McGee, RichardRichardMcGeeO'Neill, MichaelMichaelO'NeillBrabazon, AnthonyAnthonyBrabazon2011-01-202011-01-202010 IEEE2010-07978-1-4244-6909-3http://hdl.handle.net/10197/2732Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Barcelona, Spain, 18-23 JulyA significant problem in the area of stock selection is that of identifying the factors that affect a security’s return. While modern portfolio theory suggests a linear multi-factor model in the form of Arbitrage Pricing Theory it does not suggest the identity, or even the number, of risk factors in the model. Candidate factors for inclusion in a fundamental model can include hundreds of data points for each firm and with thousands of firms in the fund manager’s selection universe the model specification problem encompasses a large, computationally intense search space. Grammatical Evolution (GE) is a form of evolutionary computing that has been used successfully in model induction problems involving large search spaces. GE is applied to evolve a stock selection model with a customized mapping process developed specifically to enhance the performance of evolutionary operators for this problem. Stock selection models are rated using fitness functions commonly employed in asset management; the information coefficient and the inter-quantile return spread. The findings of the paper indicate that evolutionary computing is an excellent tool for the development of stock picking models.383941 bytesapplication/pdfenPersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Syntax of stock selectionStock picking modelsEvolutionary computationStocksPortfolio management--Computer simulationConference Publication10.1109/CEC.2010.5586001https://creativecommons.org/licenses/by-nc-sa/1.0/