DOES MORE CONSERVATIVE REVENUE RECOGNITION IMPROVE THE
INFORMATIVENESS OF EARNINGS?
Clayton L. Forester
Carlson School of Management, The University of Minnesota
claytonf@umn.edu
July 2008
I am grateful to my dissertation committee, Paul Hribar, Daniel Collins, Haidan Li, Jon Garfinkel,
and Gene Savin for their guidance and comments. I also appreciate helpful discussions and
suggestions from Brad Badertscher, John McInnis, Huishan Wan, Shibin Xie and Rong Zhao as
well as workshop participants at The University of Iowa, The University of Alberta, and The
University of Minnesota. Data is available from public sources.
DOES MORE CONSERVATIVE REVENUE RECOGNITION IMPROVE THE
INFORMATIVENESS OF EARNINGS?
Abstract
Watts (2003a) and Watts and LaFond (2007) provide evidence that more conservative
earnings serve an informational role in equity valuation. My study tests this assertion
using a sample of treatment firms that changed their revenue recognition policies to meet
the requirements of a more conservative reporting regime. I find that more conservative
revenues are more informative with respect to future cash flows and that expense
manipulations do not increase to counteract the effects on earnings of more conservative
revenue recognition. My results provide evidence of an initial decline in earnings
informativeness when more conservative practices are followed but that this decline in
informativeness is temporary. My results provide important evidence for the Financial
Accounting Standards Board who are currently considering two revenue recognition
models that differ with respect to their degrees of conservatism envisioned.
1
I. INTRODUCTION
This paper investigates the effect of conservative revenue recognition practices on
the informativeness of earnings with respect to future operating cash flows. I test the
informational role of more conservative revenue recognition using a sample of firms
whose reporting environment changed so as to require these firms to report more
conservatively.
Watts (2003a) analyzes conservatism in financial reporting and discusses the
reasons why conservatism arose in financial reporting and why it flourishes. Watts
(2003a) argues that conservatism not only serves as a natural contracting mechanism but
that “conservative accounting performance measures such as earnings also fulfill an
important role in providing information for investors”(page 214). LaFond and Watts
(2007) provide empirical evidence that conservatism serves an informational role by
reducing information asymmetry through limiting the manger’s ability to manipulate
financial reports. My paper uses a unique time frame surrounding the issuance of Staff
Accounting Bulletin No. 101 (SAB 101) to study a shift in revenue reporting
conservatism from a regime of less conservative reporting to one of increased
conservatism.
My study uses a natural experimental setting to investigate the informational role
of conservatism by studying how the informativeness of earnings is affected when a
regime shift occurs from one of less conservatism to one of more conservatism. This
study also disaggregates earnings into cash flows and the major components of accruals
to determine the source of the change in earnings’ informativeness with respect to future
cash flows for firms affected by SAB 101. Barth, Cram and Nelson (2001) demonstrate
2
that disaggregating earnings into cash flows and the major components of accruals
significantly enhances earnings’ predictive ability with respect to one period ahead
operating cash flows.
Altamuro, et al. (2005) consider two competing hypotheses – the earnings
informativeness hypothesis and the earnings management hypothesis – and find that
earnings are less managed but also less informative using their post-SAB 101 period. I
posit and find that the results of Altamuro, et al. (2005) are biased toward the finding of a
decrease in informativeness of earnings with respect to future operating cash flows in
their post-SAB 101 period. Altamuro, et al. (2005) use a relatively short post-adoption
period in the quarters immediately after adoption. This does not allow sufficient time for
previously recognized revenues that were required to be deferred by SAB 101 to re-enter
the earnings stream and creates a temporary disturbance in the earnings – cash flow
relationship.
I consider a sample of firms that were required to adopt more conservative
revenue recognition practices due to the issuance of SAB 101 (treatment firms) along
with a matched sample of firms whose revenue recognition practices did not change as a
result of SAB 101 (control firms). First, I test the relation between earnings and future
cash flows and find that this measure of earnings informativeness – the ability of earnings
to map into future cash flows – decreases in the transitional period immediately following
the increase in conservatism. This result is consistent with Altamuro, et al. (2005). Next,
I decompose earnings into current period cash flows and accruals components and
demonstrate how these components’ relations with future cash flows are affected by SAB
101 adoption. I find that the predictive ability of treatment firms’ earnings components
3
with respect to future operating cash flows declines post-SAB 101. While I do not find a
significant decline in the relation between the change in receivables and future cash flows,
there is a significant decline in the relation between the “other accruals” component of
earnings and future cash flows. Since the other accruals component contains the change
in deferred revenues, this decline provides evidence that the decline in earnings
informativeness documented in Altamuro, et al. (2005) is due to the deferral of revenues.
The decomposition results suggest that the decline in earnings informativeness for
treatment firms may be due to the disruption in the accruals brought about by the deferral
of revenue in the year of adoption of SAB 101 recognition rules. To test this hypothesis,
I reconsider whether the more conservative recognition practices recommended in SAB
101 continue to be less predictive of future operating cash flows over a longer post-SAB
101 period. Contrary to the findings in Altamuro, et al. (2005), over the longer horizon, I
find that earnings’ informativeness with respect to future cash flows actually improves in
the post-SAB 101 period. That is, earnings and the accrual components of earnings do a
better job of predicting future cash flows under the more conservative SAB 101 rules
than under the pre-SAB 101 reporting climate when firms practiced more aggressive
revenue recognition.
Further, I show that the amount of the improvement is significantly related to the
magnitude of the initial deferral required by the cumulative effect adjustment due to SAB
101 adoption. This result suggests that firms that were required to defer a large amount
of pre-SAB 101 revenues in the year of adoption will likely find a portion of these
revenues coming back into earnings in the transitional post-SAB 101 period. This reentry into earnings of revenues previously deferred as part of the cumulative effect
4
adjustment creates a disruption of the earnings – cash flow relation in the transitional
post-SAB 101 period. The significant relation between the size of the cumulative effect
adjustment (the deferral) and the change in earnings informativeness from the transitional
post-adoption period to the longer post-adoption period provides evidence of the
temporary decrease in informativeness of earnings being a result of large deferred
revenues re-entering the earnings stream.
Finally, based on Jensen’s (2005) argument that overvalued firms may be
motivated to engage in long-run value-destroying activities such as benchmark beating in
the near term, I demonstrate that benchmark beating behavior for three quarterly earnings
benchmarks (levels, changes, and analyst forecast errors) decline in the post-adoption
period for SAB 101 firms and I show that this decline is significantly greater for the
treatment firms than for the control firms. This provides evidence that these firms may
have been using aggressive revenue recognition practices to meet or beat benchmarks
with an eye to maintaining potentially overvalued equity.
Gaining a better understanding of the findings of how a change in conservatism
affects the informativeness of firms’ reported earnings is important for at least three
reasons. First, the SEC’s move to issue SAB 101 was a heavily contested regulatory
event. On the one hand, the SEC argued that SAB 101 did nothing more than provide
guidance on their interpretation of current GAAP regarding revenue recognition and was
not “new regulation” for revenue recognition. However, many firms were required to
change their revenue recognition practices in light of the issuance of SAB 101. So, for
these firms, it was tantamount to new regulation that required more conservative revenue
recognition. Second, the Financial Accounting Standards Board (FASB) is undertaking a
5
project on revenue recognition guidance that will better align with the financial statement
elements project in which it is currently involved. 1 Preliminary discussions by the FASB
suggests it is considering a less conservative approach to revenue recognition than that
embodied in SAB 101. My study’s results cast doubt on the appropriateness of moving
revenue recognition standards in that direction. I find that the more conservative SAB
101 revenue recognition policies have reduced benchmark beating (often cited as being
related to earnings management) and have increased the informativeness of earnings.
This study provides evidence on unintended consequences of financial reporting
regulation. SAB 101 was intended to more clearly communicate acceptable revenue
recognition practices in order to prevent abusive revenue recognition by firms. At the
same time, however, it may have temporarily reduced the informativeness of earnings
with respect to future cash flows in the adoption period.
This paper enhances our understanding of how regulation affects the users of
financial statements. Beatty (2007) provides a review of the literature concerning how
changes in regulation affect the behavior of management and points out the relative lack
of evidence concerning the impact of regulatory changes on social welfare. If investors
benefit from more informative earnings and an enhanced ability to predict future
operating cash flows, then my findings suggest that investors may be better off as a result
of SAB 101’s requirement for more conservative revenue recognition.
1
The FASB stated recently during a board meeting that they would be “remiss” not to address this issue of
revenue recognition. The FASB states that the reasons for reconsidering revenue recognition criteria are:
(1) there is a gap between broad conceptual guidance and detailed authoritative literature; (2) there are
flaws in the conceptual guidance provided in the FASB’s Concepts Statement No. 6; and (3) the issues of
revenue recognition are intimately tied to liability recognition issues currently under consideration by the
FASB. This paper will provide information useful to this process.
6
Finally, this paper informs the literature on conservatism by using the SAB 101
setting to compare the more aggressive revenue recognition practices of the pre-adoption
period with the more conservative revenue recognition practices subsequent to adoption.
LaFond and Watts (2006) argue that information asymmetry between informed and
uninformed equity investors generates a demand for conservatism in financial statements.
My results show that more conservative revenue recognition practices recommended in
SAB 101 create earnings that are more informative with respect to future operating cash
flows and, arguably, more useful to equity investors attempting to assess the timing,
amounts, and uncertainty of future cash flows. Consistent with the implications of
LaFond and Watts (2007), my study provides evidence that conservative revenue
recognition improves financial statement usefulness. Watts (2003b) calls for empirical
studies that focus on how GAAP changes affect conservatism and my study provides an
ideal setting to consider how a regulatory change such as SAB 101 affects the usefulness
of reported earnings numbers that are specifically made more conservative.
The remainder of the paper is organized as follows. Section II provides
background information on SAB 101 and the response of businesses and the Financial
Accounting Standards Board (FASB) and develops the hypotheses. Section III discusses
the sample selection and descriptive statistics. Section IV presents the empirical tests and
results. Finally, Section V provides a conclusion and discusses the implications of the
empirical results.
7
II. BACKGROUND AND HYPOTHESES DEVELOPMENT
Staff Accounting Bulletin (SAB) 101 was introduced by the Securities and
Exchange Commission (SEC) in response to a growing concern over firms’ use of
accelerated revenue techniques to manipulate earnings. The SEC claimed that by
accelerating revenues, firms were potentially inflating results in a way that was
misleading to investors and other users of the financial reports. Concern for these types
of abuses formed the central criticism aimed at financial reporting by Arthur Levitt in his
Numbers Game speech in 1998. 2
SAB 101 spells out the basic criteria for revenue recognition. At the time of its
introduction, there were two opposing views on the implications of this bulletin. Firms
argued that this was tantamount to a new revenue recognition standard while others
(including the SEC) maintained that this bulletin merely reiterated the current existing
requirements in GAAP. Specifically, SAB 101 requires that the following criteria be met
before revenue can be recognized: (1) Persuasive evidence of an arrangement between
buyer and seller; (2) delivery has occurred or services have been rendered; (3) the seller’s
price to the buyer is fixed or determinable; and (4) collectability is reasonably assured.
