Modelling the accruals process and assessing unexpected accruals*

eceivables to offset the positive normal accounts receivables. As normal receivables increase,
more negative abnormal receivables are needed to offset this increase.) This inherent correlation
violates the zero conditional assumption imposed by ordinary least squares (OLS) that the
expected component is orthogonal to the disturbance term. As a result, the regression approach to
extract abnormal accruals as the disturbance term is likely to be problematic.
If accruals models are ill-specified, the omitted relevant variables will be captured by the
disturbance term; and they are likely to further contaminate the resulting unexpected accruals.
This is especially the case for the Jones model, which omits lagged revenue and next period‟s
change in revenue from its explanatory variables that are intended to capture normal accruals. In
addition to normal changes in underlying performance (as discovered in Dechow et al 1995), my
paper shows that two other factors (or determinants) are likely to contaminate Jones-type
measures of unexpected accruals. These are normal changes in accrual policy and normal
deferred accruals. The modified-Jones model suffers from similar problems to the Jones model.
Finally, since conditional conservatism is an accounting distortion, its incorporation into the
Ball-Shivakumar model tends to further contaminate the measure of unexpected accruals by
attributing abnormal accruals from conditional conservatism as expected accruals.
The remainder of the paper continues as follows. Section 2 defines accruals and reconciles direct
and indirect methods to calculate accruals. Section 3 explores the accrual process and models
normal and abnormal accruals. Section 4 assesses determinants of the bias in unexpected in
capturing abnormal accruals. Section 5 demonstrates the correlation between normal and
abnormal accruals, while section 6 concludes.
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