Conservatism measures that control for the effects of economic rents on stock returns

ORI GINAL RESEARCH

Conservatism measures that control for the effectsof economic rents on stock returns

Judson A. Caskey • Kyle Peterson

� Springer Science+Business Media New York 2013

Abstract Recent studies show that regression-based estimates of accounting conserva-

tism reflect both differences in the asymmetric recognition of bad news and differences in

asset composition. In particular, a firm’s market value and returns reflect both assets-

in-place and expected future rents, while book values tend to reflect only assets-in-place.

We propose two tests that remove the effect of asset composition on cross-sectional

comparisons of accounting conservatism. First, a test based on a ratio of regression

coefficients allows for valid cross-sectional comparisons of conservatism relative to overall

news recognition. Second, in some cases, researchers can separately identify and make

cross-sectional comparisons of the fraction of good news recognized and the fraction of

bad news recognized. The estimates in this second scenario use a regression of earnings on

returns interacted with a book-to-market ratio. We validate our model by deriving and

testing several predictions based on it.

Keywords Accounting conservatism � Asymmetric timeliness � Book-to-market ratio �Returns-earnings relation

JEL Classification M41 � G10 � G30 � N20

1 Introduction

A large and growing body of accounting research studies the causes and consequences of

the tendency of accounting earnings to recognize unrealized losses earlier than unrealized

gains (Watts 2003a, b). The accounting treatment of unrealized gains and losses depends

J. A. CaskeyMcCombs School of Business, University of Texas, Austin, 1 University Station,B6400, Austin, TX 78712, USAe-mail: [email protected]

K. Peterson (&)Lundquist College of Business, University of Oregon, Eugene, OR 97403, USAe-mail: [email protected]

123

Rev Quant Finan AccDOI 10.1007/s11156-013-0360-1

on the nature of assets to which they pertain. Overall firm value consists of both assets-

in-place and future economic rents such as profits that derive from customer loyalty or

corporate culture (Roychowdhury and Watts 2007). Accounting standards, both across

industries and across countries, differ in the extent to which accounting book values reflect

future rents, if at all, whereas returns reflect investor expectations about both components

of firm value. As a result, cross-sectional differences in estimates of accounting conser-

vatism reflect both differences in the composition of firm value and, holding constant the

extent to which balance sheets include future rents, differences in firms’ policies for

recognizing gains and losses.

Many studies estimate accounting conservatism using a regression of earnings on

returns (Basu 1997) in which the incremental coefficient on negative returns serves as a

proxy for conservatism (hereafter ‘AT coefficient’). This measure reflects the extent to

which book values recognize gains and losses on both assets-in-place and future rents. If

the objective of a study is to compare the overall relationship between book and market

values (e.g. across accounting standards regimes), the effect of asset composition on this

measure poses no problems. However, in studies that examine firm-specific patterns in

recognizing gains and losses (e.g. for debt contracting purposes), it is important to consider

whether the accounting regime allows for the recognition of future rents. If not, as is the

case with US GAAP, the AT coefficient does not allow researchers to separately identify

the effects of recognition policies and the effects of asset composition. In particular, a firm

could have a high (low) AT coefficient simply because a small (large) portion of its market

value is due to expected future economic rents.

For example, consider a firm that has separable assets with a resale value of $100 and a

book value, equaling the book value of equity, of $90. Now suppose that this firm expe-

riences a negative shock in the demand for its products that reduces the resale value of its

net assets to $85 and therefore records a $5 charge to earnings for the net impairment. If

this firm does not earn abnormal profits, its market value will be roughly equal to the

market value of its separable assets and will therefore experience a decline of $15 = $100–

$85. Thus, 1/3 = $5/$15 of the decline in the market value of equity will be recorded as a

charge to earnings.

Now consider the same example, except that the firm is able to earn abnormal profits so

that its market value includes an extra $20 representing the present value of the expected

future abnormal profits. If the negative demand shock also adversely impacts this com-

ponent of firm value by, say, $5, then total decline in market value is then $20, equal to the

$15 reduction in assets in place plus the $5 reduction in expected future abnormal profits.

The earnings charge remains the same because book values do not include expected future

profits. Thus, earnings include 1/4 = $5/$20 of the reduction in market value. A typical

regression-based measure of conservatism would identify the former firm as having more

conservative earnings even though the two firms have identical accounting policies. This

represents a form of mismeasurement to the extent that the research objective is to compare

recognition policies as opposed to the overall extent to which book values track overall

news that impacts market values.

In this study, we propose two conservatism estimates that mitigate the effects of asset

composition, allowing for cross-sectional comparisons based solely on firms’ recognition

policies. The first measure utilizes a ratio of regression coefficients from an earnings-

returns regression (hereafter ‘AT ratio’). This measure is based on the fact that asset

composition affects the recognition of both good and bad news. In order to address con-

cerns that the AT ratio may have poor econometric properties, we report confidence

intervals based on both the delta method and Fieller’s Theorem (Fieller 1954), which is a

J. A. Caskey, K. Peterson

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more robust means to determine confidence intervals for ratios of regression coefficients,

particularly in small samples. Our results suggest that, at least in large samples, the delta

method confidence intervals approximate the Fieller confidence intervals very well.

The second measure utilizes a regression of earnings on returns interacted with the

firm’s book-to-market ratio. The book-to-market ratio serves as a control for the fraction of

returns attributable to future rents rather than assets-in-place. The benefit of this measure is

that it allows researchers to determine whether one group of firms incorporates more bad

news in earnings than another, while the AT ratio can only compare the relative degree of

conservatism between firms (i.e. bad news recognition relative to good news recognition).1

A comparison of ratios is unable to quantify how much good or bad news about separable

assets is included in earnings (e.g. a firm’s earnings include X % of the value of negative

shocks to separable assets and Y % of the value of positive shocks).

For researchers that want to capture cross-sectional differences in the magnitude of bad

news recognition in earnings we suggest using the regression where returns are interacted

with the book-to-market ratio. This approach uses book value as a proxy for the market

value of assets-in-place, which potentially creates measurement error. In order to assess

whether the measurement error has a major impact on AT coefficient estimates, researchers

can utilize the fact that the bias also impacts an AT ratio computed from a regression of

earnings on returns interacted with the book-to-market ratio. If the AT ratio from a

regression with book-to-market interacted returns closely matches the AT ratio from a

regression without the interaction, then this provides evidence that bias has little impact on

the AT coefficients from the regression with book-to-market interacted returns, allowing

researchers to use these coefficients for cross-sectional comparisons. If the ratios are not

similar, researchers cannot obtain a direct measure of the fraction of bad news recognized,

but can obtain a relative comparison of bad news recognition using the AT ratio from the

regression without the interaction with book-to-market.

We analyze an econometric model of the relation between market and book values to

assess the validity of conservatism estimates.2 The firm’s book value incorporates the

recognized value of separable assets, while market value includes investors’ assessment of

the value of separable assets plus the value of future rents and growth opportunities.3

Within the context of our model, we show that regression tests of asymmetric timeliness

identify the presence of conservative accounting policies, but also reflect assets

composition.

Our econometric model implies that the effect of rents on returns dampens the asso-

ciation between earnings and returns to the extent that book values reflect only assets-

in-place, consistent with the intuition of Roychowdhury and Watts (2007). Thus, firms with

high book-to-market ratios, which presumably have a large portion of value and returns

related to assets-in-place, have higher AT coefficients. This is consistent with our tests

(significant at 1 %). In contrast, we find that high book-to-market firms have an AT ratio

that is statistically equivalent to the ratio of low book-to-market firms. In other words, the

difference in AT coefficients between high and low book-to-market firms appears to be

driven by differences in firms’ composition of assets-in-place versus future rents as

opposed to differences in accounting recognition policies.

1 See Guay and Verrecchia (2006) for a discussion of the importance of distinguishing between estimates ofa firm’s bad news recognition versus its recognition of bad news relative to good news.2 See the contemporaneous paper by Ball et al. (2009) for an alternative econometric model.3 This assumption relates to Roychowdhury and Watts’ (2007) development of Watts’ (2003a) frameworkin which accounting attempts to represent the value of a firm’s separable assets.

The effects of economic rents on stock returns

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We derive and test several implications of our econometric model in order to assess its

empirical validity. We predict and find that interacting returns with the book-to-market

ratio increases AT coefficients, which is due to both a reduction in the dampening effect

that future rents have on AT coefficients and bias related to unrecognized gains on assets-

in-place. We predict a positive bias on the AT ratio, as well, but the test of that prediction

is statistically insignificant. Roychowdhury and Watts (2007) state that the inclusion of

goodwill violates the assumption that book values only reflect separable assets. We predict

and verify that the inclusion of goodwill does bias AT coefficients upward in the sense that

acquisitions that create recognized goodwill can increase the AT coefficient vis-a-vis a firm

with identical accounting policies but that did not have acquisitions. We also predict a

similar effect on the AT ratio, but fail to find statistically significant evidence in support of

that prediction.

