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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.

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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.

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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

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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 contracting-

efficiency 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

1Evidence 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

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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 contracting-efficient, but unconditional conservatism cannot. Beaver and Ryan (2005) employ the same terminology.

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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

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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

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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

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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.

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Depreciation and amortization. Accruals play a similar role in relation to longer-

cycle 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

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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.

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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).

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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

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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 contracting-

efficient 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

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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.

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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.

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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 current-

period 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.

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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 post-

1987 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.

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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 industry-

specific 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)

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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

Loss Proxy DVARt *VARt

Variable definitions

Level of cash flows

DCFt*CFt CFt : Cash flow from operations DCFt = 1 if CFt < 0, 0 otherwise

Change in cash flows

DΔCFt*ΔCFt ΔCFt : Change in cash flow from operations DΔCFt = 1 if ΔCFt < 0, 0 otherwise

Industry-adjusted cash flows

DINDt* INDADJ_CFt

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

Loss Proxy DVARt *VARt

Variable definitions

Abnormal returns

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

19

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,

20

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 r-

squareds 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

21

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

22

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 under-

estimates 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

23

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 non-

market 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.

24

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-market-

based 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 non-

market 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

25

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 r-

squareds 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%.

26

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 industry-

specific 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 r-

squareds 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 loss-

recognition 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

27

replicate the linear specification of prior studies and then demonstrate that the loss-

recognition 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 The r-squared in the McNichols (2002) pooled regression is 7.3%, compared to 8.75% in Table 1. 13 We conjecture that the asset-scaled intercept operates as an inverse proxy for growth over time.

28

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 market-

based 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 time-

series 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 Fama-

MacBeth 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 cross-

sectional correlation over-stating statistical significance.

29

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

30

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 non-

linearities 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 industry-

specific results for the Dechow-Dichev (2002) model, using DIND and DRET as

31

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.

32

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 non-

linear 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.

33

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

34

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 loss-

recognition 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 current-

period 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 Fama-Macbeth regressions are small.

35

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 three-

year-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

18 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.

36

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

37

(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).

38

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 income-

decreasing 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.

39

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, 127-169. 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.

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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 Write-Offs, 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.

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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-to-book 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.

42

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.

Pooled regressions Industry-specific regressions

CF model

DD model

Jones model

CF model

DD model

Jones model

Intercept -0.06 -0.06 -0.06 -0.03 -0.04 -0.03 (-118.36) (-109.58) (-58.51) (-13.97) (-17.42) (-11.53)CFt -0.03 -0.30 -0.27 -0.48 (-10.60) (-59.06) (-13.73) (-28.02) CFt-1 0.22 0.22 (46.70) (18.90) CFt+1 0.12 0.14 (30.24) (13.05) ΔREVt 0.12 0.10 (63.91) (14.83)GPPEt -0.04 -0.06 (-29.46) (-12.97)Nobs 57362 46821 55731 197 197 196Adj Rsq (%) 0.19 7.89 8.75 13.86 23.43 12.21

43

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 Dummy

variable Dummy definition

CFt < 0 DCFt =1 if CFt < 0, 0 otherwise ΔCFt < 0 DΔCFt =1 if ΔCF t < 0, 0 otherwise (CFt – Industry median CFt) <0 DINDt =1 if (CFt – Industry median

CFt) < 0, 0 otherwise ABNRETt = (RETt – MKTRETt) DABNRETt =1 if (RETt – MKTRETt) < 0,

0 otherwise RETt is the annual return measured over the fiscal year. MKTRETt is the CRSP equally-weighted 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 CF ΔCF INDADJ_CF ABNRET CF 0.40 0.94 0.13 ΔCF 0.50 0.42 0.12 INDADJ_CF 1.00 0.50 0.14 ABNRET 0.27 0.12 0.26 Panel B: Correlation matrix for loss proxy dummies DCF DΔCF DIND DRET DCF 0.39 0.67 0.07 DΔCF 0.30 0.46 0.07 DIND 0.57 0.36 0.10 DRET 0.09 0.08 0.13

