Discovering optimal weights in weighted-scoring stock-picking models: a mixture design approach

doi:10.1186/s40854-020-00209-x

機構典藏 > College of Engineering > Graduate Institute & Department of Civil Engineering > Journal Article > Item 987654321/119849

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/119849

Title:	Discovering optimal weights in weighted-scoring stock-picking models: a mixture design approach
Authors:	Yeh, I-Cheng;Liu, Y. C.
Keywords:	Portfolio optimization;stock-picking;weighted-scoring;mixture experimental design;multivariable polynomial regression analysis
Date:	2020-11-23
Issue Date:	2021-01-18 12:10:35 (UTC+8)
Abstract:	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.
Relation:	Financial Innovation 6, 41
DOI:	10.1186/s40854-020-00209-x
Appears in Collections:	[Graduate Institute & Department of Civil Engineering] Journal Article

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