Many prior studies utilize ratio-based measures of conservatism, but without analytical

justification for the practice. In our examination of studies that cite Basu (1997), we found

eight that measure conservatism or describe results using a ratio of regression coefficients.4

Seven of these studies define the ratio using the coefficient on positive returns in the

denominator, which raises econometric concerns because the coefficient on positive returns

often lacks statistical significance yielding an unbounded confidence interval for the ratio.5

None of these seven studies assess the statistical significance of the ratio. Callen et al.

(2010) develop a ratio measure of conservatism in an alternative framework based on a

decomposition of returns into discount rate and cash flow news. Our approach of inter-

acting returns with book-to-market also differs from Khan and Watts (2009), who develop

a measure based on linear combinations of firm size, market-to-book and leverage that are

associated with conservatism.

Of the eight studies that use a ratio measure of conservatism, only Pope and Walker

(1999) discuss the use of a ratio to correct for biases in cross-sectional comparisons of AT

coefficients. They use a ratio to correct for cross-country differences in expected returns.

Our model is distinct from theirs in that we allow for the distinction between separable

assets and future rents and we allow for a more general relation between returns and

earnings. We demonstrate that comparisons based on the AT ratio control for cross-

sectional differences in the amount of firms’ market values attributable to rents, thus

showing that the ratio controls for the biases that have yielded a negative relation between

AT coefficients and market-to-book ratios.

Roychowdhury and Watts (2007) provide an alternative means to correct for the effect

of future rents on the estimation of AT coefficients. They propose extending the returns

measurement horizon to, for example, 3 years. Beatty (2007) notes that this approach

reduces sample sizes due to data availability requirements and that the requirement of

negative returns for a multi-year period reduces the number of bad-news observations.

Moreover, Givoly et al. (2007) discuss how the aggregation of both positive and negative

news events reduces the power of asymmetric timeliness tests. The use of multi-year

returns exacerbates this effect. Lastly, while extending the measurement horizon may

reduce bias, Roychowdhury and Watts (2007) do not discuss whether or not this approach

allows for valid cross-sectional comparisons.

4 We searched articles in Contemporary Accounting Research, Journal of Accounting and Economics,Journal of Accounting Research, Review of Accounting Studies, and The Accounting Review through March2012.5 We show that controlling for lagged earnings yields statistically significant coefficients on positiveearnings in earnings/returns regressions, which is important for using a ratio to infer conservatism.


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The paper proceeds as follows. Section 2 describes measures of conservatism that

remove the effects of differences in asset composition (future rents vs. assets-in-place),

develops our econometric model and predictions, and describes how our model relates to

prior literature. Section 3 describes our data. Section 4 presents our primary tests and Sect.

5 discusses the empirical properties of the AT measures. Section 6 concludes.

2 Measures of conservatism

2.1 Econometric model

This section illustrates how to control for the effect of asset composition on estimates of

accounting conservatism. The simple model we present here establishes the intuition

necessary to progress to our predictions and empirical tests. An expanded econometric

model is available from the authors upon request. Researchers often estimate accounting

conservatism using a regression of earnings scaled by market value of equity (xit) on

returns (rit) as in Basu (1997) for firm i and year t:

xit ¼ b0 þ b11rit\0 þ b2rit þ b31rit\0rit þ eit: ð1ÞThe coefficient b3 (hereafter ‘AT coefficient’) is used as a measure of the asymmetric

timeliness of earnings with respect to recognizing bad news versus good news. In this

regression total returns are used as a proxy for news, but include the change in both the

value of separable assets and future rents as described in (2). However, earnings generally

do not include the value of future rents until they are realized. Denoting the market value of

firm i at time t by vit, we have:

vit ¼ Assestit þ Rentsit rit ¼Dvit

vi;t�1

¼ DAssetsit

vi;t�1

þ DRentsit

vi;t�1

ð2Þ

If the firm’s market value maintains the same proportion of separable assets and future

rents

(k ¼ Assetsit�1=vit�1), then the change in separable assets can be written:

DAssetsit

vi;t�1

¼ krit ð3Þ

If earnings incorporate a portion of the change in separable assets but not future rents and

the proportion recognized is asymmetric for positive and negative changes (h and �h), then

scaled earnings can be written as6:

xit ¼ 1rit\0hkrit þ 1rit � 0�hkrit: ð4Þ

If earnings as defined in (4) are estimated using the typical Basu (1997) regression as in (1)

the coefficients b2 and b3 asymptotically approach the following:

plim b2 ¼ �hk plim b3 ¼ ðh� �hÞk: ð5Þ

6 Although not modeled here, current earnings could also recognize a portion of prior unrecognized changesin separable assets and realized rents. The exclusion of these elements from the model does not affect ourinferences because we assume the current recognition of prior returns is uncorrelated with current returns.Our more elaborate econometric model available from the authors does incorporate these additionalelements.


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The coefficients in (5) identify the existence of conservatism (h and �h), and provide for

valid cross-sectional comparisons to the extent the researcher wishes to examine the

overall relation between returns and earnings. However, cross-sectional comparisons

between the coefficients b2 and b3 do not allow researchers to compare the asymmetric

timeliness of earnings with respect to separable assets, as described in Roychowdhury and

Watts (2007), because these coefficients reflect not only accounting recognition, h and �h,

but also k, the proportion of separable assets to firm value. For example, firms with a high

(low) proportion of firm value from future rents will have lower (higher) AT coefficients

because they have lower (higher) k. However, this difference in AT coefficients is not due

to more conservative accounting policies but instead results from differences in asset

composition.

As mentioned in the introduction, Roychowdhury and Watts (2007) suggest extending

the windows for returns to correct for the effect of future rents on AT coefficients. We

provide two alternative options for researchers interested in removing the effects of asset

composition from conservatism estimates. The first option involves taking a ratio of

coefficients. Since both b2 and b3 include the effect of k, taking a ratio of the slope

coefficients can eliminate the effect of k. We recommend including the sum of the coef-

ficients on positive and negative returns in the denominator to mitigate small denominator

effects (Ryan and Zarowin 2003).7 This gives:

plimb3

b2 þ b3

¼ ðh��hÞk

hk¼ h� �h

h: ð6Þ

This approach achieves consistent estimates of asymmetric timeliness but only allows for

comparisons of bad news relative to good news recognition. Researchers interested in

measuring the absolute differences in conservatism could potentially use a second option

for correction. This involves estimating a slightly altered regression specification where the

book-to-market ratio, as a proxy for k, is multiplied by returns, which attempts to remove

the effect of k on the coefficients. This altered specification is:

xit ¼ c0 þ c11rit\0 þ c2

bi;t�1

vi;t�1

rit þ c31rit\0

bi;t�1

vi;t�1

rit þ eit: ð7Þ

However, because book value is a noisy estimate of the value of separable assets, the

estimates of c now have an errors-in-variables problem. Therefore, the estimates c3 and

c2 ? c3 now have an additive bias that depends on the accuracy of bit�1

vit�1as a proxy for k. In

particular, the book-to-market ratio is:

bi;t�1

vi;t�1

¼ kbi;t�1 �Market value of assetsi;t�1

vi;t�1

: ð8Þ

From (8), we see that the bias in the coefficient estimates in (7) depend on the extent to

which the book value of separable assets differs from the market value of separable assets.

7 This does not eliminate the need to have a fairly precise measure of the coefficient on positive returns. TheAppendix discusses the Fieller method procedure for computing confidence intervals for ratios of regressioncoefficients. The regression coefficients must be statistically significant in order to have bounded confidenceintervals. For example, a 95 % confidence interval requires that the regression coefficients be significant atthe 5 % level. This implies that the ratios will be statistically insignificant in settings where the asymmetrictimeliness coefficient is statistically insignificant, such as Eng and Lin (2012) examination of Chinese cross-listing firms.


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For example, Coca-Cola’s book value includes very little of the value of its trademark,

driving a wedge between the book value and market value of its separable assets.

Researchers should be aware of this when making cross-sectional comparisons between

sets of firms with substantial differences in the extent to which their book values over- or

under-value separable asset. Because the effects of the estimation bias in (7) are difficult to

gauge ex ante, in the next section we describe a simple two-step process to determine

whether researchers can rely on the coefficient estimates in (7).

2.2 Empirical predictions

In this section, we develop several predictions that we test in Sect. 4. The coefficients in (5)

imply a positive association between the AT coefficient and the proportion k of firm value

associated with assets-in-place. Our first prediction is:

P1 The estimated AT coefficient b3 is higher for firms with a higher proportion of value

based on separable assets (high k)

Assuming a positive relation between book-to-market ratios and the proportion of value

associated with assets-in-place, P1 implies a positive relation between book-to-market

ratios and the AT coefficient as in Pae et al. (2005), Givoly et al. (2007) and

Roychowdhury and Watts (2007). To the extent that the book-to-market ratio proxies for

the portion of value associated with assets-in-place, we can also utilize regression (7) to

estimate conservatism. Given that k dampens the coefficient estimates from a typical Basu

regression, this yields the following prediction:

P2 The estimated AT coefficient c3 from (7) is higher than the estimated AT coefficient

b3 from (1). The ratio c3=ðc2 þ c3Þ from regression (7) equals the ratio b3=ðb2 þ b3Þfrom (6) if the error in estimating k is small.

