Modelling the accruals process and assessing unexpected accruals*

The main explanatory variables in the model (Xi) vary depending on whether the base model is
the modified Jones or the Dechow and Dichev (2002) model, D is equal to one if there are
unrealized losses (or bad news), and zero otherwise, VAR is the exogenous variable that serves as
a proxy for news, and 4, t j and 4 t, j 5
t, j
are the parameters that capture instances of good and
bad news, respectively. In the absence of a bias in accounting standards, conditional
conservatism is a discretionary distortion and its incorporation into the Ball and Shivakumar
model leads to more abnormal accruals being classified as expected. 20 As a result, unexpected
accruals capture a smaller proportion of abnormal accruals than a model that does not
incorporate asymmetric timeliness.
5 Correlation between normal and abnormal accruals
5.1 Correlation illustration
It may seem puzzling prima facie from the analysis in section 4 as to why the unexpected
accruals from well-specified models (like the encompassing model and the modified version of
the Dechow and Dichev (2002) model) are even contaminated. In this section, I show that such
contamination in unexpected accruals is almost unavoidable as abnormal and normal accruals are
correlated and cannot be explicitly disentangled. To see this correlation, let us make two non-
crucial assumptions – (a) accruals reverse in one period and (b) reported cash flow is unbiased.
With these assumptions, total accruals in expression (10) become
where
* *
t ttt t N D
TACC NACC DACC (21)
NACC a CNI d CNI ,
*
t
u, t u, t1
DACC m CNI ,
* ut , 1
t
20 Recall that the framework of my analysis ignores the role of accounting standard setting, see section 3.4 for details
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