Genetic programming for quantitative stock selection

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{BeckerO:2009:GEC,

• author = "Ying L. Becker and Una-May O'Reilly",
• title = "Genetic programming for quantitative stock selection",
• booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation",
• year = "2009",
• editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• pages = "9--16",
• address = "Shanghai, China",
• organisation = "SigEvo",
• DOI = "doi:10.1145/1543834.1543837",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• month = jun # " 12-14",
• isbn13 = "978-1-60558-326-6",
• keywords = "genetic algorithms, genetic programming",
• abstract = "We provide an overview of using genetic programming (GP) to model stock returns. Our models employ GP terminals (model decision variables) that are financial factors identified by experts. We describe the multi-stage training, testing and validation process that we have integrated with GP selection to be appropriate for financial panel data and how the GP solutions are situated within a portfolio selection strategy. We share our experience with the pros and cons of evolved linear and non-linear models, and outline how we have used GP extensions to balance different objectives of portfolio managers and control the complexity of evolved models.",
• notes = "Also known as \cite{DBLP:conf/gecco/BeckerO09} part of \cite{DBLP:conf/gec/2009}",

}

Genetic Programming entries for Ying L Becker Una-May O'Reilly

Citations