Given our focus on making cross-sectional comparisons, we note that regression (7) can

serve as a basis for estimating differences in conservatism between groups of firms. While

using a ratio of coefficients from (1) removes the effects of asset composition from cross-

sectional comparisons of the relative degree of conservatism, researchers who are interested

in obtaining an estimate of the amount of bad news recognition could use the coefficients from

(7), subject to the caveat that the coefficients provide a biased measure of conservatism.

Because the bias is additive, it affects both the coefficient and the ratio from (7); therefore,

researchers can check the magnitude of the bias by comparing the ratios from (1) and (7). If

the coefficient bias in (7) is small, the ratios from (1) and (7) will be roughly equal and the

coefficient estimates from (7) can be used as an estimate of the degree of conservatism.

Our remaining predictions address the effect of goodwill recognition on conservatism

estimates. The derivations in Sect. 2.1 assume that book value reflects only separable

assets. Goodwill is a notable exception to this assumption and may result in apples-

to-oranges comparisons between companies that grew via acquisitions and those that did

not. The earnings of companies that carry goodwill on their balance sheets may include

negative shocks to the value of future rents. This effect will increase the coefficient esti-

mates for b3 because goodwill writedowns induce a stronger relationship between negative

returns and negative earnings. These observations lead to the following predictions:

P3 The AT coefficient is smaller in an estimation of (1) where earnings exclude the

effects of goodwill as compared to an estimation where earnings include the effects

of goodwill.


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P4 If the AT ratio is less than (greater than) one based on estimates of (1) using earnings

that include the effects of goodwill, it is smaller (higher) when estimated using

earnings that exclude the effects of goodwill.8

2.3 Prior studies

This section provides a discussion of conservatism research since Basu (1997) in order to

frame our study’s contribution. Table 1 summarizes the conservatism measures used in

studies that cite Basu (1997) published in the following five accounting journals through

March 2012: Contemporary Accounting Research, Journal of Accounting and Economics,

Journal of Accounting Research, Review of Accounting Studies, and The AccountingReview. Table 1, Panel A shows that 76 of the 156 studies that cite Basu (1997) include

empirical tests that measure conservatism or asymmetric timeliness. Regarding these 76

studies, we highlight three observations about the literature.

First, while many papers have used some version of the Basu (1997) coefficient, other

measures have been developed, and there appears to be no real consensus on the use of

asymmetric timeliness measures. Table 1, Panel B describes the empirical measure(s) used

in these 76 empirical studies. Twelve different measures have emerged with some degree

of regularity in the literature. The first four measures listed in Panel B are all obtained from

estimates of a regression similar to (1). Others, like the incremental persistence of negative

earnings, as in Basu (1997), Ball and Shivakumar (2005), and Brauer and Westermann

(2012) are driven by data requirements such as the lack of available returns data.9 A few

researchers motivate their choice of measures by referring to analyses in Dietrich et al.

(2007) and Givoly et al. (2007) that suggest there may be problems with using a returns/

earnings framework (see Krishnan and Visvanathan 2008; Wang 2006; Ahmed et al. 2002).

However, many researchers do not discuss their choice of conservatism measures. This

lack of consensus on the use of asymmetric timeliness measures hinders the ability of

researchers to compare findings across different studies. However, this is perhaps mitigated

by the fact that most researchers (41 of the 76 studies) use multiple measures or some

combination of measures. Furthermore, most researchers (63 of the 76 studies) use some

measure estimated from a regression similar to (1), and the majority use the estimated

coefficient on negative returns, which we demonstrate leads to biased inferences in cross-

sectional comparisons.

Second, many of the papers test for cross-sectional differences in asymmetric timeli-

ness, which we highlight as problematic in tests based on the AT coefficient when

researchers do not wish to compare the accounting recognition of future rents. Of the 76

studies using some measure of conservatism, 64 of them conduct a cross-sectional

examination. For example, Givoly et al. (2007) find a negative relation between the AT

coefficient and the market-to-book ratio, which is puzzling because market-to-book

is another measure of accounting conservatism.10 Roychowdhury and Watts (2007)

hypothesize that this negative relation is due to a high proportion of returns being attrib-

utable to revisions of expected future rents, which we explicitly derive in our model.

8 Although this prediction is not immediately obvious, the derivation is available from the authors uponrequest.9 Another approach to estimating asymmetric timeliness in the absence of market return data is to measurethe frequency of large negative net income (e.g. Barth et al. 2008; Elbannan 2011).10 The market-to-book ratio reflects both asymmetric timeliness and ex ante conservatism.


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Third, of the eight studies that measure conservatism using a ratio of coefficients, only

Pope and Walker (1999) and Guay and Verrecchia (2006) provide an explanation for the

use of this measure. Pope and Walker (1999) derive a ratio-based test of conservatism in a

permanent income model to control for cross-country differences in discount rates. Our

Table 1 Basu (1997) citations and use of conservatism measures

Paper type Citations

Panel A: Basu (1997) citations by type

Citation only 80

Empirical tests 76

Total citations 156

Papers using multiple measures 41

Papers not using Basu (1997) measure 7

Conservatism measure Citations

Panel B: Basu (1997) citations by conservatism measure used

Basu coefficient 39

Basu good news coefficient 7

Basu R2 9

Basu coefficient ratio 8

Sensitivity of returns (Easton and Pae (2004)) 4

Hidden reserves (Penman and Zhang (2002)) 4

Cumulative discretionary accruals (Givoly and Hayn (2000)) 11

Skewness of earnings (Givoly and Hayn (2000)) 6

Beaver and Ryan (2000) measure 4

Persistence of earnings (Basu (1997) and Ball and Shivakumar (2005)) 19

Aggregated Basu (1997) and Roychowdhury and Watts (2007) 6

Market-to-book or book-to-market 6

Other 7

Total 130

This table reports the use of conservatism measures for all Basu (1997) citations in the following 5Accounting Journals: Contemporary Accounting Research, Journal of Accounting and Economics, Journalof Accounting Research, Review of Accounting Studies, and The Accounting Review. Panel A presentsinformation about whether the paper conducted an empirical test using a measure of conservatism, thenumber of studies using multiple measures, and those not using a measure from estimation of a Basu (1997)style regression. Panel B displays the number of papers in which a particular conservatism measure is used.The Basu coefficient (b3), Basu good news coefficient (b2), Basu R2 and Basu coefficient ratio ([b3 1 b2]/b3) are all estimated from the following regression model: EPSit/Pi,t-1 = b0 ? b1 1(rit \ 0) ? b2 rit ? b3

1(rit \ 0) rit ? eit for firm i and year t where the indicator variable 1(rit \ 0) equals one when returns arenegative and zero otherwise. The sensitivity of returns is either conservatism due to the net present value ofinvestments and/or conservatism due to accounting rules, which are estimates obtained from the regressionspecified in Easton and Pae (2004). Hidden reserves are the sum of the LIFO reserve, hypothetical capi-talized R&D expenditures and hypothetical capitalized advertising expenditures, scaled by net operatingassets as defined in Penman and Zhang (2002). Cumulative discretionary accruals and skewness of earningsare measures initially used by Givoly and Hayn (2000). The Beaver and Ryan (2000) measure is thefirm specific intercept of a pooled OLS regression of BTM ratio on current and lagged returns. ThePersistence coefficient is the estimate b3 from the following regression: Earnt = b0 ?b1 1(Earnt-1 \ 0) ? b2 Earnt-1 ? b3 1(Earnt-1 \ 0) Earnt-1 ? eit consistent with Basu (1997) and Balland Shivakumar (2005). The Aggregated Basu is the procedure conducted by Basu (1997) and Roy-chowdhury and Watts (2007) that aggregates the variables in the Basu regression over two or more years


123

model differs from theirs in that we distinguish between separable assets and future rents

and have a more general relation between returns and earnings that does not rely on the

permanent income feature used by Pope and Walker (1999) and critiqued by Basu (1999).

In addition, six of the eight studies discuss a ratio where the aggregate coefficients on

negative returns (b2 ? b3) is divided by the coefficient on positive returns (b2). The use of

this ratio provides a salient way to interpret the asymmetric timeliness estimated using the

coefficients (e.g. ‘‘the incorporation of negative news is six times that of positive news’’).

As we discuss in the next section, this particular ratio may cause econometric problems

because the estimated coefficient b2 on positive returns is often near zero.11

In addition to the AT ratio measure of conservatism, the previous section introduced a

coefficient measure from a Basu (1997)-type regression where we interact returns with the

book-to-market ratio. This approach is different from other studies that use book-to-market

as a measure of conservatism. Roychowdhury and Watts (2007; Figure 1) illustrate that the

book-to-market ratio commingles asymmetric loss recognition with expected future eco-

nomic rents included in market value but excluded from accounting measures of separable

assets. In contrast, the regression-based measure in (7), which utilizes returns interacted

with book-to-market, provides an estimate of asymmetric loss recognition.