44

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

Pooled regressions Industry-specific regressions

CF model

DD model

Jones model

CF model

DD model

Jones model

Intercept -0.03 -0.03 -0.02 -0.02 -0.03 -0.01 (-23.00) (-26.67) (-12.69) (-7.31) (-11.75) (-4.33)CFt -0.40 -0.57 -0.46 -0.45 -0.62 -0.46 (-47.68) (-61.28) (-57.57) (-22.76) (-32.04) (-25.43)CFt-1 0.20 0.22 (41.78) (19.08) CFt+1 0.11 0.14 (28.94) (12.68) ΔREVt 0.15 0.12 (79.25) (17.81)GPPEt -0.03 -0.03 (-21.26) (-7.18)DCFt 0.00 0.01 -0.01 0.01 0.01 0.00 (1.12) (3.50) (-4.72) (1.28) (1.78) (0.63)DCFt*CFt 0.53 0.45 0.58 0.49 0.46 0.46 (55.77) (43.62) (65.31) (3.69) (3.53) (5.06)Nobs 57362 46821 54838 186 186 186Adj Rsq (%) 5.74 11.83 16.78 18.44 27.37 30.20

45

Table 3 (contd.) Panel B: Proxy for economic loss: ΔCFt<0

Pooled regressions Industry-specific regressions

CF model

DD model

Jones model

CF model

DD model

Jones model

Intercept -0.06 -0.06 -0.04 -0.03 -0.04 -0.03 (-55.92) (-57.07) (-33.51) (-12.10) (-13.45) (-9.22)CFt 0.01 -0.37 -0.17 -0.55 (2.46) (-42.83) (-8.42) (-22.60) CFt-1 0.27 0.27 (35.99) (13.74) CFt+1 0.12 0.14 (30.03) (13.08) ΔREVt 0.15 0.13 (78.63) (19.84)GPPEt -0.04 -0.06 (-34.47) (-13.35)ΔCFt -0.34 -0.44 -0.32 -0.42 (-42.51) (-59.19) (-16.01) (-21.61)DΔCFt 0.01 0.01 0.01 0.00 0.00 0.00 (7.45) (8.62) (9.75) (1.31) (1.68) (1.84)DΔCFt*ΔCFt 0.25 0.17 0.38 0.15 0.12 0.11 (20.57) (13.62) (35.22) (4.02) (3.26) (2.85)Nobs 53059 46706 51551 196 197 196Adj Rsq (%) 6.73 9.00 19.86 21.29 25.19 29.97

46

Table 3 (contd.) Panel C: Proxy for economic loss: (CFt - Industry Median CFt)<0

Pooled regressions Industry-specific regressions

CF model

DD model

Jones model

CF model

DD model

Jones model

Intercept -0.05 -0.05 -0.04 -0.01 -0.03 -0.04 (-42.03) (-36.58) (-27.16) (-4.30) (-9.09) (-12.70)CFt -0.22 -0.48 -0.47 -0.61 (-30.12) (-54.84) (-20.16) (-27.34) CFt-1 0.21 0.22 (44.57) (18.58) CFt+1 0.12 0.14 (32.00) (12.57) ΔREVt 0.14 0.12 (76.19) (17.98)GPPEt -0.04 -0.03 (-34.00) (-7.49)INDADJ_CFt -0.36 -0.51 (-41.01) (-24.54)DINDt 0.03 0.02 0.02 0.01 0.01 0.01 (22.92) (16.06) (13.57) (3.44) (4.67) (2.34)DINDt*INDADJ_CFt 0.34 0.34 0.48 0.31 0.23 0.35 (37.99) (34.83) (49.46) (9.01) (6.68) (11.27)Nobs 57284 46779 54787 197 197 196Adj Rsq (%) 4.00 10.99 15.53 17.12 26.15 28.86

47

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.

