The Role of Accruals in
Asymmetrically Timely Gain and Loss Recognition
by
Ray Ball* and Lakshmanan Shivakumar**
*
Graduate School of Business
University of Chicago
5807 S. Woodlawn Ave
Chicago, IL 60637
Tel. (773) 834 5941
ray.ball@gsb.uchicago.edu
**
London Business School
Regent’s Park
London NW1 4SA
United Kingdom
Tel. (44) 207 262 5050
lshivakumar@london.edu
First version: 23 August 2004
This version: 24 April 2005
Acknowledgments
We are grateful for comments from Mark Bradshaw, Richard Frankel, Sudipta Basu,
anonymous referee and seminar participants at Emory University, Harvard Business
School and London Business School. Ball gratefully acknowledges financial support
from the University of Chicago, Graduate School of Business.
1
The Role of Accruals in
Asymmetrically Timely Gain and Loss Recognition
Abstract
We investigate the role of accrual accounting in the asymmetrically timely recognition of
unrealized gains and losses (i.e., prior to the actual realization of those losses in cash).
This role of accrual accounting has not been directly recognized in the literature. We
show that non-linear accruals models are a substantial specification improvement,
explaining up to three times the amount of variation in accruals as conventional linear
specifications such as Jones (1991). Conversely, we conclude that conventional linear
accruals models, by omitting the role of accruals in asymmetrically timely loss
recognition, offer a comparatively poor specification of the accounting accrual process.
We also conclude that linear specifications of the relation between earnings and future
cash flows, ignoring the implications of asymmetrically timely loss recognition
(conditional conservatism), substantially understate the ability of current earnings to
predict future cash flows. These findings have important implications for our
understanding of accrual accounting, and for researchers using estimates of discretionary
accruals, earnings management and earnings quality from misspecified linear accruals
models.
2
The Role of Accruals in
Asymmetrically Timely Gain and Loss Recognition
1. Introduction
An important role of accrual accounting is to align the timing of revenue and
expense recognition under the matching rule (Dechow 1994; Dechow, Kothari and Watts
1998). By adding accruals to operating cash flow, accountants produce an earnings
variable that is less noisy than operating cash flow, because accruals ameliorate the noise
in operating cash flow that arises from exogenous or manipulative variation in working
capital items such as inventory, prepayments, accounts receivable and accounts payable.
Earnings also is a less noisy variable than the sum of operating and investing cash flows,
because depreciation accounting smoothes the volatility in investment outlays.
We investigate another role of accrual accounting, the recognition of unrealized
gains and losses. By definition, timely gains and loss recognition must occur around the
time of revisions in expectations of future cash flows. This normally will be prior to the
actual realization of those losses in cash, so timely recognition generally requires
accounting accruals. This role of accrual accounting has important implications for the
interpretation of accruals. For example, we argue below that timely gain and loss
recognition increases the usefulness of financial reporting, but that it also increases the
volatility of accruals (and of earnings as well as analysts’ earnings forecast errors), which
the literature generally has taken to indicate lower reporting quality.
Furthermore, because the accounting recognition of gains and losses is
asymmetric, in that losses generally are recognized in a more timely fashion than gains,
the relation between accruals and cash flows cannot be linear (Basu 1997, Ball and
1
Shivakumar 2005). This observation challenges the linear specification that is common to
the standard accruals models, including the workhorse Jones (1991) model. The resulting
misspecification of accruals models, and thus of model-dependent measures such as
“discretionary” accruals and earnings quality, has not been directly recognized in the
literature.1
The objective of this study thus is to further explore the role of accruals in timely
gain and loss recognition. There are several reasons for doing so. First, the research
provides new insight into the function of accounting accruals, which occupy a central
position in financial reporting. It is accruals that distinguish accounting from mere
counting of cash. Accruals give accounting earnings its prime role in valuation,
contracting and performance measurement. Accrual accounting also is required to
produce all balance sheet variables other than cash. In general terms, accrual accounting
exists from a costly-contracting perspective because it improves the contractingefficiency of financial statement information (Ball 1989). However, only recently
(beginning with Dechow 1994) have researchers begun to investigate specifically how
accruals function. We therefore explore more fully the hypothesis in Basu (1997) and
Ball and Shivakumar (2005), that an important role of accrual accounting is the timely
recognition of unrealized gains and losses.
Second, this study furthers our understanding of the role of accruals in conditional
conservatism, defined as the asymmetry between gain and loss recognition timeliness. 2
1
Evidence of non-linearity in the relation between accruals and cash flows is published in Basu (1997), and
replicated for an international sample in Ball, Kothari and Robin (2000), in that cash flow and earnings
variables exhibit different incremental slopes when regressed on negative stock returns. The implication is
that accruals are a piecewise linear function of stock returns. However, this evidence does not indicate the
extent to which linear accruals models are misspecified.
2
Basu (1997) describes the asymmetry as conservatism. Ball, Kothari and Robin (2000) describe it as
“income statement” conservatism as distinct from “balance sheet” conservatism, a distinction drawn more
2
We propose that loss accruals in particular are an important determinant of earnings
quality, improving the usefulness of financial statements generally, and more specifically
in the contexts of corporate governance, management compensation and debt contracting.
Third, accruals have attracted significant attention from researchers studying
earnings management (e.g., Healy 1986, Jones 1991, Dechow and Dichev 2002) and
earnings quality (e.g., Burgstahler, Hail and Leuz 2004). Estimates of earnings
management and earnings quality in these studies rely on accruals models, particularly
the model developed by Jones (1991). Other accruals models include the Dechow,
Kothari and Watts (1998) model and the Dechow and Dichev (2002) model, which was
developed specifically for use in measuring earnings quality. This literature invariably
specifies linear models. We extend these studies by incorporating the role of accruals in
timely gain and loss recognition, and by specifying the non-linearity implied by timelier
loss recognition (conditional conservatism). We show that non-linear accruals models are
a substantial specification improvement, explaining up to three times the amount of
variation in accruals as equivalent linear specifications. We conclude that conventional
linear accruals models, by omitting the role of accruals in asymmetrically timely loss
recognition, offer a misspecification of the accounting accrual process.
Fourth, inferences drawn from studies of earnings management and earnings
quality often hinge critically on the proper specification for accruals, as these studies use
model-dependent estimates of “abnormal” or “discretionary” accruals to measure
earnings management or earnings quality. These studies have not incorporated the role of
sharply by Ball and Shivakumar (2005) as conditional conservatism, to emphasize the correlation with
economic losses and thus to differentiate it from unconditional conservatism (reporting low book values,
independent of economic gains and losses). They argue that conditional conservatism can be contractingefficient, but unconditional conservatism cannot. Beaver and Ryan (2005) employ the same terminology.
3
accruals in conditional conservatism, and their linear specification could lead to
systematic biases. In the symmetric Dechow (1994) noise reduction view of accruals,
high quality accrual accounting reduces the variance of earnings, conditional on the
variance of cash flows. Timely loss recognition has the opposite effect, increasing the
variance of earnings conditional on the variance of periodic cash flows, by including
capitalized losses in earnings. By increasing the volatility of accruals, and of earnings
relative to cash flows, timely loss recognition could be mistaken for poor earnings quality
(e.g., Leuz, Nanda, and Wysocki 2003, Burgstahler, Hail and Leuz 2004 and Graham,
Harvey and Rajgopal 2005), whereas we would argue that timely recognition of losses
through accounting accruals actually improves reporting quality (see Basu 1997, Ball,
Kothari and Robin 2000 and Ball and Shivakumar 2005).
Fifth, much has been made of the alleged declining “value relevance” of earnings.
However, Basu (1997) documents a substantial increase over three decades in the
sensitivity of earnings to economic losses. We therefore are interested in providing
evidence on model misspecification effects arising from ignoring the Basu loss
recognition asymmetry. We conclude that linear specifications of the relation between
earnings and future cash flows, ignoring the implications of asymmetrically timely loss
recognition (conditional conservatism), substantially understate the ability of current
earnings to predict future cash flows, as far as three years ahead.
We study accruals and cash flow data obtained from U.S. firms’ cash flow
statements over 1987-2002. The data confirm that a major role of accruals is to recognize
gains and losses in a timely fashion, particularly losses. Accruals therefore play a crucial
role in conditional conservatism, or the asymmetry between gain and loss recognition
4
timeliness. Because accruals are a piecewise linear function of current period operating
cash flows, standard linear accruals models (Jones 1991, Dechow and Dichev 2002) are
misspecified. We show that a piecewise linear specification of the standard models
causes a two or threefold increase in their explanatory power. Finally, we show that the
accruals asymmetry has increased in recent decades, consistent with Basu (1997) and
Givoly and Hayn (2000).
The following section outlines our hypothesis that a major role of accruals lies in
timely gain and loss recognition, particularly loss recognition. Section three describes the
sample and the variable definitions we use, section four outlines the results, and section
five presents our conclusions.
2. Role of Accruals in Timely Gain and Loss Recognition
In general terms, the economic role of accrual accounting is to improve the
contracting-efficiency of financial reporting (Ball 1989). An important insight into how
this is accomplished is gained in a literature commencing with Dechow (1994), where the
role of accruals in essence is to mitigate the noise in cash flows that arises from
exogenous or manipulative variation in firms’ working capital and investment decisions.
Removing noise from earnings presumably creates a more efficient contracting variable.
Further, accruals affect balance sheet variables at the same time as affecting earnings, and
presumably increase the contracting-efficiency of those variables as well. While Dechow
(1994) focuses on earnings, working capital accruals also remove noise from the balance
sheet items relating to working capital, which are used by short term creditors. An
5
important property of accruals as modelled in this literature is that they are symmetric.
For example, accruals respond to both increases and decreases in inventory levels.
A second economic role of accrual accounting is timely recognition of gains and
losses, particularly losses. Timely gain and loss accruals improve the timeliness of
earnings, and presumably increase its efficiency in debt and compensation contracting.
Simultaneously, gain and loss accruals improve the efficiency of contracting on balance
sheet variables, by more quickly revising the book values of assets and liabilities. The
remainder of this section describes and contrasts the two roles of accrual accounting.3
2.1 Noise Reduction Role of Accruals
Working Capital Accruals. The role of accruals proposed in Dechow (1994),
Guay, Kothari and Watts (1996) and Dechow, Kothari and Watts (1998) can be described
as mitigation of operating cash flow noise that arises from exogenous or manipulative
variation in firms’ working capital levels. Compared with accounting income, operating
cash flow is noisy because it incorporates period-to-period variation in working capital
assets such as inventory, prepayments, and accounts receivable, and in working capital
liabilities such as unearned revenue, warranty provisions and accounts payable. This
noise makes operating cash flow a less efficient contracting variable than accounting
earnings, which incorporates noise-reducing accrual accounting adjustments.
Consider a firm that consumes a service (e.g., rent) near the end of its fiscal year,
but departs from its historical accounts payable payment policy and delays paying the
account until the following year. The delay could be exogenous (e.g., due to accounts
3
We do not model the role of accruals in creating more efficient contracting variables in the context of
growth and decline. For example, firms experiencing growth in sales levels typically experience declines in
operating cash flow, other things equal, due to consequential increases in working capital requirements
6
being received late in the mail, a computer glitch, or a sick accounts payable clerk).
Alternatively, the delay could be manipulative (e.g., managers attempting to put a gloss
on current-year performance measures by timing the cash payments). The delay in
payment increases the firm’s year-end cash balance and hence its current-year operating
cash flow. The cash flow effect is only transitory, because the payment merely is delayed
by one period. When payment is made in the following year, that year’s operating cash
flow is reduced commensurably. The delay in payment causes a transitory increase in
accounts payable (a working capital liability) and thereby adds transitory noise to
operating cash flow, which reverses over time. Accrual accounting attempts to purge this
transitory noise from accounting income by expensing the cost of the service when it is
used in generating revenue, rather than when it is paid for.
Consequently, accounting earnings is a less noisy variable than cash flow from
operations. Noise in a financial statement variable adds risk to the payoffs to all parties
contracting on it. Working capital accruals are costly to produce (e.g., it is costly to count
inventory and to estimate bad debt allowances on receivables), but subject to cost
considerations they make accounting earnings a more contracting-efficient variable than
cash flow from operations. Testable implications of the noise-reduction role of working
capital accruals are that, other things equal (notably, long term gain and loss accruals):
accruals and cash flows from operations are contemporaneously negatively correlated;
cash flows are more negatively serially correlated than earnings; cash flows are more
volatile than earnings; and earnings are more highly correlated with stock returns
(Dechow 1994).
such as inventories and receivables. Accrual accounting adjusts cash flows for such effects. See our
discussion below of deflating the Jones (1991) model intercepts by total assets.
7
Depreciation and amortization. Accruals play a similar role in relation to longercycle assets and liabilities. Accrued depreciation and amortization is a source of
permanent, not transitory, difference between earnings and cash flow from operations,
because the latter does not have investments outlays deducted from it. The closest cash
equivalent to earnings is the sum of operating and investing cash flows and, here too,
accrual accounting seeks to reduce both exogenous and potentially manipulative noise in
cash flow. Earnings is less noisy than the sum of operating and investing cash flows
because depreciation and amortization accounting smoothes the volatility in investment
outlays and takes timing discretion away from managers.