Khan and Watts’ (2009) firm-year estimate of conservatism more closely resembles the

regression-based measure in (7), but differs both computationally and in terms of theo-

retical motivation. Regarding the computational differences, the Khan and Watts (2009)

C_Score measure provides an estimate of conservatism as a linear function of the firm

characteristics of Size (log market value of equity), market-to-book, and leverage. In other

words, whereas we interact returns with book-to-market, they estimate a regression with

un-interacted returns and returns interacted with size, market-to-book, and leverage.

Regarding theoretical motivation, we utilize the book-to-market ratio to proxy for the

fraction of returns associated with separable assets—the model’s k parameter. Market-

to-book, as used by Khan and Watts (2009), cannot serve this role. Also, our model also

has no particular role for size and leverage. Khan and Watts (2009) utilize these variables

in order to assess the cross-sectional drivers of conservatism rather than to correct biases in

estimates from a standard Basu (1997) regression. Thus, both the computation and theo-

retical support differ between our proposed AT measures and those in Khan and Watts

(2009).

Our study provides guidance on the use of conservatism measures in cross-sectional

studies. In the next section, we test our predictions that cross-sectional comparisons based

on regression coefficients, as in (1), are impacted by the composition of firm value to the

extent that book values exclude future rents. However, tests using a ratio of regression

coefficients, as in (6), or coefficients from a regression with returns interacted with book-

to-market, as in (7), allow for cross-sectional comparisons that reflect accounting policy

choices related to the recognition of separable assets. We test the other predictions in order

to assess the empirical validity of our model. Furthermore, we verify the AT ratio’s

econometric properties by comparing asymptotic, delta method confidence intervals to

Fieller method confidence intervals, which addresses the econometric concerns raised by

Basu (1999) in his discussion of Pope and Walker (1999).12 We describe the Fieller

method confidence intervals in the Appendix.

11 Our analysis in the next section shows that the typical Basu (1997) framework produces estimates of b2

near zero. However, including additional controls to better measure unexpected earnings and unexpectedreturns often results in a positive coefficient on positive returns.12 Pope and Walker (1999) do not assess the statistical significance of their ratio measures.


123

3 Data and sample selection

We conduct our empirical analysis on the sample of firm-years reported in Table 2, Panel

A. We begin with 254,127 firm-years in the Compustat Fundamental Annual table from

1963 to 2007. We restrict the sample to firm-years with matches to CRSP identifiers in the

CRSP/Compustat merged database (205,448 firm-years). Missing variables from Compu-stat or CRSP reduces the sample to 172,260 firm-year observations. We require that firms

have share prices greater than $2 in order to avoid small-denominator problems in the

price-deflated earnings measures and to reduce the impact of the high return variance for

firms with low share prices (Patatoukas and Thomas 2009). This requirement reduces the

sample by 17,058 firm-years. We further require that firms have positive book value of

equity, net of goodwill, for use in tests where we interact returns with the book-to-market

ratio. The final sample is 155,201 firm-years with 17,359 firms.

Panel B of Table 2 reports descriptive statistics for the sample firms. We winsorize all

variables at the 1st and 99th percentile within Compustat fiscal years. We calculate Assets

(Compustat Annual Xpressfeed item AT), Market value (PRCC_F 9 CSHO), Book value

(CEQ), and book-to-market (Book value/Market value) at the beginning of the period.

Earnings refers to earnings per share from continuing operations (EPSPX) scaled by the

beginning-of-fiscal-year price (PRCC_F), which we adjust for stock splits by multiplying

by the ratio of the current-to-prior year price adjustment factors (AJEX).13 We obtain

annual buy-and-hold returns from the CRSP monthly stock file beginning at the start of the

fourth month of the fiscal year. The descriptive statistics show the mean Assets, Market

and Book values of equity to be $2.1 billion, $880.6 million, and $514.8 million for our full

sample of firms, respectively. The mean book-to-market for our sample is 0.78.

4 Empirical results

4.1 Effect of including prior information

Before proceeding with tests of the predictions from Sect. 2, we first investigate how the

inclusion of pre-returns information impacts estimates of asymmetric timeliness. In par-

ticular, firms with high price/earnings ratios tend to have lower future returns (Basu 1977)

while the analysis in Sect. 2 assumes a risk-neutral setting with no discounting where

expected returns are zero. Furthermore, Patatoukas and Thomas (2009) show that estimates

of asymmetric timeliness in regressions similar to Basu (1997) are significantly impacted

by past earnings and size, so that the inclusion of lagged earnings acts as somewhat of a

control for this bias.

Table 3 presents estimates of asymmetric timeliness using both the specification (1) as

in Basu (1997) and a specification that includes proxies for expected earnings and expected

returns. We condition on past earnings as a component of both expected earnings and

expected returns, where the latter derives from the aforementioned relation between returns

and earnings/price ratios (Basu 1977). We also condition on factors noted in Fama and

French (1992). If returns are independent of pre-returns information, a simplifying

13 We use earnings per share from continuing operations, but note there is no consistency in the literature.Of the 36 studies that use returns in their measurement of conservatism, 18 use earnings per share fromcontinuing operations, 10 use earnings per share, 4 use both, and 4 do not provide enough detail to determinewhich measure they used.


123

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123

assumption we made when deriving our predictions, then the addition of these regressors

will not affect the coefficients on returns but will impact the regression’s explanatory

power. If including additional regressors such as lagged earnings impacts the coefficients

on returns, then these regressors are also acting as controls for expected returns.

The results in Table 3 show that estimation of specification (1) [column (1)] generates a

positive and significant coefficient on negative returns consistent with asymmetric time-

liness. The coefficient on positive returns is insignificant which is consistent with prior

Table 3 Asymmetric timeliness tests with prior information

(1) (2) (3) (4)

Negative return indicator -0.003(-0.548)

-0.003(-0.638)

-0.002(-0.360)

-0.001(-0.323)

Returns 0.003(0.281)

0.020*(1.884)

0.021**(2.220)

0.019**(2.031)

Negative indicator 9 returns 0.307***(12.661)

0.204***(10.120)

0.182***(9.324)

0.200***(10.155)

Lag EPS 0.482***(16.907)

0.730***(16.061)

0.879***(31.833)

Negative earnings indicator -0.037***(-6.732)

-0.029***(-5.940)

Neg. earnings indicator 9 earnings -0.387***(-6.887)

-0.559***(-14.117)

Beginning BTM -0.030***(-4.192)

Beta 0.008**(2.574)

MV equity -0.002*(-1.944)

Intercept 0.079***(10.771)

0.046***(8.715)

0.028***(6.981)

0.044***(4.993)

N 155,201 155,201 155,201 155,201

Firm clusters 17,359 17,359 17,359 17,359

Year clusters 45 45 45 45

v2 223.5*** 538.6*** 1,709.1*** 4,301.8***

Adjusted R2 0.147 0.365 0.386 0.396

AT ratio 0.989 0.911 0.897 0.912

95 % confidence intervals

Delta method [0.915, 1.064] [0.817, 1.005] [0.804, 0.989] [0.828, 0.997]

Feiller method [0.915, 1.065] [0.814, 1.004] [0.800, 0.988] [0.825, 0.997]

This table reports coefficient estimates from Basu (1997)-type regressions of earnings on returns with theinclusion of t - 1 information to improve the specification. Table 2 describes our sample of firms andvariable definitions. Earnings are earnings per share from continuing operations (Compustat EPSPX) scaledby the beginning-of-fiscal-year price. Returns are annual buy-and-hold returns. Forecast is the price-scaledbeginning-of-year mean analyst forecast of earnings. Lag EPS is the 1-period lagged Earnings variable(dependent variable). Beginning BTM is the beginning-of-fiscal-year book value of equity scaled by marketvalue of equity. Beta is the market model beta estimated over the prior 5 months. MV Equity is the marketvalue of equity at the beginning of the fiscal year. All variables except indicator variables are winsorized atthe 1st and 99th percentile within Compustat fiscal years. Two-tailed t-statistics are shown below coefficientestimates, with standard errors clustered by firm and fiscal year. *, **, and *** indicate significance at 10, 5,and 1 %


123

studies. Column (2) adds a control for expected earnings using lagged EPS while column

(3) splits the lagged EPS into positive and negative EPS to control for the effects docu-

mented in Patatoukas and Thomas (2009). In both of these specifications, the coefficient on

positive returns is now positive and significant (0.020 and 0.021). Although the coefficient

on negative returns remains significantly positive, it is diminished (0.307 vs. 0.204 and

0.182) relative to the original specification. The altered coefficients on returns imply that

prior earnings impact both expected returns and expected earnings.