Pooled regressions Industry-specific regressions

CF model

DD model

Jones model

CF model

DD model

Jones model

Intercept -0.04 -0.04 -0.03 -0.01 -0.02 -0.02 (-35.81) (-36.53) (-23.08) (-3.22) (-6.59) (-5.49)CFt -0.08 -0.38 -0.35 -0.56 (-28.47) (-70.90) (-16.79) (-31.48) CFt-1 0.22 0.20 (46.79) (16.94) CFt+1 0.14 0.15 (32.05) (13.21) ΔREVt 0.11 0.09 (52.72) (12.56)GPPEt -0.05 -0.06 (-34.97) (-12.64)ABNRETt -0.00 0.01 -0.01 0.00 0.00 -0.01 (-2.60) (4.52) (-8.60) (1.33) (2.07) (-3.07)DABNRETt 0.01 0.01 0.02 0.01 0.01 0.01 (8.08) (8.20) (10.57) (2.86) (3.08) (3.83)DABNRETt* ABNRETt 0.13 0.12 0.11 0.10 0.10 0.07 (44.79) (37.76) (40.30) (17.39) (16.06) (11.91)Nobs 51470 42101 50030 190 190 190Adj Rsq (%) 6.36 15.05 12.14 20.78 30.20 13.59

48

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 Industry-specific regressions

CF

modelDD

model Jones model

CF model

DD model

Jones model

Intercept 0.00 -0.01 0.01 0.01 -0.00 0.01 (2.89) (-3.67) (6.54) (3.22) (-1.37) (4.29)CFt -0.48 -0.67 -0.50 -0.54 -0.70 -0.53 (-58.36) (-72.50) (-63.21) (-25.72) (-35.32) (-27.06)CFt-1 0.19 0.20 (39.81) (17.01) CFt+1 0.13 0.15 (31.61) (13.57) ΔREVt 0.13 0.10 (66.23) (14.79)GPPEt -0.03 -0.03 (-23.70) (-6.55)ABNRETt -0.00 0.01 -0.01 0.01 0.01 -0.01 (-1.34) (4.85) (-7.66) (2.18) (2.76) (-2.24)DCFt 0.01 0.01 0.00 -0.00 0.00 -0.00 (5.38) (5.51) (0.31) (-0.01) (1.13) (-0.11)DCFt*CFt 0.59 0.50 0.61 0.30 0.30 0.31 (62.83) (49.55) (68.05) (3.94) (4.21) (3.14)DABNRETt 0.01 0.01 0.01 0.00 0.00 0.01 (5.33) (5.98) (7.76) (2.20) (2.47) (2.85)DABNRETt*ABNRETt 0.14 0.12 0.13 0.10 0.10 0.10 (48.39) (40.95) (46.59) (17.65) (16.55) (18.04)Nobs 51470 42101 49400 180 180 180Adj Rsq (%) 13.67 20.16 21.82 25.99 34.44 34.80

