Performance Analysis of Hybrid Forecasting Model In Stock Market Forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Khadka:2012:IJMIT,
  author =       "Mahesh S. Khadka and K. M. George and N. Park and 
                 J. B. Kim",
  title =        "Performance Analysis of Hybrid Forecasting Model In
                 Stock Market Forecasting",
  journal =      "International Journal of Managing Information
                 Technology",
  year =         "2012",
  volume =       "4",
  number =       "3",
  pages =        "81--88",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, concordance,
                 ARIMA, stock market forecasting, Kendall tau, gini mean
                 difference, Spearman rho, quantitative finance,
                 statistical finance, computer science, computational
                 engineering, finance, science",
  publisher =    "Academy \& Industry Research Collaboration Center
                 (AIRCC)",
  bibsource =    "OAI-PMH server at www.doaj.org",
  oai =          "oai:doaj-articles:e3186e89c872235db2c6934770967eab",
  bibsource =    "OAI-PMH server at export.arxiv.org",
  oai =          "oai:arXiv.org:1209.4608",
  ISSN =         "0975-5586",
  URL =          "http://arxiv.org/abs/1209.4608",
  URL =          "http://airccse.org/journal/ijmit/papers/4312ijmit07.pdf",
  DOI =          "doi:10.5121/ijmit.2012.4307",
  size =         "8 pages",
  abstract =     "This paper presents performance analysis of hybrid
                 model comprise of concordance and Genetic Programming
                 (GP) to forecast financial market with some existing
                 models. This scheme can be used for in depth analysis
                 of stock market. Different measures of concordances
                 such as Kendalls Tau, Ginis Mean Difference, Spearmans
                 Rho, and weak interpretation of concordance are used to
                 search for the pattern in past that look similar to
                 present. Genetic Programming is then used to match the
                 past trend to present trend as close as possible. Then
                 Genetic Program estimates what will happen next based
                 on what had happened next. The concept is validated
                 using financial time series data (S\&P 500 and NASDAQ
                 indices) as sample data sets. The forecasted result is
                 then compared with standard ARIMA model and other model
                 to analyse its performance.",
  notes =        "http://airccse.org/journal/ijmit/contact.html Also
                 known as
                 \cite{oai:doaj-articles:e3186e89c872235db2c6934770967eab},

                 Oklahoma State University, USA Also known as
                 \cite{oai:arXiv.org:1209.4608,}",
}

Genetic Programming entries for Mahesh S Khadka K M George Nohpill Park Jaebeom Kim

Citations