Finally, we include book-to-market, market model beta, and beginning market value of

equity in column (4) as additional controls for expected returns. We note that these factors

have little effect on the AT coefficients relative to specifications including controls for

expected earnings. Because the control for lagged scaled EPS [column (3)] improves the

regression specification for asymmetric timeliness significantly and produces a positive

coefficient on positive returns, we include this additional control in our subsequent tests.

4.2 Effect of rents on returns

Table 4 presents tests of prediction P1. The regressions examine cross-sectional differ-

ences in conservatism by separating firms along two dimensions: high and low book-

to-market ratios and REIT versus non-REIT. The first cross-sectional split categorizes

firm-years into high and low beginning-of-period book-to-market ratios. In specification 1

(2) of Table 4, firm-years above the median (66th percentile) are identified as high book-

to-market, while those below the median (33rd percentile) are identified as low book-

to-market. Firms with high book-to-market ratios appear to have more conservative

earnings as indicated by coefficients of 0.276 and 0.365 (significant at 1 %) on the

interaction between the high book-to-market indicator and the AT coefficient. This result is

consistent with prior studies and our prediction P1. On the other hand, the AT ratio

difference is insignificant when we partition by high book-to-market, suggesting that firms

with high book-to-market ratios have similar asymmetric timeliness to low book-to-market

firms. These results are consistent with Roychowdhury and Watts’ (2007) conjecture that

the higher AT coefficient for high book-to-market firms is, at least in part, due to the high

proportion of their value associated with separable assets that are more likely to be

reflected in earnings. The AT ratio removes the effects of cross-sectional differences in the

portion of returns associated with separable assets and shows little difference between the

samples.

In addition to reporting the AT ratios, we compare confidence intervals on the ratios

using both the Delta method and the Fieller method as a gauge of how well the Delta

method approximates the confidence interval for the ratio. In all cases, the two confidence

intervals are nearly identical. This suggests that the Delta method provides a reasonable

approximation to the standard errors for the ratio—a necessary precursor to using the ratio

in cross-sectional comparisons because a Fieller-type test is not available for comparing

two coefficient ratios.

The second regression specification provides additional support for the AT ratio by

examining cross-sectional differences in asymmetric timeliness for REITs (real estate

investment trusts) relative to other firms. We classify a firm as a REIT if the SIC is equal to

6798, but also include a broader definition of real estate firms (including firms with SIC

between 6500 and 6599 or equal to 6798 or 6799). Like high book-to-market firms, we

conjecture that REITs have returns more associated with changes in separable assets than

other firms, and test whether the AT ratio controls for this higher expected k. We do not

have any expectations that accounting rules related to REITs cause more asymmetric


123

Table 4 Results of cross-sectional estimates of asymmetric timeliness using market-to-book and REITsample partitions

High/low

BTM

(1)

Top/bottom

third BTM

(2)

REIT/

non-REIT

(3)

Alt REIT

(all real estate)

(4)

Negative return indicator 0.003

(0.954)

0.002

(0.826)

-0.003

(-0.718)

-0.003

(-0.776)

Returns 0.006

(1.035)

0.004

(0.718)

0.020**

(2.133)

0.020**

(2.088)

Negative returns indicator 9 returns 0.110***

(9.987)

0.093***

(9.523)

0.175***

(8.998)

0.173***

(9.164)

High BTM/REIT indicator 0.005*

(1.674)

0.007

(1.520)

-0.019***

(-2.921)

-0.023***

(-3.830)

High BTM/REIT 9 neg. indicator 0.013***

(2.616)

0.013*

(1.857)

0.042***

(3.864)

0.029***

(2.608)

High BTM/REIT 9 returns 0.030***

(3.435)

0.039***

(3.172)

0.008

(0.393)

0.031**

(2.017)

High BTM/REIT 9 neg.

indicator 9 returns

0.276***

(9.124)

0.365***

(9.207)

0.206*

(1.928)

0.157**

(2.477)

Lag EPS 0.745***

(16.208)

0.754***

(14.535)

0.724***

(15.998)

0.726***

(16.041)

Negative earnings indicator -0.038***

(-8.606)

-0.040***

(-9.034)

-0.035***

(-7.055)

-0.035***

(-7.059)

Neg. earnings indicator 9 earnings -0.416***

(-7.934)

-0.420***

(-7.278)

-0.374***

(-6.960)

-0.376***

(-7.019)

Intercept 0.023***

(6.969)

0.021***

(6.407)

0.029***

(7.343)

0.029**

(7.359)

N 155,201 103,466 155,201 155,201

Firm clusters 17,359 16,332 17,359 17,359


v2 2,420.7*** 1,984.6*** -3,164.8 2,360.8***

Adjusted R2 0.416 0.425 0.383 0.384

Additional tests

AT ratio

High BTM/REIT 0.914 0.915 0.932 0.866

Delta method 95 % interval [0.860, 0.968] [0.859, 0.971] [0.841, 1.023] [0.795, 0.937]

Feiller method 95 % interval [0.858, 0.967] [0.856, 0.970] [0.815, 1.026] [0.776, 0.929]

Control 0.949 0.963 0.897 0.898



Difference in ratios (F-stat) -0.035

(0.795)

-0.048

(1.099)

0.034

(0.242)

-0.032

(0.242)

This table reports coefficient estimates from Basu (1997)-type regressions of earnings on returns with cross-sectional

comparisons based on book-to-market (columns 1 and 2) and REITs (real estate investment trusts) and non-REITs

(columns 3 and 4). Table 2 describes our sample of firms and variable definitions. For BTM, the X-section indicator is set to

one for column 1 (column 2) if beginning BTM is greater than the median (66th percentile) of BTM for all sample firms and

zero if BTM is below the median (33rd percentile). For REITs, the X-section indicator is set to one if the firm’s SIC code is

equal to 6,798 and zero otherwise. Earnings are earnings per share from continuing operations scaled by the beginning-

of-fiscal-year price. Returns are annual buy-and-hold returns. All variables except indicator variables are winsorized at the

1st and 99th percentile within Compustat fiscal years. Two-tailed t-statistics are shown below coefficient estimates, with

standard errors clustered by firm and fiscal year. Significance levels for the ratio tests are computed using the delta method,

however we present confidence intervals using both the delta method and the Fieller method as explained in the Appendix

(using 5 % significance). *, **, and *** indicate significance at 10, 5, and 1 %


123

timeliness relative to other firms. The interaction coefficients for our REIT indicator and

negative returns in the two specifications are 0.206 (significant at 5 %) and 0.157 (sig-

nificant at 5 %), suggesting that REITs tend to exhibit more asymmetric timeliness.

However, the AT ratio, which controls for cross-sectional differences in the amount of

returns due to separable assets, does not indicate that REITs have greater asymmetric

timeliness. To the extent that REITs exhibit no more or less asymmetric loss recognition

than other firms, this suggests that the positive interaction coefficient is due to the high

proportion of returns that are attributable to separable assets, a bias remedied by using

comparisons based on the AT ratio.

4.3 Effect of interacting returns with the book-to-market ratio

Table 5 presents regressions that estimate regression (7) where returns are interacted with

the book-to-market ratio as a proxy for the portion k of returns attributable to separable net

assets rather than future rents. Consistent with prediction P2, the AT coefficients are higher

in the regressions where returns are interacted with the book-to-market ratio (significant at

1 %). The higher coefficient on positive returns is also consistent with the book-to-market

interaction controlling for the dampening effect of returns attributable to future rents.14

We also compare the AT ratios from the two specifications as a check on the degree to

which bias impacts the estimated coefficients in the regression where returns are interacted

with the book-to-market ratio. The ratios are nearly identical (p value for the difference in

ratios is 0.82), suggesting that, in this case, bias does not appear to have a large impact on

the coefficients. This does not suggest that this holds generally, though, and we recommend

that researchers perform this comparison before relying on coefficients estimated from a

regression where returns are interacted with the book-to-market ratio.

4.4 Effects of goodwill

Table 6 presents our tests of predictions P3 and P4 on how the accounting recognition of

goodwill impacts estimates of conservatism with respect to separable assets. We test these

predictions on both the full sample and a limited sample that includes only firms that report

goodwill. Prediction P1 states that the AT coefficient will be larger in a regression where

earnings include the effects of goodwill. While the coefficients are larger when earnings

include goodwill, the difference in coefficients is statistically insignificant.

Prediction P4 pertains to the AT ratio. The full sample regression with unadjusted

earnings yields an AT ratio of 0.897, which is equal to the AT ratio when earnings excludes

goodwill. When we restrict the sample to firms that report goodwill, the ratio is larger when

earnings include the effects of goodwill, but the difference is again insignificant. We

conclude that goodwill does not materially impact the estimation of conservatism in the

broad sample. We also compare confidence intervals on the ratios using both the Delta

method and the Fieller method. In all cases, the two confidence intervals are nearly

14 This evidence must be viewed as consistent with P2, but not as conclusive. This is because multiplyingreturns by any number between zero and one will tend to generate larger coefficients. The interpretation ofthe estimates rests on their derivation from our econometric model. If the book-to-market ratio is unrelatedto the structural construct k, then its interaction with returns when estimating (7) can be viewed as a sourceof bias. For the specification to be totally invalid would require the book-to-market ratio to be totallyunrelated to the portion of firm value due to separable net assets, which seems unlikely; however, the book-to-market ratio is an imperfect measure of k and therefore does yield some estimation bias as we notedearlier.