49

Table 5 (contd.) Panel B: Non-market-based proxy for economic loss: ΔCFt <0

Pooled regressions Industry-specific regressions

CF

modelDD

model Jones model

CF model

DD model

Jones model

Intercept -0.04 -0.04 -0.03 -0.01 -0.02 -0.01 (-28.29) (-30.50) (-16.69) (-3.30) (-5.03) (-3.56) CFt -0.04 -0.42 -0.26 -0.61 (-13.22) (-48.34) (-12.17) (-24.18) CFt-1 0.25 0.24 (33.60) (12.44) CFt+1 0.14 0.15 (32.21) (12.58) ΔREVt 0.13 0.12 (66.07) (17.92) GPPEt -0.05 -0.06 (-39.28) (-13.64) ΔCFt -0.34 -0.42 -0.29 -0.42 (-42.09) (-55.71) (-15.08) (-21.55) ABNRETt 0.01 0.01 -0.00 0.01 0.01 -0.00 (4.29) (5.43) (-1.56) (3.13) (2.54) (-1.93) DΔCFt 0.01 0.01 0.01 0.00 0.00 0.00 (8.72) (10.05) (10.74) (1.71) (2.19) (1.05) DΔCFt*ΔCFt 0.23 0.14 0.28 0.13 0.10 0.05 (18.06) (11.05) (24.99) (3.69) (2.84) (1.19) DABNRETt 0.01 0.01 0.02 0.00 0.01 0.01 (6.86) (7.57) (10.25) (2.31) (2.40) (3.23) DABNRETt*ABNRETt 0.11 0.11 0.10 0.09 0.09 0.07 (38.62) (36.65) (37.36) (15.53) (15.74) (12.43) Nobs 47742 42034 46417 190 190 190 Adj Rsq (%) 13.30 15.96 23.84 28.23 32.07 32.57

50

Table 5 (contd.) Panel C: Non-market-based proxy for economic loss: IND_CFt <0

Pooled regressions Industry-specific regressions

CF

model DD

model Jones model

CF model

DD model

Jones model

Intercept -0.03 -0.03 -0.02 0.01 -0.01 -0.02 (-17.42) (-16.62) (-10.51) (2.31) (-2.07) (-5.68) CFt -0.27 -0.55 -0.54 -0.68 (-37.69) (-62.79) (-20.84) (-26.95) CFt-1 0.20 0.19 (42.89) (17.00) CFt+1 0.14 0.15 (34.65) (13.14) ΔREVt 0.12 0.10 (62.97) (14.94) GPPEt -0.05 -0.03 (-38.44) (-7.08) IND_CFt -0.36 -0.55 (-40.85) (-23.01) ABNRETt -0.00 0.01 -0.01 0.01 0.01 -0.00 (-2.19) (4.77) (-6.26) (2.27) (2.88) (-1.99) DINDt 0.04 0.03 0.03 0.01 0.01 0.01 (28.88) (20.33) (18.84) (4.41) (5.18) (2.61) DINDt* IND_CFt 0.37 0.36 0.45 0.29 0.22 0.31 (40.17) (36.32) (45.80) (7.41) (5.61) (8.36) DABNRETt 0.01 0.01 0.01 0.01 0.01 0.01 (5.97) (6.43) (8.47) (2.74) (3.17) (3.27) DABNRETt*ABNRETt 0.13 0.12 0.12 0.10 0.10 0.09 (46.50) (38.92) (43.11) (17.94) (16.75) (17.33) Nobs 51417 42073 49334 190 190 190 Adj Rsq (%) 11.19 18.74 19.77 24.52 33.25 33.31

51

Table 5 (contd.) Panel D: Piecewise linear accruals regressions, combining all proxies

