A Comparison of Equity Valuation Models: Empirical Evidence from a Sample of UK Companies

Ja Ryong Kim
Additional contact information
Ja Ryong Kim: PhD, University of Nottingham, UK

European Journal of Multidisciplinary Studies Articles, 2019, vol. 4

Abstract: This paper aims to answer one main question: can the superior models in accounting field be superior in finance field? That is, can models that generate a better approximation to stock price also generate higher returns in the future? To answer this question, I conduct pricing errors analysis and time-series returns analysis. The most important finding is models that approximate stock price better tend to produce higher returns in the future; implying findings in accounting literature have practical implications to analysts and investors. The consistent rankings of models are observed throughout the research: forward earnings multiples perform the best, followed by fundamental valuation models and historical earnings multiples, and book value and sales multiples worst. However, multiples are ranked rather as a group in the UK. Interestingly, residual income models produce similar returns to forward earnings multiples, but the accuracy of their estimates varies depending on their terminal value assumptions.

Keywords: comparison; equity; valuation; models; empirical; evidence; sample; UK companies (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://brucol.be/index.php/ejms/article/view/6100 (text/html)
https://brucol.be/files/articles/ejms_v4_i2_19/Ryong.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eur:ejmsjr:453

DOI: 10.26417/ejms-2019.v4i2-544

Access Statistics for this article

More articles in European Journal of Multidisciplinary Studies Articles from Revistia Research and Publishing
Bibliographic data for series maintained by Revistia Research and Publishing ().