Evaluating the effectiveness of model specifications and estimation approaches for empirical accounting-based valuation models

Yun Shen and Andrew W. Stark

Accounting and Business Research, 2013, vol. 43, issue 6, 660-682

Abstract: This study considers the effectiveness of different model specifications and estimation approaches for empirical accounting-based valuation models in the UK. Primarily, we are interested in the accounting determinants of market value and, in particular, whether accounting-based valuation models can be estimated that not only have in-sample explanatory power but also potentially can be used as a tool of financial statement analysis in developing useful estimates of value out-of-sample. This requires models to be estimated on one sample, and tested for effectiveness on a different sample. Then, issues of model specification arise, together with choosing between methods of estimating the empirical models, in identifying the effectiveness of each combination. Using the criteria of bias and accuracy to capture effectiveness, we suggest estimation methods and models that, overall, provide the most effective models in this context.

Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://hdl.handle.net/10.1080/00014788.2013.840236 (text/html)
Access to full text is restricted to subscribers.

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:taf:acctbr:v:43:y:2013:i:6:p:660-682

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RABR20

DOI: 10.1080/00014788.2013.840236

Access Statistics for this article

Accounting and Business Research is currently edited by Vivien Beattie

More articles in Accounting and Business Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().