Zita Bed házi*
MODELING THE EFFECTS OF ADOPTION OF
INTERNATIONAL ACCOUNTING STANDARDS
SECTION I. INTRODUCTION TO ACCOUNTING MODELING
The first IAS was published in 1975 by the International Accounting Standards Committee
(IASC), which was formed in 1973. Since then the process for setting international accounting
standards has undergone substantial evolution culminating in the 2001 restructuring of the IASC
into the IASB1. From January 1, 2005, all publicly listed companies in the European Union are required to prepare financial statements in accordance with International Financial Reporting Standards
(IFRS). The Financial Accounting Standards Board has promulgated a comprehensive project aimed
at convergence between IFRS and US Generally Accepted Accounting Standards (GAAP). The IASB
has taken steps to limit allowable alternative accounting practices and to provide a more consistent
approach to accounting measurement, both of which it believes should increase accounting quality.
The first accounting models appeared in the earnings management literature. Earnings management consist is managing firms’ earnings toward special goals, like meeting analysts’ expectations; achieving/maximizing bonus plans compensations for executives; meeting creditors’ expectations; and, maximizing firms’ value through maximizing the value of shares. However, among researchers and practitioners it is recognized that some degree of earnings management is in the
benefit of the firms. The question remains where to set up boundaries till when earnings to be managed in the benefit of the firm, and where earnings become abusive managed and even fraud. The
earnings management literature has many trends, the most important ones considering tax-induced
earnings management, the role of auditors in earnings management, the role of analysts is earnings
management, the role of creditors in earnings management, and, earnings management with the use
of accounting accruals.
Among special attempts of earnings management literature to model, and thus to demonstrate
the existence of earnings management till the mid ‘90’s we find: earnings management of firms
subject to Alternative Minimum Tax (AMT); earnings management in response to Corporate Tax
Rate Changes (evidence from the 1986 Tax Reform Act (TRA), tax rate changes induced earnings
management in USA; tax induced earnings management by firms with net operating losses); earnings management in response to political scrutiny of effective tax rates; the effects of SFAS No.
109 referring to deferred tax valuation allowances; the growing discrepancy between book income
and tax income; and, finally, earnings management based on deferrals.
The most intensive preoccupations of earnings management literature deals with tax management, „cookie jar” reserves, „big bath” charges, creative acquisition accounting, abuse of materiality, premature revenue recognition, postponing or not recognizing expenses, round tripping or backto-back swaps and, using internal auditors suggestions. Firms may apply one or more earnings man*
1
University of Baja, Faculty of Technology and Business, Assistant Professor, ABD.
International Accounting Standards Board.
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Z. BED HÁZI: MODELING THE EFFECTS OF ADOPTION …
agement or earnings smoothing method in order to achieve purposes like meeting analysts’ expectations, meeting banks' debt covenants, paying less taxes, responding to incentives of bonus plans.
The first accounting models appeared at the time of attempts to determine accounting accruals.
Among these models we find the HEALY Model, the DEANGELO Model, the JONES Model, the
Modified JONES Model and the Industry Model. The Forward-looking model is one of the newest
models developed after 2000. The evaluation of up-mentioned models compares the specification
and power of commonly used test statistics across the measures of discretionary accruals generated
by the models. The evaluation of these models has some major insights:
1) All of the models appear to be well specified when applied to a random sample of firm years.
2) The models all generate tests of low power for earnings management of economically plausible
magnitudes.
3) All models reject the null hypothesis of no earnings management at rates exceeding the specified test-levels when applied to firms with extreme financial performance, highlighting the importance of controlling for financial performance when investigating earnings management
stimuli correlated with financial performance.
The measurement of discretionary accruals usually starts from the measurement of total accruals. After that is assumed a particular model for the process generating the nondiscretionary component of total accruals, which enables total accruals to be decomposed into nondiscretionary and
discretionary component. Most of the models require at least one parameter to be estimated. This is
typically implemented through the use of an “estimation period”, during which is no predicted any
systematic earnings management. The five models presented are general representations of those
that have been used in the extant earnings management literature. In order to facilitate the comparability, the models are presented in the same general framework rather than trying to exactly replicate the models as they have appeared in the literature.
