Multiobjective Algorithms for Financial Trading Multiobjective Out-trades Single-Objective

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Lohpetch:2011:MAfFTMOS,

• title = "Multiobjective Algorithms for Financial Trading Multiobjective Out-trades Single-Objective",
• author = "Dome Lohpetch and David Corne",
• pages = "192--199",
• booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation",
• year = "2011",
• editor = "Alice E. Smith",
• month = "5-8 " # jun,
• address = "New Orleans, USA",
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, buy and hold strategy, economics, finance, financial trading, frequent trading decision, infrequent trading strategy, multiobjective algorithm, multiobjective out-trades single-objective, multiobjective strategy, financial management",
• DOI = "doi:10.1109/CEC.2011.5949618",
• annote = "The Pennsylvania State University CiteSeerX Archives",
• bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
• language = "en",
• oai = "oai:CiteSeerX.psu:10.1.1.477.3567",
• rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.477.3567",
• URL = "http://www.macs.hw.ac.uk/~dwcorne/dldccec11.pdf",
• size = "8 pages",
• abstract = "Genetic programming (GP) is increasingly investigated in finance and economics. One area of study is its use to discover effective rules for technical trading in the context of a portfolio of equities (or an index). Early work used GP to find rules that were profitable, but were outperformed by the simple buy and hold strategy. Attempts since then report similar findings, except a handful of cases where GP has been found to outperform BH. Recent work has clarified that robust out performance of BH depends on, mainly, the adoption of a relatively infrequent trading strategy (e.g. monthly), as well as a range of other factors. Here we add a comprehensive study of multiobjective approaches to this investigation, and find that multiobjective strategies provide even more robustness in outperforming BH, even in the context of more frequent (e.g. weekly) trading decisions.",
• notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.",

}

Genetic Programming entries for Dome Lohpetch David W Corne

Citations