While these requirements seem to be a reiteration of the FASB’s revenue
recognition guidance in its Concepts Statement No. 6, SAB 101 went further, providing
additional guidance on more complex recognition issues related to the following: (1)
timing of approval for sales agreements; (2) side arrangements to the master contract; (3)
consignment arrangements; (4) criteria for delivery; (5) layaway programs; (6)
nonrefundable up-front fees; (7) cancellation provisions; and others.
At the time that SAB 101 was first introduced (implementation began with fiscal
year 2000 for most firms), the businesses most affected by this new guidance were not in
2
Readers can find copies of this September 28, 1998 speech through the SEC’s website at
http://www.sec.gov/news/speech/speecharchive/1998/spch220.txt
8
favor of its implementation. As one example, firms with revenues whose recognition
relied on customer acceptance under SAB 101guidance argued that this would create a
shift of power within the supply chain and would adversely affect their profit margins and
credit terms with customers.
In contrast to the more conservative approach to revenue recognition embodied in
SAB 101, the FASB appears to be moving toward a less conservative or more neutral
approach as part of its Conceptual Framework Project. 3 Specifically, the FASB is
considering two revenue recognition models – the Measurement Model (formerly the Fair
Value Model) and the Allocation Model (formerly the Customer Consideration Model).
At a joint meeting with the International Accounting Standards Board (IASB) on October
22, 2007, the two bodies discussed these models. Both models are based on revenue
recognition being linked to contractual rights arising from a revenue arrangement. The
Measurement Model recognizes revenues as obligations are satisfied and allows for some
revenue (and profit) to be recognized at contract inception. The Allocation Model
recognizes revenues as performance obligations are satisfied and does not allow
recognition of revenues or profits at contract inception. It appears then that the
Measurement Model is less conservative than the Allocation Model in terms of when
initial revenues may be recognized.
The FASB, along with the IASB, is currently considering these two models and it
is unclear which model the resulting revenue recognition pronouncement will favor.
However, during a May 11, 2005 board meeting, the FASB “…affirmed its past decision
to develop a standard for revenue recognition based on recognized changes in assets and
liabilities…that would not be overridden by additional recognition criteria such as
realization and completion of the earnings process….” (FASB, board meeting minutes
from May 11, 2005). In addition, the FASB seems poised to eliminate conservatism as a
3
Readers can find details on the FASB’s Conceptual Framework Project on their website at
http://www.fasb.org/project/conceptual_framework.shtml
9
desirable qualitative characteristic of accounting information. At a May 2, 2007 board
meeting to discuss the FASB’s Conceptual Framework project, the staff recommended
that the board affirm its decision to exclude conservatism as a component of faithful
representation because it is incompatible with neutrality. This explicit move away from
conservatism by the FASB diverges from the conservative reporting requirements of
SAB 101. In this paper, I consider the relative usefulness of revenue accruals in
predicting future cash flows to provide evidence of importance to this issue currently
under consideration by the FASB and the IASB.
Watts (2003a) argues that conservatism not only serves as a natural contracting
mechanism but that “conservative accounting performance measures such as earnings
also fulfill an important role in providing information for investors”(Watts, 2003a, page
214). LaFond and Watts (2007) provide empirical evidence that conservatism provides
an informational role by reducing information asymmetry through limiting the manger’s
ability to manipulate financial reports. In contrast to this view, Altamuro, et al. (2005)
show a decline in earnings informativeness with respect to future cash flows from the less
conservative pre-SAB 101 era to the more conservative post-SAB 101 era. The
seemingly contradictory evidence in Altamuro, et al. (2005) and Watts (2003a) and Watts
and LaFond (2007) brings into question whether more conservative accounting is more
informative or whether more conservative reporting decreases earnings informativeness.
I posit that the decline in earnings informativeness with respect to future cash
flows documented by Altamuro, et al. (2005) is temporary and mechanical in nature.
When firms adopted SAB 101, the adoption was treated as a change in accounting policy
by the affected firms. 4 APB No. 20 requires that this change be reflected as a cumulative
effect adjustment to earnings in the period of adoption. In most cases, this created
deferred revenue and a one-time charge to earnings for the difference between revenues
4
The pre-SAB 101 period is 1997 – 1999; the year of adoption is 2000; the transitional post-SAB 101
period is 2001, and the post-transitional period is 2002 – 2003.
10
under the more aggressive pre-SAB 101 approach versus the more conservative postSAB 101 approach. In the transitional year following adoption of SAB 101, it is likely
that for firms with an operating cycle less than one year, this deferral was recognized in
revenues. I posit that the recognition of the previously deferred revenues along with
revenues initiated in the transitional period immediately after adoption likely created a
temporary distortion in the earnings-cash flow relation that manifest itself in earnings
showing less predictive ability with respect to future cash flows. This result is interpreted
by Altamuro, et al. (2005) as evidence that the more conservative approaches to revenue
recognition embraced by SAB 101 are less informative than the more liberal approaches
followed in the pre-SAB 101 era. If this decline in earnings informativeness is
mechanical and temporary in nature as I posit, then the effect should be more
concentrated in the “other” accruals component of earnings because this is where the
change in deferred revenues is found. This leads to the statement of my first two
hypotheses.
Hypothesis 1: The predictive usefulness of earnings with respect to future
operating cash flows from before to after SAB 101 will decline in the transitional period.
Hypothesis 2: The reduction in the predictive usefulness of earnings with respect
to future operating cash flows from before to after SAB 101 is concentrated in the “other”
component of accruals.
The first hypothesis is more of a statement of expected results that confirm the
Altamuro, at al. (2005) finding of a decrease in earnings informativeness with respect to
future cash flows in the transitional period immediately after SAB 101’s adoption. The
second hypothesis provides a basis for testing the potentially mechanical relation between
an unusual change in “other” accruals during the transitional post-SAB 101 period. I
posit that this change in the transitional period is brought about by the recognition of
revenues deferred in the year of adoption. My next hypothesis builds on the first two
hypotheses and speaks more specifically to the temporary nature of the decrease in
11
earnings’ informativeness documented in Altamuro, et al. (2005). Once beyond the
transitional period, I expect an increase in the informativeness of earnings based on the
findings of Watts (2003a) and Watts and LaFond (2007) that document an information
role for conservative reporting.
Hypothesis 3: Beyond the transitional period, the predictive usefulness of
earnings with respect to future cash flows improves relative to the transitional period and
this improvement is concentrated in “other” accruals component of income.
The previous hypotheses have considered primarily the informativeness of overall
earnings with respect to future cash flows and the “other” accruals component of earnings
and its informativeness for future cash flows. I now turn my attention to the expense
components’ informativeness for future cash flows and how this informativeness is
affected by SAB 101. If managers respond to more conservative revenue recognition
guidance brought about with SAB 101 by managing expenses to help meet or beat
benchmarks, then one would expect to see the informativeness of the expense
components of earnings declining in the post-SAB 101 period and no decrease in
benchmark beating. However, if the informativeness of these components with respect to
future cash flows does not decrease with SAB 101 adoption, then it provides some
evidence that firms have not attempted to manage expense accruals to make up for the
more conservative revenue recognition required by SAB 101.
The ability of affected firms 5 to beat benchmarks is shown by Altamuro, et al.
(2005) to decrease in the transitional post-SAB 101 period. Since the more conservative
recognition requirements of SAB 101 place a constraint on firms’ revenue recognition
practices, it similarly might constrain their ability to meet or beat targets of importance to
investors and management. Jensen (2005) discusses how capital market pressure to beat
short term benchmarks is exacerbated by overvaluation of firms’ equity. If a firm’s
5
Affected firms are those that have a cumulative effect adjustment necessitated by SAB 101 adoption.
12
equity is overvalued, then market expectations with respect to earnings benchmarks may
be unreasonably high. Graham, Harvey, Rajgopal (2005) provide evidence that
executives worry about benchmarks and manage earnings to help meet benchmarks.
If expense accruals do not decrease in terms of their informativeness for future
cash flows and if declines in benchmark beating documented by Altamuro, et al. (2005)
are not temporary, then SAB 101 has even greater benefits than a reduction of benchmark
beating through aggressive revenue recognition. Not only would SAB 101 provide more
informative earnings as Watts (2003a) and Watts and LaFond (2007) suggests it should,
but it could also curb potentially value-destroying benchmark beating that can result from
overvaluation of equity. This leads to my final two hypotheses.
Hypothesis 4: Expense accruals’ informativeness with respect to future cash flows
is not expected to decrease in the late post-SAB 101 period.
Hypothesis 5: Benchmark beating will decline in the late post-SAB 101 period
relative to the pre-SAB 101 period.
13
III. SAMPLE SELECTION
SAB No. 101 was adopted by most firms during the 2000 calendar year. 6 The
adoption of SAB 101 resulted in some firms changing their revenue recognition policy.
For those firms who had been using a revenue recognition policy deemed inappropriate
under SAB 101, a cumulative effect adjustment for the effects of adoption of SAB 101
was required (following APB No. 20) in the year of adoption. 7 Using a keyword search
of firms’ 10K filings, I first identify all firms who mention “SAB 101” by itself and in
combination with “cumulative” and “adjustment”. This yields an initial sample of 1,468
firms. Of these firms, 257 report a cumulative effect adjustment to earnings due to SAB
101 adoption. Compustat data is unavailable for 28 of these firms. After eliminating
these firms from the sample, 229 firms remain as the final sample of SAB 101 firms.
Henceforth, I will refer to this as my treatment sample.
The majority of firms that mention SAB 101 and a related cumulative adjustment
do not actually report a cumulative effect adjustment on their income statement. These
firms discuss SAB 101 adoption and state either that (a) the cumulative effect of SAB
101 cannot be estimated or is immaterial or (b) the firm’s revenue recognition policy
requires no change in light of SAB 101. These firms are not included in the treatment
sample. I construct a matched control sample of firms unaffected by SAB 101 as follows.
I first match the SAB 101 firms to other Compustat firms by their two-digit SIC code and
select the firm closest in size (measured by total assets) within that industry. Each
matched firm is allowed to appear only once in the control sample.