123

identical, suggesting the Delta method provides a reasonable approximation to the standard

errors for the ratio.

4.5 Additional settings

The importance of the two prescriptions we outline depends on the nature of the study. For

example, a recent study by Ettredge et al. (2012) compare asymmetric timeliness between

restated periods for a sample of firms and control samples comprised of either other firms,

Table 5 Results of asymmetric timeliness estimates with book-to-market interactions

Returns(1)

Returns 9 BTM(2)


0.005(1.515)

Returns 0.022**(2.204)

0.031***(4.462)


0.331***(8.397)

Lag EPS 0.610***(7.562)

0.686***(8.081)


-0.378***(-4.909)


-0.051***(-6.552)

Intercept 0.038***(5.697)

0.030***(4.467)

N 155,201 155,201

Firm clusters 17,359 17,359

Year clusters 45 45

v2 1,337.0*** 3,266.1***

Adjusted R2 0.379 0.432

Additional tests

Difference in AT coefficients (F-stat) 0.148***(14.019)

AT ratio 0.894 0.914


Delta method [0.799, 0.990] [0.871, 0.956]

Feiller method [0.795, 0.988] [0.866, 0.953]

Difference in ratios (F-stat) 0.020(0.819)

This table reports coefficient and ratio estimates from Basu (1997)-type regressions of earnings on returnsusing the original specification and including interactions with book-to-market. Table 2 describes oursample of firms and variable definitions. Earnings are earnings per share from continuing operations scaledby the beginning-of-fiscal-year price. Returns are annual buy-and-hold returns. All variables except indi-cator variables are winsorized at the 1st and 99th percentile within Compustat fiscal years. Two-tailedt-statistics are shown below coefficient estimates, with standard errors clustered by firm and fiscal year.Significance levels for the ratio tests are computed using the delta method, however we present confidenceintervals using both the delta method and the Fieller method as explained in the Appendix (using 5 %significance). *, **, and *** indicate significance at 10, 5, and 1 %


123

or the same firms in the post-restatement period. They find lower asymmetric timeliness in

the periods that were later restated. Given the nature of this study using a matched control

sample and the firm itself as a control, it is unlikely that asset composition is an important

factor that potentially influences the results. However, there are other settings where our

prescriptions may prove useful. Table 7 presents regression estimates for two settings to

highlight cases where the ratio provides different inferences relative to the AT coefficient.

Table 6 Results of asymmetric timeliness estimates including and excluding the effects of goodwill

All firms Only firms with goodwill

As reported(1a)

Excl. goodwill(1b)

As reported(2a)

Excl. goodwill(2b)


-0.003(-0.622)

0.002(0.716)

0.001(0.333)

Returns 0.021**(2.220)

0.020**(2.150)

0.016**(2.085)

0.013*(1.891)


0.176***(9.060)

0.174***(9.308)

0.162***(8.119)

Lag EPS 0.730***(16.061)

0.724***(15.975)

0.485***(9.779)

0.456***(9.940)


-0.035***(-7.013)

-0.043***(-6.097)

-0.039***(-5.941)


-0.374***(-6.942)

-0.170**(-2.200)

-0.125*(-1.758)

Intercept 0.028***(6.981)

0.028***(7.280)

0.034***(8.160)

0.036***(10.157)

N 155,201 155,201 35,645 35,645

Firm clusters 17,359 17,359 7,214 7,214


v2 1,709.1*** 1,510.4*** 1,191.5*** 955.7***

Adjusted R2 0.386 0.382 0.335 0.322

Additional tests

Difference in AT coefficients (F-stat) 0.006(0.095)

0.012(0.358)

AT Ratio 0.897 0.897 0.918 0.924


Delta method [0.804, 0.989] [0.802, 0.992] [0.839, 0.997] [0.843, 1.004]

Feiller method [0.800, 0.988] [0.798, 0.991] [0.835, 0.995] [0.838, 1.003]

This table reports coefficient and ratio estimates from Basu (1997)-type regressions of earnings on returnswith both as-reported earnings and earnings excluding the effects of goodwill, such as write-downs andamortization. Table 2 describes our sample of firms and variable definitions. The first set of tests includes allfirms, while the second set includes only those firms with goodwill during the period. Earnings are earningsper share from continuing operations scaled by the beginning-of-fiscal-year price. The effects of theamortization of goodwill and goodwill write-downs on earnings per share are obtained from Compustsat(GDWLIEPS and GDWLAM). Returns are annual buy-and-hold returns. All variables except indicatorvariables are winsorized at the 1st and 99th percentile within Compustat fiscal years. Two-tailed t-statisticsare shown below coefficient estimates, with standard errors clustered by firm and fiscal year. Significancelevels for the ratio tests are computed using the delta method, however we present confidence intervals usingboth the delta method and the Fieller method as explained in the Appendix (using 5 % significance). *, **,and *** indicate significance at 10, 5, and 1 %


123

Our first setting examines differences in asymmetric timeliness for high-technology and

low-technology firms as defined in Francis and Schipper (1999). Kwon et al. (2006) find

that high-technology firms have higher AT coefficients than low technology firms, while

Chandra et al. (2004) find high technology firms have a lower AT coefficient than non

high-technology firms. The results in Table 7 suggest the AT coefficient is lower for high-

technology firms using the regression specification as in (1) (coefficients negative and

significant at 1 %), consistent with the Chandra et al. (2004) findings.15 However, when we

attempt to control for k by estimating the regression by interacting returns with book-

to-market, the coefficient becomes positive although insignificant at 10 %. The differences

in ratios for both specifications indicate high-technology firms do have greater asymmetric

timeliness relative to low-technology firms (significant at 1 %). These results highlight the

importance of estimating the ratio and not just the coefficient when comparing firms with

dissimilar portions of returns related to separable assets versus future rents.

Our second setting in Table 7 examines differences in asymmetric timeliness between

firms with high versus low probability of litigation. Basu (1997) argues and finds that an

increased probability of litigation is associated with greater asymmetric timeliness. We

estimate the probability of litigation as in Khan and Watts (2009), fitted with the param-

eters and variables as in Table 3 of Shu (2000) and estimated in the prior year.16 Firms are

designated as high probability of litigation if the predicted probability of litigation is

greater than the 90th percentile of all firms in the sample. The AT coefficients for high

probability of litigation interacted with negative returns is positive and significant at 1 % in

both specifications, suggesting that greater probability of litigation is associated with

greater asymmetric timeliness. Unlike some of the prior results, testing the differences in

ratios in this setting produces similar inferences to the AT coefficient in this case. Because

the high litigation firms have low book-to-market ratios and a lower coefficient on positive

returns, this suggests that these firms have a relatively low portion of returns associated

with separable assets, which biases against them having a higher AT coefficient than the

broader population.17 These results highlight the need to examine ratios in conjunction

with coefficients, as researchers may not know ex ante the extent of bias in the AT

coefficient.

5 Empirical properties of the asymmetric timeliness (AT) ratio

There is no theoretically ideal benchmark for measuring conservatism with which we can

test our model. Consequently, the previous section demonstrates that our model’s pre-

dictions perform well with commonly used archival data, which suggests that the model is

reasonably descriptive of actual data. This section provides additional evidence for use of

the AT ratio by showing that it exhibits intuitive empirical properties such as an association

with other empirical proxies for conservatism. Prior research has shown that AT coeffi-

cients are negatively associated with other conservatism proxies, which suggests that they

fail to accurately measure the degree of accounting conservatism (Givoly et al. 2007). We

15 The difference in coefficient results relative to Kwon et al. (2006) may be due to differences in sampleconstruction.16 The number of observations is reduced in these tests due to the variable restrictions necessary to estimatethe Shu (2000) model.17 Untabulated analysis shows that the mean (median) beginning book-to-market ratio of high litigationfirms is 0.560 (0.447) versus 0.781 (0.626) for the non-high litigation firms.


