Algorithms for Financial Modelling
- 125 Downloads
Anthony Brabazon and Michael O’Neill of the University College Dublin have just published an interesting book that introduces a wide range of biologically inspired algorithms and their applications in financial modelling. It is divided into three parts, containing in total 21 chapters. The largest, Part I, contains brief introductory explanations of three forms of artificial neural networks (multi-layer perceptrons-MLP, radial basis function network, and self-organising maps), evolutionary computation algorithms (GA, differential evolution, genetic programming, and combinations with MLP), social systems (particle swarm optimization, ant colony models, and their MLP hybrids) and artificial immune systems. The smallest of the three is Part II. It focuses on the development of market trading systems. Topics addressed range from goal determination and data collection to model construction and validation. A short chapter is also devoted to technical analysis of equity markets. In Part III,...
- 1.S.-H. Chen, L. Jain, and C.-C. Tai (eds.), Computational Economics: A Perspective from Computational Intelligence, Idea Group Publishing, 2005.Google Scholar
- 2.S.-H. Chen and P. P. Wang (eds.), Computational Intelligence in Economics and Finance, Springer, 2003.Google Scholar
- 3.P. J. Kaufman, New Trading Systems and Methods, 4th ed., John Wiley & Sons, 2005. [Includes trade station code with excel worksheet data.]Google Scholar
- 4.A. S. Soofi and L. Cao (eds.), Modelling and Forecasting Financial Data: Techniques of Nonlinear Dynamics, Springer, 2002.Google Scholar
- 5.P. D. McNelis, Neural Networks in Finance, 1st edn.: Gaining Predictive Edge in the Market, Academic Press, 2004.Google Scholar