6
There were 5 firms that reported a cumulative effect adjustment resulting from SAB No. 101 adoption
during fiscal 1999 and 37 that reported a cumulative effect adjustment in 2001. These firms have not been
included in the sample of SAB 101 firms used in this study so that a single and common year of adoption
can be used in empirical tests. Future analysis may look at the 2001 sample and compare these “late
adopters” with the majority of firms who adopted SAB No. 101 during fiscal 2000.
7
For comparison, this study follows closely the sample selection methods of Altamuro, et al. (2005) in that
firms are considered “affected” by SAB 101 based on whether or not they have a cumulative effect
adjustment related to SAB 101.
14
In constructing the matched control sample of firms that are unaffected by SAB
101, it is important to find firms that have similar revenue streams from similar activities
but did not have to change their reporting in light of SAB 101. While searching 10K
filings to identify SAB 101 firms for the treatment sample, firms were also identified that
did not have a cumulative effect adjustment but could arguably be considered affected by
SAB 101. These firms discussed the importance of SAB 101 not only to their revenue
recognition policies but also to their business practices. Some firms describe the impact
that SAB 101 had on recognition of revenues when such recognition now relies on
acceptance by the customer and how this acceptance has the potential to shift power from
supplier to customer in the supply chain.
Caution was exercised to specifically exclude these firms from the control sample
of unaffected firms. It is possible that firms considered “unaffected” by SAB No. 101
may not have a cumulative effect adjustment to income in the year of adoption because
they lack detailed information to calculate the cumulative effect. For this reason, firms
who mention that they make no cumulative effect adjustment for SAB 101 because they
lack information to calculate the effect have also been excluded from the control sample.
Table 1 provides the industry membership information for SAB 101 firms. The
industry composition of my SAB 101 treatment sample is similar to that in Altamuro, et
al. (2005), which suggests similarity in the sample selection process. Also included in
Table 1 are the industry average cumulative effect adjustments scaled by total market
value (Compustat quarterly data item 199 * data item 25) reported by the SAB 101 firms
along with the overall average. Industry average cumulative effect adjustments range
from 0.5% of total market value (Communications) to 9% of total market value (Air
Transportation) which is likely economically important to these firms. Further
corroboration of the similarity of my sample characteristics with those SAB 101 firms
selected in Altamuro, et al. (2005) is the fact that the overall average cumulative effect
adjustment recorded by firms is 3% of market value in both studies.
15
IV. EMPIRICAL TESTS AND RESULTS
Transitional Post-SAB 101 Earnings Informativeness
This section provides evidence on the first two hypotheses and demonstrates how
the informativeness of earnings with respect to future cash flows is affected by the more
conservative reporting required under SAB 101. To test the first hypothesis that the
predictive usefulness of earnings with respect to future operating cash flows declines in
the transitional post-SAB 101 period relative to the pre-SAB 101 period, I estimate the
following regression for both the treatment and control samples using firm, quarter and
industry fixed effects: 8
LeadCFO( i ,q +t ) = β 0 + β 1 Post + β 2 Earni ,q + β 3 Post * Earni ,q + ε
(1)
where LeadCFO(i ,q +t ) = accumulated cash flow from operations (Compustat data item
108) scaled by end-of period total assets (Compustat data item 44) for firm i in quarters q
+ 1, q + 2, q + 3, and q + 4.
Earni ,q = earnings (Compustat data item 69) for firm i in quarter q scaled by endof-period q total assets (Compustat data item 44).
Post = a dummy variable set equal to one if the observation is from the post-SAB
101 period, 0 otherwise.
Post * Earni ,q = the interaction of Post and Earni ,q (calculated as Post multiplied
by Earni ,q ).
8
The relation between current earnings and future cash flows as a measure of earnings informativeness is
selected as the measure of choice as the main analysis of the paper and extension of Altamuro, et al. (2005).
This choice was made since the major extension in the current paper is to consider the components of
earnings and their relation to future cash flows and how these relations are affected differentially by the
adoption of SAB 101. Announcement day abnormal stock returns’ relation to two measures of earnings
surprises are considered in section 6 of this paper.
16
The model in equation (1) was estimated for the treatment sample and the control
sample in a system of seemingly unrelated regressions. The variable of interest in
equation (1) is Post * Earni ,q . The predicted sign for the coefficient on Post * Earni ,q is
negative for the treatment sample when estimating this specification with data from the
transitional post-SAB 101 period. A negative coefficient on Post * Earni ,q represents a
decline in earnings informativeness with respect to future operating cash flows from the
pre-SAB 101 period to the transitional post-SAB 101 period. Results from the estimation
of equation (1) for a sample period from 1997 to 1999 (pre-SAB 101 period) and 2001
(transitional post-SAB 101 period) are found in the first two columns of Table 2. Panels
A through D in Table 2 present the earnings informativeness results for 1 through 4
subsequent quarters of accumulated cash flows, respectively.
The results in the first column of Table 2 show that in three out of four panels the
coefficient on Post * Earni ,q for the treatment sample is significantly negative and in the
fourth panel it is insignificantly positive. This provides evidence that the earnings
informativeness with respect to future operating cash flows declines in the transitional
post-SAB 101 period for the treatment sample of affected firms. The third column of
Table 2 reports the F-statistics resulting from tests of the differences in coefficients
derived estimating the model as a system of seemingly unrelated regressions. The
significant differences in the Post * Earni ,q coefficients reported in the third column of
Table 2 show that the decline in informativeness is stronger for the treatment firms than
for the control firms in three out of four panels. In panel C, the treatment firms have less
improvement in informativeness than do the treatment firms which is directionally
consistent with the other three panels. These results are consistent overall with the
findings in Altamuro, et al. (2005).
17
Components of Earnings and Future Cash Flows
This section investigates how the various components of earnings relate to future cash
flows. In doing so, I am able to demonstrate how the transitional impact of SAB 101
adoption affected the findings in Altamuro, et al. (2005). I investigate whether the
decline in earnings informativeness for the treatment firms in the transitional post-SAB
101 period is mechanical in nature by decomposing earnings into its cash flow and major
accrual components. As described in Section II, a mechanical decline in earnings
informativeness in the transitional period that is being driven by the large revenue
deferral would manifest itself in a decline in informativeness in the “other” accruals
component of earnings. This is due to the fact that “other” accruals captured in the cash
flow statement data in Compustat includes the change in deferred revenues.
To understand how these components of earnings are related to the decrease in
informativeness of earnings in the transitional post-adoption period found in Table 2, I
decompose earnings into its cash flow and accrual components using data from the cash
flow statement. 9 I then estimate the following regression to test whether the impact of
SAB 101 on treatment firms’ earnings is mechanically related to the deferral of revenues:
LeadCFO(i , q + t ) = β 0 + β 1CFOi , q + β 2 ΔARi , q + β 3 ΔINVi , q + β 4 ΔAPi , q
+ β 5 DEPRi , q + β 6 OTHERi , q + β 7 POST + β 8 POST * CFOi , q
+ β 9 POST * ΔARi , q + β 10 POST * ΔINVi , q + β 11 POST * ΔAPi , q
(2)
+ β 12 POST * DEPRi , q + β 13 POST * OTHERi , q + ε
where LeadCFO(i ,q +t ) = accumulated cash flow from operations (Compustat data item
108) scaled by end-of period total assets (Compustat data item 44) for firm i in quarters q
+ 1, q + 2, q + 3, and q + 4.
9
Compustat data items in the cash flow statement are provided on a cumulative basis. Throughout this
paper, cash flow statement variables have been disaggregated for fiscal quarters two, three and four.
18
CFOi ,q = cash flow from operations (Compustat data item 108) for firm i in
quarter q scaled by end-of-period q total assets (Compustat data item 44).
ΔARi ,q = the change in receivables for firm i from quarter q – 1 to quarter q,
(quarterly Compustat data item 103 from the cash flow statement), scaled by end-ofperiod total assets (Compustat data item 44).
ΔINVi ,q = the change in inventory for firm i from quarter q – 1 to quarter q,
(quarterly Compustat data item 104 from the cash flow statement) , scaled by end-ofperiod total assets (Compustat data item 44).
ΔAPi ,q = the change in accounts payable for firm i from quarter q – 1 to quarter q,
(quarterly Compustat data item 105 from the cash flow statement), scaled by end-ofperiod total assets (Compustat data item 44).
DEPRi ,q = depreciation and amortization for firm i in quarter q (Compustat data
item number 77 from the statement of cash flows), scaled by end-of-period total assets
(Compustat data item 44).
OTHERi ,q = the other component of accruals for firm i in quarter q not included
in specific categories above (calculated as Earni ,q – [ CFOi ,q + ΔARi ,q + ΔINVi ,q - ΔAPi ,q DEPRi ,q ], scaled by end-of-period total assets (Compustat data item 44).
Post = a dummy variable set equal to one if the observation is from the post-SAB
101 period, 0 otherwise.
Post * CFOi ,q = the interaction of Post and CFOi ,q (calculated as Post multiplied
by CFOi ,q ).
POST * ΔARi ,q = the interaction of Post and ΔARi ,q (calculated as Post multiplied
by ΔARi ,q ).
POST * ΔINVi ,q = the interaction of Post and ΔINVi ,q (calculated as Post
multiplied by ΔINVi ,q ).
POST * ΔAPi ,q = the interaction of Post and ΔAPi ,q (calculated as Post multiplied
by ΔAPi ,q ).
19
POST * DEPRi ,q = the interaction of Post and DEPRi ,q (calculated as Post
multiplied by DEPRi ,q ).
POST * OTHERi ,q = the interaction of Post and OTHERi ,q (calculated as Post
multiplied by OTHERi ,q ). 10
If the decline in earnings informativeness documented in the transitional postSAB 101 period is mechanically related to the deferral of revenues as required by SAB
101 adoption, I expect to see a negative coefficient on POST * OTHERi ,q for treatment
firms. Further, I expecte that this coefficient will be more negative for the treatment
firms than for the control firms. Results from the estimation of equation (2) for a sample
period from 1997 to 1999 (pre-SAB 101 period) and 2001 (transitional post-SAB 101
period) are found in the Table 3.
Comparing the pre-adoption period to the transitional SAB 101 adoption period,
the only variable that consistently loses informativeness for future cash flows is the
“other” component of accruals. Across Panels A, B, and C, the coefficient on
POST * OTHERi ,q is significantly negative. Compustat includes changes in deferred
revenues created as a result of SAB 101 adoption in the “other” accruals category. This
indicates that the “other” component of accruals which embeds the deferral adjustments
that most firms made when adopting SAB 101 is the major reason for the drop in
informativeness of earnings, providing support for Hypothesis 2. This result makes sense
if the majority of revenues were revenues received in advance and then deferred as a
result of SAB 101 adoption. 11 In all 4 panels presented, the F-Statistic on the difference
between the coefficient on POST * OTHERi ,q for SAB 101 firms and unaffected firms is
10
SAB 101 adoption created a cumulative effect adjustment for affected firms and, for the vast majority of
affected firms, a deferral of revenues previously recognized as earned that is reflected in the “other” accrual
component in Compustat’s coding of cash flow statements.
