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Impairment Write Offs Discretionary Accruals and Earnings

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					 Impairment Write-Offs, Discretionary Accruals, and Earnings Persistence




                                    HENRY JARVA*

              University of Oulu, Department of Accounting and Finance
               P.O. Box 4600, FIN–90014 University of Oulu, Finland.




                              First version: 3 October 2006
                               This version: 16 May 2007




*Contact Address: Henry Jarva, Department of Accounting and Finance, University of
Oulu, P.O. Box 4600, FIN–90014 University of Oulu, Finland. Phone: (+358) 8 553
2928; fax: (+358) 8 553 2906; e-mail: henry.jarva@oulu.fi.

Acknowledgement: I appreciate comments from Juha-Pekka Kallunki and workshop
participants at the University of Oulu. I also gratefully thank Weili Ge for her help with
the Mishkin test using SAS.




                Electronic copy available at: http://ssrn.com/abstract=947676
Abstract

This paper examines whether stock prices reflect the one-year-ahead earnings
implications of impairment write-offs. The market appears to recognize the persistence
of cash flows and nondiscretionary accruals but overestimate the persistence of
impairment write-offs and discretionary accruals. We report that impairment write-offs
are very common for U.S. corporations, appearing in 25.5% of all firm-year
observations over the period 2002 to 2005. We document that reporting impairment
write-offs is strongly linked to firm size. The results show that impairment firms have
significantly lower stock return, lower asset growth, lower sales growth, higher
bankruptcy risk, and a greater percentage of firms reporting losses and restructuring
expenses. We show that firms with impairments have higher future stock returns than
other firms, consistent with investors misunderstanding the nature of impairment write-
offs.

JEL classification: G10, M4

Keywords: Conservatism, earnings management, SFAS 142, SFAS 144, special items.

Data availability: All data are available from public sources.




                Electronic copy available at: http://ssrn.com/abstract=947676
                                                                                                  3



1. Introduction



       Basu (1997) defines conservatism as the extent to which current period accounting

income asymmetrically incorporates economic losses, relative to economic gains. He

shows that earnings are contemporaneously more sensitive to “bad news” than “good

news,” where he uses firms’ stock returns to measure news. Because the recognition of

gains and losses is asymmetric, this leads to systematic differences in the timeliness and

persistence of earnings. Although asymmetrically timely loss recognition (conditional

conservatism) is an empirically significant property of accounting earnings, its

implications for future earnings, and pricing, have not been directly examined in the

literature.

       In this paper we investigate whether stock prices rationally reflect the one-year-

ahead earnings implications of impairment write-offs1, which is one of the major

accrual components through which conservative accounting is facilitated. The

remaining accruals are decomposed into discretionary and nondiscretionary components

using a model that incorporates asymmetry in the gain and loss recognition roles of

accruals. There is a large body of evidence that shows market prices do not completely

impound predictable relations between current and future earnings (e.g., Bernard and

Thomas 1990; Sloan 1996). Although we examine how the market prices predictable

components of current earnings on future earnings, our study differs from most studies

(Xie 2001; Burgstahler et al. 2002; Dechow and Ge 2006). Our primary contribution is

to provide evidence whether the market rationally prices impairment write-offs with

respect to their one-year-ahead earnings implications. Thus, our study sheds new light

on the pricing of conditional conservatism.
1
    We use the term “write-off” to refer both complete and partial downward asset revaluations.
                                                                                            4



      The results indicate that, consistent with general view, impairment write-offs are

less persistent than the remaining accrual components. The results also indicate that

stock prices act as if investors do not anticipate lower persistence of impairment write-

offs, leading to significant security mispricing. We show that impairment write-offs are

very common and appear in 25.5% of all firm-year observations over the period 2002 to

20052. We document that reporting an impairment write-off is strongly linked to firm

size. The frequency of incurring impairment write-offs in a given year is 15.8% for the

smallest firms, compared with 41.9% for the largest firms. We find that impairment

write-off firms have performed poorly in the year that the impairment write-off is

reported. However, a positive future size-adjusted return suggests that management is

taking action to turn the firm around and the market rewards these actions.

      The remainder of our paper is organized as follows. Section 2 discusses the

background and research relating to impairment write-offs and accruals. A description

of the data and sample selection is provided in Section 3. Section 4 describes the

empirical results, which is followed by conclusions in Section 5.



2. Background and related research



2.1. Prior research



      Previous research has examined whether stock prices reflect information about

future earnings contained in different components of current earnings. Sloan (1996)

shows that earnings composed of accruals are less persistent than earnings composed of


2
    Our sample covers the post-SFAS No. 142 and No. 144 period.
                                                                                               5



cash flows. He finds that investors underweight the cash flow component and

overweight the accrual component’s implications for future earnings and, consequently,

misprice these components of earnings. Xie (2001) shows that the overpricing of total

accruals is largely due to discretionary accruals. His findings are robust after controlling

for special items. Burgstahler, Jiambalvo, and Shevlin (2002) find that investors are

fairly sophisticated in pricing special items but prices do not fully impound the

implications of special items for future earnings. Richardson et al. (2005) show that less

reliable accruals lead to lower earnings persistence and that investors do not fully

anticipate lower earnings persistence, leading to significant security mispricing. They

also show that the magnitude of security mispricing is directly related to the reliability

of underlying accruals. Dechow and Ge (2006) show that accruals improve the

persistence of earnings relative to cash flows in high accrual firms, but reduce earnings

persistence in low accrual firms. They show that the low persistence of earnings in low

accrual firms is primarily driven by special items. They also show that low accrual firms

with large negative special items earn higher positive returns than other low accrual

firms.

   There is a substantial body of accounting literature that examines special items and

asset write-offs. In addition, controlling for the effects of special items has become a

standard procedure for robustness checks. Special items refer to nonrecurring items that

are unusual or infrequent, but do not meet the criteria for classification as extraordinary.

Management have incentives to highlight the transitory nature of income-decreasing

special items (Kinney and Trezevant 1997). Prior research indicates that asset write-offs

can have an enormous impact on both accounting earnings and the book value of assets

(e.g., Alciatore et al. 1998). Francis, Hanna, and Vincent (1996) find that incentives
                                                                                             6



play a substantial role in explaining goodwill write-offs and restructuring charges but

only a small role in inventory and property, plant, and equipment (PPE) write-offs.

Elliot and Hanna (1996) find that earnings response coefficients (ERCs) generally

decrease in the presence of special items. Rees et al. (1996) find that abnormal accruals

in the asset write-down year are a credible signal about future firm performance.

Research shows that top management changes are important determinant of a write-off

decision (Strong and Meyer 1987; Elliot and Shaw 1988). In conclusion, researchers

seem particular interested in special items.

      Basu (1997) shows that the concurrent sensitivity of earnings to negative returns is

two to six times as large as the concurrent sensitivity of earnings on positive returns.

This finding implies that earnings more timely reflect publicly available bad news about

future cash flows than good news. Basu (1997) provides evidence that asymmetric

timeliness results somewhat more from accruals than from cash flows. He also finds

that negative earnings changes are less persistent than positive earnings changes.

Therefore, positive (negative) earnings changes have higher (lower) ERCs in a forward

regression of abnormal returns on earnings. The subsequent research has extensively

examined conservatism in different settings.3

      There is also a large body of literature that provides evidence of earnings

management using accruals (see e.g., Healy and Wahlen 1999; Dechow and Skinney

2000). Accrual accounting is subject to managerial discretion because of the flexibility

allowed by the Generally Accepted Accounting Principles (GAAP). A number of papers

call into question the precision and power of discretionary accruals estimates using

conventional linear accruals models (e.g., Dechow et al. 1995; Fields et al. 2001;


3
    See discussions of accounting conservatism in Watts (2003a; 2003b).
                                                                                                             7



Kothari et al. 2005). Basu (1997) points out that accruals enable accountants to

recognize bad news about future cash flow on an asymmetrically timely basis. Ball and

Shivakumar (2006) investigate the role of accrual accounting in the asymmetrically

timely recognition of gains and losses. Specifically, they argue that economic losses

(shocks to current period cash flows plus revisions in the expectation of firms’ future

cash flows) are captured by the accruals process in a more timely manner than gains.

