Forecasting High-Frequency Financial Time Series with Evolutionary Neural Trees: The Case of Hang-Seng Stock Index
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by Shu-heng Chen, Hung-shuo Wang, Byoung-tak Zhang
http://bi.snu.ac.kr/Publications/Conferences/International/ICAI99.ps
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Abstract:
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.
Citations
| 129 | Evolutionary Artificial Neural Networks – Yao |
| 59 | Balancing accuracy and parsimony in genetic programming – Zhang, Mühlenbein - 1995 |
| 55 | Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica – Dickey, Fuller - 1981 |
| 23 | Evolutionary induction of sparse neural trees – Zhang, Ohm, et al. - 1997 |
| 3 | Would Evolutionary Computation Help in Designs of ANNs in Forecasting Foreign Exchange Rates? forthcoming – Chen, Lu - 1999 |