For example, if a class of durable assets is depreciated under the straight line
method over L years with no salvage, depreciation charged against earnings is the simple
average of the last L years’ investment outlays. Accelerated depreciation is a weighted
average, giving more weight to the more recent investments. Accrued depreciation and
amortization thus is a weighted moving average function of current-period and lagged
expenditures which reduces exogenous noise in annual investment (e.g., due to the
lumpiness of durables expenditures, or exogenous variation in investment opportunities)
as well as the potential for managers to manipulate periodic earnings through the timing
of investment outlays. Here too, we note that scheduled depreciation and amortization
accruals are symmetric with respect to increases and decreases in investment outlays.
2.2 Gain and Loss Recognition Role of Accruals
Ball and Shivakumar (2005) and Kothari, Leone and Wasley (2005) discuss a
second major function for accounting accruals, timely recognition of economic gains and
losses. The economic gain or loss during a period can be thought of as the current-period
8
cash flow plus or minus any upward or downward revision in the present value of
expected future cash flows. Timely recognition of gains and losses must be
accomplished at least in part through accounting accruals, because it is based in part on
revisions of cash flow expectations made prior to their actual realization. Examples of
timely recognition involving working capital assets and liabilities include gains and
losses on trading securities, inventory write-downs due to factors such as spoilage,
obsolescence or declines in market value, receivables revaluations, and provisions for
operating costs arising from adverse events in the current period. Examples of timely
recognition involving long term assets and liabilities include restructuring charges arising
from attending to failed strategies or excessive headcounts, goodwill impairment charges
arising from negative-NPV acquisitions, and asset impairment charges arising from
negative-NPV investments in long term assets.
Timely gain and loss accruals directly improve the timeliness of accounting
earnings, and thereby (subject to cost considerations, discussed below) increase its
efficiency in debt and compensation contracting. Timely recognition simultaneously
improves the effectiveness of contracting on the basis of balance sheet variables.
Consider a long term loan agreement that requires lender approval of any new borrowing,
new investment or payment of dividends in the event the borrower violates specified
financial-statement ratios. The intent of such covenants is to transfer some decision rights
to lenders conditional on adverse outcomes. The specified ratios might be based on
income statement variables, such as the ratio of Earnings Before Interest, Taxes,
Depreciation and Amortization (EBITDA) to Interest commitments, or in terms of
balance-sheet variables, such as the ratio of Long Term Debt to Net Tangible Assets.
9
Timely recognition of gains and losses revises these ratios conditional on the outcome for
the borrower’s economic income. In contrast, untimely recognition revises the ratios with
a delay, the limit being the time it takes for all the reduced future cash flows to be
realized, and thus makes the ratios less effective. Subject to cost, untimely recognition of
gains and losses reduces the efficiency of debt contracts.
In contrast to noise-reducing operating accruals, gain and loss accruals are a
source of positive correlation between accruals and current-period operating cash flow.
This is due to revisions in the current-period cash flow from a durable asset being
positively correlated with current-period revisions in its expected future cash flows. For
example, a plant with decreased current-period cash flow due to becoming uncompetitive
most likely faces a downward revision in its expected future cash flows as well. Timely
recognition of the impaired future cash flows requires an income-decreasing accrual.
The positive correlation between gain and loss accruals and current-period cash
flow is illustrated by a durable asset that, at the beginning of period t, is an L-period
annuity of expected future cash flows, CF. 4 At the end of period t, information causes a
revision of ΔCFt in the amount of the annuity (retrospective to the beginning of t). Other
things equal, the current-period cash flow changes by ΔCFt, the cash effect in year t of
the asset’s change in value. The year-end present value of future cash flows from the
asset changes by F(L-1, r) times ΔCFt, where F(L-1,r) is the annuity factor for (L-1)
periods and an interest rate r. To the extent the asset’s value is “booked” on the balance
sheet (e.g., is not a growth option) and the change in its present value triggers an accrued
gain or loss, there is an accrued component of accounting income in year t that is
4
The example is from Ball, Robin and Wu (2003).
10
correlated with ΔCFt. The new information therefore causes a positive correlation
between accruals and cash flows.
Some of the new information about cash flows relates to “unbooked” items such
as growth options and synergies, which affect neither “bookable” gains or losses nor
current-period cash flows. Some affect one but not the other. The hypothesized positive
(though not perfect) correlation between current-period cash flows and accrued gains or
losses is based on the expectation that some information affects both current period cash
flows and bookable gains or losses arising from revisions in expected future cash flows.
A major testable implication of the gain and loss recognition role of accruals thus
is that other things equal (notably, exogenous working capital changes) accruals are
positively correlated with current-period operating cash flows. Other testable implications
of the gain and loss recognition role of accruals are that, other things equal (notably,
noise-reducing working capital accruals): earnings changes are more negatively serially
correlated than cash flows, because they incorporate transitory accrued losses; earnings
are more volatile than cash flows; and earnings are more highly correlated with stock
returns (Basu 1997). Several of these predictions are the opposite of those arising from
the Dechow (1994) noise reduction role of accruals, even though both roles serve to
increase financial reporting quality. For example: timely gain and loss recognition
induces positive correlation between accruals and current-period operating cash flow, but
noise mitigation induces negative; and one increases earnings volatile relative to cash
flows, but the other decreases it. The problem for researchers interested in discriminating
between the two roles of accruals thus is that we observe the net effect of two offsetting
11
processes. Fortunately, a discriminating test is made feasible by the asymmetric nature of
one process but not the other, a topic to which we now turn.
2.3 Asymmetry in Loss and Gain Accruals
While increased timeliness generally leads to increased contracting-efficiency of
financial statement variables, there appears to be a notable exception: timely gain
recognition. As first reported in Basu (1997), financial reporting exhibits pronounced
conditional conservatism, defined as substantively timelier recognition of losses than of
gains. Alternatively stated, accountants adopt a substantively lower verification standard
for recognizing decreases in expected future cash flows than they do for recognizing
increases (Basu 1997, Watts 2003). In this definition of conservatism, book value of
equity and (assuming clean surplus) accounting income are not biased unconditionally,
but are biased conditionally, as a function of contemporaneous economic income.
From a costly-contracting perspective, unconditional conservatism is inefficient
(Ball 2004, Ball and Shivakumar 2005). However, there are several reasons why
conditional conservatism (higher loss recognition timeliness) would be contractingefficient as compared to symmetrically timely recognition of both gains and losses. One
reason is cost. Accrual accounting is a costly activity generally, but managers’
expectations of increases in future cash flows (i.e., unrealized gains) are especially costly
for accountants to independently verify, perhaps prohibitively so. Verification costs
induce additional litigation costs, because timely gain recognition is based on estimates
of future cash flow increases that can subsequently turn out to be incorrect. 5 Another
5
Expected litigation costs are not symmetric, with lower expected cost from timely loss recognition than
from timely gain recognition (e.g., Skinner 1997; Brown, Hillegeist and Lo 2004). Ball, Kothari and Robin
(2000) argue that asymmetric expected litigation costs are costs of the common-law mechanism of
12
reason is demand. Loan agreements transfer decision rights to lenders asymmetrically,
conditional on adverse but not favourable outcomes, generating lower demand for timely
gain recognition than for timely loss recognition.6 Managers have a greater incentive to
disclose unrealized economic gains than unrealized losses (they can realize gains by
selling), so users would demand an offsetting asymmetry in the financial statements.
Managers have more incentive to disclose economic gains to potential lenders when
negotiating debt pricing, so lenders are less likely to demand timely gain recognition in
the financial statements. In corporate governance, there is a greater agency problem with
managers undertaking or continuing to operate negative-NPV investments, acquisitions
and strategies than with positive-NPV equivalents, tilting demand toward timely loss
recognition in contracting with managers. In sum, the benefits of timely loss recognition
are more likely to exceed the costs than is the case with timely gain recognition, and the
economics of contracting on the basis of financial statement variables helps explain the
observed asymmetry in gain and loss accounting.
2.4 Piecewise Linear Accruals Models
We hypothesize that conditional conservatism introduces an asymmetry in the
relation between accruals and cash flows. Economic losses are more likely to be
recognized on a timely basis, as accrued (i.e., non-cash) charges against income, whereas
economic gains are more likely to await recognition until realized in cash. This
asymmetry holds for both working capital accruals (e.g., the lower-of-cost-or-market rule
for inventories requires income-decreasing but not income-increasing accruals) and
enforcing the (implicit or explicit) contract between firms and financial statement users that financial
reporting is conditionally conservative (recognizes losses in a timely fashion).
6
Performance pricing (Beatty and Weber 2002), under which interest rates vary inversely with accounting
performance measures, would create an element of symmetric demand.
13
longer cycle accruals (e.g., impairing but not revaluing property, plant and equipment, or
goodwill). 7 It implies that the positive correlation between cash flows and accruals
arising from the timely recognition role of accruals, discussed in section 2.2 above, is
greater in periods with economic losses than in periods with economic gains. In turn, this
implies that accruals models that are linear in cash flows are misspecified, and that the
correct specification most likely is piecewise linear. No such asymmetry is predicted by
the noise reduction role of accruals.
There is some evidence of non-linearity in the prior literature. In Basu (1997),
cash flow and earnings variables exhibit different incremental slopes when regressed on
negative stock returns. A similar result is in Ball, Kothari and Robin (2000) for an
international sample. The implication is that accruals are a piecewise linear function of
stock returns, which proxy for economic gains and losses. However, this implication does
not in itself indicate the extent to which linear accruals models such as the Jones and
Dechow-Dichev models are misspecified. DeAngelo, DeAngelo and Skinner (1994) and
Butler, Leone and Willenborg (2004) show that financially distressed firms have
extremely negative abnormal accruals. Butler et al. attribute this to "liquidity enhancing
transactions (such as factoring receivables)" and DeAngelo et al. attribute it to earnings
management. However, it also is consistent with timely loss recognition, which is more
likely to occur in distressed firms. Dechow, Sloan and Sweeney (1995) and Kothari,
Leone and Wasley (2005) find that accrual models are misspecified for firms with
extreme performance, which in part could be due to timely loss recognition in the
7
The accruals function in Dechow (1994), Guay, Kothari and Watts (1996) and Dechow, Kothari and
Watts (1998) is symmetric with respect to income-increasing and income-decreasing accruals. However,
there is no reason to confine the Basu (1997) asymmetry to long term assets and liability accounting.
14
extremely poor-performing firms. Kothari, Leone and Wasley (2005) discuss the role of
timely loss recognition in accruals, but do not estimate non-linear accruals models.
To test the hypothesized asymmetry in the relation between accruals and currentperiod cash flows, we estimate versions of a piecewise-linear relation that takes the
following generic form:
ACCt = β0 + β1*Xt + β2*VARt + β3* DVARt + β4*DVARt *VARt + νt
(1)
where ACCt is accruals in year t, Xt is the set of independent variables that prior studies
have used to explain accruals, VARt is a proxy for gain or loss and DVARt is a (0,1)
dummy variable that takes the value 1 if VARt implies a loss occurs in year t.8 The above
piecewise-linear framework accommodates both roles of accruals: mitigation of noise in
cash flow and asymmetric recognition of unrealized gains and losses.
We consider three models used in prior studies to explain accruals:
Cash flow (CF) model: ACCt = α0 + α1 CFt + εt
(2.1)
Dechow-Dichev (DD) model: ACCt = α0 + α1 CFt + α2 CFt-1 + α3 CFt+1 + εt
(2.2)
Jones model: ACCt = α0 + α1 ΔREVt + α2 GPPEt + εt
(2.3)
where ΔREVt is change in total revenue and GPPEt is the undepreciated acquisition cost
of property, plant and equipment.
These models are estimated first in their linear form, replicating the results of
prior studies. The models then are re-estimated in a piecewise-linear form, using different
proxies for the existence of gains or losses in the current year. Our predictions are:
1.
β1 < 0 in the CF model, where contemporaneous cash from operations is the
sole explanatory variable (i.e., Xt = CFt). This prediction assumes the
8
We do not include VARt as a separate variable in the regressions if it induces perfect correlation with Xt.
15
negative correlation due to the noise reduction role of accruals (Dechow
1994, Dechow, Kothari and Watts 1998) exceeds the positive correlation we
hypothesize due to the timely gain recognition role. We also expect a
negative slope in the DD model.
2.
β4 > 0 in all accruals models. We predict a positive incremental coefficient
on VARt in years when the loss-proxy dummy equals one, because in those
years there is more likely to be timely recognition than in years when the
proxy indicates gains.
3.
β1 increases in magnitude in the piecewise linear specification (1), relative to
the conventional linear specification.
4.
The adjusted r-squared of the piecewise linear specification (1) exceeds its
equivalent in the conventional linear specification.
We offer no predictions for β2 and β3, due to correlation between the accrual models’
independent variables and our proxies for economic gains and losses.
3. Sample and Definition of Variables
The data are obtained from the CRSP and annual Compustat files.9 Accruals and
cash flow data are obtained from cash flow statements, and are not estimated indirectly
from balance sheet data (Hribar and Collins 2002). This restricts the sample to the post1987 period, which ends in 2003. We exclude financial firms from our sample and also
exclude firm-years in which an acquisition occurred. We Winsorize the data by excluding
the 1% extreme observations in each tail of the distribution of each variable for each year.
9
Dechow, Kothari and Watts have a discussion on the advantages of using annual data.