I.1. The HEALY Model
HEALY (1985) tests for earnings management by comparing mean total accruals scaled by
lagged total assets across the earnings management partitioning variable. Healy predicts, that systematic earnings management occurs in every period, thus his study differs from most other earnings
management studies. He uses the partitioning variable to divide the sample into three groups, with
earnings predicted to be managed upwards in one of the groups and downward in the other two
groups. Then are made inferences through pair-wise comparisons of the mean total accruals in the
groups where earnings are predicted to be managed upwards to the mean total accruals for each of the
groups where earnings are predicted to be managed downwards. The HEALY Model is equivalent to
treating the set of observations for which earnings are predicted to be managed upwards as the estimation period and the set of observations for which earnings are predicted to be managed downwards
as the event period. The measure of nondiscretionary accruals is then represented by mean total accruals from the estimation period. The model for nondiscretionary accruals is the following:
NDAτ =
where NDA
TA
t
∑ TA
t
t
(1)
T
= the estimated nondiscretionary accruals;
= the total accruals scaled by lagged total assets;
= 1, 2,…T is a year subscript for years included in the estimation period; and
= a year subscript indicating a year in the event period.
I.2. The DEANGELO Model
DEANGELO (1986) proposes a model, where first differences in total accruals are computed, and
by assuming that the differences have an expected value of zero under null hypothesis of no earnings
management. The model uses the total accruals of the last period – scaled by lagged total assets – as
the measure of nondiscretionary accruals. The DEANGELO Model for nondiscretionary accruals is:
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Z. BED HÁZI: MODELING THE EFFECTS OF ADOPTION …
NDAτ = TAτ −1
(2)
The DEANGELO model can be viewed as a special case of the HEALY Model, in which the estimation period for nondiscretionary accruals is restricted to the observation of the previous year.
A common feature of the HEALY Model and the DEANGELO Model is that they both use total
accruals from the estimation period as a proxy for expected nondiscretionary accruals. If nondiscretionary accruals are constant over time and discretionary accruals have zero mean in the estimation period, then both models measure nondiscretionary accruals without error. However, if nondiscretionary accruals change from period to period, then both the HEALY and DEANGELO models
tend to measure nondiscretionary accruals with error. The more appropriate model depends on the
nature of time-series process generating nondiscretionary accruals. If nondiscretionary accruals follow
a white noise process around a constant mean, then the HEALY model is appropriate. If nondiscretionary accruals follow a random walk, then the DEANGELO model is appropriate. DECHOW (1994) suggests that total accruals are stationary in the levels and approximate a white noise process.
The assumption that nondiscretionary accruals are constant is not likely to be empirically descriptive. KAPLAN (1985) attracts the attention that the nature of the accrual accounting process
dictates that the level of nondiscretionary accruals should change in response to changes in economic circumstances. Failure to model the impact of economic circumstances on nondiscretionary accruals – due to the omission of relevant variables – will cause increased standard errors. Furthermore,
if firms continuously experience abnormal economic circumstances, then failure to model the impact
of economic circumstances on nondiscretionary accruals will result in biased estimates.
I.3. The JONES Model
The JONES Model (1991) relaxes the assumption that nondiscretionary accruals are constant.
The model attempts to control for the effect of changes in a firm’s economic environment on nondiscretionary accruals. The JONES Model for nondiscretionary accruals in the event year is:
NDAτ = α 1 (1 / Aτ −1 ) + α 2 (∆REVτ ) + α 3 ( PPEτ )
(3)
where REV = revenues in year less revenues in year -1 scaled by total assets at -1;
PPE
= gross property, plant and equipment in year scaled by total assets at -1;
A-1
= total assets at -1; and
1, 2,3 = firm-specific parameters.
Estimation of the firm specific parameters,
in the estimation period:
1, 2 and 3 are generated using the following model
TAt = a1 (1 / At −1 ) + a 2 (∆REVt ) + a3 ( PPEt ) + υ t
(4)
where a1, a1 and a3 denote the OLS estimates of 1, 2 and 3 and TA is total accruals scaled by lagged
total assets. The model is successful at explaining around one quarter of the variation in total accruals.
The JONES model implicitly assumes that revenues are nondiscretionary. If earnings are managed through discretionary revenues, then the JONES Model removes a part of the managed earnings from the discretionary accrual proxy. For example management uses its discretion to accrue
revenues at year-end when the cash has not been received and it is questionable whether the revenues have been earned. The result of this managerial discretion – through an increase in receivables
– will be an increase in total accruals and revenues. The JONES Model extracts this discretionary
component of accruals, and causes the estimate of earnings management to be biased toward zero.
