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8/3/2019 The Role of Accruals in Asymmetrically Timely Gain and Loss Recognition ( Referensi Untuk Jones Model)

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The Role of Accruals in

Asymmetrically Timely Gain and Loss Recognitionby

Ray Ball*

and Lakshmanan Shivakumar**

*Graduate School of Business

University of Chicago

5807 S. Woodlawn Ave

Chicago, IL 60637Tel. (773) 834 5941

[email protected]

**London Business School

Regents ParkLondon NW1 4SA

United Kingdom

Tel. (44) 207 262 5050

[email protected]

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


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Asymmetrically Timely Gain and Loss Recognition

Abstract

We investigate the role of accrual accounting in the asymmetrically timely recognition ofunrealized 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 lossrecognition, offer a comparatively poor specification of the accounting accrual process.

We also conclude that linear specifications of the relation between earnings and futurecash flows, ignoring the implications of asymmetrically timely loss recognition

(conditional conservatism), substantially understate the ability of current earnings topredict 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 accrualsmodels.


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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


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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


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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


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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


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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


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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 firms 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 years 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


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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


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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


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Timely recognition of gains and losses revises these ratios conditional on the outcome for

the borrowers 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 lossaccruals are a

source ofpositive 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 anL-period

annuity of expected future cash flows, CF.4

At the end of period t, information causes a

revision ofCFtin the amount of the annuity (retrospective to the beginning of t). Other

things equal, the current-period cash flow changes by CFt, the cash effect in yeartof

the assets change in value. The year-end present value of future cash flows from the

asset changes by F(L-1, r) times CFt, where F(L-1,r) is the annuity factor for(L-1)

periods and an interest rate r. To the extent the assets value is booked on the balance


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correlated with CFt. 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 lossrecognition

induces positive correlation between accruals and current-period operating cash flow, but

noise mitigation induces negative; and one increases earnings volatile relative to cash


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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 decreasesin 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


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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


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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


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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= 0 +1*Xt + 2*VARt+ 3* DVARt +4*DVARt *VARt + t (1)

whereACCtis accruals in yeart,Xtis the set of independent variables that prior studies

have used to explain accruals, VARtis a proxy for gain or loss andDVARt is a (0,1)

dummy variable that takes the value 1 ifVARt implies a loss occurs in yeart.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= 0 + 1 CFt+ t (2.1)

Dechow-Dichev (DD) model:ACCt= 0 + 1 CFt+ 2 CFt-1 + 3 CFt+1 + t (2.2)

Jones model:ACCt= 0 + 1REVt+ 2 GPPEt + t (2.3)

where REVtis change in total revenue and GPPEtis 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:


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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. 4 > 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. 1 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 for2 and 3, 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


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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 yeart, 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 yeart, taken from the cash flow statement

(Compustat item 308), scaled by average total assets.

DCFt: Dummy variable = 1 iffCFt< 0.


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REVt: Change in revenue in yeart,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 variableDVAR). Three of these are non-market measures:

Gain/lossProxyVARt

Loss ProxyDVARt*VARt Variable definitions

Level of cash

flowsDCFt*CFt CFt : Cash flow from operations

DCFt= 1 ifCFt< 0, 0 otherwise

Change in

cash flowsDCFt*CFt CFt : Change in cash flow from operations

DCFt= 1 ifCFt< 0, 0 otherwise

Industry-

adjusted cashflows

DINDt* INDADJ_CFt INDADJ_CFt= (CFt MEDIAN_CFt)MEDIAN_CFt: Median cash flow fromoperations in three-digit SIC industry

DINDt= 1 ifINDADJ_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

returnsDABNRETt* ABNRETt ABNRETt= (RETtMKTRETt)

RETt= Stock return in fiscal year tMKTRETt= CRSP equally-weighted market

return in the fiscal year t

DABNRETt= 1 ifABNRETt< 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


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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

DCF*CFseems more likely than the level of cash flowDCF*CFto 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_CFcould 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


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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.


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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


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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. MKTRETtis the CRSP equally-weighted market return measured over the same

period asRETt. 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 onABNRETt(for positive

abnormal returns) has inconsistent signs across the regressions and tends to be

statistically insignificant. In the Jones model, the negative coefficient onABNRETt

potentially reflects the negative correlation between accruals and operating cash flow,

which is correlated withABNRETt but is not controlled for in this model. Compared with


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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


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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, DCFt*CFtand

DINDt* IND_CFt) as well as all the eighteen coefficients on DABNRETt*ABNRETtare

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 CFtgenerally 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-


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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


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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 Accounts

receivable + Inventory - Accounts payable - Tax payable + Other 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


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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 REVand

GPPEare 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.


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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 ofDVARt* VARt). The results reported in earlier tables are

qualitatively unchanged. As predicted, the coefficients on the loss proxies (DABNRETt

*ABNRETtandDVARt* VARt) are positive and significant, for all three definitions of

VARt. The large Fama-Macbeth t-statistics (ranging from 10.42 to 13.63 forDABNRETt

*ABNRETtand 4.33 to 30.13 forDVARt* 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 proxyDCFt*CFtalso is significant in each of the three accruals models, with Fama-

MacBeth t-statistics of 3.84 9.67. The other book proxies (DCFt*CFtand DINDt*


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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= [ current assets (4) cash (1)] [ current liabilities (5) - debt in

current liabilities (34) 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


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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) Receivables, (ii) Inventory, (iii) Accounts payable and accrued

liabilities, (iv) Taxes payable, (v) Other 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


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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 Taxes payable, the incremental coefficients on economics loss, namely 6 and

9, 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 Taxes 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


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either as the sign and magnitude of accumulated accruals or as Basus (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, DCFt, 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 DCFt.