16
Regressions are estimated from pooled data using either the entire sample or
separately for each 3-digit SIC industry as in Dechow and Dichev (2002). For industryspecific regressions, t-statistics are obtained from the cross-sectional distribution of
industry-specific coefficients. Each industry regression requires a minimum of 30
observations. After imposing the above data restrictions and requiring firms to have data
on accruals and cash flows, our sample consists of 57362 firm-years for the pooled
regressions, and 197 three-digit SIC industries for the industry-specific regressions.
We do not estimate regressions separately for each firm because each has at most
17 observations, from 1987 to 2003, and very few of the observations are for periods of
economic losses. As a result, estimates of conditional conservatism from firm-specific
time-series regressions are noisy and unreliable (Givoly, Hayn and Natarajan 2004).
Variable definitions are as follows:
ACCt: Accruals in year t, the dependent variable in all regressions, scaled by
average total assets (Average of Compustat item 6). Accruals are defined as
earnings taken from the cash flow statement (Compustat item 123) minus cash
flow from operations, also taken from the cash flow statement (Compustat item
308).
CFt: Cash flow from operations in year t, taken from the cash flow statement
(Compustat item 308), scaled by average total assets.
DCFt: Dummy variable = 1 iff CFt < 0.
ΔCFt: Change in cash from operations in year t, scaled by average assets.
DΔCFt: Dummy variable = 1 iff ΔCFt < 0.
REVt: Net revenue (sales) in year t (Compustat item 12)
17
ΔREVt: Change in revenue in year t, REVt - REVt-1, scaled by average total assets.
GPPEt: Gross Property, Plant and Equipment (Compustat item 7), scaled by
average total assets.
We employ four proxies VAR for fiscal-year gains and losses (and hence for the
loss-year dummy variable DVAR). Three of these are non-market measures:
Gain/loss
Proxy
VARt
Level of cash
flows
Change in
cash flows
Industryadjusted cash
flows
Loss Proxy
DVARt *VARt
DCFt*CFt
DΔCFt*ΔCFt
DINDt* INDADJ_CFt
Variable definitions
CFt : Cash flow from operations
DCFt = 1 if CFt < 0, 0 otherwise
ΔCFt : Change in cash flow from operations
DΔCFt = 1 if ΔCFt < 0, 0 otherwise
INDADJ_CFt = (CFt – MEDIAN_CFt)
MEDIAN_CFt : Median cash flow from
operations in three-digit SIC industry
DINDt = 1 if INDADJ_CFt < 0, 0 otherwise
We also consider a proxy based on stock market returns, as in Basu (1997):
Gain/loss
Proxy
VARt
Abnormal
returns
Loss Proxy
DVARt *VARt
Variable definitions
DABNRETt* ABNRETt ABNRETt = (RETt – MKTRETt)
RETt = Stock return in fiscal year t
MKTRETt = CRSP equally-weighted market
return in the fiscal year t
DABNRETt = 1 if ABNRETt < 0, 0 otherwise
Each proxy has potential strengths and weaknesses. Market returns normally have the
advantage of incorporating more information than financial statement-based “book”
variables. However, revisions to market value also incorporate information about
“unbooked” items, such as growth options and synergies, which cannot generate
“bookable” current-period gains or losses. Market returns therefore constitute a
18
potentially noisy measure of the economic losses that trigger accounting accruals, even
when they are adjusted for market-wide effects in order to control for exogenous (to
financial reporting) shifts in expected returns. However, unbooked assets such as growth
options and synergies are less likely to generate current-period cash flows, let alone
changes in cash flows. Of the three non-market proxies, the change in cash flow
DΔCF*ΔCF seems more likely than the level of cash flow DCF*CF to be correlated with
revisions in the levels of future cash flows, but it has the disadvantage that cash flow
cannot conform to a random walk process (Dechow 1994, Dechow, Kothari and Watts
1998) and hence cash flow changes do not capture the new information in that variable.
To the extent that the industry median constitutes a valid expectation for individual firms,
DIND*INDADJ_CF could be a superior proxy for economic gains and losses that trigger
accrued book gains and losses, but it ignores gains and losses from industry-wide shocks
to current and future cash flows. Because each proxy has potential strengths and
weaknesses, we explore them all individually and in combination.
4. Results
We begin by replicating prior results using three linear accruals models: a simple
cash flow model, the Dechow-Dichev (2002) model, and the popular Jones (1991) model.
We then report the improvement in each of these models when the loss recognition role
of accruals is incorporated. This is accomplished by allowing accruals to be a piecewise
linear function of the models’ independent variables, using a variety of proxies for the
existence of current-year losses. We conduct a variety of robustness checks, investigate
non-linearity in individual accruals components such as inventories and receivables,
19
report that accruals non-linearities have increased over time, and show that the non-linear
accruals specification increases the ability of accruals to predict future cash flows.
4.1 Linear accruals models
Table 1 replicates models (2.1) through (2.3) in linear form, as in prior studies,
with no allowance for asymmetric loss recognition. Current-year accruals are the
dependent variable in all specifications. Regression slopes on current-year cash flows are
negative, and slopes on prior and following year cash flows are positive, consistent with
the noise reduction role of accruals. The pooled regressions exhibit lower adjusted rsquareds than the industry-specific regression, consistent with variation across industries
in model parameters. These results generally are consistent with prior studies.
4.2 Incorporating loss recognition via piecewise linear accruals models
Table 2 provides evidence on the degree of collinearity among the four proxies
for economic gain/loss. Panel A provides the matrix of Pearson correlation coefficients
among the proxies. Coefficients above the diagonal are for pooled data and coefficients
below the diagonal are averages of the coefficients for individual industries. Panel B
provides equivalent correlations among the four loss dummy proxies. In both panels, all
are positively correlated, with the market-based proxy exhibiting the least correlation
with others. Because each variable has strengths and weaknesses as a proxy for
“bookable” economic gains and losses that can trigger accruals, in the analysis below we
explore them in different combinations.
Panels A through C of Table 3 incorporate an asymmetric, piecewise-linear
allowance for the loss recognition role of accruals, using the three non-market based
proxies for economic losses. We require at least 5 loss observations in each industry
20
regression. The loss recognition role of accruals predicts positive coefficients on the
dummy variables that proxy for economic loss when they are interacted with current-year
cash flow.10 This prediction is borne out in all specifications, i.e., for all accruals models
and all proxies, and for both the pooled and industry-specific regressions (i.e., a total of
eighteen out of eighteen specifications). The incremental loss coefficient is quite
consistent across specifications, ranging from 0.45 to 0.58 in Panel A, 0.11 to 0.38 in B,
and 0.23 to 0.48 in C. The coefficient always is statistically significant. Thus there is
consistent evidence of the role of accounting accruals in the timely recognition of
economic losses.
Compared with the linear specifications in Table 1, the adjusted r-squareds for the
piecewise linear specifications in Table 3 increase substantially. The most prominent
example is the Jones model estimated from industry data, where the increase is from 12%
under a conventional linear specification to approximately 30% under the piecewise
linear specification with proxies for gains and losses. The Dechow-Dichev model
exhibits the least improvement, which is not surprising because the model incorporates
future cash flow as an explanatory variable and hence captures some of the gain/loss
recognition role of accruals. Overall, the increase in explanatory power is consistent with
loss recognition being an important role of accounting accruals.
In the accruals regressions, the coefficients on current-period cash flows CFt
generally are higher in magnitude in the piecewise linear specification than in the
conventional linear specification. For the linear CF model, the coefficient is –0.27 (Table
1), compared with –0.45 for the equivalent piecewise linear model using current-period
10
We do not make predictions for the coefficients on the dummy variable by itself (that is, we focus on the
dummy slope but not the dummy intercept). Arguments in Beaver, Nelson and McNichols (2003) imply
21
cash flow as the gain/loss proxy (Table 3, Panel A). By failing to discriminate between
the noise mitigation role of accruals (which predicts a symmetric, negative correlation
between cash flows and accruals) and the timely loss recognition role (which predicts an
offsetting positive correlation, conditional on losses), the linear specification underestimates the extent of both. The linear specification also provides misleading estimates
of earnings quality.11
Table 4 incorporates loss recognition using a piecewise-linear market based proxy
for economic losses, as in Basu (1997). RETt is annual return measured over the fiscal
year. MKTRETt is the CRSP equally-weighted market return measured over the same
period as RETt . The proxy for economic gain or loss is the market-adjusted return,
ABNRETt ( = RETt - MKTRETt).
The coefficient on economic loss (DABNRETt *ABNRETt) is positive and
significant, as predicted. The coefficient varies between 0.11 and 0.13 in the full-sample
regression and between 0.07 and 0.10 in the industry-specific regressions. Consistent
with conditional conservatism, only the negative abnormal market returns contain
significant information about book accruals: the coefficient on ABNRETt (for positive
abnormal returns) has inconsistent signs across the regressions and tends to be
statistically insignificant. In the Jones model, the negative coefficient on ABNRETt
potentially reflects the negative correlation between accruals and operating cash flow,
which is correlated with ABNRETt but is not controlled for in this model. Compared with
the linear specification in Table 1, the adjusted r-squareds are substantially higher,
that, if anything, the coefficient should be positive.
11
Ironically, Dechow and Dichev (2002) interpret greater negative correlation as an indicator of higher
earnings quality (due to noise reduction), but Leuz, Nanda and Wysocki (2003) interpret it as an indicator
22
consistent with improved model specification. For instance, for the cash flow model in
industry-specific regressions, the adjusted r-squared increases from 13.9% to 20.8%
when the timely loss recognition role of accruals is specified - an increase of
approximately one-third. In addition, the coefficients on current-period cash flows CFt
generally increases in magnitude under the piecewise linear specification, as is the case in
Table 3 for book-based proxies: for the CF model, the coefficient increases in magnitude
from –0.27 (Table 1) to –0.35 for the equivalent piecewise linear model using abnormal
market return as the gain/loss proxy (Table 4). The market-based proxy confirms the
conclusions reached from using book-based proxies, namely that asymmetrically timely
loss recognition is a major role of accruals and that conventional linear accruals models
are substantially misspecified.
In general, the specification gains using the market-based proxy are similar to but
smaller than those from using the individual financial statement based proxies in Table 3.
This indicates that, taken by itself, the market-based proxy is inferior to book-based
proxies for the purpose of identifying “bookable” accrued gains and losses. This is not
surprising, since unbooked assets such as growth options and synergies are more likely to
generate market returns than current-period cash flows, let alone changes in cash flows.
Since all the proxies are noisy measures of economic losses that are potentially
“bookable” by accrual accounting, both the market and non-market based proxies could
provide incremental information. We therefore re-estimate the accruals models
incorporating both. Panels A through C of Table 5 report results for each of the nonmarket proxies when combined with the market proxy. The eighteen r-squareds (for all
of lower quality (they view all variance reduction as manipulative smoothing). While we prefer the former
interpretation, we note that a linear specification understates the effect whose interpretation is in dispute.
23
combinations of loss proxies, accruals models and pooling methods) exceed their Table 3
and Table 4 counterparts, consistent with both market and non-market proxies containing
incremental information about accruals. In the industry-specific regressions, the r-squared
rises to 32% - 34 % for the Jones and Dechow-Dichev accruals models. These represent
an increase of over 25% on the corresponding r-squareds in Table 1. Further, almost all
eighteen coefficients on the book-based loss proxies (DCFt*CFt, DΔCFt*ΔCFt and
DINDt* IND_CFt) as well as all the eighteen coefficients on DABNRETt*ABNRETt are
positive, as predicted. These coefficients capture the incremental sensitivity of accruals to
underlying fundamentals in loss years. Finally, the coefficients on current-period cash
flows CFt generally increase in magnitude even more when the piecewise linear
specification uses both proxies: for the CF model, the coefficient increases in magnitude
from -0.27 (Table 1), -0.45 (Table 3) and -0.35 (Table 4) to -0.54 (Table 5).
Panel D of Table 5 takes this analysis even further. It reports regression estimates
of accruals models that incorporate conditional conservatism using all four non-marketbased and market-based proxies together. Due to multicollinearity, we caution against
placing too much emphasis on the coefficients for individual variables. We do note that
this table reports higher adjusted RSQs for all accruals models and estimation methods
than in any of the panels in tables 3-5. The piecewise linear Jones model, when fitted to
industry data using all four proxies, obtains an RSQ of 42%. This compares with only
12% for a conventional linear Jones model (Table 1), 29-30% when using individual nonmarket gain/loss proxies alone (Table 3), 14% when using the market-based proxy alone
(Table 4) and 33-35% when using individual non-market gain/loss proxies in conjunction
with the market proxy (earlier panels of Table 5). We conclude that all proxies add some
24
information about bookable economic losses, that better proxies for gains and losses
strengthen the result that timely loss recognition is an important role of accounting
accruals, and that conventional linear accruals models are comparatively poor
specifications of the accounting accrual process.
4.3 Further Robustness Tests
The results reported in the previous subsection indicate that the loss-recognition
non-linearity evident in accounting accruals is robust with respect to a variety of proxies,
and combinations of proxies, for gains and losses. In this subsection we show that the
results also are robust with respect to a variety of sample and model specification
changes.
