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

Derick Kong, Cheng-Ping Lin, I-Cheng Yeh and Wei Chang

Applied Economics Letters, 2019, vol. 26, issue 5, 370-386

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.

Date: 2019
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DOI: 10.1080/13504851.2018.1486005

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