Forecasting High-Frequency Financial Time Series with Evolutionary Neural Trees: The Case of Hang-Seng Stock Index

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

  author =       "Shu-Heng Chen and Hung-Shuo Wang and 
                 Byoung-Tak Zhang",
  title =        "Forecasting High-Frequency Financial Time Series with
                 Evolutionary Neural Trees: The Case of {Hang-Seng}
                 Stock Index",
  booktitle =    "Proceedings of the International Conference on
                 Artificial Intelligence, IC-AI '99",
  year =         "1999",
  editor =       "Hamid R. Arabnia",
  volume =       "2",
  pages =        "437--443",
  address =      "Las Vegas, Nevada, USA",
  month =        "28 " # jun # "-1 " # jul,
  publisher =    "CSREA Press",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Artificial Neural Networks, Neural Trees, Sigma-Pi
                 Neural Trees, Breeder Genetic Algorithm",
  ISBN =         "1-892512-17-3",
  bibsource =    "DBLP,",
  URL =          "",
  URL =          "",
  citeseer-isreferencedby = "oai:CiteSeerPSU:407872;
  citeseer-references = "oai:CiteSeerPSU:4642; oai:CiteSeerPSU:185401;
  annote =       "The Pennsylvania State University CiteSeer Archives",
  language =     "en",
  oai =          "oai:CiteSeerPSU:454950",
  rights =       "unrestricted",
  size =         "7 pages",
  abstract =     "In this paper, the evolutionary neural trees (ENT) are
                 applied to forecasing the highfrequency stock returns
                 of Heng-Sheng stock index on December, 1998. To
                 understand what may consistute an effective
                 implementation, six experiments are conducted. These
                 experiments are different in data-preprocessing
                 procedures, sample sizes, search intensity and
                 complexity regularization. Our results shows that ENT
                 can perform more efficiently if we can associate ENT
                 with a linear filter so that it can concentrate on
                 searching in the space of nonlinear signals. Also, as
                 well demonstarted in this study, the infrequent bursts
                 (outliers) appearing in the high-frequency data can be
                 very disturbing for the normal operation of ENT.",
  notes =        "

                 This dataset (HSIX.HF) was downloaded from the Bridge
                 company. The Hong Kong stock market opens 4 hours a day
                 and five days a week. The specific period considered by
                 us has 22 working days and 4586 observations.",

Genetic Programming entries for Shu-Heng Chen Hung-Shuo Wang Byoung-Tak Zhang