123

Table 7 Results of asymmetric timeliness estimates using high-tech and high probability of litigationsample partitions

Hi-tech/low-tech High/low prob litigation

Returns

(1)

BTM 9 returns

(2)

Returns

(3)

BTM 9 returns

(4)

Negative return indicator 0.007**

(2.197)

0.005*

(1.648)

0.000

(-0.125)

0.001

(0.516)

Returns 0.034***

(4.502)

0.034***

(4.957)

0.020**

(2.127)

0.026***

(3.251)

Negative returns indicator 9 returns 0.201***

(11.170)

0.323***

(8.059)

0.188***

(10.731)

0.306***

(7.944)

Hi-tech/High-PLIT -0.008***

(-2.797)

-0.008***

(-3.588)

-0.012***

(-3.352)

-0.007**

(-2.355)

Tech/PLIT 9 neg. indicator -0.015***

(-3.448)

-0.003

(-0.690)

0.017**

(2.548)

0.004

(1.108)

Tech/PLIT 9 returns -0.029***

(-6.151)

-0.024***

(-5.452)

-0.006

(-1.246)

-0.013*

(-1.700)

Tech/PLIT 9 neg. indicator 9 returns -0.075***

(-3.445)

0.059

(1.467)

0.088***

(3.271)

0.222***

(5.406)

Lag EPS 0.714***

(15.173)

0.793***

(13.381)

0.697***

(13.184)

0.788***

(11.449)

Negative earnings indicator -0.038***

(-6.984)

-0.039***

(-9.288)

-0.040***

(-6.668)

-0.037***

(-6.846)

Neg. earnings indicator 9 earnings -0.372***

(-6.473)

-0.481***

(-7.517)

-0.361***

(-5.344)

-0.475***

(-6.325)

Intercept 0.027***

(8.175)

0.022***

(6.019)

0.030***

(7.354)

0.022***

(5.362)

N 155,201 155,201 100,765 100,765

Firm clusters 17,359 17,359 11,384 11,384


v2 2,428.1*** 3,836.6*** 2,991.8*** 3,753.6***

Adjusted R2 0.391 0.434 0.374 0.416

Additional tests

AT ratio

Hi-tech/high-PLIT 0.956 0.974 0.952 0.976



Control 0.854 0.905 0.905 0.921



Difference in ratios (F-stat) 0.102***

(10.187)

0.069***

(21.360)

0.047**

(5.424)

0.055***

(9.100)

This table reports coefficient and ratio estimates from Basu (1997)-type regressions of earnings on returns using sample

partitions of high-tech/low-tech firms and high/low probability of litigation. Each partition is estimated using a specifi-

cation using returns and returns interacted with book-to-market. Table 2 describes our sample of firms and variable

definitions. High-tech indicator is set to one if the 3-digit SIC is one of the following (283, 357, 360, 361, 362, 363, 364,

365, 366, 367, 368, 481, 737, 873) and zero otherwise. The high probability of litigation indicator equals one if the

estimated probability of litigation is greater than the 90th percentile of all sample firms. The probability of litigation is

estimated as in Khan and Watts (2009), and fitted using the parameters and variables in Table 3 of Shu (2000). All variables

except indicator variables are winsorized at the 1st and 99th percentile within Compustat fiscal years. Two-tailed t-statistics

are shown below coefficient estimates, with standard errors clustered by firm and fiscal year. Significance levels for the

ratio tests are computed using the delta method, however we present confidence intervals using both the delta method

and the Fieller method as explained in the Appendix (using 5 % significance). *, **, and *** indicate significance at 10, 5,

and 1 %


123

therefore test cross-industry associations between the AT ratio and other conservatism

measures. Furthermore, Givoly and Hayn (2000) and Givoly et al. (2007) find that the AT

coefficient is not stable over time, which is inconsistent with the stability of accounting

rules.18 We therefore test the time-series stability of the AT ratio.

5.1 AT measures across industries

We compare a number of accounting conservatism measures, including the AT coefficient

and ratio, across industries in order to measure the AT ratio’s association with other

measures of accounting conservatism. We estimate the AT coefficients and ratios using

both (1), the traditional Basu (1997) regression, and (7), where book-to-market is inter-

acted with returns. When comparing measures of conservatism across industries, we focus

on measures that reflect ex post conservatism in the form of recognizing bad news, which

should be correlated with the AT ratio.19 Ex post conservatism impacts both the book-

to-market ratio and Givoly and Hayn’s (2000) cumulative discretionary accrual (CDA)

measure because the recognition of bad news reduces both book values and cumulative

discretionary accruals.20 Basu (1997) and Ball and Shivakumar (2005) measure conser-

vatism based on the incremental persistence of negative earnings changes relative to

positive earnings changes, which reflects ex post conservatism. We therefore expect a

negative relation between the ratio and book-to-market and CDA and a positive relation

between the ratio and the relative persistence of negative earnings changes.

We also compare our ratio to a measure based on a conservatism ratio (CR) from Callen

et al. (2010) that utilize the Vuolteenaho (2002) return decomposition to infer earnings

news. Callen et al. (2010) define their conservatism ratio as the shock to current earnings

divided by the total news about earnings during the period. In other words, it represents the

amount of information about all current and future earnings recognized in the current

period earnings. We construct the CR rank as in Callen et al. (2010) by ranking the

conservatism ratio into quintiles within quintiles of negative unexpected returns

(rit � Et�1½rit�\0). We use the average rank within industry as an industry-level measure

of conservatism, and expect a positive relation with other measures of asymmetric

timeliness.

We test the relation between measures of conservatism by computing them for each of

the 48 Fama and French (1997) industry groups. Table 8 compares Pearson and Spearman

rank correlation measures across these groups. Below the correlations we have listed the

number and percentage of industries where the conservatism estimate is significant.21 The

18 We also recognize that economic circumstances affect the implementation of standards and cause var-iation in conservatism measures across time. Our model explicitly allows this. However, as Givoly et al.(2007) point out, the variation across time may partially be due to poor estimation in years with few negativenews observations.19 Measures of ex ante conservatism, including Easton and Pae’s (2004) measures of conservatism due topositive NPV projects and rules and Penman and Zhang’s (2002) ‘hidden reserves’ measure, should notnecessarily be correlated with the AT ratio.20 The market-to-book ratio is also impacted by ex ante conservatism since ex ante conservatism excludesrents from book values and can also understate separable assets via, for example, accelerated depreciation.21 For the AT coefficients, discretionary accruals, and the persistence coefficient the null to test significanceis zero. For the ratios and book-to-market the null to test significance is one, and for the CR rank significanceis tested relative to a null of three.


123

sign of the AT coefficient correlations are opposite of the predictions in several cases,

consistent with cross-sectional comparisons of the coefficient being impacted by the

portion of returns due to separable assets. Consistent with Givoly et al. (2007), the AT

coefficient is positively associated with book-to-market although the correlation is not

significant at 10 %. Moreover, the AT coefficient is positively correlated with cumulative

discretionary accruals and negatively correlated with the persistence of negative earnings

coefficient (although insignificant). However, we note that the persistence coefficient is not

estimated very precisely within industries as indicated by only 18 of 48 industries

exhibiting a significant persistence coefficient estimate. As expected, the AT coefficient is

positively associated with the CR from Callen et al. (2010).

Table 8 shows that the AT ratio exhibits the predicted correlations for all of the

reported measures of conservatism except for the Spearman correlation with the per-

sistence coefficient, which, may also be affected by the imprecise estimates of persis-

tence. The AT coefficient estimated with returns interacted with BTM has correlations

more consistent with expectations than the AT coefficient estimated with non-interacted

returns; however, the Pearson correlation with discretionary accruals is insignificant.

Both the Pearson and Spearman correlations with the persistent coefficient are negative,

inconsistent with predictions, but insignificant. This may again be due to the fact that

the persistence coefficient is fairly noisy. The ratio computed from the regression with

returns interacted with the book-to-market ratio has insignificant correlations with the

AT coefficient, the persistence coefficient and the CR Rank measure. Otherwise, it

exhibits the predicted correlations.

5.2 Time series properties of the AT ratio

In untabulated analysis, we also examine the variation across time for the AT coefficient

and ratio estimated with both returns and returns interacted with BTM. The coefficients of

variation (standard deviation divided by the mean) for the coefficient, coefficient with

BTM interaction, ratio, and ratio with BTM interaction for our sample are 0.518, 0.424,

0.172 and 0.103. The ratios have less variability over time than the coefficients, a desirable

property since asymmetric timeliness, as required by accounting standards, does not

change dramatically from year to year. Estimating using returns interacted with BTM

reduces the variation over time, although not as much as the reduction achieved by the ratio

measures.

The time series data also indicate that there was a fundamental shift in both the AT

coefficient and the ratio in the mid-1970 s. The coefficients of variation for years prior

to 1975 are 1.175, 0.738, 1.082, and 1.165 for the four measures, substantially higher

than years following 1975. This shift could be explained by the emergence of the

Financial Accounting Standards Board (FASB) in 1973 and the early standards written

by the board. In 1975, the FASB issue Statement No. 5, Accounting for Contingencies,

which required the recognition of loss contingencies but not gain contingencies (FASB

1975). Statement No. 15, Accounting by Debtors and Creditors for Troubled DebtRestructuring, was issued in 1977, requiring creditors to reduce yields for adjustments

of payment terms of troubled debt if the aggregate payments were not less than the

carrying amount; however, if the aggregate payments were less than the carrying

amount, the creditor must reduce the carrying amount and record a loss. Therefore,

smaller losses adjusted future gains, while larger losses required current period recog-

nition consistent with asymmetric timeliness.


