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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/104666

    Title: Building Multi-Factor Stock Selection Models Using Balanced Split Regression Trees with Sorting Normalization and Hybrid Variables
    Authors: Yeh, I-Cheng;Lien, Che-hui;Ting, Tao-Ming
    Keywords: stock markets;stock selection models;multi-factor selection models;balanced split regression trees;sorting normalisation;hybrid variables;Taiwan;modelling;bull markets;bear markets
    Date: 2015-06-30
    Issue Date: 2016-01-06 11:06:34 (UTC+8)
    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.
    Relation: International Journal of Foresight and Innovation Policy 10(1), pp.48-74
    DOI: 10.1504/IJFIP.2015.070081
    Appears in Collections:[土木工程學系暨研究所] 期刊論文

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