11
For the vast majority of firms required to make cumulative effect adjustments for SAB 101 adoption,
revenues received in advance of performance of services or provision of goods was the main reason for the
adjustment. I estimate equation (2) for a sub-sample of firms (183 firms) whose cumulative effect
adjustment clearly related to revenues received in advance and the results are qualitatively similar to those
found in Table 3 with slightly more significant results for the Post*Other variable, as expected.
20
significant, providing further evidence that the other component of earnings is the main
component in which a reduction in informativeness occurs and that this result is stronger
for treatment firms than for control firms. The coefficient on POST * ΔARi ,q is positive
and insignificant in Panels A through C of Table 3 and significantly positive in Panel D.
This suggests there is no change in the relation between accounts receivable and future
cash flows from the pre-SAB 101 period to the transitional post-SAB 101 period.
Similarly, the coefficients on most of the expense accruals do not decrease in
informativeness in the transitional post-adoption period. This provides some evidence
that managers did not use expense manipulations to counteract the impact of more
conservative revenue recognition on their ability to meet benchmarks of importance.
Taken together, the results of this and the previous section provide evidence that the
decrease in earnings’ informativeness during the transitional post-SAB 101 period is
mechanical in nature and related to the deferral of revenues re-entering the earnings
stream and creating a disruption in the earnings – future cash flow relation.
Transitory Nature of Informativeness Results
SAB 101 adoption created a cumulative effect adjustment for affected firms and,
for the vast majority of affected firms, a deferral of revenues previously recognized as
earned that is reflected in the “other” accrual component in Compustat’s coding of cash
flow statements. Accordingly, I posit that the reduction in the informativeness of
earnings due to SAB 101 documented by Altamuro, et al (2005) was temporary. If
treatment firms defer a portion of previously recognized earnings until a period just
shortly after SAB 101 adoption, and related operating cash flows are unaffected, then one
would expect a temporary distortion in the relation between the “other” accrual
component (that contains the adjustment to the deferred revenue) and future operating
cash flows. Whether this reduction is permanent or temporary is an important empirical
21
question. It is important for researchers to recognize that in testing the relative
informativeness of alternative revenue recognition procedures that they conduct their
tests outside of the transitional period in which the initial adoption of the new procedures
affects the various accrual accounts.
To test whether the reduction of earnings informativeness in the post-SAB 101
period is permanent or temporary, I re-estimate equations (1) and (2) using a longer
sample period, including all quarters in the post-SAB 101 period for 2002 and 2003. 12 I
eliminate 2000 as the year of adoption and also eliminate 2001 as it is the year in which
deferrals from 2000 are likely to reverse and, therefore, does not represent an equilibrium
for the “other” accruals in the SAB 101 regime. The post-transitional period of 2002 and
2003 provides us a “cleaner” post-adoption period once the deferrals from the one-time
adjustments have had a chance to completely unwind. The results of these estimations
can be found in Tables D4 and D5.
The results in Table 4 for the post-transitional SAB 101 period exhibit a dramatic
reversal of the results in Table 2 from the transitional post-SAB 101 period. Based on the
post-transitional time period, SAB 101 firms’ earnings informativeness improves
dramatically from the pre-adoption period to the post-transition period. The F-Statistic
for the difference between SAB 101 firms and control firms becomes is significant in all
panels of Table 4. This provides evidence that the improvement for the treatment firms is
greater than that of the control firms, suggesting that SAB 101 has actually improved
earnings’ informativeness with respect to future cash flows through more conservative
revenue recognition.
Additional evidence that firms affected by SAB 101 were not in equilibrium by
the end of the transitional time period is found by considering β 1 + β 2 in Table 4. The
sum of these two coefficients represents the informativeness of cash flows and revenues
12
I have also estimated these regressions including 2001 in the longer post-adoption period. Qualitatively,
the results are similar to those reported.
22
from receivables. This suggests that earnings are not related to future operating cash
flows in the transitional post period. This provides further support for my hypothesis that
the transitional post-adoption period is in disequilibrium with respect to the relation
between earnings and cash flows.
In the post-transitional period, the treatment firms exhibit the expected significant
relation between earnings and future operating cash flows in three out of four panels.
β 1 + β 2 is significant in the post-transitional period and the sum of these coefficients is
significantly larger than in the transitional post-adoption period. These results are
consistent with the decline in informativeness being temporary in nature.
In Table 5, the results for the post-transitional SAB 101 time period demonstrate a
similar reversal relative to the transitional time period results in presented in Table 3. In
the transitional period, the SAB 101 firms exhibit significantly negative coefficients on
the Post*Other variable in three out of four panels, indicating that the “other” component
of accruals (which is where changes in the deferred revenues resides) is where the
reduction in the informativeness of earnings with respect to future cash flows is
concentrated. However, in Table 5, the POST*OTHER variable becomes either positive
and significant (Panels A and B) or insignificantly positive (Panel C) or insignificantly
negative (Panel D). POST*OTHER loses significance in all Panels except B. The
transitional finding of a negative relation between POST * OTHERi ,q and future cash
flows becomes insignificant. This suggests that the relation between other accruals and
future cash flows was temporarily negative and that the difference between treatment and
control samples’ changes was also temporary. Taken together these results support
Hypothesis 3.
Table 5 also presents evidence that supports results in the previous section that
expense accruals do not demonstrate a significant decrease in informativeness in the posttransitional period. This provides support for the idea that the managers have not used
expense accruals manipulation to overcome the limitations of a more conservative
23
revenue recognition regime. This provides support for Hypothesis 4. In a subsequent
section, I will investigate the target beating behavior of the treatment and control firms to
further understand if conservative revenue recognition that resulted from SAB 101
provides a disciplining force to reduce potentially value-destroying benchmark beating
activities.
Magnitude of Deferral
In this section I investigate whether the temporary decline in the predictive
usefulness of earnings and the “other” accrual component with respect to future cash
flows that is exhibited during the transitional SAB 101 period is related to the cumulative
effect adjustment that firms made when they adopted the more conservative revenue
recognition procedures recommended by SAB 101. I split the sample into large and
small cumulative adjustments and I then estimate the following equation to determine
how the size of the SAB 101 cumulative adjustment affected the reversal:
LeadCFO(i ,q +t ) = β 0 + β 1 Late + β 2 Earni ,q + β 3 Size _ Adji ,q +
β 4 Late * Earni ,q + β 5 Size _ Adj * Late * Earni ,q + ε
(3)
where LeadCFO(i ,q +t ) = accumulated cash flow from operations (Compustat data item
108) scaled by end-of period total assets (Compustat data item 44) for firm i in quarters q
+ 1, q + 2, q + 3, and q + 4.
Size _ Adji ,q = a dummy variable set to 1 if the observation comes from the
quartile of firms with the highest cumulative effect adjustment (scaled by total market
value) due to SAB 101 adoption and 0 if the observation comes from firms in the smallest
quartile of cumulative effect adjustment.
Late * Earni ,q = the interaction of the dummy variable Late and Earni ,q
24
Size _ Adj * Late * Earni ,q = the interaction of the size of the cumulative effect
adjustment and the change in earnings informativeness from the early post period to the
later post period.
Late = a dummy variable set equal to one if the observation is from the posttransitional SAB 101 period, 0 if the observation is from the transitional SAB 101 period.
The results from estimating equation (3) are presented in Table 6. The positive
and significant coefficient on Size _ Adj * Late * Earni ,q provides additional corroborating
evidence that the decrease in the informativeness of earnings with respect to future cash
flows documented in Table 3 in the transitional SAB 101 period was temporary. Since
the magnitude of the deferral of revenues (as measured by the cumulative effect
adjustment) is positively correlated with the shift from earnings being less informative in
the transitional SAB 101 period to earnings being more informative in the posttransitional period, this provides corroborative evidence that the decrease in earnings’
informativeness reported by Altamuro, et al (2005) was temporary in nature and tied to
the mechanics of the deferral of revenues.
Benchmark Beating Analysis
The previous sections have provided evidence of the temporary decline in
earnings informativeness in the transitional SAB 101 period followed by an increase in
earnings informativeness in the post-transitional period. In addition, I show that this
result is likely driven by “other” accruals component of earnings and that expense
accruals have not decreased in terms of there predictive usefulness with respect to future
operating cash flows. Jensen (2005) posits that managers of overvalued firms may
manipulate earnings in order to meet or beat important benchmarks. This behavior (along
with other short-sighted activities) on the part of managers can have real value destroying
implications for firms. SAB 101 created a regime in which revenue recognition is more
25
conservative. In the previous sections, I find that greater conservatism in revenue
recognition does not appear to have motivated managers to manipulate expense accruals.
This is evidenced by no significant decline in informativeness of expense accruals with
respect to future cash flows with the adoption of SAB 101 reporting.
I now perform test my final hypothesis concerning the benchmark beating
activities of firms and how those activities are affected by more conservative revenue
recognition required by SAB 101. I use three benchmarks shown in prior literature to be
important to managers – zero quarterly earnings, zero seasonally adjusted quarterly
earnings changes, and consensus analyst quarterly earnings forecasts.13 The pre-SAB
101 period includes all quarters for 1997, 1998 and 1999. I test to see if benchmark
beating declines with the introduction of SAB 101 using two different post-SAB 101
periods as I have done in the previous empirical tests of this section. For both the
treatment firms and the control firms, histograms of quarterly earnings (Compustat data
item 69) scaled by end-of period total assets (Compustat data item 44), the seasonally
adjusted change in quarterly earnings scaled by end-of-period total assets, and quarterly
analyst forecast errors are created for the pre-SAB 101 period as well as the post-SAB
101 period.
I construct histograms for quarterly earnings levels and seasonally adjusted
changes in quarterly earnings (both scaled by end of period total assets). Bin widths for
the distributions are 0.75 percent. For the earnings level histogram, I construct a variable,
Earn_Diff, which is measured according to Burgstahler and Dichev (1997) as the
difference between the actual number of firms in each distribution bin and the expected
number of firms in each bin. The expected number of firms in each bin is calculated as
the mean number of firms in the two adjacent bins. A similar variable,
13
Burgstahler and Dichev (1997) and Brown and Caylor (2005) document the importance of these three
benchmarks. Additionally, Graham, Harvey, and Rajgopal (2005) report survey evidence that benchmarks
are important to managers.