Constant Sample Results. The results in Tables 1 to 5 are not directly comparable
because their samples vary with data availability. To check whether this affects our
inferences, we replicate the earlier regressions using a constant sample. For
comparability, we restrict the full-sample regressions in this table to include only the
firms that are included in the industry-specific regressions. The constant sample consists
of 35134 observations in pooled regressions that span 167 3-digit SIC codes. Results are
reported in Table 6. Since the results are qualitatively similar to those in Tables 1 to 5,
Table 6 reports only the adjusted r-squares across the different models. The adjusted rsquareds from models incorporating asymmetric loss recognition (rows 2 to 4) exhibit
substantial increases relative to conventional linear accrual models (row 1), particularly
in the pooled regressions. Even in the industry-specific regressions, the Jones and CF
models, which use only contemporaneously available data to explain accruals, exhibit
increases in adjusted r-squareds from approximately 13% to 24-36%.
25
Working Capital Accruals. The dependent variable in our regressions is total
accruals. However, the Dechow and Dichev (2002) model was developed to explain
working capital accruals, and their empirical results are based on working capital
accruals. As McNichols (2002) points out, their model may offer a noisy specification for
total accruals. While our hypothesis of non-linearity in accruals due to timely loss
recognition applies to working capital accruals as well as longer-cycle accruals (due, for
example, to losses from inventory write-downs, receivables revaluations and accrued
expense provisions), the distinction makes it difficult to compare our results directly with
Dechow and Dichev (2002). Hence for comparison we re-estimate our earlier regressions
using working capital accruals as the dependent variable, defined as ΔAccounts
receivable + ΔInventory - ΔAccounts payable - ΔTax payable + ΔOther assets, net.
The results are presented in Panel A of Table 7. We reports results from industryspecific regressions only; regressions based on pooled data yield similar conclusions. In
this replication, the average coefficients on contemporaneous, lagged and one-year-ahead
cash flows are –0.45, 0.18 and 0.14 respectively. These are comparable with -0.51, 0.19
and 0.15 in Dechow and Dichev (2002, Table 3). Moreover, the average adjusted rsquareds for our replication is 32%, comparable to the average of 34% reported by
Dechow and Dichev (2002). When this model is extended to incorporate the lossrecognition non-linearity, the coefficient on contemporaneous cash flows increases in
magnitude to as much as –0.57 and the adjusted r-squareds increase to as much as 42%, a
proportional increase of over 30% relative to the linear model. Thus, when we confine
the dependent variable to include only working capital accruals, we are able to closely
26
replicate the linear specification of prior studies and then demonstrate that the lossrecognition non-linearity still substantially improves the model specification.
Standardizing the Intercept. When estimating the Jones model, we do not
standardize the intercept by average total assets. This is consistent with the intent of the
Jones model, facilitates comparison with the CF-model and the DD-model, and is
consistent with the approach of McNichols (2002).12 Several studies standardize the
intercept, although we are aware of no specific theory or evidence to prefer such
standardization.13 Hence, for comparability with prior studies, we repeat the Jones model
regressions with a standardized intercept.
The results are presented in Table 7, Panel B. The coefficients on ΔREV and
GPPE are 0.11 and -0.10 (both are statistically significant), and the adjusted r-squared is
33%. These statistics compare with 0.17, –0.06 and 39% reported in Jeter and
Shivakumar (1999), who standardize the intercept in a cross-sectional estimation of the
Jones model. Moreover, in this specification of the Jones model, the adjusted r-squareds
increase substantially when non-linear loss-recognition proxies are introduced, from 33%
for the linear model to as much as 58% in a regression that combines all proxies – a
proportional increase of over 75%. Thus, when we standardize the Jones model intercept
by total assets, we again are able to closely replicate the linear specification of prior
studies and then demonstrate that the loss-recognition non-linearity still substantially
improves the model specification.
12
13
The r-squared in the McNichols (2002) pooled regression is 7.3%, compared to 8.75% in Table 1.
We conjecture that the asset-scaled intercept operates as an inverse proxy for growth over time.
27
In addition to these tests, we also checked the robustness of our results to
standardizing variables by beginning of year total assets rather than average total assets.
Our conclusions are unaffected by this modification.
Fama-MacBeth Statistics. One concern is cross-sectional correlation, both among
firms in the pooled sample and among the individual-industries. Table 8 reports averages
of coefficients and adjusted-r-squareds from separate yearly cross-sectional regressions
and the associated Fama-Macbeth t-statistics. Panel A reports results when the marketbased loss proxy (DABNRETt *ABNRETt) is combined with each individual book-based
proxy (each version of DVARt * VARt). The results reported in earlier tables are
qualitatively unchanged. As predicted, the coefficients on the loss proxies (DABNRETt
*ABNRETt and DVARt * VARt) are positive and significant, for all three definitions of
VARt. The large Fama-Macbeth t-statistics (ranging from 10.42 to 13.63 for DABNRETt
*ABNRETt and 4.33 to 30.13 for DVARt * VARt) indicate a surprising degree of timeseries stationarity in the role of accruals in timely loss recognition.
Panel B repeats the analysis with the four proxies combined in a single regression.
Again, the results are qualitatively unchanged, though due to correlation among the
proxies not all coefficients are significant. The market-based loss proxy is significant in
each of the three accruals models, with Fama-MacBeth t-statistics of 11.64 – 13.73. The
book proxy DCFt*CFt also is significant in each of the three accruals models, with FamaMacBeth t-statistics of 3.84 – 9.67. The other book proxies (DΔCFt*ΔCFt and DINDt*
IND_CFt) are significant in two of the three models. Our earlier conclusion, that timely
loss recognition is a significant role of accounting accruals, does not appear due to crosssectional correlation over-stating statistical significance.
28
Accruals estimated from balance sheet changes. We obtain accruals data from
firms’ cash flow statements because accruals estimated from balance sheet changes are
noisy and potentially biased (Hribar and Collins 2002). For comparison, and to test the
robustness of our results to a different time period, we also estimate accruals from
balance sheet changes during 1964 to 1986. Accruals, ACCit, then are estimated as
(Compustat data item numbers in parentheses):
ACCit = [Δ current assets (4) – Δ cash (1)] – [Δ current liabilities (5) - Δ debt in
current liabilities (34) – Δ tax payable (71)].
In untabulated results, the statistical significance of the market-based proxy for
economic losses increases relative to those reported in earlier tables, while that of the
non-market proxies based on balance sheet data all decrease, consistent with increased
noise in balance sheet accruals estimation. Nevertheless, the adjusted r-squareds in the
piecewise linear specifications always increase significantly relative to their equivalents
in the conventional linear specifications. For example, the adjusted r-squareds from the
industry-specific estimates increase from 19% in the conventional linear Jones model to
as much as 61% when non-linearity is introduced with all proxies for economic loss. We
conclude that our results are robust with respect to using balance sheet accruals estimates
and a longer time period.
4.4 Accruals components and the effect of taxes
An interesting issue is the extent to which the non-linear relation between accruals
and cash flows can be attributed to individual accruals components, such as inventories,
loss provisions, and receivables. That at least some components will exhibit non-linearity
is beyond doubt, because in each firm/year the total accrual that is the dependent variable
29
in previous regressions is a simple sum of its individual components. Conditional
conservatism (“anticipate all losses but await realization of all gains”) is feasible for most
accruals, but to our knowledge it has not been studied at such a micro level.
Accruals decomposition also is of interest in helping isolate any effect of nonlinearities in income taxation on accrued short term taxes payable. For example, if firms
making a current-year loss for tax purposes cannot fully offset the loss against prior
taxable income, and hence do not obtain a tax refund, there is a non-linearity in the
relation between taxable income and either current-period tax payments or period-ending
taxes payable. However, non-linearities in taxes payable as a function of taxable income
do not necessarily imply non-linearities in taxes as a function of book income, cash flow,
change in cash flow, or stock returns.
For the above reasons, we replace total accruals with individual accruals
components as dependent variables in the accrual models. The components of accruals
considered are: (i) ΔReceivables, (ii) ΔInventory, (iii) ΔAccounts payable and accrued
liabilities, (iv) ΔTaxes payable, (v) ΔOther assets and liabilities, (vi) Depreciation and
amortization and (vii) Miscellaneous accruals. These components are obtained from cash
flow statements and are signed positive if they are income increasing and negative if they
are income decreasing. Thus, increases in taxes payable, which are income decreasing,
are negative accruals. The accruals components are likely to be correlated, so in the
regressions for each component we include all other components (defined as total accrual
less the dependent variable) as an additional explanatory variable.
The results are reported in Table 9. To conserve space we report only industryspecific results for the Dechow-Dichev (2002) model, using DIND and DRET as
30
indicators of economic loss; pooled regressions, other accrual models and other proxies
for economic losses yield qualitatively similar results. For the components of accruals,
including ΔTaxes payable, the incremental coefficients on economics loss, namely α6 and
α9, tend to be significantly positive. The magnitudes of the coefficients on both
DIND*INDADJ_CFt and DRET*ABNRETt are substantially smaller than for total
accruals in Panel C of Table 5, which is not surprising in view of the generally smaller
magnitudes of the individual accruals components. For instance, the coefficient on
DIND*INDADJ_CFt is 0.36 for total accruals, while it is never more than 0.10 for the
accrual components. The non-linear specification increases the adjusted r-squares for all
accruals components, consistent with conditional conservatism being a pervasive feature
of accrual accounting. The asymmetric relation between ΔTaxes payable and proxies for
economic income is consistent with non-linearity in the tax code, due for example to the
inability to completely offset tax losses against prior taxable income.14 The non-linearity
exists in all other components of accruals as well, suggesting that our earlier results
cannot be explained away as a manifestation of the non-linearity in tax rules.15
4.5 Time-series properties of conservatism
Basu (1997) documents a steady increase in conditional conservatism from the
mid 1970s onward. Givoly and Hayn (2000) document a persistent increase in the degree
of reporting conservatism over the past four decades, where conservatism is measured
14
The Tax payable accrual studied here is a current liability, and is distinguished from accrued long term
Deferred taxes. There is a mechanical asymmetry in short term Tax payable due to accrued tax refunds
(arising from firms offsetting current-period tax losses against prior taxed income) being reported as
receivables, rather than as a negative values for taxes payable.
15
A puzzling result is that depreciation and amortization accruals are significantly negatively correlated
with contemporaneous, lagged and year-ahead cash flows. A potential explanation is that managers use
depreciation and amortization to smooth reported earnings. Another explanation is that firms increase
depreciation when they incur economic losses, by shortening asset lives or incorporating non-scheduled
depreciation (effective asset impairments). These are conjectures.
31
either as the sign and magnitude of accumulated accruals or as Basu’s (1997) incremental
slope in a regression of earnings on returns. In this section, we examine whether a
similar pattern emerges for conditional conservatism in accruals. We annually estimate
piecewise linear accrual models across all firms, for each of the years 1972 to 2002.16
Accruals are estimated from changes in balance sheet items when they are not available
from the cash flow statement.
Figure 1 plots the coefficients on proxies for economic news (contemporaneous
cash flows or abnormal stock returns) and on the incremental loss coefficient in each of
the sample years. The results are reported only for estimates obtained from the nonlinear version of the Dechow-Dichev (2002) model, although the conclusions are robust
to alternative accrual models. For clarify of presentation, Figure 1 reports results from
regressions that include only one proxy for economic loss at a time: Panels A to D plot
the coefficients when the proxy for economic loss is DCFt, DΔCFt, DINDt and DRETt
respectively.
In Panels A to C, the coefficients on cash flows always are negative and exhibit
no obvious trend over time. In contrast, the coefficient on the economic loss, irrespective
of how it is measured, tends to be close to zero until the mid 1970s and then steadily
increases, reaching a peak in 2001. This trend is clearer in panels A and C, which use
either DCF or DIND to proxy for economic loss, than in Panel B which uses DΔCFt.
The coefficients on incremental loss in Panel B are also more volatile.
Panel D of Figure 1 plots the coefficients when abnormal returns are used as
proxy for economic news. The coefficients on positive abnormal returns are close to zero
16
The sample starts in 1972, when we have at least 1000 firms in each regression. The sample size
increases steeply in the 1960s, raising concerns about Compustat’s selection procedures and coverage.
32
throughout the sample period, but the coefficients on negative abnormal returns (i.e., the
coefficients on DRET*ABNRETt) are positive in all the years and steadily increase from
the early 1970s. This pattern is very similar to that reported in Basu (1997). These
results suggest that the trends in conservatism documented in Basu (1997) and Givoly
and Hayn (2000) also are observed using accrual-based measures of conservatism.
4.6 Timely loss recognition and the ability of cash flows and accruals (hence
earnings) to predict future cash flows
If loss recognition accruals function to incorporate information about reductions
in expected future cash flows in current earnings, they should improve the ability of
earnings (and its cash flow and accruals components) to predict future cash flows from
operations. This hypothesis is tested in a preliminary fashion for one-year-ahead cash
flows and on U.K. data in Ball and Shivakumar (2005, Table 7). This subsection reports
more extensive tests for cash flows up to three years ahead and on U.S. data.
