Analytical and empirical analyses on fixed asset write-offs

The objective of the International Financial Reporting Standards (IFRS) is to provide useful information to the users of financial statements to assist in making economic decisions. To be useful, information has to be relevant and reliable, but the reliability of information suffers when the guidelines for the reporting of specific issues are not clear and managerial discretion arises. Write-offs are one of those accounting issues that are regularly related to earnings management. By now it is seen as common knowledge that write-offs, especially those on goodwill, do not reflect declines in asset value; rather, they are used as a device to manipulate financial reports. However, there is a striking lack of grounded theoretical research that can confirm this assessment. The aim of this dissertation is to provide valuable analytical and empirical insights on fixed asset write-offs under IFRS. In a first step, the practical implementation of IAS 36 in Europe has to be analyzed, which is best done empirically. Based on the findings from these empirical surveys, the most substantial questions remaining are subject to an in-depth analytical discussion. Since IAS 36 entails different measurement issues that have their origins in finance theory, this dissertation also aims to introduce some basic techniques from theoretical finance to accounting research. Lastly, as the analyses presented in this dissertation do not cover all open questions on fixed asset write-offs, the author hopes to encourage further research on this important topic.:1. Analytical and Empirical Analyses on Fixed Asset Write-Offsffs: An Overview... 1 2. Determinants of the Write-Off Decision under IFRS: Evidence from Germany... 20 3. What Drives Companies? An Analysis of Fixed Asset Write-Offs in Europe in the Context of Different Institutional Settings... 60 4. A Critical Analysis of the Requirements of IAS 36 - A Pre-Tax CAPM?... 134 5. A New Perspective on Fixed Asset Write-Offs - When is Earnings Management Optimal... 16