The predictive ability of entity-wide geographic sales disclosures: IAS 14R versus IFRS 8

Sandra J. Cereola, Nancy B. Nichols and Donna L. Street

Research in Accounting Regulation, 2018, vol. 30, issue 2, 121-130

Abstract: This report examines the predictive value of geographic revenue disclosures under IFRS 8 in forecasting company revenues using four forecast models. The findings show that the predictive accuracy of IFRS 8 entity-wide geographic sales significantly outperform consolidated sales in forecasting consolidated sales one year out. The results indicate that the predictive ability of country specific entity wide geographic sales improves on average by six percent when geographic sales are reported for country of domicile or by each individually material country. The study also finds that geographic sales disclosures by companies located in countries with high and moderate enforcement regimes improve the predictive accuracy of geographic sales by five percent. These results provide evidence that the disclosure of finer geographic sales data is more decision useful and associated with improved predictive accuracy for large listed companies in Europe, Australia and New Zealand.

Keywords: IFRS 8; Segment reporting; Predictive ability (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1052045718300262
Full text for ScienceDirect subscribers only

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:eee:reacre:v:30:y:2018:i:2:p:121-130

DOI: 10.1016/j.racreg.2018.09.002

Access Statistics for this article

Research in Accounting Regulation is currently edited by G. Previts

More articles in Research in Accounting Regulation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().