26
Earn_Change_Diff, is constructed for the histograms for the seasonally adjusted change
in earnings. Also, Forecast_Error_Diff is constructed for the histograms of analyst
quarterly earnings forecast errors in a similar manner. Analyst forecast errors are
computed as the difference between I/B/E/S actual quarterly earnings per share minus the
median of the most recent (prior to the quarterly earnings announcement date) consensus
forecast. The difference between the actual number of firms in a bin and the expected
number of firms (average of adjacent bins) is Forecast_Error_Diff. I create the variable
Netbin that is 1 if it is the bin just to the right of the benchmark of interest, -1 if it is in the
bin just to the left of that benchmark, and zero otherwise.
The histogram in Figure 1 is derived from the pre-adoption period for the SAB
101 affected firms. Figure 2 is derived from the post-adoption period. Visually, the
discontinuity around zero earnings seems to have declined following the adoption of SAB
101. Figures E3 and E4 provide a similar comparison for the histograms of the
seasonally adjusted change in quarterly earnings for the SAB 101 firms while Figures E5
and E6 provide a comparison of analysts’ quarterly earnings per share forecast errors
(I/B/E/S actual earnings per share less the most recent consensus forecast of earnings per
share prior to the quarterly earnings announcement date).
To test whether the observed differences are statistically significant, the following
regressions are estimated to determine whether there has been a significant decrease in
the amount of benchmark beating behavior from the pre-SAB 101 period to the post-SAB
101 periods and whether any such change is more significant for SAB 101 firms than for
the sample of unaffected firms:
Earn _ Diff (b ,t ) = β 0 + β1 Post + β 3 Netbin(b,t ) + β 3 Post * Netbin(b,t ) + ε
Earn _ Change _ Diff ( b ,t ) = β 0 + β1 Post + β 2 Netbin( b ,t )
+ β 3 Post * Netbin( b ,t ) + ε
(4)
(5)
27
Forecast _ Error _ Diff (b ,t ) = β 0 + β 1 Post + β 2 Netbin( b ,t )
+ β 3 Post * Netbin(b ,t ) + ε
(6)
where Earn _ Diff (b ,t ) = the difference between the expected number of firms and the
actual number of firms for each bin b in each period t for the histogram of net income
scaled by end-of-period total assets.
Earn _ Change _ Diff (b ,t ) = the difference between the expected number of firms
and the actual number of firms for each bin b in each period t for the histogram of
seasonally adjusted change in net income.
Forecast _ Error _ Diff (b ,t ) = the difference between the expected number of
firms and the actual number of firms for each bin b in each period t for the histogram of
analysts’ consensus quarterly earnings per share forecasts.
Post = a dummy variable set equal to one if the observation is from the post-SAB
101 period, 0 otherwise.
Netbin(b ,t ) = a dummy variable set equal to 1 if the firm is in the bin just to the
right of the appropriate benchmark (zero earnings, zero change in earnings and 0 and 1
cent per share above analyst forecasts, respectively), -1 if the firm is in the bin just to the
left of the benchmark of interest and 0 otherwise.
Post * Netbin(b ,t ) = the interaction of Post and Netbin(b ,t ) (calculated as Post
multiplied by Netbin(b ,t ) ).
Table 7 presents the results from the estimation of equations (4), (5), and (6) using
the transitional post-adoption period. Panel A presents the results where the benchmark
of interest is zero earnings, Panel B presents the results where the benchmark of interest
is last year’s earnings for the corresponding quarter, and Panel C presents results for the
analyst forecast benchmark.
28
The significantly negative coefficient on Post * Netbin(b ,t ) for the SAB 101 firms
(first column) found in all three panels indicates that the level of unexpected number of
firms around the benchmark of interest declines significantly in the post-SAB 101 period.
This suggests that benchmark beating has declined overall. The F-Statistics in the third
columns of Panels A and B of Table 7 provide evidence that SAB 101’s conservative
reporting requirements curbed benchmark beating around earnings levels and changes
benchmarks. However, Panel C does not provide evidence that the treatment firms
declined more than the control firms with regard to meeting or beating analysts’ quarterly
earnings forecast. It seems that the benchmark beating results are mixed in the
transitional SAB 101 period.
Finally, Table 8 presents the results from the estimation of equations (4), (5), and
(6) using the post-transitional period. Panel A presents the results where the benchmark
of interest is zero earnings, Panel B presents the results where the benchmark of interest
is last year’s earnings for the corresponding quarter, and Panel C presents results for the
analyst forecast benchmark. Overall, the earnings levels and changes benchmarks
provide some evidence of a decrease in benchmark beating for the treatment firms
(relative to the control firms) from the pre-SAB 101 regime to the post-SAB 101 regime.
However, evidence from the analyst forecast benchmark in Panel C is inconsistent with
significantly lower benchmark beating for treatment firms relative to control firms.
One potential issue that might create difficulty interpreting the results observed in
the main tests is the differential incidence of loss firms across samples and time periods.
To test whether loss firms are driving any of my results, I re-estimated the main tests
separately, excluding loss firms from the analysis. Results (not tabled here) of the main
tests for profitable firms only remain qualitatively unchanged and provide confidence that
the changes in the accruals – cash flow relation documented in Tables 2 and 4 are not
driven by losses in the various samples and time periods.
29
Since there are some inconsistencies across quarters in the results prior to this, I estimate
equation (1) again using annual data due to concerns over seasonality. Overall, the annual
estimations (not tabled here) are qualitatively similar to the results in the quarterly
analysis.
Another possible confounding factor in the tests for changes in informativeness of
earnings and its accruals components over time is that the volatility of earnings may be
different across treatment and control samples and between pre-SAB 101 and post-SAB
101 periods. To test for differences in earnings volatility across samples and across time
periods, I calculate mean earnings (cash flows) volatility of quarterly earnings for the preSAB 101 period and post-SAB 101 period for both samples. This was calculated as the
variance of quarterly earnings for the time series pre-SAB 101 and post-SAB 101. Each
firm has 12 quarterly observations in the pre-SAB 101 period and 12 quarterly
observations in the post-SAB 101 period. I perform t-tests and find that neither
differences across time periods nor across samples are significant.
30
V. CONCLUSION
This paper investigates the impact of conservative revenue recognition practices
on the informativeness of earnings with respect to future operating cash flows. In a prior
paper, Altamuro, et al. (2005) find evidence that more conservative revenue recognition
practices are less informative relative to more aggressive revenue recognition procedures
that firms were using prior to SAB 101. I posit that this finding is due to distortions in
the earnings-cash flow relationship during the SAB 101 transition period over which
Altamuro, et al. (2005) conduct their analysis.
Given the concern over accounting manipulations and scandals related to
aggressive financial reporting and the fact that the FASB is in the beginning stages of
revising revenue recognition criteria in conjunction with its conceptual framework project,
this paper reconsiders the affects of SAB 101 on earnings informativeness and provides
important additional evidence that both complements and extends the results of Altamuro,
et. al (2005).
I find that the decrease in earnings informativeness with respect to future
operating cash flows documented in Altamuro, et al. (2005) appears to be limited to the
period shortly after adoption of SAB 101. This decrease in informativeness reverses
when the informativeness tests are conducted on SAB 101 data from periods beyond the
transitional SAB 101 period (i.e., after 2001 fiscal quarters) leading to an overall increase
in earnings informativeness for the sample of SAB 101 firms. This finding has important
implications for future revenue recognition projects under consideration by the FASB.
My findings suggest that more conservative revenue recognition enhances the predictive
usefulness of earnings and its accrual components with respect to future cash flows.
Moreover, I present findings that the more conservative revenue recognition under SAB
101 reduces management’s benchmark beating behavior. This finding suggests that
31
worries over SAB 101 and its potential to diminish the informativeness of earnings may
not be as serious as previously thought.
LaFond and Watts (2007) provide empirical evidence that conservatism serves an
informational role and reduces information asymmetry between investors. My study,
while not specifically focused on information asymmetry, does provide complementary
evidence that supports LaFond and Watts’ (2007) conjecture. My finding that more
conservative revenues are more informative with respect to future operating cash flows
provides evidence on one of the mechanisms by which investor information asymmetry
may be reduced.
A potential area of future research is to consider how this temporary shift in the
earnings-cash flow relationship in the transitional post-SAB 101 period affected analysts’
forecast accuracy. This area of future research will allow a better understanding of
whether and how analysts anticipated the consequences of SAB 101 for affected firms.
Future work could use analyst forecast dispersion as a measure of information asymmetry
to test for a shift in asymmetry after the imposition of SAB 101.
Additional future research could investigate whether more conservative SAB 101
revenue reporting changed firms’ business models as some claimed it would. Watts
(2003b) calls for empirical studies that focus on how GAAP changes affect conservatism
and how contracting responds to these changes. Critics of SAB 101 argued that these
requirements would place greater power in the hands of the customer in the supply chain.
Since one implication of the more conservative revenue recognition required by SAB 101
was that firms were required to wait for customer approval prior to recognition of
revenues, firms that have a greater concentration of revenues with relatively few
customers might be more affected operationally than would firms that have a wider
customer base. For example, if 90% of a firm’s revenues come from one customer, then
SAB 101 could impact that firm more than a firm with many customers. Of interest
32
would be whether the reduction of informativeness of earnings was concentrated in firms
more likely to lose power in the supply chain as a result of SAB 101.
Additionally, the pre- and post-SAB 101 setting of my study provides a unique
context to test the sensitivity of a growing array of firm-specific measures of accounting
conservatism that have recently been proposed. Khan and Watts (2007) propose the
C_Score to be used in event studies and other contexts. Since SAB 101 reduces the
manager’s flexibility in choosing when to recognize revenues, it provides an ideal setting
in which to test the sensitivity of the C_Score measure to real changes in the
conservatism of a firm’s reported earnings.
33
Table1: Average industry cumulative adjustment scaled by market value of equity
Industry
Pharmaceuticals and Chemicals
Machinery
Electrical
Measuring Instruments
Transportation By Air
Communications
Miscellaneous Retail
Holding Companies
Computer and Business Services
Educational Ser SAB
101vices
Management Services
All Other Industries
Total
Number
of
Firms
48
28
8
35
5
8
10
6
24
SIC
Code
28
35
36
38
45
48
59
67
73
Cumulative
Adjustment
0.026
0.034
0.020
0.036
0.091
0.005
0.024
0.010
0.038
82
87
0.008
0.076
5
8
44
0.031
229
Note: Industries with at least companies of membership were considered in the
classifications in this table based on 2-digit SIC membership codes. The
cumulative adjustment reported is the average scaled cumulative effect adjustment
reported by the firms in the industry. The adjustment was scaled by the market
value of equity at the end of fiscal year 2000.