Ball and Shivakumar (2006) show that piecewise linear accruals models that incorporate

asymmetry in gain and loss recognition offer a substantial specification improvement,

and they explain substantially more variation in accruals than equivalent linear

specifications. Their results suggest that standard linear accruals models exhibit

substantial attenuation bias and are misspecified to some degree.

    In contrast to prior work that often focuses on the average behavior of special items,

our analysis focuses on impairment write-offs.4 Our empirical tests attempt to measure

the pricing of impairment write-offs in the post-SFAS No. 142 and No. 144 period.



2.2. Accounting for asset impairments



    The increasing frequency of large special item adjustments has attracted the

attention of the business press, accounting standard-setting bodies, and regulatory

authorities. The Financial Accounting Standards Board (FASB) adopted SFAS No. 142

(Goodwill and Other Intangible Assets) in June 2001. SFAS 142 requires that goodwill

and intangible assets that have indefinite useful lives will not be amortized but rather

4
  Burgstahler et al. (2002, 587) note that while special items are commonly viewed as transitory on
average, it is not clear if individual special items have different implications for future earnings. Also
Dechow and Ge (2006, 293) raise a question of whether different types of special items have different
implications for earnings persistence and future returns. One reason for scarce research on individual
special items is that Compustat has classified different types of special items only since 2001.
                                                                                             8



will be tested at least annually for impairment. Intangible assets that have finite useful

lives are amortized over their useful lives. The standard provides specific guidance for

testing goodwill for impairment using a two-step process. The first step is a screen for

potential impairment that begins with an estimation of the fair value of a reporting unit.

If the carrying amount exceeds the estimated fair value, then the second step measures

the amount of impairment. The impaired amount is the difference between the carrying

amount of the reporting unit’s goodwill and its implied fair value. The standard

prohibits the loss recognized to exceed the carrying amount of goodwill. Also,

subsequent reversal of a previously recognized impairment loss is prohibited. Under

SFAS 142, the amortization expense and impairment losses for intangible assets shall be

presented in income statement line items within continuing operations. Impairment

losses that arise due to the initial application of the standard are to be reported as

resulting from a change in accounting principle. Basu (1997, 10) argues that asset

impairment recognition standards have increased the U.S.’s accounting conservatism.

Watts (2003a, 217) claims that the recent adoption of FASB 142 appears inconsistent

with conservative accounting and enables impairment manipulation. This is because

assessing impairment requires valuation of future cash flows and those estimates are

unlikely to be verifiable and contractible.

   The Financial Accounting Standards Board adopted SFAS No. 144 (Accounting for

the Impairment or Disposal of Long-Lived Assets) in August 2001. SFAS No. 144

superseded SFAS No. 121 and replaced the amortization of goodwill with periodic

assessment of goodwill impairment. The standard requires firms to a) recognize an

impairment loss only if the carrying amount (book value) of a long-lived asset (asset

group) is not recoverable from its undiscounted cash flows and b) measure an
                                                                                           9



impairment loss as the difference between the carrying amount and its fair value. SFAS

144 (par. 8) requires a long-lived asset to be tested for recoverability whenever events

or changes in circumstances indicate that its carrying amount may not be recoverable. If

the test is triggered then it also may be necessary to change depreciation estimates and

method, or the amortization period. The recoverability of long-lived asset is tested by

the entity’s own assumptions about estimates of future cash flows (SFAS 144, par. 17).

Under SFAS 144, an impairment loss recognized for a long-lived asset to be held and

used shall be included in income for continuing operations before income taxes.

   Managers’ accounting choices affect the timing and the amount of impairment

recognition since subjectivity is inherent in the two-step impairment test prescribed by

SFAS 142 and SFAS 144. Richardson et al. (2005, 449) point out that to estimate the

amount of impairment write-offs involves great subjectivity. Riedl (2004) finds that

write-offs reported after the adoption of SFAS No. 121 (predecessor of SFAS No. 144)

have lower associations with economic factors, and a higher association with “big bath”

reporting behavior, relative those reported prior to the standard. On the contrary,

management could record impairment write-offs to smooth income in a period of higher

than normal earnings to maintain a steady and predictable earnings growth. However, it

is more likely that impairment write-offs lower earnings persistence. In summary, SFAS

142 and SFAS 144 give substantial latitude for management to select the timing and

amount of asset impairment.



3. Data
                                                                                                            10



    The sample comprises all firms listed on the New York (NYSE), American

(AMEX), and NASDAQ markets for which requisite financial and return data are

available. We exclude foreign companies. Our empirical tests employ data from three

sources. Financial statement data are obtained from the Worldscope database, stock

return data are obtained from the Datastream, and analyst data are obtained from the

I/B/E/S research files. Our sample consists of financial statement and return data for

firms with required one-year-ahead, current, and lagged values during the 4-year period

2002 to 2005. The sample period is relatively short because impairment write-off data

are not available in Worldscope until 2001. In addition, we exclude year 2001 because

SFAS 142 and SFAS 144 are effective in fiscal years beginning after December 15,

2001 (SFAS 142, par. 48; SFAS 144, par. 49).5

    The following data filters are applied to 15,864 firm-year observations during the 4-

year period 2002 to 2005. Firms in the financial services, insurance, and real estate

industries (SIC 6000–6999) and firms with missing SIC codes are removed (772 firm-

years). Observations with less than $1 million in average total assets are deleted to

control for the potential influence of observations with relatively low values of deflating

variable (458 firm-years). We replace missing values of impairments with zero. Firms

with positive total impairment values are deleted (48 firm-years). To minimize the

effect of outliers, we delete the firm-year observations that are in the top or bottom 1 %

of the distributions of the earnings, cash flows, and accruals variables (576 firm-years).

Finally, we delete observations with less than 15 observations in any three-digit SIC



5
  Beatty and Weber (2006) examine SFAS 142 adoption decisions, focusing on the trade-off between
recording certain current goodwill impairment charges below the line and uncertain future impairment
charges included in income from continuing operations. They find that both contracting and market
incentives affect firms’ accounting choices relating to the trade-off between the timing and presentation
of expense recognition on income statements.
                                                                                                                         11



code combinations (846 firm-years). These procedures result in 13,164 observations for

primary tests.

    Following earlier research, we compute accruals based on cash flow statements.

Hribar and Collins (2002) show that merger and acquisition activities, foreign currency

translations, and divestitures introduce significant measurement error into accruals

estimated using the balance sheet approach. We calculate Accrualst by subtracting

operating cash flow from earnings before extraordinary items, both taken from the cash

flow statement. Pre-impairment accrualst are calculated by subtracting Impairmentst

from Accrualst.6

    To estimate discretionary accruals we use the piecewise linear Jones (1991) model,

modified by Ball and Shivakumar (2006). We run the following regression from the

pooled data separately for each three-digit SIC industry (159 industries):



      Pre − impairment accrualsit = α 0 + α1 ∆Salesit + α 2 PPEit + α 3Cash flowsit

                                             + α 4 DCFit + α 5Cash flowsit × DCFit + εit                         (1)



where ∆Salesit is the change in sales for firm i in year t; PPEit is gross property, plant,

and equipment; Cash flowsit is cash flow from operations and it is our proxy for gain or

loss;7 DCFit is a dummy variable having the value of one if Cash flowsit is negative,

zero otherwise; Cash flowsit × DCF is the interaction variable; and εit is the error term.

All variables are standardized by average total assets in an attempt to reduce


6
  Although we subtract impairment write-offs from total accruals, there are still many accrual components
in which the timely recognition of losses is accomplished. Examples are losses on trading securities,
inventory write-downs, receivables revaluation, and restructuring charges arising from attending to failed
strategies or excessive head accounts (Ball and Shivakumar 2006, 213).
7
  Cash flow proxy gives the highest R2 (40%) but our results are similar when we use different proxies to estimate the
nondiscretionary accruals. See Ball and Shivakumar (2006) for more details.
                                                                                              12



heteroskedasticity. Nondiscretionary accruals, NACCit, is the fitted value from Eq. (1)

and discretionary accruals, DACCit, is the residual from Eq. (1).

