The Syntax of Stock Selection: Grammatical Evolution of a Stock Picking Model

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@InProceedings{mcgee_etal:cec2010,

• author = "Richard McGee and Michael O'Neill and Anthony Brabazon",
• title = "The Syntax of Stock Selection: Grammatical Evolution of a Stock Picking Model",
• booktitle = "2010 IEEE World Congress on Computational Intelligence",
• pages = "4347--4354",
• year = "2010",
• address = "Barcelona, Spain",
• month = "18-23 " # jul,
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, grammatical evolution",
• isbn13 = "978-1-4244-6910-9",
• DOI = "doi:10.1109/CEC.2010.5586001",
• abstract = "A 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 customised 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.",
• notes = "WCCI 2010. Also known as \cite{5586001}",

}

Genetic Programming entries for Richard McGee Michael O'Neill Anthony Brabazon

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