34
Table 2: Changes in Earnings Informativeness from Pre-SAB 101 Period (1997 – 1999)
to the Transitional SAB 101 Period (2001)
Panel A: Cash flows for quarter q+1
Variable
Intercept
Post
Earn
Post*Earn
β1 + β 2
Number of Observations
Adjusted R2
SAB 101
Firms
Coefficients
(t-statistics)
Unaffected
Firms
Coefficients
(t-statistics)
0.002*
(1.85)
0.004**
(2.02)
0.199***
(27.98)
-0.128***
(-5.57)
-0.012
(-0.42)
3,370
0.003**
(2.14)
0.006**
(2.32)
0.376***
(41.34)
-0.096***
(-5.81)
-0.051***
(-1.92)
3,370
0.12
F-Statistic for
Difference in
Coefficients
0.88
1.03
697.69***
3.89**
0.23
Panel B: Cash flows for accumulated to quarter q+2
Variable
Intercept
Post
Earn
Post*Earn
β1 + β 2
Number of Observations
Adjusted R2
SAB 101
Firms
Coefficients
(t-statistics)
0.002
(0.97)
0.007**
(2.20)
0.288***
(23.40)
-0.108***
(-3.93)
-0.042
(-0.89)
3,370
0.50
Unaffected
Firms
Coefficients
(t-statistics)
0.003
(1.37)
0.013***
(3.12)
0.599***
(38.46)
-0.050
(-1.44)
0.175***
(5.03)
3,370
0.62
F-Statistic for
Difference in
Coefficients
0.70
3.63*
764.07***
5.30**
35
Table 2. Continued
Panel C: Cash flows accumulated to quarter q+3
Variable
Intercept
Post
Earn
Post*Earn
β1 + β 2
Number of Observations
Adjusted R2
SAB 101
Firms
Coefficients
(t-statistics)
Unaffected
Firms
Coefficients
(t-statistics)
0.001
(0.08)
0.012***
(2.59)
0.001
(0.45)
0.021***
(3.32)
0.380***
(3.51)
0.021
(0.47)
0.049
(0.80)
3,370
0.810**
(35.31)
0.189***
(3.34)
0.178 ***
(2.93)
3,370
0.57
F-Statistic for
Difference in
Coefficients
0.14
3.27*
640.13***
16.14***
0.61
Panel D: Cash flows accumulated to quarter q+4
Variable
Intercept
Post
Earn
Post*Earn
β1 + β 2
Number of Observations
Adjusted R2
SAB 101
Firms
Coefficients
(t-statistics)
0.001
(0.20)
0.010**
(1.62)
0.470***
(20.59)
-0.306***
(-2.67)
0.221***
(2.69)
3,370
Unaffected
Firms
Coefficients
(t-statistics)
0.002
(0.50)
0.016
(1.92)
1.023**
(34.73)
- 0.170**
(-2.42)
0.239***
(3.46)
3,370
0.59
0.63
F-Statistic for
Difference in
Coefficients
0.23
0.83
666.39***
6.85**
36
Table 2. Continued
Note: Variables are defined in text.
Note: This table presents regression results using a transitional post-adoption period
consisting of fiscal year 2001.
Note: The dependent variable in panel A is one quarter ahead cash flows from operations.
Note: The dependent variable in panel B is cumulative cash flows from operations for
two quarters ahead.
Note: The dependent variable in panel C is cumulative cash flows from operations for
three quarters ahead.
Note: The dependent variable in panel D is cumulative cash flows from operations for
four quarters ahead.
Note: * Denotes statistical significance at the .10 level, two-tailed t statistics.
Note: ** Denotes statistical significance at the .05 level, two-tailed t statistics.
Note: *** Denotes statistical significance at the 0.01 level, two-tailed t statistics.
Note: Results in the table were obtained using firm, quarter and industry fixed effects.
Also, a system of seemingly unrelated regressions was estimated to determine
differences in coefficients for the variables of interest.
37
Table 3:
Disaggregated Earnings Informativeness – Changes from Pre-SAB 101 Period
(1997 – 1999) to Transitional SAB 101 Period (2001)
Panel A: Cash flows for quarter q+1
Variable
Intercept
Post
CFO i , q
ΔARi ,q
ΔINVi ,q
ΔAPi ,q
DEPRi ,q
OTHERi ,q
POST * CFOi ,q
POST * ΔARi ,q
POST * ΔINVi ,q
POST * ΔAPi ,q
POST * DEPRi ,q
POST * OTHERi ,q
β 6 + β13
Number of Observations
Adjusted R2
SAB 101
Firms
Coefficients
(t-statistics)
-0.009
(-0.24)
0.012***
(2.95)
0.097***
(4.68)
0.241***
(7.01)
0.147***
(3.10)
-0.053
(-1.27)
0.042
(0.26)
0.070***
(6.45)
-0.220***
(-4.56)
-0.006
(-0.08)
-0.085
(-0.83)
0.006
(0.06)
0.228
(1.34)
-0.181***
(-3.61)
-0.111***
(2.25)
Unaffected
Firms
Coefficients
(t-statistics)
0.026
(0.76)
0.003
(0.62)
0.162***
(7.60)
0.245***
(6.07)
-0.078*
(-1.53)
-0.469***
(-9.67)
-0.181
(-0.89)
0.012
(0.44)
-0.257***
(-6.10)
-0.053
(-0.66)
0.217**
(1.84)
0.613***
(10.44)
0.283
(1.02)
0.135***
(2.62)
0.147***
(3.16)
2,930
0.43
2,376
0.55
F-Statistic for
Difference in
Coefficients
0.82
0.12
0.93
0.42
0.25
6.53***
11.23***
3.53*
1.12
0.85
4.67**
3.58*
-0.055
14.25***
38
Table 3. Continued
Panel B: Cash flows accumulated to quarter q+2
Variable
Intercept
Post
CFO i , q
ΔARi ,q
ΔINVi ,q
ΔAPi ,q
DEPRi ,q
OTHERi ,q
POST * CFOi ,q
POST * ΔARi ,q
POST * ΔINVi ,q
POST * ΔAPi ,q
POST * DEPRi ,q
POST * OTHERi ,q
β 6 + β13
Number of Observations
Adjusted R2
SAB 101
Firms
Coefficients
(t-statistics)
-0.012
(-0.20)
0.020***
(2.99)
0.071**
(2.06)
0.274***
(4.77)
0.172**
(2.18)
-0.100
(-1.46)
0.059
(0.22)
0.075***
(4.14)
-0.141*
(-1.75)
0.012
(0.10)
-0.066
(-0.38)
0.166
(1.01)
0.263
(0.93)
-0.235***
(-2.79)
-0.161***
(-1.94)
Unaffected
Firms
Coefficients
(t-statistics)
0.054
(1.03)
0.004
(0.52)
0.164***
(5.04)
0.332***
(5.39)
-0.005
(-0.06)
-0.525***
(-7.10)
-0.663***
(-2.13)
0.059*
(1.46)
-0.239***
(-3.65)
0.380***
(3.03)
0.330**
(1.81)
0.186***
(2.07)
0.887***
(2.04)
0.157***
(1.96)
0.216***
(2.88)
2,923
0.50
2,362
0.65
F-Statistic for
Difference in
Coefficients
0.49
0.23
0.89
0.72
5.66**
78.36***
789.36***
7.58**
6.12**
48.36***
6.68**
0.89
5.66**
725.36***
39
Table 3. Continued
Panel C: Cash flows accumulated to quarter q+3
Variable
Intercept
Post
CFO i , q
ΔARi ,q
ΔINVi ,q
ΔAPi ,q
DEPRi ,q
OTHERi ,q
POST * CFOi ,q
POST * ΔARi ,q
POST * ΔINVi ,q
POST * ΔAPi ,q
POST * DEPRi ,q
POST * OTHERi ,q
β 6 + β13
Number of Observations
Adjusted R2
SAB 101
Firms
Coefficients
(t-statistics)
0.008
(0.10)
0.029***
(3.20)
-0.044
(-0.96)
0.256***
(3.40)
0.173*
('1.67)
-0.109
(-1.21)
0.639*
(1.81)
0.074***
(3.10)
0.014
(0.13)
0.016
(0.10)
0.343
(1.52)
0.009
(0.05)
0.035
(0.09)
-0.186*
(-1.68)
-0.113
(-1.03)
2,916
0.57
Unaffected
Firms
Coefficients
(t-statistics)
0.109 ***
(2.38)
-0.011
(-1.31)
0.393 ***
(8.89)
0.268 ***
(2.92)
0.315 ***
(2.74)
-0.734 ***
(-6.68)
-1.29 ***
(-3.01)
0.375 ***
(6.39)
-0.196 ***
(-2.76)
0.195
(1.47)
0.130
(0.78)
0.275 ***
(2.20)
1.776 ***
(3.63)
0.135
(1.04)
0.210 ***
(3.54)
2,351
0.63
F-Statistic for
Difference in
Coefficients
365.34***
0.40
365.43***
0.25
544.22***
658.23***
459.34***
455.66***
452.30***
778.23***
262.36***
0.78
456.38***
765.32***
40
Table 3. Continued
Panel D: Cash flows accumulated to quarter q+4
Variable
Intercept
Post
CFO i , q
ΔARi ,q
ΔINVi ,q
ΔAPi ,q
DEPRi ,q
OTHERi ,q
POST * CFOi ,q
POST * ΔARi ,q
POST * ΔINVi ,q
POST * ΔAPi ,q
POST * DEPRi ,q
POST * OTHERi ,q
β 6 + β13
Number of Observations
Adjusted R2
SAB 101
Firms
Coefficients
(t-statistics)
0.026
(0.25)
0.024**
(2.03)
0.062
(1.04)
0.165*
(1.67)
0.153
(1.12)
-0.132
(-1.11)
3.124***
(6.38)
0.032
(1.03)
0.493***
(3.52)
0.598***
(2.76)
0.751***
(2.46)
-0.508*
(-1.81)
0.092
(0.18)
0.274*
(1.86)
0.306***
(2.11)
Unaffected
Firms
Coefficients
(t-statistics)
0.129
(1.22)
-0.033***
(-2.06)
0.148***
(2.26)
0.336***
(2.70)
-0.371***
(-2.37)
-0.306***
(-2.05)
0.693
(1.09)
0.266***
(3.26)
0.081
(0.60)
0.628***
(2.40)
0.828***
(1.96)
0.147
(0.79)
3.751***
(4.05)
0.414***
(2.30)
0.679***
(4.14)
2,908
0.60
2,340
0.64
F-Statistic for
Difference in
Coefficients
0.45
0.72
0.89
3.98**
369.35***
12.12**
38.96***
402.36***
423.65***
1.32
0.89
42.36***
465.90***
778.93***
41
Table 3. Continued
Note: Variables are defined in text.