    Stock returns are measured using compounded buy-hold size-adjusted returns

inclusive of dividends and other distributions. Following previous research, returns are

calculated for a 12-month period beginning eight months prior to the end of the fiscal

year. The size-adjusted return is calculated by deducting the value-weighted average

return for firms in the same size-matched decile, where size is measured as the market

value at the beginning of return cumulation period.

    Analyst EPS and sales forecast data are obtained from I/B/E/S. We used the median

consensus forecast outstanding in the eighth month before fiscal year-end. We also

obtained the number of analysts providing the forecast in the eighth month before fiscal

year-end.



4. Empirical results



    We present results in five sections. Section 4.1 begins with descriptive statistics for

our sample. Section 4.2 presents results from tests of persistence concerning the

different earnings components. Section 4.3 reports the market pricing of impairment

write-offs and discretionary accruals. The results of market perceptions are discussed in

Section 4.4 and Section 4.5 examines the predictive ability of impairments.



4.1. Descriptive statistics
                                                                                           13



   Panel A of Table 1 provides descriptive statistics of the financial variables used in

our analysis. All variables are scaled by average total assets. The median earnings

amount is 0.025, while median cash flow cash from operations is 0.069. This difference

leads to negative total accruals (–0.060), mostly due to depreciation. Total impairments

have a median value of 0.000. However, the mean is –0.012, and this suggests that the

distributions of impairments are highly skewed. Consistent with Xie (2001)

discretionary accruals are more variable than nondiscretionary accruals. For our

subsample of impairment firms, the median earnings is negative (–0.006) and the

median cash flow is positive (0.058). The mean value of total impairments is –0.048.

The results indicate that firms have performed poorly during the fiscal year they report

an impairment write-off.



(Insert Table 1 about here)



   Panel B of Table 1 reports both Spearman and Pearson correlations. For ease of

exposition, we discuss the Spearman correlations. Consistent with prior research, we

document a positive correlation between earnings and total accruals (0.384). We also

report a positive relation between earnings and cash flows (0.760). Consistent with

Dechow (1994), who proposes that accruals offset transitory cash flow effects, our pre-

impairment accruals are negatively related to cash flows (–0.203). There is a negative

correlation between nondiscretionary and discretionary accruals (–0.096) which can be

interpreted as an evidence of management smoothing income. Ball and Shivakumar

(2006, 213) argue that the gain and loss recognition role of accruals is a source of

positive correlation between accruals and current-period operating cash flow.
                                                                                                                          14



Impairments are positively correlated with cash flows (0.080) implying timely

recognition of impairments. Impairments are also positively related to pre-impairment

accruals (0.081) and discretionary accruals (0.078). We return to this issue in Fig. 1.

     Table 2 presents the relative frequency of impairments during the four-year period

from 2002 to 2005.8 Impairments are fairly common, appearing in 25.5% of all firm-

years. The impairment phenomenon is strongly linked to firm size. The results reveal an

increasing relation between firm size and the frequency of impairments. The frequency

of incurring impairment write-offs in a given year is 15.8% for the smallest firms

(portfolio 1), compared with 41.9% for the largest firms (portfolio 10). In untabulated

results, we observe that the probability of reporting an impairment increasing from

25.5% for all firms to 49.6% for firms reporting impairment in the previous year. This

finding suggests that impairments are not as transitory as management claims them to

be. The results in Table 2 indicate that large firms make more balance sheet adjustments

than small firms. This is consistent with Dechow and Ge’s (2006) view that accounting

rules lead to negative accruals with a balance sheet perspective (in contrast to an income

statement perspective). Conservative accounting requires declining firms with large

asset bases to record impairments to write assets down at fair value.



(Insert Table 2 about here)



     Fig. 1a plots the proportion of firms reporting impairment write-offs based on ranks

of accruals. Impairment write-offs are defined as impairments as a percentage of

average total assets greater than 0%, 1%, 2%, or 5%. Approximately 30% of firms in


8
  Ten portfolios are formed each year based on the value of firms’ total assets at the end of the previous fiscal year-
end.
                                                                                              15



decile 1 report impairment write-offs that are greater than 5% average total assets. The

results indicate that the proportion of firms reporting impairments is largest in deciles 1

and 2.



(Insert Fig. 1 about here)



   Fig. 1b plots the proportion of firms reporting impairment write-offs based on ranks

before impairment write-offs. The largest impairments are still in deciles 1 and 2.

However, this ranking leads to a smoother frequency of impairments and reflects a

convex pattern with larger impairment write-offs. The results show that large

impairment write-offs are not just an issue for low accrual firms, but these write-offs

make the total accruals exceedingly negative. In all deciles at least 8% of firms have

impairment write-offs that are greater than 1% average total assets. Moreover, in all

deciles except in decile 10 at least 20% of firms have reported impairment write-offs.

Dechow and Ge (2006, 254) argue that large negative accruals indicate that a firm is

reducing assets and downsizing, while firms with large positive accruals indicate that a

firm is investing in assets, generating sales, and expanding their business. The results in

Fig. 1 b imply that both “growing” and “declining” firms make large impairment write-

offs if the sign of accruals is the measure of growth. However, these results support the

view that declining firms are more likely to make impairment write-offs to correct their

balance sheet.



4.2. Tests of persistence
                                                                                                                            16



     Next we investigate whether impairment write-offs and discretionary accruals are

important for explaining why the accrual component of earnings is less persistent than

the cash flow component. Table 3 provides the results of estimating the following

regressions:



     Earningst + 1 = α + βEarningst + εt                                                                            (2)

     Earningst + 1 = α + β1Earningst + β2DIt + β 3 Earningst × DIt + εt                                             (3)

     Earningst + 1 = α + δ1Cash flowst + δ2Accrualst + εt                                                           (4)

     Earningst + 1 = α + δ1Cash flowst + δ2Pre − impairment accrualst

                          + δ3Ιmpairmentst + εt                                                                     (5)

     Earningst + 1 = α + δ1Cash flowst + δ2NACCt + δ3DACCt + δ4Ιmpairmentst + εt                                    (6)



where DI is a dummy variable equal to one for firms that report impairment write-offs,

zero otherwise. Since we are interested in the persistence of earnings (not a return on

assets) we scale earnings in period t+1 by average total assets in period t (see, Fairfield,

Whisenant, and Yohn, 2003; Fairfield, 2006). The use of a contemporaneous deflator

would be particularly puzzling in our setting since impairment write-offs affect both the

denominator and the numerator of the dependent variable.9

     The results in Table 3 indicate that the persistence parameter on earnings is equal to

0.886. This suggests that profitability follows a mean-reverting process, as documented

in past research. Eq. (3) includes an interactive indicator variable equal to one for firms

that report impairment write-offs. Note that after controlling for the impairments, the

coefficient on earnings increases to 0.982. The interactive slope coefficient, β3 ,
9
  We find that our results are robust with respect to using average total assets in period t+1 as an alternative deflator
for t+1 earnings. Interested readers may obtain a full set of results by contacting the author.
                                                                                          17



indicates that the persistence of earnings is lower for impairment firms (0.982–0.286).

This finding is consistent with Basu (1997) in that conservatism affects earnings

persistence. The adjusted R-square increases from 38.77% to 39.92%.



(Insert Table 3 about here)



   Consistent with prior research, decomposing earnings into cash flows and accruals

indicates that the cash flow component of earnings is more persistent than the accrual

component of earnings (1.081 vs. 0.568). An untabulated F-test rejects the hypothesis

that the coefficients are equal (F=483.48). Decomposing accruals into the impairment

component and the pre-impairment component indicates that impairment component is

less persistent than other accrual components (0.084 vs. 0.664). If impairments are

purely transitory, the coefficient should move toward –1. A transitory impairment

coefficient is strongly rejected (F=611.78, untabulated). Consistent with Xie (2001),

further decomposing reveals that the discretionary accrual component is less persistent

than the nondiscretionary component of accruals (0.581 vs. 0.928). The equality of

these two coefficients is rejected (F=61.10, untabulated). In summary, these results

indicate that different accrual components have incremental information content and

impairment write-offs have a strong affect on earnings persistence.



