The syntax of stock selection : grammatical evolution of a stock picking model

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Title: The syntax of stock selection : grammatical evolution of a stock picking model
Authors: McGee, Richard
O'Neill, Michael
Brabazon, Anthony
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Date: Jul-2010
Online since: 2011-01-20T14:39:03Z
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 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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: IEEE Press
Copyright (published version): 2010 IEEE
Keywords: Syntax of stock selectionStock picking models
Subject LCSH: Evolutionary computation
Portfolio management--Computer simulation
DOI: 10.1109/CEC.2010.5586001
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Language: en
Status of Item: Peer reviewed
Is part of: 2010 IEEE Congress on Evolutionary Computation (CEC) [proceedings]
Conference Details: Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Barcelona, Spain, 18-23 July
ISBN: 978-1-4244-6909-3
Appears in Collections:FMC² Research Collection
Computer Science Research Collection
Business Research Collection
CASL Research Collection

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