Note: This table presents results using an earlier post-adoption period consisting of fiscal
year 2001.
Note: The dependent variable in panel A is one quarter ahead cash flows from operations.
Note: The dependent variable in panel B is cumulative cash flows from operations for
two quarters ahead.
Note: The dependent variable in panel C is cumulative cash flows from operations for
three quarters ahead.
Note: The dependent variable in panel D is cumulative cash flows from operations for
four quarters ahead.
Note: * Denotes statistical significance at the .10 level, two-tailed t statistics.
Note: ** Denotes statistical significance at the .05 level, two-tailed t statistics.
Note: *** Denotes statistical significance at the 0.01 level, two-tailed t statistics.
Note: Based on concern regarding cross-correlation, results found in the table were also
estimated using robust t-statistic estimation and those results do not change the
qualitative results.
42
Table 4: Changes in Earnings Informativeness from Pre-SAB 101 Period (1997 – 1999)
to the Post-Transitional SAB 101 Period (2002 – 2003)
Panel A: Cash flows for quarter q+1
Variable
Intercept
Post
Earn
Post*Earn
β1 + β 2
Number of Observations
Adjusted R2
SAB 101
Firms
Coefficients
(t-statistics)
Unaffected Firms
Coefficients
(t-statistics)
F-Statistic for
Difference in
Coefficients
0.002
(1.61)
0.001
(0.16)
0.199***
(24.36)
0.079***
(7.89)
0.290***
(20.40)
4,186
0.003*
(1.87)
-0.002
(-1.06)
0.376***
(36.01)
-0.325***
(-25.11)
-0.124***
(-17.13)
3,324
0.83
0.47
0.48
3.22*
654.31***
815.89***
Panel B: Cash flows accumulated to quarter q+2
Variable
Intercept
Post
Earn
Post*Earn
β1 + β 2
Number of Observations
Adjusted R2
SAB 101
Firms
Coefficients
(t-statistics)
Unaffected Firms
Coefficients
(t-statistics)
F-Statistic for
Difference in
Coefficients
0.002
(0.74)
0.002
(0.48)
0.288***
(17.76)
0.224***
(3.49)
0.416***
(14.79)
5,070
0.003*
(1.58)
-0.001
(-0.31)
0.198***
(9.31)
-0.403***
(-18.14)
-0.126***
(-12.10)
3,338
0.72
0.45
0.58
1.53
791.68***
690.16***
43
Table 4. Continued
Panel C: Cash flows accumulated to quarter q+3
Variable
Intercept
Post
Earn
Post*Earn
β1 + β 2
Number of Observations
Adjusted R2
SAB 101 Firms
Coefficients
(t-statistics)
Unaffected Firms
Coefficients
(t-statistics)
F-Statistic for
Difference in
Coefficients
0.001
(0.16)
0.003
(0.62)
0.001***
(0.36)
0.006
(0.49)
0.15
0.380***
(15.05)
0.091**
(2.88)
0.324***
(7.13)
4,158
0.211***
(6.61)
-0.508***
(-13.88)
-0.063***
(-5.60)
3,265
0.43
0.57
0.01
706.60***
822.76***
Panel D: Cash flows accumulated to quarter q+4
Variable
Intercept
Post
Earn
Post*Earn
β1 + β 2
Number of Observations
Adjusted R2
SAB 101 Firms
Coefficients
(t-statistics)
0.001
(0.14)
0.018
(2.48)
0.469***
(14.36)
0.334***
(6.25)
0.015
(0.24)
4,144
Unaffected Firms
Coefficients
(t-statistics)
0.139
(0.40)
0.014***
(3.16)
0.285***
(27.81)
0.09
(1.49)
0.177***
(2.84)
3,349
0.40
0.59
F-Statistic for
Difference in
Coefficients
0.27
3.12*
774.29***
56.27***
44
Table 4. Continued
Note: Variables are defined in text.
Note: This table presents results using a later post-adoption period consisting of fiscal
years 2002 and 2003.
Note: The dependent variable in panel A is one quarter ahead cash flows from operations.
Note: The dependent variable in panel B is cumulative cash flows from operations for
two quarters ahead.
Note: The dependent variable in panel C is cumulative cash flows from operations for
three quarters ahead.
Note: The dependent variable in panel D is cumulative cash flows from operations for
four quarters ahead.
Note: * Denotes statistical significance at the .10 level, two-tailed t statistics.
Note: ** Denotes statistical significance at the .05 level, two-tailed t statistics.
Note: *** Denotes statistical significance at the 0.01 level, two-tailed t statistics.
Note: Based on concern regarding cross-correlation, results found in the table were also
estimated using robust t-statistic estimation and those results do not change the
qualitative results.
45
Table 5: Disaggregated Earnings Informativeness – Changes from Pre-SAB 101 Period
(1997 – 1999) to Post-Transitional SAB 101 Period (2002 – 2003)
Panel A: Cash flows for quarter q+1
Variable
Intercept
Post
CFO i , q
ΔARi ,q
ΔINVi ,q
ΔAPi ,q
DEPRi ,q
OTHERi ,q
POST * CFOi ,q
POST * ΔARi ,q
POST * ΔINVi ,q
POST * ΔAPi ,q
POST * DEPRi ,q
POST * OTHERi ,q
β 6 + β13
Number of Observations
Adjusted R2
SAB 101
Firms
Coefficients
(t-statistics)
-0.028
(-0.97)
0.011***
(3.07)
0.171***
(8.12)
0.275***
(7.46)
0.199***
(3.90)
-0.100***
(-2.25)
0.046
(0.35)
0.080***
(6.84)
0.213***
(10.78)
0.063
(0.98)
0.196***
(2.58)
0.058
(0.84)
-0.128
(-0.83)
0.092***
(3.24)
0.171 ***
(6.63)
Unaffected
Firms
Coefficients
(t-statistics)
0.031
(1.14)
-0.007*
(-1.60)
0.252***
(11.27)
0.273***
(6.08)
-0.03
(-0.54)
-0.541***
(-10.03)
-0.535***
(-2.49)
0.06***
(2.05)
-0.104***
(-2.71)
0.14***
(1.93)
0.363***
(4.07)
-0.049
(-0.66)
0.702***
(2.68)
0.077**
(1.80)
0.137 ***
(4.14)
4,478
0.47
3,566
0.57
F-Statistic for
Difference in
Coefficients
0.23
0.78
0.96
0.25
56.35***
0.166
458.32***
0.04
456.87***
725.69***
0.67
0.71
366.52***
0.015
46
Table 5. Continued
Panel B: Cash flows accumulated to quarter q+2
Variable
Intercept
Post
CFO i , q
ΔARi ,q
ΔINVi ,q
ΔAPi ,q
DEPRi ,q
OTHERi ,q
POST * CFOi ,q
POST * ΔARi ,q
POST * ΔINVi ,q
POST * ΔAPi ,q
POST * DEPRi ,q
POST * OTHERi ,q
β 6 + β13
Number of Observations
Adjusted R2
SAB 101
Firms
Coefficients
(t-statistics)
-0.054
(-0.93)
0.013**
(1.74)
0.181***
(4.23)
0.327***
(4.34)
0.277***
(2.66)
-0.184***
(-2.03)
0.295
(1.11)
0.091***
(3.83)
0.692***
(12.41)
-0.067
(-0.51)
0.290**
(1.83)
0.596***
(4.14)
0.053
(0.17)
0.119***
(2.06)
0.210***
(3.96)
Unaffected
Firms
Coefficients
(t-statistics)
0.066*
(1.66)
-0.014***
(-2.20)
0.352***
(10.80)
0.389***
(5.96)
0.088
(1.08)
-0.677***
(-8.62)
-1.364***
(-4.37)
0.157***
(3.70)
0.044
(0.77)
0.258***
(2.39)
0.271***
(2.07)
-0.018
(-0.16)
1.464***
(3.82)
-0.024
(-0.37)
0.133***
(2.72)
4,458
0.47
3,521
0.58
F-Statistic for
Difference in
Coefficients
6.24**
54.68***
0.73
0.98
0.189***
456.39**
789.36***
0.75
455.23***
412.36***
0.88
378.96***
453.69***
656.34***
47
Table 5. Continued
Panel C: Cash flows accumulated to quarter q+3
Variable
Intercept
Post
CFO i , q
ΔARi ,q
ΔINVi ,q
ΔAPi ,q
DEPRi ,q
OTHERi ,q
POST * CFOi ,q
POST * ΔARi ,q
POST * ΔINVi ,q
POST * ΔAPi ,q
POST * DEPRi ,q
POST * OTHERi ,q
β 6 + β13
Number of Observations
Adjusted R2
SAB 101
Firms
Coefficients
(t-statistics)
-0.067
(-0.73)
0.019*
(1.66)
0.049
(0.71)
0.295 ***
(2.45)
0.290 **
(1.75)
-0.197
(-1.36)
0.975 ***
(2.29)
0.089 ***
(2.33)
0.781 ***
(8.77)
-0.121
(-0.574)
0.218
(0.85)
1.14 ***
(4.94)
-0.124
(-0.25)
0.077
(0.82)
0.166 ***
(1.96)
Unaffected
Firms
Coefficients
(t-statistics)
0.114***
(1.95)
-0.011
(-1.13)
0.35***
(7.19)
0.296***
(3.07)
0.338***
(2.79)
-0.757***
(-6.49)
-1.573***
(-3.40)
0.382***
(6.06)
-0.059
(-0.62)
0.239*
(1.45)
0.098
(0.51)
0.091
(0.52)
1.771***
(3.11)
-0.242***
(-2.54)
0.141***
(1.96)
4,437
0.45
3,482
0.59
F-Statistic for
Difference in
Coefficients
0.77
3.53*
89.64***
0.62
0.11
7.24**
796.14***
0.72
665.32***
6.33**
0.64
885.63***
776.33***
286.32***
48
Table 5. Continued
Panel D: Cash flows accumulated to quarter q+4
Variable
Intercept
Post
CFO i , q
ΔARi ,q
ΔINVi ,q
ΔAPi ,q
DEPRi ,q
OTHERi ,q
POST * CFOi ,q
POST * ΔARi ,q
POST * ΔINVi ,q
POST * ΔAPi ,q
POST * DEPRi ,q
POST * OTHERi ,q
β 6 + β13
Number of Observations
Adjusted R2
SAB 101
Firms
Coefficients
(t-statistics)
0.029
(0.11)
0.026**
(1.78)
0.039
(0.41)
0.150
(0.88)
0.187
(0.79)
-0.181
(-0.88)
1.968***
(3.26)
0.032
(0.59)
0.477***
(3.77)
-0.063
(-0.21)
-0.339
(-1.01)
1.163***
(5.15)
-0.529
(-0.92)
-0.068
(-0.62)
0.017
(0.13)
Unaffected
Firms
Coefficients
(t-statistics)
0.137**
(1.84)
-0.006
(-0.49)
0.524***
(8.46)
0.386***
(3.16)
-0.188
(-1.22)
-0.521***
(-3.52)
-1.186***
(-2.01)
0.398***
(4.97)
0.054
(0.44)
0.205
(0.96)
0.395*
(1.60)
-0.022
(-0.09)
1.593***
(2.19)
-0.284***
(-2.34)
0.114
(1.21)
4,413
0.43
3,451
0.60
F-Statistic for
Difference in
Coefficients
6.35**
5.66**
75.88***
7.12**
0.77
3.65*
74.36***
365.33***
744.12***
0.34
5.69**
488.93***
776.63***
236.33***
49
Table 5. Continued
Note: Variables are defined in text.