Pooled regressions Industry-specific regressions

CF

modelDD

model Jones model

CF model

DD model

Jones model

Intercept -0.00 -0.00 -0.02 -0.00 -0.01 -0.01 (-1.50) (-2.50) (-9.75) (-0.49) (-1.58) (-3.78)CFt -0.36 -0.68 -0.38 -0.70 (-35.79) (-56.94) (-12.60) (-22.32) CFt-1 0.19 0.21 (25.88) (10.36) CFt+1 0.13 0.15 (32.15) (12.35) ΔREVt 0.14 0.12 (70.82) (17.07)GPPEt -0.05 -0.04 (-39.63) (-9.78)ΔCFt -0.26 -0.33 -0.26 -0.30 (-33.04) (-42.89) (-12.71) (-14.65)IND_CFt -0.23 -0.37 (-25.91) (-14.03)ABNRETt 0.00 0.01 -0.00 0.01 0.01 -0.00 (3.86) (5.06) (-1.38) (3.63) (3.06) (-0.75)DCFt 0.01 0.01 0.01 0.01 0.01 0.01 (5.79) (5.73) (9.20) (1.12) (1.28) (1.74)DCFt*CFt 0.45 0.44 0.12 0.55 0.48 0.48 (28.69) (26.39) (10.69) (1.06) (0.93) (1.40)DΔCFt 0.00 0.00 0.00 -0.00 -0.00 -0.00 (0.81) (1.20) (3.78) (-0.69) (-0.40) (-0.39)DΔCFt*ΔCFt 0.09 0.01 0.11 0.04 0.01 0.02 (7.38) (0.65) (9.03) (1.12) (0.24) (0.58)DINDt -0.00 -0.00 0.01 0.01 0.01 0.00 (-0.33) (-0.10) (7.10) (1.02) (1.09) (0.60)DINDt* IND_CFt 0.03 0.08 0.25 -0.29 -0.27 -0.15 (2.62) (5.49) (16.07) (-0.56) (-0.54) (-0.44)DABNRETt 0.01 0.01 0.01 0.01 0.01 0.01 (5.31) (5.89) (9.03) (2.32) (2.32) (2.97)DABNRETt*ABNRETt 0.12 0.12 0.11 0.09 0.09 0.08 (42.65) (40.30) (39.46) (15.49) (15.87) (15.30)Nobs 47701 42009 45879 180 180 180Adj Rsq (%) 17.66 20.44 27.47 31.63 35.38 42.29

52

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-market-based 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 Industry-specific regressions

CF model

DD model

Jones model

CF model

DD model

Jones 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

53

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 II III IV V

Intercept 0.03 0.04 0.03 0.04 0.04 (15.77) (14.64) (11.55) (12.93) (12.15)CFt -0.45 -0.57 -0.49 -0.55 -0.53 (-27.01) (-28.46) (-21.57) (-23.50) (-20.66)CFt-1 0.18 0.16 0.15 0.16 0.14 (17.96) (15.58) (10.00) (16.30) (7.64)CFt+1 0.14 0.16 0.16 0.16 0.16 (11.96) (14.39) (13.80) (13.77) (14.16)ABNRETt 0.01 0.01 0.01 0.01 (4.43) (3.99) (4.29) (3.94)DCFt 0.01 0.00 (2.73) (0.27)DCFt*CFt 0.14 0.24 (3.38) (0.65)DΔCFt 0.01 0.01 (5.39) (2.40)DΔCFt*ΔCFt 0.08 -0.03 (1.88) (-0.87)DINDt 0.01 -0.00 (3.89) (-0.32)DINDt* IND_CFt 0.10 -0.12 (3.69) (-0.33)DABNRETt 0.00 -0.00 0.00 -0.00 (0.44) (-0.01) (0.55) (-0.07)DABNRETt*ABNRETt 0.04 0.04 0.04 0.04 (6.99) (6.62) (6.98) (6.80)Nobs 140 140 140 140 140Adj Rsq (%) 31.87 40.88 38.98 38.99 41.55

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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.

I II III IV V

Intercept -0.22 -0.36 -0.15 -0.41 -0.26 (-3.34) (-5.81) (-1.58) (-6.51) (-3.28)ΔREVt 0.10 0.11 0.12 0.10 0.12 (13.47) (15.44) (17.48) (14.91) (17.30)GPPEt -0.10 -0.02 -0.07 -0.04 -0.05 (-30.06) (-4.21) (-20.32) (-11.38) (-13.64)CFt -0.50 (-29.05)ΔCFt -0.44 -0.29 (-23.18) (-14.57)IND_CFt -0.59 -0.41 (-23.27) (-15.19)ABNRETt 0.00 -0.01 -0.01 -0.00 (1.85) (-2.33) (-2.61) (-1.17)DCFt 0.01 0.01 (1.65) (1.66)DCFt*CFt 0.19 0.35 (2.10) (1.29)DΔCFt -0.00 -0.00 (-0.40) (-1.14)DΔCFt*ΔCFt 0.07 0.02 (1.93) (0.57)DINDt 0.00 -0.00 (0.88) (-0.31)DINDt* IND_CFt 0.31 -0.08 (8.02) (-0.30)DABNRETt 0.01 0.00 0.00 0.00 (6.39) (1.29) (1.69) (2.31)DABNRETt*ABNRETt 0.08 0.07 0.09 0.08 (14.84) (11.61) (17.37) (15.41)Nobs 180 180 180 180 180Adj Rsq (%) 33.29 53.44 51.06 52.45 58.46

