Stock Market Modeling Using Genetic Programming Ensembles
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InCollection{grosan:2006:GSP,
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author = "Crina Grosan and Ajith Abraham",
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title = "Stock Market Modeling Using Genetic Programming
Ensembles",
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year = "2006",
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booktitle = "Genetic Systems Programming: Theory and Experiences",
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pages = "131--146",
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volume = "13",
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series = "Studies in Computational Intelligence",
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editor = "Nadia Nedjah and Ajith Abraham and
Luiza {de Macedo Mourelle}",
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publisher = "Springer",
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address = "Germany",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-29849-5",
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URL = "http://www.cs.ubbcluj.ro/~cgrosan/stock-chapter.pdf",
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DOI = "doi:10.1007/3-540-32498-4_6",
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abstract = "The use of intelligent systems for stock market
predictions has been widely established. This chapter
introduces two Genetic Programming (GP) techniques:
Multi-Expression Programming (MEP) and Linear Genetic
Programming (LGP) for the prediction of two stock
indices. The performance is then compared with an
artificial neural network trained using
Levenberg-Marquardt algorithm and Takagi-Sugeno
neuro-fuzzy model. We considered Nasdaq-100 index of
Nasdaq Stock Market and the S&P CNX NIFTY stock index
as test data. Empirical results reveal that Genetic
Programming techniques are promising methods for stock
prediction. Finally formulate an ensemble of these two
techniques using a multiobjective evolutionary
algorithm. Results obtained by ensemble are better than
the results obtained by each GP technique
individually.",
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notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html",
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size = "17 pages",
- }
Genetic Programming entries for
Crina Grosan
Ajith Abraham
Citations