4.3. Tests of the pricing of impairment write-offs and discretionary accruals



   Following prior research we use the Mishkin (1983) test to investigate whether

investors appear to rationally price publicly available information. Our application of
                                                                                             18



the Mishkin (1983) test provides a statistical comparison between (a) a measure of the

market’s pricing of impairment write-offs and discretionary accruals, and (b) a measure

of the ability of impairment write-offs and discretionary accruals to predict one-year-

ahead earnings. If the market’s valuation coefficients on accrual components

significantly differ from the forecasting coefficient of these accrual components for one-

year-ahead earnings, the Mishkin (1983) test would suggest that the market misprices

these accrual components.

   Table 4 reports the results of the Mishkin (1983) tests. Panel A of Table 4 shows

results of estimating Eq. (5), where we decompose earnings into cash flow from

operations, pre-impairment accruals, and impairments. For cash flows, the valuation

coefficient (δ*1=0.907) is not significantly smaller than the forecasting coefficient

(δ1=1.089), indicating that investors correctly price the cash flow component. Investors

appear to overweight the pre-impairment accruals (0.919 vs. 0.664), but this difference

is not statistically significant. Investors appear to overweight the persistence of

impairment write-offs (0.084 vs. 2.133). The likelihood ratio statistics strongly reject

the null hypothesis of rational pricing of impairment write-offs (p<0.001).



(Insert Table 4 about here)



   Panel B of Table 4 provides the results for regression Eq. (6). This regression

decomposes pre-impairment accruals into nondiscretionary and discretionary accruals.

The forecasting coefficient on nondiscretionary (discretionary) accruals is 0.927

(0.581). The valuation coefficient on nondiscretionary accruals is 0.871, while the

valuation coefficient on discretionary accruals is 0.934. The results indicate that
                                                                                              19



investors correctly weight nondiscretionary accruals, but overweight discretionary

accruals. However, the untabulated likelihood ratio statistic of 0.03 (marginal

significance level = 0.859) suggest that investors do not distinguish these two

components. Finally, the null hypothesis that the market rationally prices all four

earnings components is rejected (p<0.001).



4.4. Market perceptions



   The objective of this section is to provide further insights into why the market

appears to overprice impairment write-offs. Table 5 provides various measures of firm

characteristics across samples formed by the magnitude of impairment write-offs

relative to average total assets. We evaluate the financial conditions of firms by using

Altman’s (1968) Z-score model. Altman’s Z score is computed as:



    Z = 1 .2 X 1 + 1 .4 X 2 + 3 .3 X 3 + 0 .6 X 4 + 1 .0 X 5                           (7)



where X1 is the working capital/total assets, X2 is the retained earnings/total assets, X3

is the earnings before interest and taxes/total assets, X4 is the market value of equity at

previous year end/book value of total liabilities, and X5 is sales/total assets. Altman

(1968, 607) renders that the Z score of 2.675 discriminates best between bankrupt and

non-bankrupt firms.

   Panel A of Table 5 confirm that earnings, cash flows, accruals, asset growth, and

sales growth declines systematically with the relative magnitude of impairment write-

offs. Although discretionary accruals become more negative when the relative
                                                                                              20



magnitude of impairments increases, it is difficult to conclude that those firms are

taking a big bath. Impairment write-off firms report a much higher percentage of losses

and restructuring charges than other firms. Low Altman’s Z-scores for impairment

write-off firms indicates that those firms are more likely in financial distress. These

results indicate that impairment firms have performed very poorly during the fiscal year

in which they report the impairment write-off.

   Panel B of Table 5 indicates that impairment firms have poorer market performance

than other firms. This is in the spirit of Basu (1997). For example, firms that have

impairment write-offs greater than 1% average total assets have size-adjusted returns of

–9.4% versus 2.5% for no impairment firms. Analyst coverage is correlated with firm

size and it is highest for impairment sample (70.3%). Analysts’ median EPS forecast

errors indicate that no impairment firms have higher earnings than analysts expect,

while impairment firms perform worse than analysts expect. Analysts may exclude

impairment write-offs from their forecasts of Street earnings (Bradshaw and Sloan

2002, 46). Therefore, we also report analysts’ sales forecast errors because, unlike

earnings, sales are not affected by impairment write-offs. Results also indicate that sales

forecasts are too optimistic for impairment firms. The poor performance of impairment

firms appears to be a surprise for analysts.



(Insert Table 5 about here)



   Table 5 Panel C reports the stock price performance and investor recognition in

period t+1. The size-adjusted return of impairment firms is 7.8% whereas for no

impairment firms the return is 1.3%. A similar finding holds in other impairment
                                                                                                 21



subsamples. Note that impairment write-offs are accounting entries and do not require

any action to be taken by management to improve performance. However, a positive

size-adjusted return suggests that management is taking action to turn the firm around

and the market rewards these actions. Investors appear to believe that poor performance

of impairment firms will continue because realized earnings outperform analysts’

forecasts. Increase in analyst coverage (change in number of EPS/sales forecasts) is

largest for no impairment firms and lowest for the 5% subsample. This result suggests

that an analyst response is correlated with firm performance.

    Finally, Panel D compares selected values that are statistically different between

firms that report impairment write-offs and firms that have no impairments. All

performance measures confirm that impairment firms have performed significantly

worse than firms without impairments. Results on the market show that analysts’

median sales forecast error in year t and EPS forecast error in year t+1 is significantly

different between the samples. Change in the number of EPS forecasts in year t + 1 is

significantly smaller for impairment firms. For impairment firms the mean size-adjusted

return is not statistically smaller in period t, but it is significantly higher in period t+1.

    Table 5 suggests that firms that recognize impairments differ from other firms.

Table 6 investigates whether impairments predict future returns after controlling for

other potential determinants of future returns. We include the level of total accruals as a

control variable because Sloan (1996) shows that there is a negative relation between

accruals and future returns.

    The results indicate that impairments continue to explain future returns after

controlling for other variables. Size (measured as the natural logarithm of the fiscal

year-end market value of equity) is negative and significant suggesting that smaller
                                                                                            22



firms earn higher future returns. There is a negative relation between total accruals and

future size-adjusted returns. Book-to-market ratio is significantly negative, and this

suggests that value stocks perform slightly worse in the future. There is a negative

relation between current size-adjusted returns and future size-adjusted returns. The

analyst EPS forecast indicator variable is positive suggesting that firms followed by

analyst earn higher future returns. These results demonstrate that predictive ability of

impairments is not subsumed by these other variables.

   In summary, impairment firms have performed poorly, have higher bankruptcy risk,

and earn negative size-adjusted stock returns in the year that an impairment write-off is

reported. Analysts give pessimistic earnings forecasts for impairment firms suggesting

that they believe that poor performance will continue. Investors seem to believe that

impairment firms are more risky or misunderstand the nature of impairments and,

therefore, impairment firms earn higher returns than other firms.



4.5. Predictive ability of impairments



   Barth, Cram, and Nelson (2001) show that different accrual components reflect

different information relating to future cash flows. If impairments incorporate into

current earnings information about changes in expected future cash flows, they should

improve the ability of earnings (and its cash flow and accruals components) to predict

future cash flows. By definition an impairment loss must be recognized if the carrying

amount of a long-lived asset is not recoverable from its undiscounted cash flows. We

next investigate whether cash flows and accruals contain incremental information for

impairment write-off firms about the next period’s cash flows. We adopt a similar
                                                                                                  23



regression than Ball and Shivakumar (2006, 236) to test the predictive ability of

impairments. In particular, if management recognizes an impairment loss in a timely

manner, we expect that this specification conveys information about future cash flows.

Table 7 provides the following regression:



    Cash flowst + j = α + δ1Cash flowst − 1 + δ2Accrualst − 1 + δ 3Cash flowst + δ 4 Accrualst

                      + δ 5 DIt + δ 6Cash flowst × DIt + δ 7 Accrualst × DIt + εt +   j     (8)

                       j = 1 to 3.



Cash flowst–1 and Accrualst–1 control for expected cash flows at the beginning of year t.

