Mandated disclosures under IAS 36 Impairment of Assets and IAS 38 Intangible Assets: value relevance and impact on analysts’ forecasts

ABSTRACT

Drawing on a large sample of European firms, we examine whether variant compliance levels with mandated disclosures under IAS 36 Impairment of Assets and IAS 38 Intangible Assets are value relevant and affect analysts’ forecasts. Our results indicate a mean (median) compliance level of about 84% (86%) but high variation among firms and disclosure levels regarding IAS 36 being much lower than those regarding IAS 38. In depth, analysis reveals that non-compliance relates mostly to proprietary information and information that reveals managers’ judgment and expectations. Furthermore, we find a positive (negative) relationship between average disclosure levels and market values (analysts’ forecast dispersion). Results, however, hold more specifically for disclosures related to IAS 36, and these also improve analysts’ forecast accuracy. Our findings add knowledge regarding the economic consequences of mandatory disclosures, have an appeal to regulators and financial statement preparers and reflect on the IASB’s concerns to increase the guidance and principles on presentation and disclosure.

KEYWORDS:

• Mandatory disclosures
• value relevance
• analysts’ forecasts
• intangibles
• impairments
• IAS 36
• IAS 38

JEL CLASSIFICATION:

• M40
• M41
• M48
• G10

Acknowledgment

We gratefully acknowledge helpful comments received from two anonymous reviewers, Barbara Davidson, Mark Clatworthy, Richard Martin, Francesco Mazzi, Michael Stewart and the participants of the 16^th Financial Reporting and Business Communication Conference (Bristol, July 2012). Thanks to Yin Wang and Michael Mavromatis for excellent research assistance. We also thank the Association of Chartered Certified Accountants (ACCA), the Carnegie Trust for the Universities of Scotland, the ESSEC KPMG Financial Reporting Centre and the Accounting and Finance Division at the University of Stirling for funding this study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ For example, an Ernst & Young (2009) study of over 700 deals that took place in 2007 indicates that, on average, 23% of the deal values were allocated to identifiable assets, whilst close to 50% was allocated to goodwill. Tsalavoutas et al. (2014, 21) report that the mean (median) percentage of goodwill recognized over the purchase price is 54% (51%) for a sample of large international firms reporting under IFRS in 2010.

² http://www.ifrs.org/Current-Projects/IASB-Projects/Disclosure-Initiative/Pages/Disclosure-Initiative.aspx.http://www.ifrs.org/Current-Projects/IASB-Projects/Disclosure-Initiative/Principles-of-Disclosure/Pages/Exposure-Draft-and-Comment-letters.aspx.

³ André, Filip, and Paugam (2016), report similar values for companies in the US for the same period.

⁴ Further, McInnis and Monsen (2017), whose dataset contains 4166 firm-years containing at least one acquisition in the US for the period between 2003 to 2014, report that intangibles (other than goodwill) make up on average 35.0% of the purchase price, resulting identifiable intangible assets of $730 billion.

⁵ In order to avoid penalizing a company for non-compliance with a standard or item which might not be applicable, a thorough reading of the complete annual is needed prior to proceeding with examining compliance (Cooke 1992). This approach was followed in the present study.

⁶ To ensure the reliability of the research instrument, we scored 10 randomly selected companies independently. We then compared our findings. Given that the final research instrument had been agreed by all investigators, differences in the compliance scores across the investigators were insignificant.

⁷ We exclude financial companies because of the differences in the nature of their operations and because they are subject to different regulations. This is common in the relevant literature on compliance with mandatory disclosures (e.g. Mazzi et al. 2017; Abdullah et al. 2015) as well as the literature examining analysts’ forecasts (e.g. Bozzolan, Trombetta, and Beretta 2009; Glaum et al. 2013a).

⁸ This results in the exclusion of six potential observations. However, although a firm may be listed in more than one stock markets, its financial statements are the same. Hence, we cannot include the same compliance scores and financial statements-related items twice in the sample.

⁹ For example, higher earnings’ quality could result in higher value relevance of earnings. Additionally, the more readable a company’s financial statements, the easier for financial statement users to comprehend and analyse the information in them. This would lead to higher value relevance of accounting information. Further, the higher the enforcement mechanisms of accounting and security regulations, the higher the value relevance of accounting information would be. Finally, the more developed a market is, the more sophisticated investors would follow listed firms and analyse their financial statements. This would lead to higher value relevance of accounting information. Following along these lines, these characteristics would result in lower analysts’ forecasts’ errors and dispersion.

¹⁰ AWCA = (WCt – WCt-1*St/St-1)/TAt. WC stands for working capital accruals, computed as current assets (WC02201) – cash & equivalents (WC02001) – current liabilities (WC03101) + short term debt (WC03051).

¹¹ Ind equals one when a firm operates in one of the following industry sectors: basic materials, consumer goods, industrials, oil and gas or technology. Consequently, it takes a value of zero when the company is in the consumer services, healthcare, telecommunications or utilities industries. Arguably, this dummy may not capture the diversity of industries in the sample. Hence, we repeat all our analyses by substituting the one dummy variable we now have, with industry fixed effects based on the ICB Level 2 as this indicated in Table 1. These results confirm the findings of our main analyses.

¹² Repeating the analysis by using the number of shares outstanding as an alternative scaling factor does not lead to qualitatively different conclusions.

¹³ The findings regarding Aver_2 are qualitatively similar and are not discussed for brevity.

Additional information

Funding

We also thank the Association of Chartered Certified Accountants (ACCA), the Carnegie Trust for the Universities of Scotland, the ESSEC KPMG Financial Reporting Centre and the Accounting and Finance Division at the University of Stirling for funding this study.