Stock Market Modeling Using Genetic Programming Ensembles

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

@InCollection{grosan:2006:GSP,

• author = "Crina Grosan and Ajith Abraham",
• title = "Stock Market Modeling Using Genetic Programming Ensembles",
• year = "2006",
• booktitle = "Genetic Systems Programming: Theory and Experiences",
• pages = "131--146",
• volume = "13",
• series = "Studies in Computational Intelligence",
• editor = "Nadia Nedjah and Ajith Abraham and Luiza {de Macedo Mourelle}",
• publisher = "Springer",
• address = "Germany",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-29849-5",
• URL = "http://www.cs.ubbcluj.ro/~cgrosan/stock-chapter.pdf",
• DOI = "doi:10.1007/3-540-32498-4_6",
• 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.",
• notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html",
• size = "17 pages",

}

Genetic Programming entries for Crina Grosan Ajith Abraham

Citations