DIt is a dummy variable equal to one when the magnitude of impairments is greater than

1% average total assets, zero otherwise. Variables are standardized by average total

assets in period t. If impairments incorporate information about expected future cash

flow changes, the specification in Eq. (8) will better predict future cash flow. The

incremental coefficients, δ6 and δ7, during impairment years will be negative, because

recognition of impairment will incorporate a multiperiod cash flow effect, not simply

current-year effects. We offer no predictions for δ5 in Eq. (8).



(Insert Table 7 about here)



   The results in Panel A of Table 7 indicate that the incremental coefficient δ6 on

current year cash flows during impairment years is negative and significant for each of

the three future-year cash flow. The accruals in impairment-recognition years

incorporate incremental cash flow effects for year t+1 and t+2. The negative coefficient
                                                                                             24



on the interaction terms indicates that firms with impairment suffer a multiperiod

reduction in cash flows. This is consistent with impairments incorporating information

about expected future cash flow changes.

   Elliot and Shaw (1988) point out that, “In general, economic events precede

accounting recognition; an event occurs and then it is disclosed. For material write-offs,

this sequence implies that assets suffer an impairment of value, management realizes

that impairment, and then an accounting entry is created to record the impairment.”

Hayn and Hughes (2006) find that goodwill write-offs lag behind the economic

impairment of goodwill by an average of three to four years. To examine conjecture that

firms delay disclosing asset impairment issue we re-estimate Eq. (8). We give dummy

variable value of one when the magnitude of impairments is greater than 1% average

total assets for period t+1 but zero for period t. Our untabulated results do not support

the view that firms delay recognition of impairment write-offs. However, this result

should be interpreted with caution and we leave this issue for future research.



5. Conclusions



   Our research relates closely to two streams of accounting research. The first stream

of research is based on Sloan (1996). Sloan (1996) shows that stock prices do not fully

reflect information about future earnings in accruals and cash flows. He finds that

investors appear to overweight (underweight) total accruals (cash flows) when forming

future earnings expectations. The second stream of research is based on Basu (1997).

He shows that earnings report publicly available bad news about future cash flows more
                                                                                         25



timely than good news. Basu (1997, 16) argues that accruals incorporating write-offs

are more likely to reflect conservatism than others.

    This paper examines whether stock prices reflect the one-year-ahead earnings

implications of impairment write-offs, which is one of the major accrual components

through which conservative accounting is facilitated. Recognition of impairment write-

offs indicates that the carrying amount of the asset have exceeded its recoverable

amount. The remaining accruals we decompose into nondiscretionary and discretionary

accruals using the piecewise linear Jones (1991) model, modified by Ball and

Shivakumar (2006). We show that impairments and discretionary accruals are less

persistent than other accruals, which, in turn, are less persistent than cash flow

components or earnings. The market appears to recognize the persistence of cash flows

but overestimate the persistence of impairment write-offs. Moreover, the market is

fairly sophisticated in pricing the one-year-ahead earnings implications of the

nondiscretionary accruals but overweight discretionary accruals. Although investors

appear to misprice impairment write-offs, we do not claim that conservative accounting

is the reason for this.

    We report that impairment write-offs are very common for U.S. corporations,

appearing in 25.5% of all firm-year observations over the period 2002 to 2005. We

document that reporting an impairment write-off is strongly linked to firm size. The

frequency on incurring impairment write-offs in a given year is 15.8% for the smallest

firms, compared with 41.9% for the largest firms. Firms that report impairment write-

offs have performed very poorly. The results show that impairment firms have

significantly lower stock returns, lower asset growth, lower sales growth, higher

bankruptcy risk, and a greater percentage of firms reporting losses and restructuring
                                                                                            26



expenses. The average change in analyst coverage is lowest for firms that report large

impairment write-offs. This result suggests that analysts believe that poor performance

of impairment firms will continue. We document that firms with impairments have

higher future stock returns than other firms. This is consistent with the conjecture that

investors misunderstand the nature of impairments and the actions that the management

is taking to turn the firm around. Finally, empirical results reveal that cash flows and

accruals in impairment write-off firms contain incremental information about future

cash flows.

   Our conclusions are subject to a number if limitations. First, our findings should be

interpreted with caution because our sample period is relatively short. Second, we focus

on the overall impairment write-offs and do not examine its components. Our study

addresses the average behavior of impairment write-offs. For example, while both

goodwill write-offs and PPE write-offs indicate that the carrying amount of the asset

have exceeded its recoverable amount, their implications for future performance may be

different. This raises questions for future research.
                                                                                          27



References



Alciatore, M., Dee, C. C., Easton, P., & Spear, N. (1998). Asset write-downs: a decade

   of research. Journal of Accounting Literature 17, 1–39.

Altman, E. I. (1968). Financial ratios, discriminant analysis, and the prediction of

   corporate bankruptcy. The Journal of Finance 23(4), 589–609.

Ball, R. & Shivakumar, L. (2006). The role of accruals in asymmetrically timely gain

   and loss recognition. Journal of Accounting Research 44(2), 207–242.

Barth, M. E., Cram, D. P. & Nelson, K. K. (2001). Accruals and the prediction of future

   cash flows. The Accounting Review 76(1), 27–58.

Basu, S. (1997). The conservatism principle and the asymmetric timeliness of earnings.

   Journal of Accounting and Economics 24, 3–37.

Beatty, A. & Weber, J. (2006). Accounting discretion in fair value estimates: an

   examination of SFAS 142 goodwill impairments. Journal of Accounting Research

   44(2), 257–288.

Bernard, V. L. & Thomas, J. K. (1990). Evidence that stock prices do not fully reflect

   the implications of current earnings for future earnings. Journal of Accounting and

   Economics 13, 305–340.

Bradshaw, M. T. & Sloan, R. G. (2002). GAAP versus the Street: an empirical

   assessment of two alternative definitions of earnings. Journal of Accounting

   Research 40(1), 41–66.

Burgstahler, D., Jiambalvo, J., & Shevlin, T. (2002). Do stock prices fully reflect the

   implications of special items for future earnings? Journal of Accounting Research

   40(3), 585–612.
                                                                                            28



Dechow, P. M. (1994). Accounting earnings and cash flows as measures of firm

   performance: the role of accounting earnings. Journal of Accounting and Economics

   18, 2–42.

Dechow, P. M. & Ge, W. (2006). The persistence of earnings and cash flows and the

   role of special items: implications for the accrual anomaly. Review of Accounting

   Studies 11(2/3), 297–303.

Dechow, P. M. & Skinner, D. J. (2000). Earnings management: reconciling the views of

   accounting academics, practitioners, and regulators. Accounting Horizons 14(2),

   235–250.

Dechow, P. M., Sloan, R. G. and Sweeney, A. P. (1995). Detecting earnings

   management. The Accounting Review 70(2), 193–225.

Elliot, J. A. & Hanna, J. D. (1996). Repeated accounting write-offs and the information

   content of earnings. Journal of Accounting Research 34(supplement), 135–155.

Elliot, J. A. & Shaw, W. H. (1988). Write-offs as accounting procedures to manage

   perceptions. Journal of Accounting Research 26(supplement), 91–119.

Fairfield, P. M. (2006). Discussion of “The persistence of earnings and cash flows and

   the role of special items: implications for the accrual anomaly.” Review of

   Accounting Studies 11(2/3), 297–303.

Fairfield, P. M., Whisenant, J. S., & Yohn, T. L. (2003). The differential persistence of

   accruals and cash flows for future operating income versus future profitability.

   Review of Accounting Studies 8, 221–243.

Fields, T. D., Lys, T. Z. & Vincent, L. (2001). Empirical research on accounting choice.

   Journal of Accounting and Economics 31, 255–307.
                                                                                             29



Financial Accounting Standards Board (2002). Statement of financial accounting

   concepts No. 142: goodwill and other intangible assets. Norwalk, CT: FASB.

Financial Accounting Standards Board (2002). Statement of financial accounting

   concepts No. 144: accounting for the impairment or disposal of long-lived assets.

   Norwalk, CT: FASB.

Francis, J., Hanna, J. D., & Vincent, L. (1996). Causes and effects of discretionary asset

   write-offs. Journal of Accounting Research 34(supplement), 117–134.

