By Shu-Heng Chen (auth.), Shu-Heng Chen (eds.)
After a decade of improvement, genetic algorithms and genetic programming became a greatly accredited toolkit for computational finance. Genetic Algorithms and Genetic Programming in Computational Finance is a pioneering quantity dedicated solely to a scientific and accomplished overview of this topic. Chapters conceal numerous parts of computational finance, together with monetary forecasting, buying and selling options improvement, funds move administration, choice pricing, portfolio administration, volatility modeling, arbitraging, and agent-based simulations of man-made inventory markets. instructional chapters also are incorporated to assist readers quick take hold of the essence of those instruments. ultimately, a menu-driven software, basic GP, accompanies the amount, for you to allow readers and not using a powerful programming history to achieve hands-on adventure in facing a lot of the technical fabric brought during this work.
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Additional resources for Genetic Algorithms and Genetic Programming in Computational Finance
13 The usual argument that agents will eventually learn one of these equilibria (in particular, the Pareto superior one) is anything but well grounded. The chapter by Thomas Riechmann, "A Model of Bounded Rational Consumer Choice" , uses a standard general equilibrium model to show that even finding the optimal consumption bundle of three goods can be an extremely complicated issue for consumers. The model is very simple. It has 500 consumers. Each is endowed with the same utility function and the same budget constraint.
An Introduction to Genetic Algorithms: A Mutual Fund Screening Example," Neurove$t Journal, 2(4), 16-19. Bauer, R J. Jr. (1995). , Nissen V. ), Evolutionary Algorithms in Management Applications. 253-263, Heidelberg and New York: Springer. Bauer, R J. Jr. and G. E. Liepins (1992). "Genetic Algorithms and Computerized Trading Strategies," in O'leary D. ), Expert Systems in Finance. North Holland. , O. Pictet, and G. Zumbach (1998). "Representational Semantics for Genetic Programming Based Learning in HighFrequency Financial Data," in Koza J.
9 As the theory and practice of genetic algorithms were being developed, they were being used in a wide spectrum of real-world applications. The benefits of genetic algorithms in engineering can be illustrated by their application in the design of turbine blades for jet engines, as done 32 GA AND GP IN COMPUTATIONAL FINANCE by General Electric. The design of turbines involves more than 100 variables. I O The use of genetic algorithms in this case rendered an improvement of 2 percent in efficiency, an immense gain in this field.