We estimate the following piecewise regression of future cash flow from
operations on current period earnings components (accruals and cash flows):
CFit+j = α0 + α1CFit-1 + α2ACCit-1 + α3CFit + α4 ACCit + α5DVARit
+ α6 CFit *DVARit + α7 ACCit *DVARit + εit+j
(3)
All variables are as defined earlier and are standardized by average of ending total assets
in years t and t-1. CFit-1 and ACCit-1 are included in the regression to control for expected
cash flows at the beginning of year t. The regressions are estimated separately for each
3-digit industry with at least 30 observations in total and 5 observations indicating an
33
economic loss that year. We report the average coefficients and t-statistics computed
from the distribution of coefficients across industries.17
The hypothesis that loss recognition accruals incorporate information about
reductions in expected future cash flows in current earnings, and that gain recognition is
not symmetric, implies the piecewise-linear specification in (3) improves the ability of
the cash flow and accruals components of current earnings to predict future cash flow
from operations: that is, it implies an increase in the explanatory power of (3) over a
conventional linear model. In addition, the hypothesis implies the incremental coefficient
α7 on current-year accruals during loss years is negative, because accruals in lossrecognition years incorporate capitalized multi-period cash flow effects, not simply
current-year effects. We discuss the coefficient α6 on current-period cash flows below.
Results are reported in Table 10. The columns present separate results when the
dependent variable is operating cash flow in each of the three following years (i.e., for
j=1 to 3). In each case, results also are presented for a conventional linear model that does
not incorporate the loss versus gain recognition asymmetry (i.e., that restricts α5 ,α6 and
α7 to equal zero). The row entitled “% increase in adj r-sq” presents the proportional
increase in adjusted r-square obtained by introducing the non-linearity to the prediction
model. Panels A through D repeat the analysis for each of the four proxies for currentperiod economic loss, that is when the dummy variable DVARit proxying for economic
loss is either DCFit, DΔCFit, DINDit or DABNRETit.
17
We repeat this analysis using Fama-Macbeth annual regressions. Average annual regression slopes yield
qualitatively similar results, though as might be expected the increases in adjusted r-squares in FamaMacbeth regressions are small.
34
For every proxy except ΔCFit, and for each of the three future-year cash flows
(i.e., in nine of nine cases), the incremental coefficient α7 on current-year accruals during
loss years is negative, economically substantial (generally in the order of one-half the
coefficient on accruals in non-loss years), and statistically significant, as predicted. This
is consistent with accruals in loss-recognition years incorporating capitalized multi-year
cash flow effects that are greater in scale than individual-year effects. Interestingly, the
coefficient α6 on current-period cash flows during loss years also is negative and
significant in all nine cases (three loss proxies and three cash flow forecasting horizons),
possibly because loss-recognition years are likely to incorporate negative cash flow
effects from managers dealing with the losses. Together, the incremental coefficients α6
and α7 on current-period cash flows and accruals during loss years imply that the ability
of earnings to predict future cash flows is enhanced substantially by differentiating
between gain and loss years. 18
Furthermore, the explanatory power of the regression increases substantially
relative to the conventional linear specification, for all four proxies and all three
prediction horizons (i.e., in twelve of twelve cases). The increases in adjusted r-squareds
range from approximately one quarter to one half. For example, the proportion of threeyear-ahead cash flow from operations predicted by current-year earnings (decomposed
into its cash flow and accruals components) increases from 18-20% to 26-28% when the
loss/gain asymmetry is taken into account, under various proxies for losses. It would be
reasonable to assume that the true effect (i.e., what we would observe with an error-free
A similar result is implicit in the difference between α3 and α4 in Ball and Shivakumar (2005, Table 7),
with net income rather than its cash and accrual components as independent variable and using UK data in
a one-year-ahead cash flow prediction.
18
35
proxy for economic losses) is even larger. We conclude that linear specifications of the
relation between earnings and future cash flows, ignoring the implications of
asymmetrically timely loss recognition (conditional conservatism), substantially
understate the predictive ability of current earnings.19
5. Conclusions
We propose there are at least two important economic roles of accounting
accruals. One role, recognized in the literature since Dechow (1994), is the mitigation of
noise in operating cash flows that arises from variation in working capital levels and the
mitigation of noise in investment cash flows due to variation in the level of periodic net
investment. The second role, an unrecognised implication of Basu (1997), is the timely
recognition of gains and losses arising from both working capital assets and liabilities and
long term assets and liabilities. Both roles of accruals increase the timeliness of earnings:
one by removing negative serial correlation in changes in cash flows (and hence
incorporating in current earnings information about reversion in future cash flows); and
the other by removing positive serial correlation in operating cash flows (and hence
incorporating in current earnings the information about continuation in future cash
flows). These roles of accrual accounting help explain why (contrary to widespread
belief among financial economists) stock returns are more highly correlated with earnings
than with cash flows (e.g., Ball and Brown 1968, Dechow 1994, Basu 1997, Nichols and
Wahlen 2004), why analysts typically issue forecasts of earnings rather than cash flows,
why analysts use price to earnings valuation models rather than price to cash flow models
19
This conclusion helps explain the puzzle of the alleged declining “value relevance” of earnings (Lev
1989) when at the same time the sensitivity of earnings to negative stock returns (a proxy for economic
36
(Demirakos, Strong and Walker 2004), and why loan and compensation agreements
typically are written in terms of accrual variables (such as earnings, total tangible assets
and total liabilities) rather than cash variables.
We document that recognizing gains and losses in a timely fashion, prior to their
actual cash flow realization, is in fact a major role of accounting accruals. We also show
that, consistent with Basu (1997), accrued loss recognition is more prevalent than accrued
gain recognition. We conclude that conditional conservatism, defined as asymmetry
between gain and loss recognition timeliness, is an important property of accounting
accruals.
One implication of asymmetric timeliness is that accruals are a piecewise linear
function of current period operating cash flows. Standard linear accruals models (Jones
1991, Dechow and Dichev 2002) thus are misspecified. We report that a piecewise linear
specification increases their explanatory power two or threefold. In this regard, the results
in Ball and Shivakumar (2005) for U.K. private and public firms apply in the U.S.
context.
We do not believe the accruals non-linearities are due to tax effects, for several
reasons. First, non-linearities in taxes payable as a function of taxable income do not
necessarily imply non-linearities in taxes as a function of book income, cash flow, change
in cash flow, or stock returns, and hence are not directly relevant to our results. Second,
the tax code generally requires deductible losses to be realized and does not allow the
deduction of accrued losses, presumably due to the opportunism that would be
encouraged if tax deductions were based on expectations. Third, our analysis of
losses) has increased over time (Basu 1997).
37
individual accruals components shows there are asymmetries in accruals generally, so the
result is by no means confined to tax accruals.
Nor do we believe the results are due to firms taking “big baths.” Unlike the
timely loss recognition hypothesis, the big bath notion does not predict that incomedecreasing accruals are a function of real variables, notably current-period cash flows and
stock returns. The big bath notion could be modified to predict that firms exaggerate
losses when they are recognized in a timely fashion, and hence that the accruals model
coefficients we report are in some sense “too large.” This modified big bath notion would
not contradict the timely loss recognition hypothesis; it simply would suggest that timely
loss recognition accruals can be exaggerated for purposes of earnings management.
The inferences drawn from studies of earnings management and earnings quality
hinge on their specification of expected or “non-discretionary” accruals (e.g., Healy
1986, Jones 1991, Dechow and Dichev 2002). These studies rely largely on accruals
models, particularly the models developed by Jones (1991), Dechow, Kothari and Watts
(1998) and Dechow and Dichev (2002). We extend these studies by specifying piecewise
linear accruals models that incorporate loss recognition, and obtain a two or threefold
increase in the explanatory power of accruals models. We conclude that conventional
linear accruals models are substantially misspecified and produce potentially misleading
measures of discretionary accruals, earnings management and earnings quality.
38
References
Ball, R., 1989. The Firm as a Specialist Contracting Intermediary: Application to
Accounting and Auditing. Unpublished, University of Rochester.
Ball, R., 2001. Infrastructure requirements for an economically efficient system of public
financial reporting and disclosure, Brookings-Wharton Papers on Financial Services, 127169.
Ball, R., 2004. Daimler-Benz AG: Evolution of corporate governance from a code-law
stakeholder to a common-law shareholder value system. In: Hopwood, A., Leuz, C. and
Pfaff, D. (Eds.), The Economics and Politics of Accounting: International Essays.
Oxford, England: Oxford University Press.
Ball, R., Kothari, S.P., Robin, A., 2000. The effect of international institutional factors on
properties of accounting earnings, Journal of Accounting & Economics 29, 1-51.
Ball, R., Robin, A., Wu, J.S., 2003. Incentives versus standards: Properties of accounting
income in four East Asian countries and implications for acceptance of IAS, Journal of
Accounting & Economics 36, 235-270.
Ball, R., Shivakumar, L., 2005. Earnings quality in U.K. private firms: comparative loss
recognition timeliness, Journal of Accounting & Economics 39, 83-128..
Basu, S., 1997. The conservatism principle and asymmetric timeliness of earnings,
Journal of Accounting & Economics 24, 3-37.
Beatty, A., Weber, J., 2002. Performance pricing in debt contracts, working paper,
Massachusetts Institute of Technology.
Beaver, W.H., McNichols, M.F. and Nelson, K.K., 2003. An alternative interpretation of
the discontinuity in earnings distributions. Unpublished, Stanford University.
Beaver, W.H. and Ryan, S.G., 2005. Conditional and Unconditional Conservatism:
Concepts and Modeling, Review of Accounting Studies, forthcoming.
Brown, S., Hillegeist, S.A. and Lo, K., 2004. Conference calls and information
asymmetry, Journal of Accounting & Economics 37, 343-366.
Burgstahler, D., Hail, L. and Leuz, C., 2004. The importance of reporting incentives:
Earnings management in European private and public firms. Unpublished, University of
Pennsylvania.
Butler, M., A. Leone and M. Willenborg, 2004. An empirical analysis of auditor
reporting and its association with abnormal accruals, Journal of Accounting & Economics
37, 139–165.
39
DeAngelo, H., DeAngelo, L. and Skinner, D.J., 1994. Accounting choice in troubled
companies. Journal of Accounting & Economics 17, 113–143.
Dechow, P., 1994. Accounting earnings and cash flows as measures of firm performance:
The role of accounting accruals, Journal of Accounting & Economics 18, 3-42.
Dechow, P. and Dichev, I., 2002, The quality of accruals and earnings: The role of
accrual estimation errors, The Accounting Review 77, 35-59.
Dechow, P., Kothari, S.P. and Watts, R.L., 1998. The relation between earnings and cash
flows, Journal of Accounting & Economics 25, 131-214.
Dechow, P.M., R. G. Sloan and A. P. Sweeney, 1995. Detecting earnings management,
The Accounting Review 70, 193-225.
Demirakos, E.G., Strong, N.C. and Walker, M., 2004. What valuation models do analysts
use? Accounting Horizons 18, 221-240.
Francis, J., Hanna, J.D., Vincent, L., 1996. Causes and Effects of Discretionary Asset WriteOffs, Journal of Accounting Research 34 (Supplement), 117-134.
Graham, J., Harvey, C. and Rajgopal, S., 2005, "The Economic Implications of Corporate
Financial Reporting." forthcoming, Journal of Accounting and Economics.
Givoly, D., Hayn, C. and Natarajan, A., 2004, Measuring reporting conservatism,
Working paper, Pennsylvania State University.
Guay, W., Kothari S.P., Watts, R.L., 1996. A market-based evaluation of discretionary
accruals, Journal of Accounting Research Supplement 24, 83-115.
Hribar, P., Collins, D.W., 2002. Errors in estimating accruals: Implications for empirical
research, Journal of Accounting Research 40, 105-134.
Jeter, D., Shivakumar, L., 1999. Cross-sectional estimation of abnormal accruals using
quarterly and annual data: effectiveness in detecting event-specific earnings management.
Accounting and Business Research 29, 299-319.
Jones, J., 1991, Earnings Management during import relief investigations, Journal of
Accounting Research 29, 193-228.
Kothari, S.P., A. Leone and C. Wasley, 2005. Performance matched discretionary accrual
measures, Journal of Accounting and Economics 39 163–197.
Kraft, A., Leone, A. and Wasley, C., 2004, Research design issues and related inference
problems underlying tests of the market pricing of accounting information. Working
paper, London Business School.
40
Leuz, C., Nanda, D.J. and Wysocki, P., 2003. Earnings Management and Investor
Protection: An International Comparison, Journal of Financial Economics 69, 505-527.
McNichols, M.F., 2002. Discussion of the Quality of Accruals and Earnings: The Role of
Accrual Estimation Errors, The Accounting Review, 77 Supplement 61-69.
Nichols, D.C. and Wahlen, J.M., 2004. How do earnings numbers relate to stock returns?
A review of classic accounting research with updated evidence, Accounting Horizons 18,
263-286.
Roychowdhury, S. and Watts, R.L., 2004. Asymmetric timeliness of earnings, market-tobook and conservatism in financial reporting. Unpublished, MIT and University of
Rochester, December.
Skinner, D., 1997, Earnings disclosures and stockholder lawsuits, Journal of Accounting
& Economics 23, 249-282.
Watts, R.L., 1993. A proposal for research on conservatism, unpublished, University of
Rochester.
Watts, R.L., 2003. “Conservatism in accounting part 1: Explanations and implications,
Accounting Horizons 17, 207-221.
Watts, R.L., Zimmerman, J.L., 1986. Positive Accounting Theory. Englewood Cliffs,
N.J.: Prentice-Hall.