123

Ta

ble

8C

orr

elat

ion

sb

etw

een

con

serv

atis

mm

easu

res

Pre

dic

tion

(coef

f&

rati

o)

AT

coef

f

pos

Rat

io

pos

AT

coef

fw

/BT

M

pos

Rat

iow

/BT

M

pos

Book-t

o-m

arket

neg

Dis

cac

crual

s

neg

Per

sist

coef

f

pos

CR

rank

pos

AT

coef

fici

ent

pos

0.1

82

(0.2

36)

0.4

72

(0.0

01)

0.0

52

(0.7

38)

0.2

27

(0.1

38)

0.1

61

(0.2

97)

-0.2

42

(0.1

14)

0.3

75

(0.0

12)

Rat

iopos

0.2

22

(0.1

48)

0.6

31

(0.0

00)

0.8

05

(0.0

00)

-0.6

13

(0.0

00)

-0.4

64

(0.0

02)

0.1

25

(0.4

19)

0.2

83

(0.0

63)

AT

coef

fw

/BT

Mpos

0.6

80

(0.0

00)

0.3

27

(0.0

30)

0.5

25

(0.0

00)

-0.4

30

(0.0

04)

-0.4

39

(0.0

03)

-0.0

85

(0.5

83)

0.2

83

(0.0

63)

Rat

iow

/BT

Mpos

0.0

40

(0.7

99)

0.7

58

(0.0

00)

0.3

36

(0.0

26)

-0.3

69

(0.0

14)

-0.3

47

(0.0

21)

-0.0

04

(0.9

81)

0.1

62

(0.2

93)

Book-t

o-m

arket

neg

0.2

26

(0.1

40)

-0.5

06

(0.0

01)

-0.2

76

(0.0

70)

-0.4

02

(0.0

07)

0.7

12

(0.0

00)

-0.3

49

(0.0

20)

0.0

63

(0.6

84)

Dis

cret

ionar

yac

crual

sneg

0.2

79

(0.0

67)

-0.2

85

(0.0

61)

-0.1

57

(0.3

10)

-0.3

28

(0.0

30)

0.7

21

(0.0

00)

-0.2

30

(0.1

33)

-0.0

20

(0.8

95)

Per

sist

ence

coef

fici

ent

pos

-0.1

91

(0.2

14)

0.3

08

(0.0

42)

-0.1

15

(0.4

59)

0.0

72

(0.6

41)

-0.3

44

(0.0

22)

-0.2

14

(0.1

64)

-0.0

45

(0.7

70)

CR

rank

pos

0.4

12

(0.0

06)

0.3

23

(0.0

32)

0.3

08

(0.0

42)

0.1

04

(0.5

02)

0.0

68

(0.6

59)

-0.0

49

(0.7

51)

0.1

14

(0.4

60)

Num

ber

signifi

cant

46

34

46

38

36

43

18

30

Per

cent

signifi

cant

95.8

%70.8

%95.8

%79.2

%75.0

%89.6

%37.5

%68.2

%

Null

for

signifi

cance

test

s0.0

00

1.0

00

0.0

00

1.0

00

1.0

00

0.0

00

0.0

00

3.0

00

This

table

report

sth

eco

rrel

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ns

bet

wee

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fere

nt

mea

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acco

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ng

conse

rvat

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bas

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esti

mat

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rth

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Fam

aan

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(1997

)in

dust

rygro

ups.

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table

dis

pla

ys

Pea

rson

corr

elat

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bel

ow

the

dia

gonal

and

Spea

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corr

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above,

wit

hp

val

ues

report

edin

par

enth

esis

bel

ow

the

corr

elat

ion

esti

mat

es.

The

asym

met

ric

tim

elin

ess

(AT

)

coef

fici

ent

isth

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tim

ate

of

b3

ina

regre

ssio

nof

pri

ce-s

cale

dea

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per

shar

eon

retu

rns:

EP

Sit/P

i,t2

1=

b0

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11(r

it\

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?b

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iable

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retu

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are

neg

ativ

ean

dze

rooth

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ise.

Rat

ioeq

ual

sth

ees

tim

ates

b3/(b

2?

b3).

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ula

tive

dis

cret

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yac

crual

sar

eth

esu

mof

tota

lac

crual

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etin

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cash

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ns)

less

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gac

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pay

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)sc

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sets

inth

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a(G

ivoly

and

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).T

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Per

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coef

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tim

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regre

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=b

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b1

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arn

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len

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)in

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ted

retu

rns.

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use

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age

rank

wit

hin

indust

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ry-l

evel

mea

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rvat

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elat

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wit

hth

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effi

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tan

dra

tio

mea

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sonly

list

edas

‘pos’

for

posi

tive

and

‘neg

’fo

r

neg

ativ

e.*,

**,

and

***

indic

ate

signifi

cance

at10,

5,

and

1%


123

6 Conclusion

Many empirical studies on accounting conservatism employ regression-based measures of

earnings’ asymmetric recognition of bad news as in Basu (1997). Recent studies have

shown that such tests may be unsuitable for some comparisons of conservatism across

groups of firms or across time (Givoly, Hayn and Natarajan 2007). We develop an

econometric model that shows that coefficient estimates appear to provide valid indications

of the presence of conservatism but also reflect the portion of firm value attributable to

future rents. This occurs because book values primarily represent a firm’s separable net

assets while market values and returns incorporate the value of both separable assets and

the value of non-separable assets and expected future economic rents. Two groups of firms

with identical accounting policies can therefore have different asymmetric timeliness

coefficients simply because one group’s returns are more associated with future rents that

are not included in book values. We show that a simple test using a ratio of regression

coefficients controls for the effects of value composition. This allows for comparisons of

conservatism across groups.

We derive several predictions from our empirical model and test them on a large sample

from the CRSP and Compustat databases often used in tests of accounting conservatism.

We find support for our predictions, which suggests that the model provides a reasonable

description of actual data. Furthermore, we verify that the ratio tests are well-behaved, in a

statistical sense, by demonstrating that delta method confidence intervals for the ratios

provide very close approximations to more robust confidence intervals based on Fieller’s

Theorem (Fieller 1954).

We conclude by noting that the usefulness of coefficient ratios extends beyond studies

of accounting conservatism. The test design in this study compared the incremental

response of an event, bad news, across groups of firms. A variety of studies in accounting,

such as those that compare the effects of regulation across groups of firms, utilize a similar

test design. We conjecture that inferences based on coefficient ratios may be more

appropriate than inferences based on the coefficients themselves, in cases where coefficient

biases may be associated with the sample partition.

Acknowledgments We thank an anonymous referee, David Aboody, Sudipta Basu, Peter Demerjian,Carla Hayn, Jack Hughes, Steve Matsunaga, Karl Muller, Eddie Riedl and workshop participants at AAAFARS 2009 Mid-Year Meeeting, Pennsylvania State University, UCLA and University of Southern Cali-fornia for their helpful comments. This paper was previously titled ‘‘On the estimation of the asymmetrictimeliness of earnings: Inference and bias corrections.’’

Appendix

This appendix describes the construction of confidence intervals for ratios of regression

coefficients based on Fieller’s Theorem (Fieller 1954) as implemented in Zerbe (1978).22

The confidence intervals are based on transforming a hypothesis for the ratio to an

equivalent linear hypothesis. Given a value k, a vector b of p estimated coefficients and

vectors n and d, the ratio n0b=d0b [ k if and only if n0b=kd0b [ 0.23 Given estimated

coefficients b and covariance matrix R , a t-statistic for the inequality is:

22 Also see Staiger, Stock and Watson (1997) for an example application of this approach.23 If d0b\ 0 the second inequality is reversed, but (A2) still determines the confidence interval.


123

T ¼ ðn0b� kd0bÞ=ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

n0Rn� 2kn0Rd þ k2d0Rdp

: ð9ÞGiven a confidence level a, sample size N and critical t-statistic ta/2 with N - p degrees

of freedom such that P(-ta/2 \ T \ ta/2) = 1 - a, the confidence intervals on the ratio

level k solve:

a0 þ 2a1k þ a2k2� 0; ð10Þ

a0 ¼ ðn0bÞ2 � t2a=2n0Rn a1 ¼ t2

a=2n0Rd � ðn0bÞðd0bÞ a2 ¼ ðd0bÞ2 � t2a=2d0Rd ð11Þ

If a2 [ 0, then the 1 - a confidence interval for the ratio is ð�a1 �ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

a21 � a0a2

p

Þ=a2.24

If a2 \ 0 then the confidence interval is unbounded.25 In this case, if a21 � a0a2\0, then

the confidence interval is the entire real line. If a2 \ 0 and a21 � a0a2 [ 0, then the con-

fidence interval is the complement of ð�a1 �ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

a21 � a0a2

p

Þ=a2.26

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Conservatism measures that control for the effects of economic rents on stock returns

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