Table 8: Fama-Macbeth cross-sectional regressions This table presents the average coefficients and adjusted r-squares from yearly cross-sectional 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 CFt ΔCFt IND_CFt

CF

model DD

model Jones model

CF model

DD model

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DD model

Jones model

Intercept 0.00 -0.01 0.01 -0.04 -0.04 -0.02 -0.02 -0.03 -0.02 (0.75) (-1.80) (2.94) (-9.45) (-10.37) (-3.74) (-7.24) (-7.88) (-3.47)CFt -0.50 -0.67 -0.51 -0.08 -0.43 -0.30 -0.57 (-36.53) (-52.65) (-39.37) (-4.83) (-25.08) (-17.58) (-31.63) CFt-1 0.17 0.23 0.19 (19.34) (15.27) (21.72) CFt+1 0.13 0.14 0.15 (26.77) (27.15) (27.27) ΔREVt 0.12 0.12 0.11 (22.11) (30.53) (21.16)GPPEt -0.03 -0.05 -0.05 (-9.93) (-12.08) (-12.73)VARt -0.30 -0.40 -0.34 (-15.88) (-31.58) (-20.04)ABNRETt 0.01 0.01 -0.01 0.01 0.01 -0.00 0.00 0.01 -0.00 (2.31) (4.53) (-3.95) (4.14) (4.55) (-1.45) (2.12) (4.42) (-2.21)DVARt 0.01 0.01 0.00 0.01 0.02 0.01 0.03 0.03 0.03 (1.91) (2.08) (0.21) (7.37) (9.63) (6.84) (11.67) (8.38) (9.46)DVARt * VARt 0.56 0.49 0.58 0.17 0.12 0.20 0.34 0.35 0.38 (26.45) (22.08) (30.13) (4.85) (4.33) (8.32) (18.79) (23.61) (14.71)DABNRETt 0.01 0.01 0.01 0.01 0.02 0.02 0.01 0.01 0.02 (5.86) (5.76) (6.54) (5.02) (5.81) (6.39) (5.89) (5.53) (7.28)DABNRETt* ABNRETt 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)Adj Rsq (%) 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.

Dependent variable α0 α1 α2 α3 α4 α5 α6 α7 α8 α9 Adj R-sq

(%) Δ Accounts Receivables 0.01 -0.12 -0.17 0.05 0.06 13.98(= –Compustat Data Item 302) (10.38) (-11.04) (-15.49) (8.37) (10.60) 0.02 -0.15 -0.22 0.05 0.08 0.01 0.03 -0.00 0.01 0.03 21.75 (7.64) (-13.76) (-14.43) (7.18) (12.17) (4.85) (2.08) (-1.68) (3.92) (9.15) Δ Inventory 0.01 -0.07 -0.16 0.06 0.04 13.04(= –Compustat Data Item 303) (12.33) (-6.02) (-13.43) (11.63) (7.13) 0.01 -0.11 -0.19 0.06 0.05 0.01 0.00 0.00 0.01 0.02 17.80 (6.48) (-8.81) (-12.42) (10.73) (7.91) (5.07) (0.08) (0.01) (5.35) (7.42) - Δ (Accounts payable and accrued liabilities) -0.01 -0.20 -0.17 0.06 0.06 26.89(= –Compustat Data Item 304) (-16.94) (-21.89) (-17.26) (11.40) (12.64) -0.004 -0.21 -0.25 0.05 0.06 -0.00 0.10 0.01 -0.00 0.02 30.61 (-2.71) (-20.51) (-21.47) (9.15) (11.71) (-0.38) (6.95) (5.39) (-1.72) (7.35)

Table 9 (contd.)