Hayn, C. & Hughes, P. (2006). Leading indicators of goodwill impairment. Journal of

   Accounting, Auditing and Finance (forthcoming).

Healy, P. M. & Wahlen, J. M. (1999). A review of the earnings management literature

   and its implications for standard setting. Accounting Horizons 13(4), 365–383.

Hribar, P. & Collins, D. W. (2002). Errors in estimating accruals: implications for

   empirical research 40(1), 105–134. Journal of Accounting Research 40(1), 105–134.

Jones, J. (1991). Earnings management during import relief investigations. Journal of

   Accounting Research 29, 193–228.

Kinney, M. & Trezevant, R. (1997). The use of special items to manage earnings and

   perceptions. Journal of Financial Statement Analysis 3(1), 45–53.

Kothari, S. P., Leone, A. J., & Wasley, C. E. (2005). Performance matched

   discretionary accrual measures. Journal of Accounting and Economics 39, 163–197.

Mishkin, F. (1983). A Rational Expectations Approach to Macroeconomics: Testing

   Policy Effectiveness and Efficient-Market Models. Chicago, IL: University of

   Chicago Press.
                                                                                            30



Rees, L., Gill, S., & Gore, R. (1996). An investigation of asset write-downs and

   concurrent abnormal accruals. Journal of Accounting Research 34(supplement),

   157–169.

Richardson, S. A., Sloan, R. G., Soliman, M. T., & Tuna, I. (2005). Accrual reliability,

   earnings persistence and stock prices. Journal of Accounting and Economics 39,

   437–485.

Riedl, E. J. (2004). An examination of long-lived asset impairments. The Accounting

   Review 79(3), 823–852.

Sloan, R. G. (1996). Do stock prices fully reflect information in accruals and cash flows

   about future earnings? The Accounting Review 71(3), 289–315.

Strong, J. S. & Meyer, J. R. (1987). Asset write-downs: managerial incentives and

   security returns. Journal of Finance 42(3), 643 – 661.

Watts, R. L. (2003a). Conservatism in accounting part I: explanations and implications.

   Accounting Horizons 17(3), 207–221.

Watts, R. L. (2003b). Conservatism in accounting part II: evidence and research

   opportunities. Accounting Horizons 17(3), 287–301.

White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a

   direct test for heteroskedasticity. Econometrica 48(4), 817–838.

Xie, H. (2001). The pricing of abnormal accruals. The Accounting Review 76(3), 357–

   373.
                                                                                                                                31



Table 1
Summary statistics for 13,164 firm-year observations for the period 2002 to 2005.
Panel A: Descriptive statistics
                                              Full Sample (N=13,164)                   Total Impairments ≠ 0 Sample (N=3,360)
Variable                          Mean      Std dev   25%       Median    75%       Mean      Std dev    25%        Median      75%
Earnings                          –0.064     0.271    –0.089     0.025     0.072    –0.117     0.292     –0.173     –0.006           0.049
Pre–impairment earnings           –0.052     0.257    –0.071     0.028     0.074    –0.069     0.248     –0.106      0.014           0.060
Total accruals                    –0.087     0.151    –0.115    –0.060    –0.020    –0.133     0.182     –0.159     –0.083      –0.039
Pre–impairment accruals           –0.075     0.136    –0.106    –0.055    –0.017    –0.086     0.139     –0.118     –0.061      –0.025
Cash flows                         0.023     0.201    –0.016     0.069     0.131     0.017     0.186     –0.025      0.058           0.119
Total impairments                 –0.012     0.059    –0.000     0.000     0.000    –0.048     0.109     –0.038     –0.009      –0.002
NACC                              –0.075     0.069    –0.102    –0.069    –0.041    –0.077     0.067     –0.101     –0.070      –0.043
DACC                               0.000     0.118    –0.025     0.008     0.042    –0.009     0.122     –0.033      0.002           0.034
                                                                                                                                         32



Table 1 continued
Panel B Pearson (above diagonal) and Spearman (below diagonal) correlations
                                                                              Pre-
                                                              Total           impairment                                       Total
                             Earnings         Cash flows      accruals        accruals         NACC            DACC            impairments
Earnings                                       0.835           0.681           0.612            0.461           0.440           0.329
Cash flows                    0.760                            0.166            0.133           0.264           0.000            0.118
Total accruals                0.384           –0.160                            0.921           0.475           0.789            0.433
Pre-impairment accruals       0.325           –0.203           0.959                            0.504           0.864            0.047
NACC                          0.156           –0.128           0.505            0.518                           0.000            0.051
DACC                          0.328           –0.074           0.686            0.717          –0.096                            0.024
Total impairments             0.209            0.080           0.225            0.081           0.025           0.078

All correlations are significant at <0.01 level except for the Pearson correlation between cash flows and NACC, and the Pearson correlation
between NACC and DACC. The full sample consists of 13,164 firm-year observations from 2002 to 2005. The subsample where total
impairments ≠ 0 consist of 3,360 observations. The variables are defined as follows: Earnings is earnings before extraordinary items. Pre-
impairment earnings is earnings minus total impairments. Cash flows is cash flow from operations. Total accruals is earnings before
extraordinary items minus cash flow from operations. Pre-impairment accruals is total accruals minus total impairments. Total impairments
equals the sum of impairment of fixed assets, impairment of goodwill, impairment of other intangibles, and impairment of PPE. NACC is
nondiscretionary accruals and it is the fitted value from the Eq. (1). DACC is discretionary accruals and it is the residual from the Eq. (1).
All above variables are scaled by average total assets
                                                                                  33



Table 2
Frequency of impairments by portfolios ordered by firm size, where firm size is
measured as total assets at the end of previous fiscal year-end

                     Number of         Number of           % of impairment
Portfolio            firm-years        impairment years    years
All portfolios       13,164            3,360               25.5
1 (smallest firms)   1,314             208                 15.8
2                    1,317             212                 16.1
3                    1,316             246                 18.7
4                    1,317             288                 21.9
5                    1,316             305                 23.2
6                    1,318             330                 25.0
7                    1,317             375                 28.5
8                    1,316             409                 31.1
9                    1,317             436                 33.1
10 (largest firms)   1,316             551                 41.9
                                                                                                                        34



                                      (a)

                               45 %

                               40 %
    Frequency of Impairments




                               35 %

                               30 %

                               25 %

                               20 %

                               15 %

                               10 %

                               5%

                               0%
                                            1   2      3       4         5       6          7   8      9      10
                                                                   Rank of Total Accruals
                                Frequency of Impairments greater than 5%     Frequency of Impairments greater than 2%
                                Frequency of Impairments greater than 1%     Frequency of Impairments greater than 0%


                                      (b)

                               35 %

                               30 %
    Frequency of Impairments




                               25 %

                               20 %

                               15 %

                               10 %

                               5%

                               0%
                                            1   2      3       4         5       6          7   8      9      10
                                                           Rank of Pre-impairment Accruals
                                Frequency of Impairments greater than 5%     Frequency of Impairments greater than 2%
                                Frequency of Impairments greater than 1%     Frequency of Impairments greater than 0%


Fig. 1 Frequency of impairments across accrual deciles (a) Based on ranks of total
accruals. (b) Based on pre-impairment accruals. The sample consist 13,164 firm-years
from 2002 to 2005. Firm-year observations are ranked annually and assigned in
ascending order to decile portfolios based on total accruals and pre-impairment accruals.
Decile 1 consists of firms with the most negative accruals. Decile 10 consists of firms
with the most positive accruals
                                                                                            35



Table 3
Results from ordinary least squares regressions of future earnings on cash flows,
accruals, and impairments for 13,164 firm-year observations for the period 2002 to
2005

                                             One-year-ahead earnings
Variable                       (2)          (3)         (4)          (5)          (6)
Intercept                      0.009       0.001      –0.023       –0.022       –0.002
                              (5.15)      (0.40)      (–6.05)      (–5.61)      (–0.28)
Earnings                       0.886       0.982
                             (30.76)     (26.58)
DI                                         0.022
                                          (5.81)
Earnings × DI                            –0.286
                                         (–4.98)
Cash flow                                               1.081        1.089        1.066
                                                      (44.17)      (44.44)      (42.05)
Total accruals                                          0.568
                                                      (10.42)
Pre-impairment accruals                                              0.664
                                                                   (10.03)
Impairments                                                          0.084        0.082
                                                                    (0.90)       (0.87)
NACC                                                                              0.928
                                                                                (10.63)
DACC                                                                              0.581
                                                                                 (6.76)
Adjusted R2(%)                38.77        39.92       40.94        41.57        41.84
Figures in parenthesis denote t-statistics based on the heteroskedasticity-consistent
covariance matrix (White, 1980). A t-statistic of 2.58 implies a significance level of
0.01 using a two-tailed test. A t-statistic of 1.96 implies a significance level of 0.05
using a two-tailed test.