I.4. The Modified JONES Model
The modification of the JONES Model is designed to eliminate the conjectured tendency of the
model to measure discretionary accruals with error when discretion is exercised over revenues. In
the modified model, nondiscretionary accruals are estimated during the event period (i.e., during
periods in which earnings management is hypothesized) as:
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NDAτ = α 1 (1 / Aτ −1 ) + α 2 (∆REVτ − ∆RECτ ) + α 3 ( PPEτ )
(5)
where
REC = net receivables in year less net receivables in year -1 scaled by total assets at -1.
The estimates of 1, 2 and 3 and nondiscretionary accruals during the estimation period – in
which no systematic earnings management is hypothesized – are those obtained from the JONES
model. The original JONES model assumes that discretion is not exercised over revenue in the estimation period or the event period. The modified JONES Model assumes that all changes in credit sales in
the event period result from earnings management. This assumption is based on the reasoning that it is
easier to manage earnings by exercising discretion over the recognition of revenue on cash sales. In
this way the estimate of earnings management should no longer be biased toward zero in samples
where earnings management has taken place through the management of revenues.
I.5. The Industry Model
The Industry Model was used by DECHOW and SLOAN (1991). This model – similar to the
JONES model – relaxes the assumption that nondiscretionary accruals are constant over time. Instead of directly model the determinants of nondiscretionary accruals, the Industry Model assumes
that variation in the determinants of nondiscretionary accruals are common across firms in the same
industry. The Industry Model for nondiscretionary accruals is:
NDAτ = γ 1 + γ 2 mediant (TAτ )
(6)
where
mediant(TA ) = the median value of total accruals scaled by lagged assets for all non-sample firms in
the same 2-digit SIC code. The use of 2-digit SIC levels represent a trade-off between defining industry groupings narrowly enough that the Industry Model to capture the industry specific effects versus
having enough firms in each industry grouping so that the model can effectively diversify firmspecific effects. The firm specific parameters 1 and 2 are estimated with OLS regression on the observations in the estimation period.
The ability of Industry Model to mitigate measurement error in discretionary accruals hinges on
two factors. First, the Industry Model removes only variation in nondiscretionary accruals that is
common across firms in the same industry. If changes in nondiscretionary accruals reflect responses to changes in firm-specific circumstances, then the Industry Model will not extract all nondiscretionary accruals from the discretionary accruals proxy. Second, the Industry Model removes
variation in discretionary accruals that is correlated across firms in the same industry. The severity
of this problem depends on the extent to which the earnings management stimulus is correlated
across firms in the same industry.
Each of the five models considered contains an earnings management partitioning variable.
DECHOW, SLOAN and SWEENEY (1995) argue, that if this earnings partitioning variable is correlated with firm performance, then tests for earnings management are potentially misspecified for all
of the models considered. Appropriate measures of firm performance include both earnings performance and cash from operations performance. When facing this problem, the next recommendations followed: first, the researcher can evaluate the nature of misspecification and conduct a
qualitative assessment of how it affects statistical inferences; second, the researcher can directly
control for the performance related misspecification. Potential approaches can include the firm performance in the earnings management regression (e.g., DEANGELO et al. 1994), the use of control
sample (e.g., HEALY, 1985), or other form of analysis of variance that controls for firm performance (e.g., HOLTHAUSEN et al, 1995).
Because the model of nondiscretionary accruals may unintentionally extract the discretionary
component of accruals, it is also important to consider the relation between the context in which
earnings management is hypothesized and the model of nondiscretionary accruals that is employed.
For example, if the JONES model is used in a context, where discretion is exercised over revenues,
then it is likely to extract the discretionary component of total accruals. If the Industry Model is
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used in a context, where intra-industry correlation in discretionary accruals is expected, then it is
also likely to extract the discretionary component of total accruals. Consideration of a sample help
avoiding the use of a model of nondiscretionary accruals that unintentionally extracts discretionary
accruals.
