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Building growth and value hybrid valuation model with errors-in-variables regression

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  • Derick Kong
  • Cheng-Ping Lin
  • I-Cheng Yeh
  • Wei Chang

Abstract

Growth value model (GVM) considers stock intrinsic value as the synergy of book value and return on equity (ROE), which contains two parameters, value factor and growth factor. This study addresses the problem of independent variables having measurement errors by utilizing errors-in-variables regression to estimate accurate model parameters. Research findings show the following: (1) The regression curve derived by traditional regression analysis exhibits severe bias. Errors-in-variables regression is capable of correcting the bias. (2) Large-scale firms exhibit lower value factor and higher growth factor, which indicates that large-scale firms possess better profit persistence.

Suggested Citation

  • Derick Kong & Cheng-Ping Lin & I-Cheng Yeh & Wei Chang, 2019. "Building growth and value hybrid valuation model with errors-in-variables regression," Applied Economics Letters, Taylor & Francis Journals, vol. 26(5), pages 370-386, March.
    • Handle: RePEc:taf:apeclt:v:26:y:2019:i:5:p:370-386
    DOI: 10.1080/13504851.2018.1486005
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    Cited by:

    1. I-Cheng Yeh & Yi-Cheng Liu, 2020. "Discovering optimal weights in weighted-scoring stock-picking models: a mixture design approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-28, December.
    2. I-Cheng Yeh & Yi-Cheng Liu, 2023. "Exploring the growth value equity valuation model with data visualization," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-37, December.

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