Optimizing a Pseudo Financial Factor Model with Support Vector Machines and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/ai/ButlerK09a,
  title =        "Optimizing a Pseudo Financial Factor Model with
                 Support Vector Machines and Genetic Programming",
  author =       "Matthew Butler and Vlado Keselj",
  booktitle =    "22nd Canadian Conference on Artificial Intelligence,
                 Canadian AI 2009",
  year =         "2009",
  editor =       "Yong Gao and Nathalie Japkowicz",
  volume =       "5549",
  series =       "Lecture Notes in Computer Science",
  pages =        "191--194",
  address =      "Kelowna, Canada",
  month =        may # " 25-27",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, support
                 vector machines, financial forecasting, principle
                 component analysis",
  isbn13 =       "978-3-642-01817-6",
  DOI =          "doi:10.1007/978-3-642-01818-3_21",
  bibdate =      "2009-05-18",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/ai/ai2009.html#ButlerK09a",
  abstract =     "We compare the effectiveness of Support Vector
                 Machines (SVM) and Tree-based Genetic Programming (GP)
                 to make accurate predictions on the movement of the Dow
                 Jones Industrial Average (DJIA). The approach is
                 facilitated though a novel representation of the data
                 as a pseudo financial factor model, based on a linear
                 factor model for representing correlations between the
                 returns in different assets. To demonstrate the
                 effectiveness of the data representation the results
                 are compared to models developed using only the monthly
                 returns of the inputs. Principal Component Analysis
                 (PCA) is initially used to translate the data into PC
                 space to remove excess noise that is inherent in
                 financial data. The results show that the algorithms
                 were able to achieve superior investment returns and
                 higher classification accuracy with the aid of the
                 pseudo financial factor model. As well, both models
                 outperformed the market benchmark, but ultimately the
                 SVM methodology was superior in terms of accuracy and
                 investment returns.",
}

Genetic Programming entries for Matthew Butler Vlado Keselj

Citations