Discovering optimal weights in weighted-scoring stock-picking models: a mixture design approach
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inancial Innovation
Open Access
METHODOLOGY
Discovering optimal weights in weighted‑scoring stock‑picking models: a mixture design approach I‑Cheng Yeh1* and Yi‑Cheng Liu2 *Correspondence: [email protected] 1 Department of Civil Engineering, Tamkang University, 151 Yingzhuan Rd., Tamsui Dist., New Taipei City 25137, Taiwan Full list of author information is available at the end of the article
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 pre‑ diction 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 sig‑ nificant 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 methodol‑ ogy can fill the three drawbacks of the classical weighted-scoring approach. Keywords: Portfolio optimization, Stock-picking, Weighted-scoring, Mixture experimental design, Multivariable polynomial regression analysis
Highlights • Finding the connection between weights of stock-picking concepts and performances. • Discovering the optimal combination of weights of stock-picking concepts. • Meeting various investors’ preferences.
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