Building multi-factor stock selection models using balanced split regression trees with sorting normalisation and hybrid variables Online publication date: Thu, 25-Jun-2015
by I-Cheng Yeh; Che-Hui Lien; Tao-Ming Ting
International Journal of Foresight and Innovation Policy (IJFIP), Vol. 10, No. 1, 2015
Abstract: This research employed regression trees to build the predictive models of the rate of return of the portfolio and conducted an empirical study in the Taiwan stock market. Our study employed the sorting normalisation approach to normalise independent and dependent variables and used balanced split regression trees to improve the defects of the traditional regression trees. The results show (a) using the sorting normalised independent and dependent variables can build a predictive model with a better capability in predicting the rate of return of the portfolio, (b) the balanced split regression trees perform well except in the training period from 1999 to 2000. One possible reason is that the dot-com bubble achieved its peak in 2000 which changes investors' behaviour, (c) during the training period, the predictive ability of the model using data from the bull market outperforms the model using data from the bear market.
Online publication date: Thu, 25-Jun-2015
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 Foresight and Innovation Policy (IJFIP):
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 firstname.lastname@example.org