Dependent variable α0 α1 α2 α3 α4 α5 α6 α7 α8 α9 Adj R-sq

(%) -Δ (Taxes payable) -0.001 -0.01 -0.02 0.01 0.003 4.23(= –Compustat Data Item 305) (-6.17) (-5.50) (-9.71) (7.92) (4.76) 0.001 -0.02 -0.04 0.01 0.004 0.00 0.02 0.00 -0.00 -0.00 5.91 (2.73) (-5.59) (-10.54) (6.84) (4.73) (0.26) (6.35) (1.42) (-0.57) (-0.22) Δ (Other assets and liabilities) 0.00 -0.09 -0.13 0.07 0.02 14.00(= –Compustat Data Item 307) (0.86) (-14.24) (-18.26) (14.49) (6.54) 0.003 -0.11 -0.17 0.06 0.03 0.002 0.03 0.002 0.00 0.01 17.78 (1.92) (-16.13) (-13.63) (12.59) (7.22) (1.96) (2.40) (2.99) (0.46) (6.27) - (Depreciation & Amortization) -0.05 0.02 -0.01 -0.02 -0.02 11.72(= –Compustat Data Item 125) (-46.67) (3.71) (-1.44) (-4.22) (-10.66) -0.04 0.00 -0.05 -0.02 -0.02 0.004 0.06 -0.00 -0.00 0.01 15.60 (-29.53) (0.69) (-5.08) (-4.00) (-8.70) (5.39) (5.04) (-0.92) (-1.78) (5.56) Miscellaneous accruals 0.001 -0.05 -0.04 0.01 0.00 6.92

(3.44) (-8.53) (-8.51) (5.27) (1.31)

(= – (Compustat Data Item 124+ data item 126+ data item 106+ data item 213)) 0.002 -0.06 -0.05 0.01 0.00 0.001 0.01 0.001 -0.00 0.01 10.60 (1.65) (-9.06) (-5.69) (4.91) (0.75) (2.12) (0.53) (2.48) (-1.22) (5.09)

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 CFit+1 CFit+2 CFit+3 I II III IV V VI

Intercept 0.026 0.031 0.038 0.037 0.047 0.039 (15.10) (14.40) (17.06) (13.03) (14.68) (11.20) CFit-1 0.298 0.175 0.308 0.211 0.324 0.210 (18.46) (10.06) (16.12) (8.81) (10.38) (6.70) ACCit-1 0.071 0.036 0.047 0.035 0.086 0.036 (5.26) (2.54) (3.18) (1.70) (3.09) (1.22) CFit 0.390 0.588 0.343 0.586 0.356 0.696 (20.75) (24.78) (16.50) (16.70) (12.78) (13.83) ACCit 0.026 0.219 0.033 0.240 0.020 0.281 (1.75) (13.09) (1.72) (10.73) (0.79) (7.65) DCFit 0.000 -0.017 0.005 (0.04) (-2.95) (0.37) DCFit* CFit -0.073 -0.301 -0.184 (-0.73) (-2.81) (-0.72) DCFit ACCit -0.128 -0.163 -0.049 (-2.93) (-3.45) (-0.90) No. of obs 185 185 178 178 173 173 Adj R-sq (%) 33.33 42.71 23.81 32.31 18.22 27.74 % Increase in adj. r-sq 28.16 35.66 52.25

Panel B: Proxy for economic gain or loss is ΔCFit Dependent variable CFit+1 CFit+2 CFit+3

