Modelling the accruals process and assessing unexpected accruals*

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Hence, the correlation between abnormal and normal accruals could be a plausible explanation as to why even well-specified accruals models are powerful in detecting abnormal accruals only in very restrictive circumstances. When abnormal and normal accruals are correlated, the expected component will unavoidably embed some abnormal accruals. As a result, unexpected accruals extracted from these models will not capture abnormal accruals in full. Specifically, when there is no heterogeneity between firms, the r-squared of these models will be close to one and the model fully explains the variation in accruals which consist of both abnormal and normal accruals. The resulting unexpected accruals will be close to zero even for serious earnings managers. Since the high r-squared of a model can be due to its ability in correctly attributing normal accruals as the expected component or incorrectly attributing abnormal accruals as the expected component, this raises doubt over whether one should judge a model‟s ability to correctly extract abnormal accruals by its r-squared. In short, the correlation between abnormal and normal accruals inevitably leads well-specified accruals models to incorrectly attribute abnormal accruals as the expected component and less well-specified accruals models to incorrectly attribute normal accruals as the unexpected component. The zero conditional assumption imposed by ordinary least squares requires the disturbance term to be uncorrelated with the explanatory variables. The correlation between abnormal and normal accruals exactly violates this assumption. As a result, the approach to identify earnings management via a least square disturbance term of the regression is likely to be problematic. This echoes the points made in McNicols (2000). 40
6 Conclusion My analysis reveals that the factors that drive normal accruals are growth in income and changes in accruals and deferrals policies, while abnormal accruals are driven by growth in income and a change in accounting policies. Since income growth is the common driver for both accruals, the two are inherently correlated. This inherent correlation violates the ordinary least squares zero conditional assumption (that the expected component is orthogonal to the disturbance term). As a result, even the unexpected accruals from well-specified accrual models are biased by two factors – income distortion in the benchmark firms involved in the estimation procedure and deviation in operating policies between the firm in question and the benchmark firms. Researchers who use unexpected accruals to identify earnings management need to ensure that the correlation between normal and abnormal accruals is minimized. This can be achieved by employing the idea of the matching procedure in Kothari et al (2005) with three modifications. First, instead of utilizing the ill-specified Jones-type models, well-specified models like the encompassing model or the modified version of the Dechow and Dichev (2002) model should be used. The latter model could be more empirically desirable if measurement errors are taken into account as revenue tends to be relatively inferior to cash flow in measuring their respective underlying items. Second, instead of matching firms on ROA alone, firms need to be matched by their operating policy. As shown in this paper, the drivers for this operating policy are profit margin, accrual policy, deferral policy, and economic depreciation rate. Finally, the additional criteria for the matching firms is that their income distortion should, on average, be zero or unbiased, i.e., they have no incentives to systematically manage earnings. 41
Page 1 and 2: Modelling the accruals process and
Page 3 and 4: insights into what unexpected accru
Page 5 and 6: attributed to the firm‟s biased a
Page 7 and 8: 2 Direct and indirect method to cal
Page 9 and 10: flow associated with income recogni
Page 11 and 12: change in net financial obligations
Page 13 and 14: t t TACC CNICFt T t t t titti CNI
Page 15 and 16: period of income recognition and ca
Page 17 and 18: expenses or receive less revenue in
Page 19 and 20: any accounting distortions (conserv
Page 21 and 22: this paper). Combining the normal a
Page 23 and 24: 4 Assessing unexpected accruals mea
Page 25 and 26: firm j and the benchmark firms. The
Page 27 and 28: If there is no deviation in operati
Page 29 and 30: accruals is highly positive (negati
Page 31 and 32: These restrictions respectively imp
Page 33 and 34: The second source of contamination
Page 35 and 36: Even if the use of changes in cash
Page 37 and 38: The item m m CNI , 1 a a CNI
Page 39: Hence, to manage earnings downward
Page 43 and 44: References Ball, R., Shivakumar, L.
Page 45 and 46: Appendix 1: Derivation of Normal Ac
Page 47 and 48: accrued (initiated deferred) propor
Page 49 and 50: The portion of underlying income in
Page 51 and 52: Hence, the correlation between norm
Page 53 and 54: Table 2: Summary of Accruals Compon
Page 55 and 56: Table 4: Summary of the Bias in Une

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