Pereira, Robert (2000): Genetic Algorithm Optimisation for Finance and Investments.
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This paper provides an introduction to the use of genetic algorithms for financial optimisation. The aim is to give the reader a basic understanding of the computational aspects of these algorithms and how they can be applied to decision making in finance and investment. Genetic algorithms are especially suitable for complex problems characterised by large solution spaces, multiple optima, nondifferentiability of the objective function, and other irregular features. The mechanics of constructing and using a genetic algorithm for optimisation are illustrated through a simple example.
|Item Type:||MPRA Paper|
|Original Title:||Genetic Algorithm Optimisation for Finance and Investments|
|Subjects:||C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics
G - Financial Economics > G0 - General
|Depositing User:||Rob Pereira|
|Date Deposited:||10. Jun 2008 06:39|
|Last Modified:||11. Feb 2013 10:07|
Bauer, R.J. Jr. (1994), Genetic Algorithms and Investment Strategies, Wiley Finance Editions, John Wiley and Sons, New York.
Deboeck, G.J. (1994), Trading On the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets, John Wiley and Sons, New York.
Dorsey, R.E. and Mayer,W.J. (1995), Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features, Journal of Business and Economic Statistics, 13 (1), 53-66.
Holland, J.H. (1975), Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, Michigan.
Kahneman, D. and Tversky, A. (1982), Intuitive Prediction: Biases and Corrective Procedures, in Kahneman, D. Slovic, P. and Tversky, A. (eds.), Judgement Under Uncertainty: Heuristics and Biases, Cambridge University Press, New York, pp 414-421.