I II III IV V VI Intercept 0.027 0.030 0.037 0.039 0.047 0.047 (14.58) (13.60) (17.17) (12.94) (14.74) (12.84) CFit-1 0.272 0.229 0.296 0.247 0.267 0.268 (15.45) (9.28) (13.80) (8.98) (8.43) (6.48) ACCit-1 0.049 0.047 0.043 0.023 0.058 0.053 (3.98) (2.89) (2.56) (1.08) (2.05) (1.62) CFit 0.418 0.541 0.374 0.518 0.415 0.559 (22.85) (20.19) (19.44) (13.07) (15.40) (11.49) ACCit 0.060 0.177 0.068 0.178 0.064 0.170 (3.96) (7.66) (3.74) (6.43) (2.61) (3.77) DΔCFit -0.002 -0.006 -0.009 (-0.82) (-1.53) (-1.61) DΔCFit* CFit 0.041 0.005 -0.020 (1.41) (0.12) (-0.39) DΔCFit ACCit 0.000 0.013 0.043 (0.02) (0.37) (0.80) No. of obs 194 194 185 185 179 179 Adj R-sq (%) 33.43 41.56 24.51 32.12 18.74 25.80 % Increase in adj. r-sq 24.33 31.07 37.71 Panel C: Proxy for economic gain or loss is INDADJ_CFit

Dependent variable CFit+1 CFit+2 CFit+3 I II III IV V VI

Intercept 0.027 0.036 0.038 0.043 0.045 0.036 (15.17) (9.35) (17.11) (7.29) (14.74) (5.56) CFit-1 0.297 0.180 0.325 0.200 0.357 0.199 (17.02) (10.11) (16.57) (8.46) (11.15) (6.51) ACCit-1 0.074 0.040 0.059 0.018 0.112 0.036 (5.39) (2.49) (3.56) (0.86) (4.01) (1.21) CFit 0.390 0.599 0.340 0.606 0.354 0.806 (19.59) (18.42) (17.29) (12.09) (13.60) (10.09) ACCit 0.022 0.299 0.028 0.341 -0.005 0.407 (1.26) (12.35) (1.43) (10.11) (-0.18) (8.34) DINDit -0.010 -0.011 0.002 (-2.25) (-1.85) (0.22) DINDit* CFit -0.096 -0.160 -0.375 (-1.97) (-2.69) (-4.06) DINDit ACCit -0.181 -0.207 -0.256 (-5.52) (-5.36) (-4.24) No. of obs 195 195 186 186 179 179 Adj R-sq (%) 33.27 42.67 24.69 32.65 19.54 27.37 % Increase in adj. r-sq 28.22 32.25 40.08

Panel D: Proxy for economic gain or loss is ABNRETit

Dependent variable CFit+1 CFit+2 CFit+3 I II III IV V VI

Intercept 0.027 0.014 0.037 0.034 0.046 0.037 (13.32) (4.56) (15.82) (9.58) (13.83) (8.72) CFit-1 0.318 0.216 0.324 0.221 0.340 0.251 (17.21) (11.13) (14.63) (10.14) (10.08) (7.82) ACCit-1 0.076 0.034 0.065 0.016 0.102 0.057 (4.86) (2.17) (3.43) (0.75) (3.66) (1.77) CFit 0.350 0.724 0.335 0.638 0.337 0.756 (18.44) (23.15) (18.13) (16.09) (13.32) (15.92) ACCit 0.004 0.265 0.019 0.300 -0.020 0.367 (0.27) (6.86) (1.09) (7.50) (-0.82) (6.36) DABNRETit 0.020 0.004 0.006 (6.36) (1.14) (1.24) DABNRETit* CFit -0.245 -0.155 -0.245 (-7.40) (-4.12) (-4.97) DABNRETit ACCit -0.119 -0.128 -0.191 (-2.86) (-3.01) (-2.91) No. of obs 195 195 186 186 179 179 Adj R-sq (%) 31.62 43.13 23.90 32.05 18.23 26.95 % Increase in adj. r-sq 36.38 34.08 47.83

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

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