Genetic Algorithms and Genetic Programming in Computational by Shu-Heng Chen (auth.), Shu-Heng Chen (eds.)

By Shu-Heng Chen (auth.), Shu-Heng Chen (eds.)

After a decade of improvement, genetic algorithms and genetic programming became a commonly authorized toolkit for computational finance. Genetic Algorithms and Genetic Programming in Computational Finance is a pioneering quantity dedicated totally to a scientific and entire assessment of this topic. Chapters conceal a variety of components of computational finance, together with monetary forecasting, buying and selling ideas improvement, money circulate administration, choice pricing, portfolio administration, volatility modeling, arbitraging, and agent-based simulations of man-made inventory markets. educational chapters also are incorporated to aid readers fast clutch the essence of those instruments. ultimately, a menu-driven software, basic GP, accompanies the quantity, in order to let readers with out a robust programming history to realize hands-on adventure in facing a lot of the technical fabric brought during this work.

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The termination criterion is satisfied when either the maximum number of generations is achieved or when the genotypes (the structures) of the population of individuals converges. The maximum number of generations is set by the creator of the genetic algorithm before running it, which ensures that the genetic algorithm does not continue indefinitely. Convergence may occur in two ways: first, convergence of the genotype structure occurs when all bit positions in all strings are identical. In this case, crossover will have no further effect.

For this test, the authors incorporate into the fitness function a penalty depending on node complexity, as shown in the previous chapter. Interestingly, neither imposing a penalty for complexity nor expanding the set of data functions leads to any appreciable improvement in the performance of the genetic program. Adding a penalty function does not help in this case, probably because the program already has a validation step, which itself is a design for over-fitting avoidance. 5. Arbitrage The last chapter of Part IV, Evolutionary Decision Trees for Stock Index Options and Futures Arbitrage, by Sheri Markose, Edward Tzang and Hakan Er applies genetic programming to stock index options and futures arbitrage; more precisely, the short P-C-F arbitrage.

Concluding Remarks What is the current state of financial applications of genetic algorithms and genetic programming? From a review of the 20 chapters distributed over the five parts of the volume, one can see the following observations. First, the application coverage is continuously enlarging. There is little doubt that new application domains will emerge in the next few years. In fact, apart from what have been said on the volume, the recent publication Noe (2000), which applies genetic algorithms to the study of takeover behavior, shows another novel application.

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