Forecasting Financial Volatility Using Nested Monte Carlo Expression Discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Tristan Cazenave and Sana Ben Hamida",
  booktitle =    "2015 IEEE Symposium Series on Computational
  title =        "Forecasting Financial Volatility Using Nested Monte
                 Carlo Expression Discovery",
  year =         "2015",
  pages =        "726--733",
  abstract =     "We are interested in discovering expressions for
                 financial prediction using Nested Monte Carlo Search
                 and Genetic Programming. Both methods are applied to
                 learn from financial time series to generate non linear
                 functions for market volatility prediction. The input
                 data, that is a series of daily prices of European
                 S&P500 index, is filtered and sampled in order to
                 improve the training process. Using some assessment
                 metrics, the best generated models given by both
                 approaches for each training sub sample, are evaluated
                 and compared. Results show that Nested Monte Carlo is
                 able to generate better forecasting models than Genetic
                 Programming for the majority of learning samples.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/SSCI.2015.110",
  month =        dec,
  notes =        "Also known as \cite{7376684}",

Genetic Programming entries for Tristan Cazenave Sana Ben Hamida