Implementation errors during the transition to the International Financial Reporting Standards, Chief Financial Officer's compensation and turnover and earnings quality metrics
- Publication Type:
- Thesis
- Issue Date:
- 2011
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First, the thesis investigates the relation between the Chief Financial Officer's (CFO's)
accounting talent, his/her compensation and his/her turnover. The thesis contends that
accounting talent of the CFO can be measured by implementation errors, when a country
moves to the International Financial Reporting Standards (IFRS) by adopting a "big bang"
approach where all firms have to adopt IFRS within the same accounting period without the
opportunity of early or late adoption. Eighteen different accounting errors are hand-collected
for a sample of 280 Australian companies, which is used in constructing the CFO's
accounting talent. The thesis finds (i) a positive relation between the CFO's accounting talent
and the CFO's compensation ex-ante in the transition year, (ii) a positive relation between the
CFO's accounting talent and the CFO's bonus compensation in the subsequent year (adoption
year) and (iii) an inverse relation between the CFO's accounting talent and the CFO's
turnover in the subsequent year (adoption year). Further tests on the Chief Executive
Officer's (CEO's) accounting talent and the CEO's compensation and turnover and
alternative specifications of our variables confirm our results. Overall the findings bring into
question the outcomes of government intervention in setting executive compensation.
Second, the thesis investigates the extent to which commonly used earnings quality metrics
capture implementation errors. The metric used to measure implementation errors is the same
as the measure used for the CFO's accounting talent. A positive relation is expected, between
some commonly used earnings quality metrics and implementation errors as these metrics
have been claimed to capture the extent to which earnings are calculated with errors. Ranging
from highest to lowest in terms of explanatory power, from OLS regressions are: total
accruals, earnings persistence, accruals quality and earnings predictability. Implementation
errors in reported earnings however do not explain variations in "abnormal" accruals as
estimated from a firm-specific time-series regressions of the modified Jones model and in
earnings smoothness. Overall the results have implications for researchers and provide
guidance regarding the appropriateness of earnings quality metrics selected in their research
setting. The results also point to the fact that total accruals may be a "better" proxy for
implementation errors compared to more "sophisticated" models.
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