Discovering optimal weights in weighted-scoring stock-picking models: a mixture design approach
學年 109
學期 1
出版（發表）日期 2020-11-23
作品名稱 Discovering optimal weights in weighted-scoring stock-picking models: a mixture design approach
作品名稱（其他語言）
著者 Yeh, I-Cheng; Liu, Y. C.
單位
出版者
著錄名稱、卷期、頁數 Financial Innovation 6, 41
摘要 Certain literature that constructs a multifactor stock selection model adopted a weighted-scoring approach despite its three shortcomings. First, it cannot effectively identify the connection between the weights of stock-picking concepts and portfolio performances. Second, it cannot provide stock-picking concepts’ optimal combination of weights. Third, it cannot meet various investor preferences. Thus, this study employs a mixture experimental design to determine the weights of stock-picking concepts, collect portfolio performance data, and construct performance prediction models based on the weights of stock-picking concepts. Furthermore, these performance prediction models and optimization techniques are employed to discover stock-picking concepts’ optimal combination of weights that meet investor preferences. The samples consist of stocks listed on the Taiwan stock market. The modeling and testing periods were 1997–2008 and 2009–2015, respectively. Empirical evidence showed (1) that our methodology is robust in predicting performance accurately, (2) that it can identify significant interactions between stock-picking concepts’ weights, and (3) that which their optimal combination should be. This combination of weights can form stock portfolios with the best performances that can meet investor preferences. Thus, our methodology can fill the three drawbacks of the classical weighted-scoring approach.
關鍵字 Portfolio optimization;stock-picking;weighted-scoring;mixture experimental design;multivariable polynomial regression analysis
語言 en
ISSN 2199-4730
期刊性質 國外
收錄於 SSCI
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
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/119849 )

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