%0 Conference Paper %1 oai:CiteSeerPSU:454950 %A Chen, Shu-Heng %A Wang, Hung-Shuo %A Zhang, Byoung-Tak %B Proceedings of the International Conference on Artificial Intelligence, IC-AI '99 %C Las Vegas, Nevada, USA %D 1999 %E Arabnia, Hamid R. %I CSREA Press %K Algorithm Artificial Breeder Evolutionary Genetic Networks, Neural Sigma-Pi Trees, algorithms, genetic programming, %P 437--443 %T Forecasting High-Frequency Financial Time Series with Evolutionary Neural Trees: The Case of Heng-Sheng Stock Index %U http://citeseer.ist.psu.edu/454950.html %V 2 %X 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. %Z The Pennsylvania State University CiteSeer Archives %@ 1-892512-17-3

@inproceedings{oai:CiteSeerPSU:454950, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Las Vegas, Nevada, USA}, annote = {The Pennsylvania State University CiteSeer Archives}, author = {Chen, Shu-Heng and Wang, Hung-Shuo and Zhang, Byoung-Tak}, bibsource = {DBLP, http://dblp.uni-trier.de}, biburl = {https://www.bibsonomy.org/bibtex/20d47534ce93dc1e12b5e2d5d94f5cb83/brazovayeye}, booktitle = {Proceedings of the International Conference on Artificial Intelligence, IC-AI '99}, citeseer-isreferencedby = {oai:CiteSeerPSU:407872; oai:CiteSeerPSU:67015}, citeseer-references = {oai:CiteSeerPSU:4642; oai:CiteSeerPSU:185401; oai:CiteSeerPSU:103144}, editor = {Arabnia, Hamid R.}, interhash = {478c1fb612e40170ee96cf56c6efc309}, intrahash = {0d47534ce93dc1e12b5e2d5d94f5cb83}, isbn = {1-892512-17-3}, keywords = {Algorithm Artificial Breeder Evolutionary Genetic Networks, Neural Sigma-Pi Trees, algorithms, genetic programming,}, language = {en}, month = {28 June-1 July}, notes = {http://www.sigmod.org/sigmod/dblp/db/conf/icai/icai1999-2.html}, oai = {oai:CiteSeerPSU:454950}, pages = {437--443}, publisher = {CSREA Press}, rights = {unrestricted}, size = {7 pages}, timestamp = {2008-06-19T17:37:42.000+0200}, title = {Forecasting High-Frequency Financial Time Series with Evolutionary Neural Trees: The Case of Heng-Sheng Stock Index}, url = {http://citeseer.ist.psu.edu/454950.html}, volume = 2, year = 1999 }