Evolutionary Computation for Modelling and Optimization in Finance

Sandra Paterlini ()
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Sandra Paterlini: University of Modena and Reggio E., Department of Economics, CEFIN and RECent

A chapter in Proceedings of COMPSTAT'2010, 2010, pp 265-274 from Springer

Abstract: Abstract In the last decades, there has been a tendency to move away from mathematically tractable, but simplistic models towards more sophisticated and real-world models in finance. However, the consequence of the improved sophistication is that the model specification and analysis is no longer mathematically tractable. Instead solutions need to be numerically approximated. For this task, evolutionary computation heuristics are the appropriate means, because they do not require any rigid mathematical properties of the model. Evolutionary algorithms are search heuristics, usually inspired by Darwinian evolution and Mendelian inheritance, which aim to determine the optimal solution to a given problem by competition and alteration of candidate solutions of a population. In this work, we focus on credit risk modelling and financial portfolio optimization to point out how evolutionary algorithms can easily provide reliable and accurate solutions to challenging financial problems.

Keywords: population-based algorithms; multi-objective optimization; clustering; credit risk modelling; financial portfolio optimization (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2604-3_24

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DOI: 10.1007/978-3-7908-2604-3_24

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