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:1.8120

@InProceedings{oai:CiteSeerPSU:454950,

• 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, http://dblp.uni-trier.de",
• URL = "http://bi.snu.ac.kr/Publications/Conferences/International/ICAI99.ps",
• URL = "http://citeseer.ist.psu.edu/454950.html",
• citeseer-isreferencedby = "oai:CiteSeerPSU:407872; oai:CiteSeerPSU:67015",
• citeseer-references = "oai:CiteSeerPSU:4642; oai:CiteSeerPSU:185401; oai:CiteSeerPSU:103144",
• 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 = "broken 2022 http://www.sigmod.org/sigmod/dblp/db/conf/icai/icai1999-2.html

  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

Citations