Earnings is earnings before extraordinary items. Cash flows is cash flow from
operations. Total accruals is earnings before extraordinary items minus cash flow from
operations. Impairments equals the sum of the impairment of fixed assets, impairment
of goodwill, impairment of other intangibles, and impairment of PPE. Pre-impairment
earnings is earnings minus impairments. Pre-impairment accruals is total accruals minus
impairments. NACC is nondiscretionary accruals and it is the fitted value from the Eq.
(1). DACC is discretionary accruals and it is the residual from the Eq.(1). All variables
are scaled by average total assets
                                                                                             36



Table 4
Nonlinear generalized least squares estimation (the Mishkin test) of the market pricing
of cash flows, nondiscretionary accruals, discretionary accruals, and impairments with
their respect to their implications for one-year-ahead earnings

Panel A: Earningst+1 = α + δ1Cash flowst + δ2Pre-impairment accrualst
+ δ3Impairmentst + εt+1
Size-adjusted returnt+1 = β(Earningst+1 – α – δ*1Cash flowst
– δ*2Pre-impairment accrualst – δ*3Impairmentst) + εt+1
Forecasting coefficients                       Valuation coefficients
Parameter Coefficient estimate (t-statistic) Parameter Coefficient estimate (t-statistic)
δ1             1.089 (84.00)                   δ*1          0.907 (8.83)
δ2             0.664 (34.88)                   δ*2          0.919 (6.09)
δ3             0.084 (1.92)                    δ*3          2.133 (5.73)
                                               β            0.521 (14.58)
Test of market efficiency
Null hypothesis                Likelihood ratio statistic      Marginal significance level
δ1 = δ*1                        3.15                            0.076
δ2 = δ*2                        2.85                            0.091
δ3 = δ*3                       34.75                           <0.001
All δ* = δ*                    11.11                            0.004
All δj = δ*j                   39.02                           <0.001
j = 1 to 3
                                                                                             37



Table 4 continued
Panel B: Earningst+1 = α + δ1Cash flowst + δ2NACCt + δ3DACCt + δ4Impairmentst + εt+1
Size-adjusted returnt+1 = β(Earningst+1 – α – δ*1Cash flowst – δ*2NACCt – δ*3DACCt
– δ*4Impairmentst) + εt+1
Forecasting coefficients                       Valuation coefficients
Parameter Coefficient estimate (t-statistic) Parameter Coefficient estimate (t-statistic)
δ1             1.065 (80.20)                   δ*1          0.911 (8.65)
δ2             0.927 (23.97)                   δ*2          0.871 (2.85)
δ3             0.581 (26.63)                   δ*3          0.934 (5.38)
δ4             0.082 (1.88)                    δ*4          2.135 (5.72)
                                               β            0.521 (14.54)
Test of market efficiency
Null hypothesis                Likelihood ratio statistic      Marginal significance level
δ1 = δ*1                        2.14                            0.144
δ2 = δ*2                        0.03                            0.856
δ3 = δ*3                        4.18                            0.041
δ4 = δ*4                       34.83                           <0.001
All δ* = δ*                    11.13                            0.011
All δj = δ*j                   40.37                           <0.001
j = 1 to 3

Earnings is earnings before extraordinary items. Cash flows is cash flow from
operations. Total accruals is earnings before extraordinary items minus cash flow from
operations. Impairments equals the sum of impairment of fixed assets, impairment of
goodwill, impairment of other intangibles, and impairment of PPE. Pre-impairment
accruals is total accruals minus impairments. Pre-impairment accruals is total accruals
minus impairments. NACC is nondiscretionary accruals and it is the fitted value from
Eq. (1). DACC is discretionary accruals and it is the residual from Eq. (1). All above
variables are scaled by average total assets

Annual returns are calculated from the start of the fourth month subsequent to the fiscal
year-end. The size-adjusted return is calculated by deducting the value-weighted
average return for firms in the same size-matched decile, where size is measured as the
market value at the beginning of return cumulation period.
                                                                                                                                       38



Table 5
Descriptive statistics for samples of firms formed by the magnitude of impairment write-offs relative to average total assets.
Panel A: Values of select characteristics
                                         Full Sample         IM = 0          IM ≠ 0           IM > 1%         IM > 2%            IM > 5%
                                         (N=13,164)          (N=9,804)       (N=3,360)        (N=1,602)       (N=1,165)          (N=728)
Earnings (mean)                          –0.064              –0.046          –0.117           –0.246          –0.304             –0.400
Cash flows (mean)                         0.023               0.025           0.017           –0.039          –0.060             –0.085
Total accruals (mean)                    –0.087              –0.072          –0.133           –0.207          –0.244             –0.316
NACC (mean)                              –0.075              –0.075          –0.077           –0.087          –0.091             –0.099
DACC (mean)                               0.000               0.003          –0.009           –0.023          –0.025             –0.032
Asset growth (median)                     0.049               0.066          –0.003           –0.089          –0.137             –0.206
Sales growth (median)                     0.084               0.097           0.049            0.006          –0.008             –0.023
Percentage of loss firms (%)              38.7                34.1            52.0             77.0            85.3               93.5
Percent having restructuring expense (%) 18.4                 14.8            28.9             30.5            31.4               31.9
Altman’s Z-score (median)                 3.00                3.28            2.29             1.36            0.88               0.22
Total assetst–1 (million $) (median)      200.1               153.0           408.9            170.0           131.6              119.4
Book to market (median)                   0.438               0.425           0.470            0.481           0.479              0.492

Panel B: Stock price performance and investor recognition in period t
                                          Full Sample     IM = 0             IM ≠ 0           IM > 1%         IM > 2%            IM > 5%
                                          (N=13,164)      (N=9,804)          (N=3,360)        (N=1,602)       (N=1,165)          (N=728)
Return (mean)                              0.311            0.332             0.249            0.232           0.240              0.256
Size-adjusted return (mean)                0.018            0.025            –0.003           –0.094          –0.091             –0.086
Percent having EPS forecast (%)            62.6             59.9              70.3             57.1            54.2               51.8
Analyst EPS forecast error (median)        0.005            0.008            –0.005           –0.037          –0.042             –0.036
No. of EPS forecasts (median)              5                5                 7                5               5                  5
Percent having sales forecast (%)          57.2             54.6              64.8             52.3            49.4               48.1
Analyst sales forecast error (median)      0.004            0.010            –0.008           –0.041          –0.050             –0.072
No. of sales forecasts (median)            4                3                 4                4               4                  4
                                                                                                                                  39



Table 5 continued
Panel C: Stock price performance and investor recognition in period t+1
                                          Full Sample     IM = 0           IM ≠ 0           IM > 1%            IM > 2%      IM > 5%
                                          (N=13,164)      (N=9,804)        (N=3,360)        (N=1,602)          (N=1,165)    (N=728)
Return (mean)                              0.387            0.367           0.445            0.609              0.670        0.714
Size-adjusted return (mean)                0.029            0.013           0.078            0.118              0.135        0.142
Percent having EPS forecast (%)            64.5             62.7            69.6             54.2               50.6         48.1
Analyst EPS forecast error (median)        0.013            0.010           0.025            0.035              0.008        0.037
Change in no. of EPS forecasts (%)         19.6             22.6            12.2             3.6                1.0         –6.7
Percent having sales forecast (%)          61.2             59.5            66.0             51.4               47.9         44.6
Analyst sales forecast error (median)      0.010            0.011           0.007            0.001             –0.003       –0.008
Change in no. of sales forecasts (%)       43.6             45.7            38.5             26.3               23.5         16.5