41
Table 1: Replication of Linear Accruals Regressions
The table presents regression results for the following accruals models:
CF model:
ACCt = α0 + α1 CFt + εt
Dechow-Dichev (DD) model: ACCt = α0 + α1 CFt + α2 CFt-1 + α3 CFt+1 + εt
Jones model:
ACCt = α0 + α1 ΔREVt + + α2 GPPEt + εt
CFt is operating cash flow in year t, taken from the cash flow statement (Compustat item
308); ACCt is accruals in year t, defined as income before extraordinary items
(Compustat item 123) minus operating cash flow in year t; ΔREVt is change in revenue in
year t; and GPPEt is gross undepreciated property, plant and equipment in year t.
All variables are standardized by average total assets. For each variable, the extreme 1%
of observations on either side is deleted in each year.
Pooled regression statistics are from a single regression using the full sample of
firm/years. Industry-specific statistics are mean coefficients from the cross-sectional
distribution of individual 3-digit SIC industry-specific regressions and t-statistics based
on the standard deviation of that distribution. A minimum of 30 observations is required
in each industry. On average, each industry-specific regression uses 220 observations.
The sample period is from 1987 to 2003.
Intercept
CFt
CFt-1
CFt+1
ΔREVt
GPPEt
Nobs
Adj Rsq (%)
Pooled regressions
Industry-specific regressions
CF
DD
Jones
CF
DD
Jones
model
model
model
model
model
model
-0.06
-0.06
-0.06
-0.03
-0.04
-0.03
(-118.36) (-109.58) (-58.51) (-13.97) (-17.42) (-11.53)
-0.03
-0.30
-0.27
-0.48
(-10.60) (-59.06)
(-13.73) (-28.02)
0.22
0.22
(46.70)
(18.90)
0.12
0.14
(30.24)
(13.05)
0.12
0.10
(63.91)
(14.83)
-0.04
-0.06
(-29.46)
(-12.97)
57362
46821
55731
197
197
196
0.19
7.89
8.75
13.86
23.43
12.21
42
Table 2: Collinearity among gain and loss proxies
This table reports the correlations among both non-market-based and market-based
proxies for economic gain and loss, and correlations among the corresponding loss
dummy proxies.
Proxy for economic loss
CFt < 0
ΔCFt < 0
(CFt – Industry median CFt) <0
Dummy
variable
DCFt
DΔCFt
DINDt
Dummy definition
ABNRETt = (RETt – MKTRETt)
DABNRETt
=1 if CFt < 0, 0 otherwise
=1 if ΔCF t < 0, 0 otherwise
=1 if (CFt – Industry median
CFt) < 0, 0 otherwise
=1 if (RETt – MKTRETt) < 0,
0 otherwise
RETt is the annual return measured over the fiscal year. MKTRETt is the CRSP equallyweighted market return measured over the same period as RETt.
Panel A provides the Pearson correlation coefficients among the proxies for economic
gain/loss. Coefficients above (below) the diagonal are for the pooled data (averages of
coefficients for each industry meeting the data requirements for the regressions). Panel B
provides equivalent correlations among the loss dummy proxies.
Panel A: Correlation matrix for gain and loss proxies
ΔCF
CF
INDADJ_CF ABNRET
0.94
0.13
0.42
0.12
0.50
0.14
0.12
0.26
0.40
CF
ΔCF
INDADJ_CF
ABNRET
0.50
1.00
0.27
Panel B: Correlation matrix for loss proxy dummies
DCF
DCF
DΔCF
DIND
DRET
DΔCF
0.39
0.30
0.57
0.09
0.36
0.08
DIND
0.67
0.46
DRET
0.07
0.07
0.10
0.13
43
Table 3: Piecewise linear accruals regressions, book-based proxies
Accruals models that incorporate conditional conservatism using non-market-based
proxies for economic loss and corresponding dummy variable to capture timely loss
recognition.
The variables are defined in Table 2. We require at least 5 observations in each
regression with an economic loss, i.e., dummy variable capturing economic loss should
take the value 1 for at least 5 observations.
Panel A: Proxy for economic loss: CFt<0
Intercept
CFt
CFt-1
CFt+1
ΔREVt
GPPEt
DCFt
DCFt*CFt
Nobs
Adj Rsq (%)
Pooled regressions
Industry-specific regressions
CF
DD
Jones
CF
DD
Jones
model
model
model
model
model
model
-0.03
-0.03
-0.02
-0.02
-0.03
-0.01
(-23.00) (-26.67) (-12.69)
(-7.31) (-11.75) (-4.33)
-0.40
-0.57
-0.46
-0.45
-0.62
-0.46
(-47.68) (-61.28) (-57.57) (-22.76) (-32.04) (-25.43)
0.20
0.22
(41.78)
(19.08)
0.11
0.14
(28.94)
(12.68)
0.15
0.12
(79.25)
(17.81)
-0.03
-0.03
(-21.26)
(-7.18)
0.00
0.01
-0.01
0.01
0.01
0.00
(1.12)
(3.50) (-4.72)
(1.28)
(1.78)
(0.63)
0.53
0.45
0.58
0.49
0.46
0.46
(55.77) (43.62) (65.31)
(3.69)
(3.53)
(5.06)
57362
46821
54838
186
186
186
5.74
11.83
16.78
18.44
27.37
30.20
44
Table 3 (contd.)
Panel B: Proxy for economic loss: ΔCFt<0
Intercept
CFt
CFt-1
CFt+1
ΔREVt
GPPEt
ΔCFt
DΔCFt
DΔCFt*ΔCFt
Nobs
Adj Rsq (%)
Pooled regressions
Industry-specific regressions
CF
DD
Jones
CF
DD
Jones
model
model
model
model
model
model
-0.06
-0.06
-0.04
-0.03
-0.04
-0.03
(-55.92) (-57.07) (-33.51) (-12.10) (-13.45)
(-9.22)
0.01
-0.37
-0.17
-0.55
(2.46) (-42.83)
(-8.42) (-22.60)
0.27
0.27
(35.99)
(13.74)
0.12
0.14
(30.03)
(13.08)
0.15
0.13
(78.63)
(19.84)
-0.04
-0.06
(-34.47)
(-13.35)
-0.34
-0.44
-0.32
-0.42
(-42.51)
(-59.19) (-16.01)
(-21.61)
0.01
0.01
0.01
0.00
0.00
0.00
(7.45)
(8.62)
(9.75)
(1.31)
(1.68)
(1.84)
0.25
0.17
0.38
0.15
0.12
0.11
(20.57) (13.62) (35.22)
(4.02)
(3.26)
(2.85)
53059
46706
51551
196
197
196
6.73
9.00
19.86
21.29
25.19
29.97
45
Table 3 (contd.)
Panel C: Proxy for economic loss: (CFt - Industry Median CFt)<0
Intercept
CFt
CFt-1
CFt+1
ΔREVt
GPPEt
INDADJ_CFt
DINDt
DINDt*INDADJ_CFt
Nobs
Adj Rsq (%)
Pooled regressions
Industry-specific regressions
CF
DD
Jones
CF
DD
Jones
model model model model
model
model
-0.05
-0.05
-0.04
-0.01
-0.03
-0.04
(-42.03) (-36.58) (-27.16) (-4.30)
(-9.09) (-12.70)
-0.22
-0.48
-0.47
-0.61
(-30.12) (-54.84)
(-20.16) (-27.34)
0.21
0.22
(44.57)
(18.58)
0.12
0.14
(32.00)
(12.57)
0.14
0.12
(76.19)
(17.98)
-0.04
-0.03
(-34.00)
(-7.49)
-0.36
-0.51
(-41.01)
(-24.54)
0.03
0.02
0.02
0.01
0.01
0.01
(22.92) (16.06) (13.57)
(3.44)
(4.67)
(2.34)
0.34
0.34
0.48
0.31
0.23
0.35
(37.99) (34.83) (49.46)
(9.01)
(6.68) (11.27)
57284 46779
54787
197
197
196
4.00
10.99
15.53
17.12
26.15
28.86
46
Table 4: Piecewise linear accruals regressions, market proxy
Accruals model that allow for conditional conservatism using market-based proxies for
economic loss and corresponding dummy variable to capture timely loss recognition.
The variables are defined in Table 2. We require at least 5 observations in each
regression with an economic loss, i.e., dummy variable capturing economic loss should
take the value 1 for at least 5 observations.
Intercept
CFt
CFt-1
CFt+1
ΔREVt
GPPEt
ABNRETt
DABNRETt
DABNRETt*
ABNRETt
Nobs
Adj Rsq (%)
Pooled regressions
Industry-specific regressions
CF
DD
Jones
CF
DD
Jones
model
model
model
model
model
model
-0.04
-0.04
-0.03
-0.01
-0.02
-0.02
(-35.81) (-36.53) (-23.08)
(-3.22)
(-6.59)
(-5.49)
-0.08
-0.38
-0.35
-0.56
(-28.47) (-70.90)
(-16.79) (-31.48)
0.22
0.20
(46.79)
(16.94)
0.14
0.15
(32.05)
(13.21)
0.11
0.09
(52.72)
(12.56)
-0.05
-0.06
(-34.97)
(-12.64)
-0.00
0.01
-0.01
0.00
0.00
-0.01
(-2.60)
(4.52)
(-8.60)
(1.33)
(2.07)
(-3.07)
0.01
0.01
0.02
0.01
0.01
0.01
(8.08)
(8.20) (10.57)
(2.86)
(3.08)
(3.83)
0.13
(44.79)
51470
6.36
0.12
(37.76)
42101
15.05
0.11
(40.30)
50030
12.14
0.10
(17.39)
190
20.78
0.10
(16.06)
190
30.20
0.07
(11.91)
190
13.59
47
Table 5: Piecewise linear accruals regressions, market and non-market proxies
Accruals model that allow for conditional conservatism using both non-market-based and
market-based proxies for economic loss. The variables are as defined in Tables 2.
Panel A: Non-market-based proxy for economic loss: CFt<0
Pooled regressions
CF
DD
Jones
model model model
0.00
-0.01
0.01
Intercept
(2.89) (-3.67)
(6.54)
-0.48
-0.67
-0.50
CFt
(-58.36) (-72.50) (-63.21)
0.19
CFt-1
(39.81)
0.13
CFt+1
(31.61)
0.13
ΔREVt
(66.23)
-0.03
GPPEt
(-23.70)
-0.00
0.01
-0.01
ABNRETt
(-1.34)
(4.85) (-7.66)
0.01
0.01
0.00
DCFt
(5.38)
(5.51)
(0.31)
0.59
0.50
0.61
DCFt*CFt
(62.83) (49.55) (68.05)
0.01
0.01
0.01
DABNRETt
(5.33)
(5.98)
(7.76)
0.14
0.12
0.13
DABNRETt*ABNRETt
(48.39) (40.95) (46.59)
51470
42101
49400
Nobs
13.67
20.16
21.82
Adj Rsq (%)
Industry-specific
regressions
CF
DD
Jones
model model model
0.01
-0.00
0.01
(3.22) (-1.37)
(4.29)
-0.54
-0.70
-0.53
(-25.72) (-35.32) (-27.06)
0.20
(17.01)
0.15
(13.57)
0.10
(14.79)
-0.03
(-6.55)
0.01
0.01
-0.01
(2.18)
(2.76) (-2.24)
-0.00
0.00
-0.00
(-0.01)
(1.13) (-0.11)
0.30
0.30
0.31
(3.94)
(4.21)
(3.14)
0.00
0.00
0.01
(2.20)
(2.47)
(2.85)
0.10
0.10
0.10
(17.65) (16.55) (18.04)
180
180
180
25.99
34.44
34.80
48
Table 5 (contd.)
Panel B: Non-market-based proxy for economic loss: ΔCFt <0
Pooled regressions
CF
DD
Jones
model model model
-0.04
-0.04
-0.03
Intercept
(-28.29) (-30.50) (-16.69)
-0.04
-0.42
CFt
(-13.22) (-48.34)
0.25
CFt-1
(33.60)
0.14
CFt+1
(32.21)
0.13
ΔREVt
(66.07)
-0.05
GPPEt
(-39.28)
-0.34
-0.42
ΔCFt
(-42.09)
(-55.71)
0.01
0.01
-0.00
ABNRETt
(4.29)
(5.43) (-1.56)
0.01
0.01
0.01
DΔCFt
(8.72) (10.05) (10.74)
0.23
0.14
0.28
DΔCFt*ΔCFt
(18.06) (11.05) (24.99)
0.01
0.01
0.02
DABNRETt
(6.86)
(7.57) (10.25)
0.11
0.11
0.10
DABNRETt*ABNRETt
(38.62) (36.65) (37.36)
47742
42034
46417
Nobs
13.30
15.96
23.84
Adj Rsq (%)
Industry-specific
regressions
CF
DD
Jones
model model model
-0.01
-0.02
-0.01
(-3.30) (-5.03) (-3.56)
-0.26
-0.61
(-12.17) (-24.18)
0.24
(12.44)
0.15
(12.58)
0.12
(17.92)
-0.06
(-13.64)
-0.29
-0.42
(-15.08)
(-21.55)
0.01
0.01
-0.00
(3.13)
(2.54) (-1.93)
0.00
0.00
0.00
(1.71)
(2.19)
(1.05)
0.13
0.10
0.05
(3.69)
(2.84)
(1.19)
0.00
0.01
0.01
(2.31)
(2.40)
(3.23)
0.09
0.09
0.07
(15.53) (15.74) (12.43)
190
190
190
28.23
32.07
32.57
49
Table 5 (contd.)