The coefficients on incremental loss in Panel B are also more volatile.


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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 = 0 + 1CFit-1 + 2ACCit-1 + 3CFit + 4ACCit+ 5DVARit

+ 6CFit*DVARit+ 7ACCit*DVARit+ it+j (3)

All variables are as defined earlier and are standardized by average of ending total assets

in years tand t-1. CFit-1 andACCit-1 are included in the regression to control for expected

cash flows at the beginning of yeart. The regressions are estimated separately for each


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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

7on 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 6on 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 5 ,6and

7to 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 variableDVARitproxying for economic

loss is either DCFi DCFi DINDi or DABNRETi


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For every proxy except CFit, and for each of the three future-year cash flows

(i.e., in nine of nine cases), the incremental coefficient 7on 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 6on 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 6

and 7on 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


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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


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(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


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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.


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Table 1: Replication of Linear Accruals Regressions

The table presents regression results for the following accruals models:

CF model: ACCt= 0 + 1 CFt+ t

Dechow-Dichev (DD) model: ACCt= 0 + 1 CFt+ 2 CFt-1 + 3 CFt+1 + t

Jones model: ACCt= 0 + 1REVt+ + 2 GPPEt + t

CFtis operating cash flow in yeart, taken from the cash flow statement (Compustat item308);ACCt is accruals in yeart, defined as income before extraordinary items

(Compustat item 123) minus operating cash flow in yeart; REVt is change in revenue inyeart; and GPPEtis gross undepreciated property, plant and equipment in yeart.

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 offirm/years. Industry-specific statistics are mean coefficients from the cross-sectional

distribution of individual 3-digit SIC industry-specific regressions and t-statistics basedon the standard deviation of that distribution. A minimum of 30 observations is requiredin 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)

REVt 0.12 0.10 (63.91) (14.83)

GPPEt -0.04 -0.06


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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 lossdummy proxies.

Proxy for economic loss Dummyvariable

Dummy definition

CFt< 0 DCFt =1 ifCFt< 0, 0 otherwise

CFt< 0 DCFt =1 ifCFt< 0, 0 otherwise

(CFt Industry median CF

t)


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Table 3 (contd.)

Panel B: Proxy for economic loss: CFt


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Table 3 (contd.)

Panel C: Proxy for economic loss: (CFt - Industry Median CFt)


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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 eachregression with an economic loss, i.e., dummy variable capturing economic loss should

take the value 1 for at least 5 observations.


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)

REVt 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 190 190 190

Adj Rsq (%) 6.36 15.05 12.14 20.78 30.20 13.59


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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


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Table 5 (contd.)

Panel B: Non-market-based proxy for economic loss: CFt


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Table 5 (contd.)

Panel C: Non-market-based proxy for economic loss: IND_CFt


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Table 5 (contd.)

Panel D: Piecewise linear accruals regressions, combining all proxies

Pooled regressionsIndustry-specific

regressions

CF

model

DD

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)

REVt 0.14 0.12 (70.82) (17.07)

GPPEt -0.05 -0.04 (-39.63) (-9.78)

CFt -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)

DCFt 0.00 0.00 0.00 -0.00 -0.00 -0.00 (0.81) (1.20) (3.78) (-0.69) (-0.40) (-0.39)

D

CFt*

CFt 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

Table 6: Constant Sample Results


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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 analysisin 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 1and 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-specificregressions.


CF

model

DD

model

Jones

model

CF

model

DD

model

Jones

model

Models without conditional

conservatism0.97 9.08 11.29 13.44 22.86 12.85

Models using CFt


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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 andShivakumar (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 sampleacross 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 accounts receivable + Inventory - Accounts payable - Tax

payable + Other 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)

DCFt 0.01 0.01 (5.39) (2.40)

DCFt*CFt 0.08 -0.03 (1.88) (-0.87)

DINDt 0.01 -0.00

(3.89) (-0.32)

DINDt* IND_CFt 0.10 -0.12

Table 7 (contd.)


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( )

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)REVt 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)

CFt -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)

DCFt -0.00 -0.00 (-0.40) (-1.14)

DCFt*CFt 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)

Table 8: Fama-Macbeth cross-sectional regressions


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g

This table presents the average coefficients and adjusted r-squares from yearly cross-

sectional regressions of the following accrual models with conditional conservatism:

ACCit= 0 + 1CFit+2CFit-1 +3CFit+1 +4DVARit+5DVARit* VARit

+6ABNRETit+7DABNRETit+8ABNRETit* DABNRETit+ it

VARitis the non-market proxy for economic gain or loss, CFit, CFitorINDADJ_CFit.DVARit takes the value 1 ifLOSSit< 0, 0 otherwise. Variables are as defined in tables 1and 2. VARit is not separately included in the regression if it causes perfect correlation

with other explanatory variables.

VARt CFt CFt IND_CFtCF

model

DD

model

Jones

model

CF

model

DD

model

Jones

model

CF

model

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)

REVt 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)

the role of accruals in asymmetrically timely gain and loss recognition ( referensi untuk jones...

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