Panel D: Descriptive statistics for firms without impairment write-offs and firms with impairment write-offs
                                             IM = 0                 IM ≠ 0
Independent variable                         (N=9,804)              (N=3,360)               Difference               (p-value)
DACC (mean)a                                  0.003                 –0.009                   0.012                   (<0.001)
                        b
Asset growth (median)                         0.066                 –0.003                   0.069                   (<0.001)
Sales growth (median)b                        0.097                   0.049                  0.048                   (<0.001)
                              c
Percentage of loss firms (%)                  34.1                    52.0                  –17.9                    (<0.001)
Percent having restructuring expense (%)c 14.8                        28.9                  –14.1                    (<0.001)
Altman’s Z-score (median)b                    3.28                    2.29                   0.99                    (<0.001)
Analyst EPS forecast errort (median)b         0.008                 –0.005                   0.013                   (0.146)
Analyst sales forecast errort (median)b       0.010                 –0.008                   0.018                   (<0.001)
                                        b
Analyst EPS forecast errort+1 (median)        0.010                   0.025                 –0.015                   (<0.001)
Analyst sales forecast errort+1 (median)b     0.011                   0.007                  0.004                   (0.406)
                                     a
Change in no. of EPS forecasts (%)            22.6                    12.2                   10.4                    (<0.001)
Change in no. of sales forecasts (%)a         45.7                    38.5                   7.2                     (0.007)
                              a
Size-adjusted returnt (mean)                  0.024                 –0.003                   0.027                   (0.465)
Size-adjusted returnt+1 (mean)a               0.013                   0.078                 –0.065                   (0.008)
                                                                                                                                           40




a
 Reported numbers are means and p-value is based on t-test.
b
  Reported numbers are medians and p-value is based on Wilcoxon signed-rank test.
c
 Reported numbers are means and p-value is based on chi-square test.

The full sample covers 13,164 firm-year observations for the period 2002 to 2005. Subsamples are formed based on the relative magnitude
of impairments to average total assets. Earnings is earnings before extraordinary items. Cash flows is cash flow from operations. Total
accruals is earnings before extraordinary items minus cash flow from operations. NACC is non-discretionary accruals and it is the fitted
value from Eq. (1). DACC is discretionary accruals and it is the residual from Eq. (1). Asset growth is calculated as (total assetst – total
assetst–1)/total assetst–1. Sales growth is calculated as (salest – salest–1)/salest–1. Percent of loss firms is the percentage of firm-years that
have negative earnings before extraordinary items. Percent having restructuring expense is the percentage of firm-years that have reported
restructuring expenses. Altman’s Z-score is calculated as Z = 1.2 (working capital/total assets) + 1.4 (retained earnings/total assets) + 3.3
(earnings before interest and taxes/total assets) + 0.6 (market value of equity at the end of previous year/total liabilities) + 1.0 (sales/total
assets). Total assetst–1 is the total assets at year t–1. Book to market is the ratio of the book value of equity to the market value of equity at
the end of fiscal year t. Percent having EPS/sales forecast refers to the percentage of firm-year observations where there is an analyst
EPS/sales forecasting during the fourth month after the fiscal year-end of year t in IBES. Analyst EPS forecast error equals (Realized EPSt
– Analyst EPS forecastt)/Realized EPSt–1 obtained from IBES. Analyst sales forecast error equals (Realized salest – Analyst sales
forecastt)/Realized salest–1 obtained from IBES. No. of EPS/sales forecasts is the number of analysts that issue EPS/sales forecast during
the eighth month before the fiscal year-end of year t. Change in no. of EPS/sales forecast is the percentage change in the number of analysts
that issue EPS/sales forecast, which is calculated as (No. of EPS/sales forecasts at year t+1 – No. of EPS/sales forecasts at year t)/No. of
EPS/sales forecasts at year t.

Annual returns are calculated from the start of the eighth month before the fiscal year-end. The size-adjusted return is calculated by
deducting the value-weighted average return for firms in the same size-matched decile, where size is measured as the market value at the
beginning of the return cumulation period.
                                                                                            41



Table 6
Future size-adjusted returns on impairment write-offs and other predictors of returns
Variable                                     One-year-ahead size-adjusted return
Intercept                                                       0.545
                                                                (4.83)
Impairment write-offs indicator                                 0.063
                                                                (2.54)
Size                                                           –0.031
                                                               (–4.81)
Total accruals                                                 –0.149
                                                               (–2.03)
Size-adjusted return from t –1 to t                            –0.029
                                                               (–3.75)
EPS forecast indicator                                          0.088
                                                                (2.88)
∆EPS forecasts × EPS forecast indicator                        –0.022
                                                               (–1.12)
Book to market                                                 –0.003
                                                               (–3.03)
No. of obs.                                                    13,081
Adj. R2 (%)                                                      0.44
The t-statistics are in the parentheses.

Impairment write-off indicator is an indicator variable, taking the value of 1 when the
firm reports impairment write-offs. Size is the natural logarithm of the market value of
equity measured at fiscal year-end. Total accruals is earnings before extraordinary items
minus cash flow from operations. EPS forecast indicator is an indicator variable, taking
the value of 1 when there is an analyst EPS forecast during the fourth month after the
fiscal year-end of year t in IBES. ∆EPS forecasts is the percentage change in the
number of analysts that issue EPS forecast, which is calculated as (No. of EPS forecasts
at year t+1 – No. of EPS forecasts at year t)/No. of EPS forecasts at year t. Book to
market is the ratio of the book value of equity to the market value at the end of fiscal
year t.

Annual returns are calculated from the start of the fourth month subsequent to the fiscal
year-end. The size-adjusted return is calculated by deducting the value-weighted
average return for firms in the same size-matched decile, where size is measured as the
market value at the beginning of the return cumulation period.
                                                                                           42



Table 7
The ability of impairments to predict future cash flows for the period 2002 to 2005.

Panel A
                       Cash flowst+1           Cash flowst+2          Cash flowst+3
Variable                I           II          I          II           I          II
Intercept              0.031      0.031        0.048       0.050       0.061      0.061
                     (13.66)    (12.37)      (12.43)     (11.58)      (8.01)     (7.23)
Cash flowt–1           0.005      0.003        0.005       0.004     –0.002     –0.004
                      (0.21)     (0.13)       (0.10)      (0.07)     (–0.02)    (–0.05)
Accrualst–1          –0.011     –0.009        –0.016     –0.014      –0.017     –0.017
                     (–1.38)    (–1.24)      (–1.04)     (–0.88)     (–1.13)    (–1.05)
Cash flowt            0.896       0.933        0.966       1.019       1.077      1.135
                     (35.51)    (36.84)      (17.66)     (18.84)     (10.69)    (10.89)
Accrualst             0.206       0.268        0.306       0.410       0.275      0.367
                      (8.55)     (8.04)       (7.03)      (7.05)      (4.55)     (4.62)
DIt                               0.009                    0.004                  0.012
                                 (1.20)                   (0.32)                 (0.58)
Cash flowt × DIt                –0.228                   –0.303                 –0.278
                                (–5.80)                  (–4.22)                (–2.34)
Accrualst × DIt                 –0.107                   –0.194                 –0.137
                                (–2.30)                  (–2.55)                (–1.21)
No. of obs.           13,146    13,146         9,530      9,530       6,064      6,064
       2
Adj. R (%)             54.58     55.27        37.67       38.51       23.66      24.01
Figures in parenthesis denote t-statistics based on the heteroskedasticity-consistent
covariance matrix (White, 1980). A t-statistic of 2.58 implies a significance level of
0.01 using a two-tailed test. A t-statistic of 1.96 implies a significance level of 0.05
using a two-tailed test.

Cash flows is cash flow from operations. Accruals is earnings before extraordinary
items minus cash flow from operations. DI is a dummy variable (N=1,602), taking the
value of 1 when the magnitude of impairment is greater than or equal to the 1% average
total assets. Variables are standardized by average total assets in period t.

				
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