Conservatism and the Information Content of Earnings

Mary E. Barth,1 Wayne R. Landsman,2 Vivek Raval,2 and Sean Wang2

October 2014

1. Graduate School of Business, Stanford University, Stanford, CA, 94305,

mbarth@stanford.edu. 2. Kenan-Flagler Business School, University of North Carolina at Chapel Hill, Chapel Hill, NC

27599, wayne_landsman@unc.edu, vraval@kenan-flagler.unc.edu, sean_wang@kenan-flagler.unc.edu.

We thank Alfred Wagenhofer and participants at the Columbia Business School Burton Workshop, University of Melbourne, and University of Washington, Bothell, for helpful comments. We appreciate funding from the Center for Finance and Accounting Research at UNC-Chapel Hill. Corresponding author is Mary E. Barth.

Conservatism and the Information Content of Earnings

Abstract

This study finds that more conservative earnings have lower information content in that higher conditional conservatism decreases the speed with which equity investor disagreement and uncertainty resolve at earnings announcements. We find that a measure of conditional conservatism that varies cross-sectionally and intertemporally is negatively related to the ratio of average daily equity trading volume (return volatility) during the annual earnings announcement window to average daily volume (volatility) during the post-announcement window. We also find positive returns subsequent to the earnings announcement for firms with higher conditional conservatism, which is consistent with investors being unable to adjust fully for the negative bias in conservative earnings. We find higher levels of insider purchases in the post-announcement window for firms with more conservative earnings, which is consistent with insiders taking advantage of the investor underreaction.

Conservatism and the Information Content of Earnings

1. Introduction

This study examines whether earnings that are more conservative have lower information

content. In particular, we examine whether higher conditional conservatism decreases the speed

with which equity investor disagreement and uncertainty resolve at earnings announcements.

Conditional conservatism is a property of accounting earnings that recognizes good news on a

less timely basis than bad news. Although prior studies find evidence of benefits to

equityholders from conditional conservatism, there is less evidence relating to costs to

equityholders. One potential cost is lower information content of earnings, which impedes

equityholders’ ability to discern the valuation implications of earnings when they are announced.

We find evidence of such costs, in that higher conditional conservatism decreases the speed with

which equity investor disagreement and uncertainty resolve at earnings announcements. We also

find that firms with higher levels of conditional conservatism have positive stock returns and

higher levels of stock purchases by insiders subsequent to earnings announcements.

One of the most widely documented empirical regularities in the accounting literature is

conditional conservatism, which is evidenced by a larger response coefficient for negative return

than for positive return in an earnings-return regression. The benefits of conservative accounting

practices have been widely discussed in the literature. For example, prior literature finds that

conservatism helps to minimize contracting costs between debtholders and the firm, and to

reduce managers’ incentives and ability to manipulate accounting amounts. Thus, conservatism

benefits both debtholders and equityholders. However, the literature also provides evidence that

conservatism varies by industry and over time, which is consistent with there being costs to

conservative accounting that offset benefits.

2

We address our research question by developing a measure of conditional conservatism

that varies cross-sectionally and intertemporally, and testing whether the measure has a

significantly negative relation with two measures of information content of earnings at earnings

announcements. Finding a negative relation implies that investor disagreement and uncertainty

resolve more slowly with the announcement of annual earnings the greater is conditional

conservatism. Our measure of conditional conservatism is based on the Basu (1997) asymmetric

timeliness coefficient, and uses a two-step estimation approach adapted from Barth, Konchitchki,

and Landsman (2013). Our two information content measures are based on earnings

announcement equity trading volume and stock return volatility, which are two commonly

employed measures of information content. Specifically, our measures are the ratios of average

daily volume and volatility during the four-day annual earnings announcement window to

average daily volume and volatility during the 18-day window beginning the third day after the

earnings announcement. We test our predictions by estimating the relation between our earnings

information content measures and our conservatism measure, including controls for known

determinants of trading volume and equity volatility. Findings from our primary tests that

include as controls size, the equity book-to-market ratio, and leverage are based on 39,539

annual earnings announcements associated with fiscal year ends from 1994 to 2011; findings

from tests that include additional controls are based on 23,771 earnings announcements.

The findings from all of our tests are consistent with the prediction that conditional

conservatism is significantly negatively related to both earnings information content measures.

These findings indicate that conservatism is associated with a delay in the time it takes for

resolution of investor disagreement as reflected in trading volume, and a delay in the time it

takes for average investor beliefs to update fully as reflected in equity volatility.

3

Because higher conditional conservatism requires investors to spend more time

interpreting the information in earnings announcements, and information processing is costly, we

expect the price adjustments to announcements of conservative earnings also to be delayed. If

earnings are conservative and investors are unable to adjust fully for the negative bias in earnings

resulting from conservatism, then stock returns subsequent to the announcement will be more

positive for firms with more conservative earnings. Thus, we test whether firms with higher

levels of conditional conservatism have positive stock returns subsequent to earnings

announcements, and find that they do. This finding suggests that insiders can take advantage of

this investor underreaction by purchasing the firm’s shares in the post-announcement period, and

we find evidence that they do.

We contribute to the conservatism literature as it relates to equity markets by examining

the extent to which conservatism affects the information content of earnings announcements. In

particular, we find that higher conservatism decreases the speed with which investor

disagreement and uncertainty resolve at earnings announcements. We also contribute to the

literature by finding that firms with higher levels of conditional conservatism have positive stock

returns and higher levels of stock purchases by insiders subsequent to earnings announcements.

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

predictions and related research. Section 3 develops the research design, and section 4 describes

the sample. Section 5 presents our results and section 6 concludes the study.

