The full text of this article
Empirical mode decomposition-based models for predicting direction of stock index movement
by Youqin Pan; Yong Hu
International Journal of Data Science (IJDS), Vol. 1, No. 3, 2016
Abstract: In this paper, novel forecasting models based on empirical mode decomposition (EMD) are proposed to predict the direction of stock market movement. The proposed models first use EMD to adaptively decompose the complicated stock index into a small number of intrinsic mode functions (IMFs). Then, these IMFs were used as explanatory variables to predict the signs of stock market movement. The Dow Jones industrial average (DJIA) index, Hang Seng index (HSI) and Shanghai stock exchange composite (SSE) index were used to evaluate the performance of the proposed models. The proposed learning algorithms generate about a 70% hit ratio on weekly stock indices except that of the logistic regression on SSE. Moreover, the learning algorithms seem to perform equally well on the monthly stock indices and the weekly Dow index. However, there are significant differences among the model performances of the three learning algorithms on weekly SSE and HSI indices.
Online publication date: Tue, 12-Apr-2016
is only available to individual subscribers or to users at subscribing institutions.
Go to Inderscience Online Journals to access the Full Text of this article.
Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.
Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Science (IJDS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable).
See our Orders page to subscribe.
If you still need assistance, please email email@example.com