Volatility forecasting using time series data mining and evolutionary computation techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Irwin Ma and Tony Wong and Thiagas Sankar",
  title =        "Volatility forecasting using time series data mining
                 and evolutionary computation techniques",
  booktitle =    "GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation",
  year =         "2007",
  editor =       "Dirk Thierens and Hans-Georg Beyer and 
                 Josh Bongard and Jurgen Branke and John Andrew Clark and 
                 Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and 
                 Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and 
                 Julian F. Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Riccardo Poli and Kumara Sastry and 
                 Kenneth Owen Stanley and Thomas Stutzle and 
                 Richard A Watson and Ingo Wegener",
  volume =       "2",
  isbn13 =       "978-1-59593-697-4",
  pages =        "2262--2262",
  address =      "London",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2262.pdf",
  DOI =          "doi:10.1145/1276958.1277397",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, Real-World
                 Applications: Poster, data mining, economics, financial
                 volatility, forecasting, S&P 100",
  abstract =     "Traditional parametric methods have limited success in
                 estimating and forecasting the volatility of financial
                 securities. Recent advance in evolutionary computation
                 has provided additional tools to conduct data mining
                 effectively. The current work applies the genetic
                 programming in a Time Series Data Mining framework to
                 characterise the S&P100 high frequency data in order to
                 forecast the one step ahead integrated volatility.
                 Results of the experiment have shown to be superior to
                 those derived by the traditional methods.",
  notes =        "GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071",

Genetic Programming entries for Irwin Ma Tony Wong Thiagas Sankar