I.6. The Forward-looking model
The Forward-looking model was developed by DECHOW et al. in 2002 and has the following form:
(7)
where
TACCit = total accruals of firm i in year t;
∆Salesit = the change in firm i’s sales from year t-1 to t;
K
= the slope efficient from a regression of ARit on Salesit;
∆ARit = the change in firm i’s accounts receivable from operating activities from year t-1 to t;
TACC it = α + β1 ( ∆Salesit − (1 − k ) ∆ARit ) + β 2 PPE it + β 3TACC it −1 + β 4 GR _ Sales(t +1) + ε it
PPEit = gross property, plant and equipment of firm i in year t;
TACCit −1 = total accruals of firm i from year t-1, scaled by year t-2 total assets;
GR _ Sales(t +1) = the change in firm i’s sales from year t to t+1, scaled by year t sales;
ε it
= the error term.
The Forward-looking model includes three adjustments to the modified JONES model. First, rather
than assuming all credit sales are discretionary, the model treats part of the increase in credit sales as
normal accrual by regressing ARit on Salesit where the estimated parameter k ranges from 0 to 1.
Hence, the change in sales in Equation (7) is reduced by less than 100 percent of the increase in receivables. Second, a portion of total accruals is assumed to be predictable and captured by including last
year’s accruals in the model. Third, the modified JONES model treats increases in inventory made in anticipation of higher sales as an abnormal accrual reflecting earnings manipulation and not a rational increase in inventory. (e.g. HUNT et al, 1996). Future sales growth corrects for such misclassifications, although it means the Forward-looking model uses future period data to estimate current period normal
and abnormal accruals. Under assumption of no earnings management, PHILLIPS et al. (2003) estimate
the model by excluding (1–k) ARit and using non-EM = 1 firm-years for each two-digit SIC-group year.
The resulting parameter estimates in Equation (7) are used to compute abnormal accruals.
SECTION II. MODELING ACCOUNTING QUALITY
II.1. The Deferred Tax Expense Model
For testing the deferred tax expense (DTE) model, PHILLIPS et al (2003) consider three situations
in which earnings management is likely present: firm years with zero or slightly positive earnings
changes, firm years with zero or slightly positive earnings levels, and firm years where earnings exactly
equal or slightly exceed analysts’ forecasts. The following pooled cross-sectional model is estimated:
EM it = α + β1 DTEit + β 2 ACit + β 3 ∆CFOit + β j ∑ j Ind it + ε it
(8)
where
EMit = 1 if the change in firm i’s net income form year t-1 to t divided by the market value of equity
at the end of year t-2 is 0 and < 0,01, and 0 if the change in net income -0,01 and < 0;
DTEit = firm i’s deferred tax expense in year t, scaled by total assets at the end of year t-1;
ACit = a measure of firm i’s accruals in year t;
CFOit = the change in firm i’s cash flows form continuing operations from year t-1 to t, scaled by
total assets at the end of year t-1;
∑ j Ind it = 1 (0) if firm i is (is not) in industry j in year t, based on two-digit SIC codes;
ε it
30
= the error term.
Z. BED HÁZI: MODELING THE EFFECTS OF ADOPTION …
II.2. The model of BARTH et al. (2005)
BARTH et al (2005) compare characteristics of accounting amounts for firms that apply International Accounting Standards (IAS) to a matched sample of firms that do not to investigate whether
reporting under IAS is associated with higher accounting quality and lower cost of capital. In particular, they investigate whether applying IAS is associated with less earnings management, more
timely loss recognition, higher value relevance of accounting amounts, and a lower cost of capital.
They first conduct a cross-sectional comparison of IAS and matched non-IAS firms in the period
after IAS firms adopt. They then conduct a time-series examination of whether IAS-adopting firms
increase accounting quality and decrease their cost of capital after applying the international standards. Their results suggest that IAS firms have higher accounting quality and may have a lower
cost of capital than non-IAS firms. Their results also suggest that IAS firms exhibit an improvement in accounting quality and a reduction in cost of capital with the application of IAS; such
changes are not present to the same extent in their non-adoption sample over the same period.