Panel C: Non-market-based proxy for economic loss: IND_CFt <0
Pooled regressions
CF
DD
Jones
model model model
-0.03
-0.03
-0.02
Intercept
(-17.42) (-16.62) (-10.51)
-0.27
-0.55
CFt
(-37.69) (-62.79)
0.20
CFt-1
(42.89)
0.14
CFt+1
(34.65)
0.12
ΔREVt
(62.97)
-0.05
GPPEt
(-38.44)
-0.36
IND_CFt
(-40.85)
-0.00
0.01
-0.01
ABNRETt
(-2.19) (4.77) (-6.26)
0.04
0.03
0.03
DINDt
(28.88) (20.33) (18.84)
0.37
0.36
0.45
DINDt* IND_CFt
(40.17) (36.32) (45.80)
0.01
0.01
0.01
DABNRETt
(5.97) (6.43)
(8.47)
0.13
0.12
0.12
DABNRETt*ABNRETt
(46.50) (38.92) (43.11)
51417 42073
49334
Nobs
11.19 18.74
19.77
Adj Rsq (%)
Industry-specific
regressions
CF
DD
Jones
model model model
0.01
-0.01
-0.02
(2.31) (-2.07) (-5.68)
-0.54
-0.68
(-20.84) (-26.95)
0.19
(17.00)
0.15
(13.14)
0.10
(14.94)
-0.03
(-7.08)
-0.55
(-23.01)
0.01
0.01
-0.00
(2.27)
(2.88) (-1.99)
0.01
0.01
0.01
(4.41)
(5.18)
(2.61)
0.29
0.22
0.31
(7.41)
(5.61)
(8.36)
0.01
0.01
0.01
(2.74)
(3.17)
(3.27)
0.10
0.10
0.09
(17.94) (16.75) (17.33)
190
190
190
24.52
33.25
33.31
50
Table 5 (contd.)
Panel D: Piecewise linear accruals regressions, combining all proxies
Pooled regressions
CF
DD
Jones
model model model
-0.00
-0.00
-0.02
Intercept
(-1.50) (-2.50) (-9.75)
-0.36
-0.68
CFt
(-35.79) (-56.94)
0.19
CFt-1
(25.88)
0.13
CFt+1
(32.15)
0.14
ΔREVt
(70.82)
-0.05
GPPEt
(-39.63)
-0.26
-0.33
ΔCFt
(-33.04)
(-42.89)
-0.23
IND_CFt
(-25.91)
0.00
0.01
-0.00
ABNRETt
(3.86)
(5.06) (-1.38)
0.01
0.01
0.01
DCFt
(5.79)
(5.73)
(9.20)
0.45
0.44
0.12
DCFt*CFt
(28.69) (26.39) (10.69)
0.00
0.00
0.00
DΔCFt
(0.81)
(1.20)
(3.78)
0.09
0.01
0.11
DΔCFt*ΔCFt
(7.38)
(0.65)
(9.03)
-0.00
-0.00
0.01
DINDt
(-0.33) (-0.10)
(7.10)
0.03
0.08
0.25
DINDt* IND_CFt
(2.62)
(5.49) (16.07)
0.01
0.01
0.01
DABNRETt
(5.31)
(5.89)
(9.03)
0.12
0.12
0.11
DABNRETt*ABNRETt
(42.65) (40.30) (39.46)
47701
42009
45879
Nobs
17.66
20.44
27.47
Adj Rsq (%)
Industry-specific
regressions
CF
DD
Jones
model model model
-0.00
-0.01
-0.01
(-0.49) (-1.58) (-3.78)
-0.38
-0.70
(-12.60) (-22.32)
0.21
(10.36)
0.15
(12.35)
0.12
(17.07)
-0.04
(-9.78)
-0.26
-0.30
(-12.71)
(-14.65)
-0.37
(-14.03)
0.01
0.01
-0.00
(3.63)
(3.06) (-0.75)
0.01
0.01
0.01
(1.12)
(1.28)
(1.74)
0.55
0.48
0.48
(1.06)
(0.93)
(1.40)
-0.00
-0.00
-0.00
(-0.69) (-0.40) (-0.39)
0.04
0.01
0.02
(1.12)
(0.24)
(0.58)
0.01
0.01
0.00
(1.02)
(1.09)
(0.60)
-0.29
-0.27
-0.15
(-0.56) (-0.54) (-0.44)
0.01
0.01
0.01
(2.32)
(2.32)
(2.97)
0.09
0.09
0.08
(15.49) (15.87) (15.30)
180
180
180
31.63
35.38
42.29
51
Table 6: Constant Sample Results
This table presents results for a constant sample of firms. Only firms with sufficient
observations to estimate all the regressions in Panels A to E are included for the analysis
in this table. The regressions allow for conditional conservatism using both non-marketbased and market-based proxies for economic loss. The variables are defined in Tables 1
and 2. To conserve space, the table reports only the percentage adjusted r-squares from
each regression. The pooled regressions are based on 35134 observations, while the
industry-specific regressions report average adjusted r-squares from 167 industry-specific
regressions.
Pooled regressions
CF
DD
Jones
model
model
model
Industry-specific regressions
CF
DD
Jones
model
model
model
Models without conditional
conservatism
0.97
9.08
11.29
13.44
22.86
12.85
Models using CFt<0 and
ABNRETt<0 to proxy for
economic loss
13.68
19.52
23.25
26.33
33.47
35.61
Models using ΔCFt<0 and
ABNRETt<0 to proxy for
economic loss
13.73
15.43
25.49
27.27
30.16
32.54
Models using IND_CFt <0
and ABNRETt<0 to proxy
for economic loss
11.04
18.16
20.80
24.26
31.69
33.90
Models that combine all
non-market and market
proxies for economic loss
17.87
19.61
28.24
31.33
34.08
42.03
52
Table 7 Comparison with prior studies
This table presents results for more direct comparison with prior studies, particularly
Dechow and Dichev (2002) who study only working capital accruals and Jeter and
Shivakumar (1999) who study the Jones model with the intercept scaled by lagged total
assets. The data requirements for this analysis are as in table 6, i.e., a constant sample
across all accrual models. The table presents results from industry-specific regressions.
In Panel A, working capital accruals are the dependent variable, defined as in Dechow
and Dichev (2002) as Δaccounts receivable + ΔInventory - ΔAccounts payable - ΔTax
payable + ΔOther assets, net. The variable is computed from Compustat cash flow
statement items as - (data 302 + data 303 + data304 + data305 + data307).
Panel A: Change in working capital as the dependent variable
I
Intercept
CFt
CFt-1
CFt+1
ABNRETt
DCFt
DCFt*CFt
DΔCFt
DΔCFt*ΔCFt
DINDt
DINDt* IND_CFt
DABNRETt
DABNRETt*ABNRETt
Nobs
Adj Rsq (%)
II
III
IV
V
0.03
0.04
0.03
0.04
0.04
(15.77) (14.64) (11.55) (12.93) (12.15)
-0.45
-0.57
-0.49
-0.55
-0.53
(-27.01) (-28.46) (-21.57) (-23.50) (-20.66)
0.18
0.16
0.15
0.16
0.14
(17.96) (15.58) (10.00) (16.30)
(7.64)
0.14
0.16
0.16
0.16
0.16
(11.96) (14.39) (13.80) (13.77) (14.16)
0.01
0.01
0.01
0.01
(4.43)
(3.99)
(4.29)
(3.94)
0.01
0.00
(2.73)
(0.27)
0.14
0.24
(3.38)
(0.65)
0.01
0.01
(5.39)
(2.40)
0.08
-0.03
(1.88)
(-0.87)
0.01
-0.00
(3.89) (-0.32)
0.10
-0.12
(3.69) (-0.33)
0.00
-0.00
0.00
-0.00
(0.44) (-0.01)
(0.55) (-0.07)
0.04
0.04
0.04
0.04
(6.99)
(6.62)
(6.98)
(6.80)
140
140
140
140
140
31.87
40.88
38.98
38.99
41.55
53
Table 7 (contd.)
Panel B uses total accruals and replicates the Jones model after standardizing all variables
and the intercept by the average of total assets at the end of years t-1 and t.
Panel B: Results from scaling intercept by average total assets.
II
I
Intercept
ΔREVt
GPPEt
CFt
ΔCFt
IND_CFt
ABNRETt
DCFt
DCFt*CFt
DΔCFt
DΔCFt*ΔCFt
DINDt
DINDt* IND_CFt
DABNRETt
DABNRETt*ABNRETt
Nobs
Adj Rsq (%)
III
IV
V
-0.22
(-3.34)
0.10
(13.47)
-0.10
(-30.06)
-0.36
-0.15
-0.41
-0.26
(-5.81) (-1.58) (-6.51) (-3.28)
0.11
0.12
0.10
0.12
(15.44) (17.48) (14.91) (17.30)
-0.02
-0.07
-0.04
-0.05
(-4.21) (-20.32) (-11.38) (-13.64)
-0.50
(-29.05)
-0.44
-0.29
(-23.18)
(-14.57)
-0.59
-0.41
(-23.27) (-15.19)
0.00
-0.01
-0.01
-0.00
(1.85) (-2.33) (-2.61) (-1.17)
0.01
0.01
(1.65)
(1.66)
0.19
0.35
(2.10)
(1.29)
-0.00
-0.00
(-0.40)
(-1.14)
0.07
0.02
(1.93)
(0.57)
0.00
-0.00
(0.88) (-0.31)
0.31
-0.08
(8.02) (-0.30)
0.01
0.00
0.00
0.00
(6.39)
(1.29)
(1.69)
(2.31)
0.08
0.07
0.09
0.08
(14.84) (11.61) (17.37) (15.41)
180
180
180
180
180
33.29
53.44
51.06
52.45
58.46
54
Table 8: Fama-Macbeth cross-sectional regressions
This table presents the average coefficients and adjusted r-squares from yearly crosssectional regressions of the following accrual models with conditional conservatism:
ACCit= α0 + α1CFit +α2CFit-1 +α3CFit+1 +α4DVARit +α5DVARit * VARit
+α6 ABNRETit +α7DABNRETit +α8ABNRETit * DABNRETit + εit
VARit is the non-market proxy for economic gain or loss, CFit, ΔCFit or INDADJ_CFit.
DVARit takes the value 1 if LOSSit < 0, 0 otherwise. Variables are as defined in tables 1
and 2. VARit is not separately included in the regression if it causes perfect correlation
with other explanatory variables.
VARt
Intercept
CFt
CF
model
0.00
(0.75)
-0.50
(-36.53)
CFt-1
CFt+1
ΔREVt
GPPEt
VARt
ABNRETt
DVARt
DVARt * VARt
DABNRETt
DABNRETt*
ABNRETt
Adj Rsq (%)
0.01
(2.31)
0.01
(1.91)
0.56
(26.45)
0.01
(5.86)
CFt
ΔCFt
DD
Jones
CF
DD
Jones
model model model model model
-0.01
0.01
-0.04
-0.04
-0.02
(-1.80) (2.94) (-9.45) (-10.37) (-3.74)
-0.67
-0.51
-0.08
-0.43
(-52.65) (-39.37) (-4.83) (-25.08)
0.17
0.23
(19.34)
(15.27)
0.13
0.14
(26.77)
(27.15)
0.12
0.12
(22.11)
(30.53)
-0.03
-0.05
(-9.93)
(-12.08)
-0.30
-0.40
(-15.88)
(-31.58)
0.01
-0.01
0.01
0.01
-0.00
(4.53) (-3.95) (4.14) (4.55) (-1.45)
0.01
0.00
0.01
0.02
0.01
(2.08) (0.21) (7.37) (9.63) (6.84)
0.49
0.58
0.17
0.12
0.20
(22.08) (30.13) (4.85) (4.33) (8.32)
0.01
0.01
0.01
0.02
0.02
(5.76) (6.54) (5.02) (5.81) (6.39)
IND_CFt
CF
DD
Jones
model model model
-0.02
-0.03
-0.02
(-7.24) (-7.88) (-3.47)
-0.30
-0.57
(-17.58) (-31.63)
0.19
(21.72)
0.15
(27.27)
0.11
(21.16)
-0.05
(-12.73)
-0.34
(-20.04)
0.00
0.01
-0.00
(2.12) (4.42) (-2.21)
0.03
0.03
0.03
(11.67) (8.38) (9.46)
0.34
0.35
0.38
(18.79) (23.61) (14.71)
0.01
0.01
0.02
(5.89) (5.53) (7.28)
0.14
0.13
0.14
0.12
0.12
0.11
0.14
0.12
0.13
(13.63) (13.51) (13.34) (11.35) (11.91) (10.42) (12.59) (12.26) (12.63)
17.00 22.97 24.87 15.84 18.33 26.59 14.06 21.17 22.07
Table 9: Piecewise linear regressions for accrual components, market and non-market proxies
The table presents estimated coefficients from the following regression:
ACC_COMPONENTit= α0 + α1OTHER_ACCit + α2CFit +α3CFit-1 +α4CFit+1 +α5DINDit
+α6DINDit * INDADJ_CFit +α7 ABNRETit +α8DRETit +α9ABNRETit * DRETit + εit
where ACC_COMPONENT is a component of accruals, either ΔReceivables, ΔInventory, -Δ(Payables and accrued liabilities),
-Δ(Taxes payable), Δ(Other assets and liabilities), Depreciation & Amortization, or Miscellaneous accruals. OTHER_ACCit is
defined as total accruals minus the dependent variable. Other variables are as defined in Tables 1 and 2. The table reports mean
coefficients from the cross-sectional distribution of individual 3-digit SIC industry-specific regressions and t-statistics (within
parenthesis) based on the standard deviation of that distribution.
