Is transparent accounting information more value relevant?: A Scandinavian study on the removal of the corridor method.
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
This paper examines if the value relevance of pension accounting information has increased after the implementation of IAS 19R and its removal of the corridor method, requiring all firms with defined benefit pension plans utilising the corridor method to recognise previously disclosed actuarial gains and losses in the financial statement. With a sample of 185 Scandinavian firms and 370 firm-year observations for 2012 (disclosure year) and 2013 (recognition year) we perform two separate stock-price regressions (one recognition model and one recognition versus disclosure model). The recognition model tests if value relevance has increased for the balance sheet during the first full recognition year of IAS 19R and our recognition versus disclosure model tests whether recognised actuarial gains and losses are more value relevant than when they are disclosed. Our results indicate that previously disclosed pension amounts in the form of actuarial gains and losses increases the value relevance of the balance sheet when they are recognised. However, there is no significant difference between the valuation of this pension amount by investors whether it is disclosed or recognised. Therefore, even though our recognition test suggests an increase in value relevance, we can not conclude that the value relevance of pension accounting information has increased for the average investor after the implementation of IAS 19R or that higher accounting quality is achieved through more transparent financial information.
Place, publisher, year, edition, pages
2015. , 38 p.
Actuarial gains and losses, Corridor method, Disclosure, IAS 19R, IASB, Pension accounting, Recognition, Value relevance
IdentifiersURN: urn:nbn:se:uu:diva-256250OAI: oai:DiVA.org:uu-256250DiVA: diva2:824858
Master Programme in Accounting, Auditing and Analysis