Building Multi-Factor Stock Selection Models Using Balanced Split Regression Trees with Sorting Normalization and Hybrid Variables
學年 103
學期 2
出版（發表）日期 2015-06-30
作品名稱 Building Multi-Factor Stock Selection Models Using Balanced Split Regression Trees with Sorting Normalization and Hybrid Variables
作品名稱（其他語言）
著者 Yeh, I-Cheng; Lien, Che-hui; Ting, Tao-Ming
單位
出版者
著錄名稱、卷期、頁數 International Journal of Foresight and Innovation Policy 10(1), pp.48-74
摘要 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.
關鍵字 stock markets;stock selection models;multi-factor selection models;balanced split regression trees;sorting normalisation;hybrid variables;Taiwan;modelling;bull markets;bear markets
語言 en_US
ISSN 1740-2816
期刊性質 國外
收錄於
產學合作
通訊作者 Yeh, I-Cheng
審稿制度 否
國別 USA
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/104666 )