α0
α1
α3
α4
-0.17
(-15.49)
0.05
(8.37)
0.06
(10.60)
-0.15
(-13.76)
-0.07
(-6.02)
-0.22
(-14.43)
-0.16
(-13.43)
0.05
(7.18)
0.06
(11.63)
0.01
(6.48)
-0.11
(-8.81)
-0.19
(-12.42)
-0.01
(-16.94)
-0.20
(-21.89)
-0.004
(-2.71)
-0.21
(-20.51)
Dependent variable
Δ Accounts Receivables
(= –Compustat Data Item 302)
0.01
(10.38)
-0.12
(-11.04)
Δ Inventory
(= –Compustat Data Item 303)
0.02
(7.64)
0.01
(12.33)
- Δ (Accounts payable and
accrued liabilities)
(= –Compustat Data Item 304)
α2
α5
α6
α7
α8
α9
0.08
(12.17)
0.04
(7.13)
0.01
(4.85)
0.03
(2.08)
-0.00
(-1.68)
0.01
(3.92)
0.03
(9.15)
0.06
(10.73)
0.05
(7.91)
0.01
(5.07)
-0.17
(-17.26)
0.06
(11.40)
0.06
(12.64)
-0.25
(-21.47)
0.05
(9.15)
0.06
(11.71)
Adj R-sq
(%)
13.98
21.75
13.04
0.00
(0.08)
0.00
(0.01)
0.01
(5.35)
0.02
(7.42)
17.80
26.89
-0.00
(-0.38)
0.10
(6.95)
0.01
(5.39)
-0.00
(-1.72)
0.02
(7.35)
30.61
Table 9 (contd.)
Dependent variable
-Δ (Taxes payable)
(= –Compustat Data Item 305)
α0
α1
α2
α3
α4
-0.001
(-6.17)
-0.01
(-5.50)
-0.02
(-9.71)
0.01
(7.92)
0.003
(4.76)
Δ (Other assets and liabilities)
(= –Compustat Data Item 307)
0.001
(2.73)
0.00
(0.86)
-0.02
(-5.59)
-0.09
(-14.24)
-0.04
(-10.54)
-0.13
(-18.26)
0.01
(6.84)
0.07
(14.49)
0.003
(1.92)
-0.11
(-16.13)
-0.17
(-13.63)
-0.05
(-46.67)
0.02
(3.71)
-0.04
(-29.53)
0.001
(3.44)
0.002
(1.65)
- (Depreciation &
Amortization)
(= –Compustat Data Item 125)
Miscellaneous accruals
(= – (Compustat Data Item
124+ data item 126+ data item
106+ data item 213))
α5
α6
α7
α8
α9
0.004
(4.73)
0.02
(6.54)
0.00
(0.26)
0.02
(6.35)
0.00
(1.42)
-0.00
(-0.57)
-0.00
(-0.22)
0.06
(12.59)
0.03
(7.22)
0.002
(1.96)
-0.01
(-1.44)
-0.02
(-4.22)
-0.02
(-10.66)
0.00
(0.69)
-0.05
(-8.53)
-0.05
(-5.08)
-0.04
(-8.51)
-0.02
(-4.00)
0.01
(5.27)
-0.02
(-8.70)
0.00
(1.31)
0.004
(5.39)
-0.06
(-9.06)
-0.05
(-5.69)
0.01
(4.91)
0.00
(0.75)
0.001
(2.12)
Adj R-sq
(%)
4.23
5.91
14.00
0.03
(2.40)
0.002
(2.99)
0.00
(0.46)
0.01
(6.27)
17.78
11.72
0.06
(5.04)
-0.00
(-0.92)
-0.00
(-1.78)
0.01
(5.56)
15.60
6.92
0.01
(0.53)
0.001
(2.48)
-0.00
(-1.22)
0.01
(5.09)
10.60
Table 10: Timely loss recognition and the ability of cash flow from operations and
accruals to predict future cash flow from operations
The table presents estimates from the following regression of future cash flow from
operations on current period earnings components (accruals and cash flows):
CFit+j = α0 + α1CFit-1 + α2ACCit-1 + α3CFit + α4ACCit + α5DVARit
+ α6CFit *DVARit + +α7ACCit *DVARit + εit+j
where j=1 to 3. DVARit is a dummy for economic loss, either DCFit, DΔCFit, DINDit or
DABNRETit. Variables are as defined in tables 1 and 2 and all variables are standardized
by average total assets in period t. CFit-1 and ACCit-1 are included in the regression to
control for expected cash flows at the beginning of period t. The regressions are
estimated separately for each 3-digit industry with at least 30 observations, of which at
least 5 observations correspond to an economic loss, i.e., at least 5 observations take the
value 1 for DABNRETit. The table presents the average coefficients and t-statistics
computed from the distribution of coefficients across industries. The t-statistics are
presented in parentheses. For each continuous independent variable in the regression, the
extreme 1% of observations on either side is deleted in each year. The row titled “%
increase in adj r-sq” presents the proportional increase in adjusted r-squares from
considering non-linearity in the prediction model relative to the model that does not
consider non-linearity (i.e., restricts α5 through α7 to be zero).
Panel A: Proxy for economic gain or loss is CFit
Dependent variable
Intercept
CFit-1
ACCit-1
CFit
ACCit
CFit+1
CFit+3
I
0.026
(15.10)
0.298
(18.46)
0.071
(5.26)
0.390
(20.75)
0.026
(1.75)
II
0.031
(14.40)
0.175
(10.06)
0.036
(2.54)
0.588
(24.78)
0.219
(13.09)
0.000
(0.04)
-0.073
(-0.73)
-0.128
(-2.93)
III
0.038
(17.06)
0.308
(16.12)
0.047
(3.18)
0.343
(16.50)
0.033
(1.72)
IV
0.037
(13.03)
0.211
(8.81)
0.035
(1.70)
0.586
(16.70)
0.240
(10.73)
-0.017
(-2.95)
-0.301
(-2.81)
-0.163
(-3.45)
V
0.047
(14.68)
0.324
(10.38)
0.086
(3.09)
0.356
(12.78)
0.020
(0.79)
VI
0.039
(11.20)
0.210
(6.70)
0.036
(1.22)
0.696
(13.83)
0.281
(7.65)
0.005
(0.37)
-0.184
(-0.72)
-0.049
(-0.90)
185
33.33
185
42.71
28.16
178
23.81
178
32.31
35.66
173
18.22
173
27.74
52.25
DCFit
DCFit* CFit
DCFit ACCit
No. of obs
Adj R-sq (%)
% Increase in adj. r-sq
CFit+2
Panel B: Proxy for economic gain or loss is ΔCFit
Dependent variable
Intercept
CFit-1
ACCit-1
CFit
ACCit
CFit+1
I
0.027
(14.58)
0.272
(15.45)
0.049
(3.98)
0.418
(22.85)
0.060
(3.96)
DΔCFit
DΔCFit* CFit
DΔCFit ACCit
No. of obs
Adj R-sq (%)
% Increase in adj. r-sq
194
33.43
II
0.030
(13.60)
0.229
(9.28)
0.047
(2.89)
0.541
(20.19)
0.177
(7.66)
-0.002
(-0.82)
0.041
(1.41)
0.000
(0.02)
194
41.56
24.33
CFit+2
III
0.037
(17.17)
0.296
(13.80)
0.043
(2.56)
0.374
(19.44)
0.068
(3.74)
185
24.51
IV
0.039
(12.94)
0.247
(8.98)
0.023
(1.08)
0.518
(13.07)
0.178
(6.43)
-0.006
(-1.53)
0.005
(0.12)
0.013
(0.37)
185
32.12
31.07
CFit+3
V
0.047
(14.74)
0.267
(8.43)
0.058
(2.05)
0.415
(15.40)
0.064
(2.61)
179
18.74
VI
0.047
(12.84)
0.268
(6.48)
0.053
(1.62)
0.559
(11.49)
0.170
(3.77)
-0.009
(-1.61)
-0.020
(-0.39)
0.043
(0.80)
179
25.80
37.71
Panel C: Proxy for economic gain or loss is INDADJ_CFit
Dependent variable
Intercept
CFit-1
ACCit-1
CFit
ACCit
CFit+1
I
0.027
(15.17)
0.297
(17.02)
0.074
(5.39)
0.390
(19.59)
0.022
(1.26)
DINDit
DINDit* CFit
DINDit ACCit
No. of obs
Adj R-sq (%)
% Increase in adj. r-sq
195
33.27
II
0.036
(9.35)
0.180
(10.11)
0.040
(2.49)
0.599
(18.42)
0.299
(12.35)
-0.010
(-2.25)
-0.096
(-1.97)
-0.181
(-5.52)
195
42.67
28.22
CFit+2
III
0.038
(17.11)
0.325
(16.57)
0.059
(3.56)
0.340
(17.29)
0.028
(1.43)
186
24.69
IV
0.043
(7.29)
0.200
(8.46)
0.018
(0.86)
0.606
(12.09)
0.341
(10.11)
-0.011
(-1.85)
-0.160
(-2.69)
-0.207
(-5.36)
186
32.65
32.25
CFit+3
V
0.045
(14.74)
0.357
(11.15)
0.112
(4.01)
0.354
(13.60)
-0.005
(-0.18)
179
19.54
VI
0.036
(5.56)
0.199
(6.51)
0.036
(1.21)
0.806
(10.09)
0.407
(8.34)
0.002
(0.22)
-0.375
(-4.06)
-0.256
(-4.24)
179
27.37
40.08
Panel D: Proxy for economic gain or loss is ABNRETit
Dependent variable
Intercept
CFit-1
ACCit-1
CFit
ACCit
CFit+1
CFit+3
I
0.027
(13.32)
0.318
(17.21)
0.076
(4.86)
0.350
(18.44)
0.004
(0.27)
II
0.014
(4.56)
0.216
(11.13)
0.034
(2.17)
0.724
(23.15)
0.265
(6.86)
0.020
(6.36)
-0.245
(-7.40)
-0.119
(-2.86)
III
0.037
(15.82)
0.324
(14.63)
0.065
(3.43)
0.335
(18.13)
0.019
(1.09)
IV
0.034
(9.58)
0.221
(10.14)
0.016
(0.75)
0.638
(16.09)
0.300
(7.50)
0.004
(1.14)
-0.155
(-4.12)
-0.128
(-3.01)
V
0.046
(13.83)
0.340
(10.08)
0.102
(3.66)
0.337
(13.32)
-0.020
(-0.82)
VI
0.037
(8.72)
0.251
(7.82)
0.057
(1.77)
0.756
(15.92)
0.367
(6.36)
0.006
(1.24)
-0.245
(-4.97)
-0.191
(-2.91)
195
31.62
195
43.13
36.38
186
23.90
186
32.05
34.08
179
18.23
179
26.95
47.83
DABNRETit
DABNRETit* CFit
DABNRETit ACCit
No. of obs
Adj R-sq (%)
% Increase in adj. r-sq
CFit+2
Figure 1: Trend in conditional conservatism, 1972 – 2002
Coefficients from annual cross-sectional regressions of a Dechow-Dichev (2002) model modified to incorporate conditional
conservatism. Panels A to C are based on non-market-based proxies for economic loss, while Panel D is based on abnormal stock
returns as the proxy. The models are described in Tables 3 and 4. Accruals are taken from cash flow statements when available, but
otherwise are computed from Compustat balance sheet data items as: ACC = Δ[{Current assets (data 4) – Cash (data 1)} – {Current
liabilities (data 5) – Debt in current liabilities (data 34) – taxes payable (data 71)}] – Depreciation (data 14)
Panel A: Proxy for economic loss: CFt<0
0.8
0.6
0.2
0
19
72
19
73
19
74
19
75
19
76
19
77
19
78
19
79
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
Coefficient value
0.4
-0.2
-0.4
-0.6
-0.8
Year
Cf(t)
"DCF*CF(t)"
CF(t)+DCF*CF(t)
19
72
19
73
19
74
19
75
19
76
19
77
19
78
19
79
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
Coefficient value
Figure 1 (contd.)
Panel B: Proxy for economic loss: ΔCFt<0
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
Year
Cf(t)
DCHCF*CF(t)
CF(t)+DCHCF*CF(t)
19
72
19
73
19
74
19
75
19
76
19
77
19
78
19
79
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
Coefficient value
Figure 1 (contd.)
Panel C: Proxy for economic loss: INDADJ_CFt)<0
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
Year
Cf(t)
DIND*CF(t)
CF(t)+DIND*CF(t)
19
72
19
73
19
74
19
75
19
76
19
77
19
78
19
79
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
Coefficient value
Figure 1 (contd.)
Panel D: Proxy for economic loss: ABNRETt<0
0.25
0.2
0.15
0.1
0.05
0
-0.05
Year
ABNRET(t)
DRET*ABNRET(t)
ABNRET(t)+DRET*ABNRET(t)