Note: This table presents results using a later post-adoption period consisting of fiscal
years 2002 and 2003.
Note: The dependent variable in panel A is one quarter ahead cash flows from operations.
Note: The dependent variable in panel B is cumulative cash flows from operations for
two quarters ahead.
Note: The dependent variable in panel C is cumulative cash flows from operations for
three quarters ahead.
Note: The dependent variable in panel D is cumulative cash flows from operations for
four quarters ahead.
Note: * Denotes statistical significance at the .10 level, two-tailed t statistics.
Note: ** Denotes statistical significance at the .05 level, two-tailed t statistics.
Note: *** Denotes statistical significance at the 0.01 level, two-tailed t statistics.
Note: Based on concern regarding cross-correlation, results found in the table were also
estimated using robust t-statistic estimation and those results do not change the
qualitative results.
50
Table 6: Size of Cumulative Effect Adjustment and the Change in Earnings
Informativeness from Transitional to Post-Transitional SAB 101 Period
Coefficients
Variable
Intercept
Late
Earn
Late*Earn
Size_Adj
Size_Adj*Late*Earn
Number of
Observations
2
Adjusted R
(t-statistics)
0.011
(-0.39)
0.0004
(0.05)
-0.159
*
(1.80)
0.142
(1.03)
-0.039
(-1.40)
0.319 ***
(2.66)
850
0.54
Note: Variables are defined in text.
Note: This table presents results comparing the early and late post-adoption periods for
affected firms, how this post-adoption relation between earnings and cash flows
changes, and how the size of the cumulative adjustment affects this relationship.
Note: * Denotes statistical significance at the .10 level, two-tailed t statistics.
Note: ** Denotes statistical significance at the .05 level, two-tailed t statistics.
Note: *** Denotes statistical significance at the 0.01 level, two-tailed t statistics.
51
Table 7:
Benchmark Beating in Treatment and Control Firms – Changes from Pre-SAB
101 Period (1997 – 1999) and Transitional SAB 101 Period (2001)
Panel A: Earnings levels
SAB 101
Firms
Coefficients
(t-statistics)
Variable
Intercept
Post
Netbin
Post*Netbin
Number of
Observations
2
Adjusted R
-0.03
(-0.03)
0.07
(0.04)
100.25***
(14.26)
-29.25***
(-2.94)
Unaffected
Firms
Coefficients
(t-statistics)
-0.02
(-0.01)
0.002
(0.00)
65.25 ***
(8.01)
14.75
(1.28)
135
135
0.72
0.58
F-Statistic for
Difference in
Coefficients
0.41
0.24
456.32***
298.56***
Panel B: Earnings changes
SAB 101
Variable
Intercept
Post
Netbin
Post*Netbin
Firms
Coefficients
(t-statistics)
0.00
(0.00)
0.04
(0.01)
177.50***
(12.81)
-116.50***
(-5.94)
Unaffected
Firms
Coefficients
(t-statistics)
0.00
(0.00)
0.04
(0.01)
161.75 ***
(8.69)
-60.50 ***
(-2.30) **
Number of
Observations
114
114
Adjusted R2
0.57
0.44
F-Statistic for
Difference in
Coefficients
0.00
0.00
1.12
362.55**
52
Table 7. Continued
Panel C: Analyst forecasts
SAB 101
Firms
Coefficients
(t-statistics)
Variable
Intercept
-0.71
(-0.68)
0.22
(0.15)
70.9***
(11.86)
-20.74***
(-2.45)
Post
Beat
Post*Beat
Number of
Observations
2
Adjusted R
Unaffected
Firms
Coefficients
(t-statistics)
-0.64
(-0.82)
0.33
(0.30)
46.38 ***
(12.05)
-22.61 ***
(-4.15)
106
106
0.51
0.55
F-Statistic for
Difference in
Coefficients
0.23
0.48
236.85***
0.32
Note: Variables are defined in text.
Note: Panel A represents the results based on the distribution of earnings levels.
Note: Panel B represents the results based on the distribution of earnings changes.
Note: Panel C represents the results based on the distribution of analyst forecast errors.
Note: * Denotes statistical significance at the .10 level, two-tailed t statistics.
Note: ** Denotes statistical significance at the .05 level, two-tailed t statistics.
Note: *** Denotes statistical significance at the 0.01 level, two-tailed t statistics.
53
Table 8:
Benchmark Beating in Treatment and Control Firms – Changes from Pre-SAB
101 Period (1997 – 1999) and Post-Transitional SAB 101 Period (2002 –
2003)
Panel A: Earnings levels
SAB 101
Firms
Coefficients
(t-statistics)
Variable
Intercept
Post
Netbin
Post*Netbin
Number of
Observations
2
Adjusted R
-0.02
(-0.04)
0.08
(0.04)
97.63***
(11.21)
-18.68***
(-2.21)
Unaffected
Firms
Coefficients
(t-statistics)
-0.01
(-0.01)
0.10
(0.00)
72.25***
(6.45)
9.35
(1.23)
142
142
0.66
0.54
F-Statistic for
Difference in
Coefficients
0.23
0.12
76.23***
54.66***
Panel B: Earnings changes
SAB 101
Firms
Coefficients
(t-statistics)
Variable
Intercept
Post
Netbin
Post*Netbin
Number of
Observations
2
Adjusted R
0.00
(0.00)
0.06
(0.11)
123.32***
(12.81)
-86.50***
(-5.94)
Unaffected
Firms
Coefficients
(t-statistics)
0.00
(0.00)
0.07
(0.02)
109.56***
(7.45)
-42.30*
(-1.99)
111
111
0.54
0.38
F-Statistic for
Difference in
Coefficients
0.56
0.11
15.75
77.36**
54
Table 8. Continued
Panel C: Analyst forecasts
SAB 101
Firms
Coefficients
(t-statistics)
Variable
Intercept
-0.71
(-0.88)
0.47
(0.23)
66.36***
(11.86)
-21.24***
(-2.45)
Post
Beat
Post*Beat
Number of
Observations
2
Adjusted R
Unaffected
Firms
Coefficients
(t-statistics)
-0.58
(-0.43)
0.36
(0.19)
44.89***
(12.05)
-19.37***
(-2.34)
115
115
0.51
0.55
F-Statistic for
Difference in
Coefficients
0.21
0.44
77.63***
1.24
Note: Variables are defined in text.
Note: Panel A represents the results based on the distribution of earnings levels.
Note: Panel B represents the results based on the distribution of earnings changes.
Note: Panel C represents the results based on the distribution of analyst forecast errors.
Note: * Denotes statistical significance at the .10 level, two-tailed t statistics.
Note: ** Denotes statistical significance at the .05 level, two-tailed t statistics.
Note: *** Denotes statistical significance at the 0.01 level, two-tailed t statistics.
55
Figure 1: Earnings level histogram for SAB 101 firms in the pre-adoption period
350
300
250
200
150
100
50
0
-20
-10
0
10
20
Note: The vertical axis measures the frequency (in number of firms) while the horizontal
axis measures the quarterly earnings per share (in cents). Each bar in the figure
indicates the number of firms in a particular earning per share bin.
56
Figure 2: Earnings level histogram for SAB 101 firms in the post-adoption period
350
300
250
200
150
100
50
0
-20
-10
0
10
20
Note: The vertical axis measures the frequency (in number of firms) while the horizontal
axis measures the quarterly earnings per share (in cents). Each bar in the figure
indicates the number of firms in a particular earning per share bin.
57
Figure 3: Seasonally adjusted earnings change scaled by end-of-period total assets for
the pre-adoption period
600
500
400
300
200
100
0
-20
-10
0
10
20
Note: The vertical axis measures the frequency (in number of firms) while the horizontal
axis measures the seasonally adjusted change in quarterly earnings (scaled by endof-period total assets). Each bar in the figure indicates the number of firms in a
particular seasonally adjusted quarterly earnings change bin.
58
Figure 4: Seasonally adjusted earnings change scaled by end-of-period total assets for
the post-adoption period
700
600
500
400
300
200
100
0
-20
-10
0
10
20
Note: The vertical axis measures the frequency (in number of firms) while the horizontal
axis measures the seasonally adjusted change in quarterly earnings (scaled by endof-period total assets). Each bar in the figure indicates the number of firms in a
particular seasonally adjusted quarterly earnings change bin.
59
Figure 5: Distribution of forecast errors (in cents) for SAB 101 firms in the pre-adoption
period
350
300
250
200
150
100
50
0
-30
-27
-24
-21
-18
-15
-12
-9
-6
-3
0
3
6
9
12
15
18
21
24
27
30
Note: The vertical axis measures the frequency (in number of firms) while the horizontal
axis measures the quarterly earnings per share forecast error. This error is
calculated as the difference between the consensus analyst forecast of earnings per
share minus the actual earnings per share. Consensus forecast used was the most
recent median analyst forecast of quarterly earnings per share prior to the earnings
announcement date. Each bar in the figure indicates the number of firms in a
forecast error share bin.
60
Figure 6: Distribution of forecast errors (in cents) for SAB 101 firms in the postadoption period
350
300
250
200
150
100
50
0
-30
-27
-24
-21
-18
-15
-12
-9
-6
-3
0
3
6
9
12
15
18
21
24
27
30
Note: The vertical axis measures the frequency (in number of firms) while the horizontal
axis measures the quarterly earnings per share forecast error. This error is
calculated as the difference between the consensus analyst forecast of earnings per
share minus the actual earnings per share. Consensus forecast used was the most
recent median analyst forecast of quarterly earnings per share prior to the earnings
announcement date. Each bar in the figure indicates the number of firms in a
forecast error share bin.
61
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