Following LANG, RAEDY, and WILSON (2005), BARTH et al (2005) define the first earnings
smoothing measure which is based on the variability of the change in net income scaled by total assets, NI. A smaller variance in the change in net income is evidence consistent with earnings
smoothing. However, net income is likely to be sensitive to a variety of other factors unrelated to
earnings smoothing, e.g., firm size and industry. Their matching procedure mitigates the confounding effect of these factors. However, some differences may remain. Therefore, their measure
of earnings variability is the variance of the residual from the following regression of change in net
income on control variables:
∆NI it = α 0 + α1SIZEit + α 2GROWTH it + α 3 EISSUEit + α 4 LEVit +
α 5 DISSUEit + α 6TURN it + α 7 CFit + α 8 AUDit + α 9 NUMEX it + ε it
(9)
where SIZE is the natural log of end of year market value of equity, GROWTH is percentage change
in sales, EISSUE is percentage change in common stock, LEV is end of year total liabilities divided
by end of year total equity book value, DISSUE is percentage change in total liabilities, TURN is
sales divided by end of year total assets, CF is annual net cash flow from operating activities, AUD
is an indicator variable that equals one if the firm’s auditor is PwC, KPMG, Arthur Andersen,
E&Y, or D&T, and zero otherwise2, and NUMEX is the number of exchanges on which a firm’s
stock is listed. Equation (9) also includes country, industry, and year fixed-effects.
The second measure of earnings smoothing used by BARTH et al (2005) is based on the ratio of
the variability of the change in net income, NI , to the variability of the change in operating cash
flows, CF. Firms with more volatile cash flows typically have more volatile net income, and their
second measure controls for this. If firms use accruals to manage earnings, the variability of the
change in net income should be lower than that of operating cash flows. This measure requires the
variability of CF in addition to the variability of NI. Therefore, they estimate an equation similar to equation (9), but with CF as the dependent variable:
∆CFit = α 0 + α1SIZEit + α 2GROWTH it + α 3 EISSUEit + α 4 LEVit +
α 5 DISSUEit + α 6TURN it + α 7 CFit + α 8 AUDit + α 9 NUMEX it + ε it
(10)
As with equation (9), they estimate equation (10), using country and industry fixed effects,
pooling observations appropriate for the particular comparison. The variability of CF is the crosssectional variance of groups of residuals from equation (10), where the composition of the groups depends on the particular comparison they test. The resulting variable that they use for their comparisons, variability of NI over CF, is the ratio of the variability of NI to the variability of CF.
2
The Big Five in the meantime remained Big Four after the Arthur Andersen’s collapse.
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The third measure of earnings smoothing used by BARTH et al (2005) is based on the Spearman
correlation between accruals and cash flows. Because accruals reverse over time, they expect that
accruals and cash flows are negatively correlated. As with the two variability measures based on
equations (9) and (10), because their matching procedure not fully eliminates the sensitivity of the
accruals and cash flows correlation to factors unrelated to earnings smoothing, they compare correlations of residuals from equations (11) and (12) rather than correlations between CF and ACC
directly. As with the equations (9) and (10), both CF and ACC are regressed on the control variables, but excluding CF:
∆CFit = α 0 + α1SIZEit + α 2GROWTH it + α 3 EISSUEit + α 4 LEVit +
α 5 DISSUEit + α 6TURN it + α 7 AUDit + α 8 NUMEX it + ε it
ACCit = α 0 + α1 SIZEit + α 2GROWTH it + α 3 EISSUEit + α 4 LEVit +
α 5 DISSUEit + α 6TURN it + α 7 AUDit + α 8 NUMEX it + ε it
(11)
(12)
They test for differences in these correlations based on the squared correlation as derived in
CRAMER (1987).
BARTH et al (2005) find that in the post-adoption period, adopting firms evidence less earnings
management, more timely loss recognition, and more value relevance of accounting amounts than
do non-adopting firms. Firms adopting IAS have significantly higher variance of the change in net
income, a higher ratio of the variances of the change in net income and change in cash flows, and a
lower frequency of small positive net income. They have a higher frequency of large negative net
income and generally higher value relevance of accounting amounts. Differences between adopting
and non-adopting firms in the pre-adoption period do not explain the post-adoption differences in
accounting quality. Adopting firms generally exhibit higher accounting quality in the post-adoption
period than they do in the pre-adoption period. The increase in accounting quality is generally
higher for adopting firms than for non-adopting firms. They also find weak evidence suggesting
that the use of IAS may be associated with a lower cost of equity capital. Overall, their results suggest an improvement in accounting quality associated with using IAS.
CONCLUSIONS
The first section of my paper deals with early accounting modeling techniques like the HEALY
Model, the DEANGELO Model, the JONES Model, the Modified JONES Model and the Industry
Model. The Forward-looking model is one of the newest models developed after 2000. Section II
deals with more complex accounting models, like the DTE model and the model of BARTH et al.
(2005). Modeling of adoption of IAS is only at the beginning, effects of special IAS adoption could
be ways of future accounting modeling attempts.
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