2. Basis for Predictions and Related Research

Basu (1997) and subsequent studies (Ball, Kothari, and Robin, 2000; Qiang, 2007;

Beaver, Landsman, and Owens, 2012) find evidence of conditional conservatism for US firms.

Basu (1997) adopts the view that return reflects the change in the firm’s economics and thus a

4

firm has good (bad) news when return is positive (negative). Conditional conservatism is a

property of accounting earnings that recognizes good news on a less timely basis than bad news.

To the extent that a firm’s earnings exhibits conditional conservatism its earnings more closely

reflects the change in its economics when its return is negative than when it is positive. Hence,

Basu (1997) measures conditional conservatism by a larger coefficient for negative than for

positive annual return in an earnings-return regression.

Ball, Kothari, and Robin (2000) and Ball, Robin, and Wu (2003), among others, find

evidence of conservatism for firms in several other countries, where the extent of conservatism

depends on a variety of institutional factors. Taken together, these studies show that

conservatism is generally higher for US firms, which is interpreted as indirect evidence that

conservatism is beneficial because the US is generally viewed as having the most efficient

capital market in the world. Other studies find that conditional conservatism has increased in the

US in the past few decades (Watts, 2003; Beaver, Landsman, and Owens, 2012), which suggests

that the demand for conservative accounting continues to increase.

Several studies identify benefits to equityholders from conservative accounting. Watts

(2003) explains that conservative accounting helps minimize conflicts between debtholders and

equityholders, thereby enabling more efficient contracting, which results in benefits to

equityholders in the form of lower debt costs. LaFond and Watts (2008) posits that conservatism

reduces a manager’s incentives and ability to manipulate accounting amounts, thereby reducing

information asymmetry between firm insiders and outside equity investors. This results in higher

equity values. LaFond and Watts (2008) finds support for this supposition by providing

evidence that information asymmetry is significantly negatively related to conservatism after

controlling for other demands for conservatism. Penman and Zhang (2013) finds that

5

conservative accounting reveals information about the riskiness of the firm and thus the

appropriate discount rate to apply to the firm’s expected cash flows. D’Augusta, Bar-Yosef, and

Prencipe (2012) finds that conservatism reduces investor disagreement at earnings

announcements as measured by lower abnormal trading volume in the three-day announcement

window. The study interprets this finding as evidence that conservatism improves informational

efficiency. However, other studies (Beaver, 1968, Kim and Verrecchia, 1991a; Landsman and

Maydew, 2002) interpret lower trading volume as lower information content. Regardless,

D’Augusta, Bar-Yosef, and Prencipe (2012) does not control for the total amount of trading

volume associated with the earnings announcement. Thus, the study does not examine the speed

with which equity investor disagreement resolves at earnings announcements, which is a focus of

our study.

Kim, Li, Pan, and Zuo (2013) builds on LaFond and Watts (2008) by predicting that

accounting conservatism reduces financing costs in seasoned equity offerings (SEO) because

conservatism mitigates the negative impact of information asymmetry. New investors engage in

less price protection when new shares are issued and therefore are willing to pay higher prices

for the shares. Consistent with predictions, Kim et al. (2013) finds that issuers with higher

conservatism experience less negative equity returns at SEO announcements. Louis, Sun, and

Urcan (2012) finds that conservatism benefits equityholders by providing incentives for more

efficient real investment decisions by managers. This occurs because conservatism provides

better monitoring of managers’ investment decisions, and therefore mitigates the value

destruction associated with cash holdings. Using various proxies for conservatism, Louis, Sun,

and Urcan (2012) finds that the equity market values an additional dollar of cash holdings more

for firms with greater accounting conservatism, suggesting that conservatism is associated with a

6

more efficient use of cash. Francis, Hasan, and Wu (2013) finds that firms with more

conservative accounting had less significant losses in equity values during the 2009 financial

crisis. Balakrishnan, Watts, and Zuo (2013) finds a similar result, and concludes that accounting

conservatism improved borrowing capacity, reduced underinvestment, constrained managerial

opportunism, and enhanced firm value. Finally, García Lara, García Osma, and Penalva (2011)

provides evidence that higher conditional conservatism is associated with lower implied cost of

capital.

Other research suggests that conservatism is associated with costs for debtholders and

equityholders. Regarding debtholders, Gigler, Kanodia, Sapra, and Venugopalan (2009) shows

analytically that accounting conservatism can decrease the efficiency of debt contracts if it

results in unwarranted covenant violations. Regarding equityholders, costs can arise from the

increase in investor uncertainty regarding the valuation implications of a firm’s earnings

associated with higher conservatism. Mensah, Song, and Ho (2004) finds evidence that analysts’

earnings forecast errors and dispersion of analysts’ forecasts are positively associated with the

Penman and Zhang (2002) measure of accounting conservatism. These findings suggest that

conservative accounting makes it more difficult for analysts to forecast earnings. However,

Mensah, Song, and Ho (2004) does not address whether more conservative accounting results in

lower information content of earnings, which makes it more difficult for investors to discern the

valuation implications of earnings when they are announced.

We contribute to the conservatism literature as it relates to equity markets by examining

the extent to which conditional conservatism affects the information content of earnings

announcements. In particular, we predict that higher conditional conservatism decreases the

speed with which investor disagreement and uncertainty resolve at earnings announcements.

7

When the difference between the positive and negative return coefficient in the earnings-return

relation is large, the range of the implied appropriate earnings multiple is large. Thus, the

difference between the coefficients measures the extent to which investors are uncertain about

the valuation implication of, and therefore how to interpret, the firm’s earnings. This greater

uncertainty manifests as investors taking more time to assess the valuation implication of

earnings.

We also predict that during the post-earnings announcement period higher conditional

conservatism is associated with greater positive price drift. If investors do not adjust fully for the

negative bias in earnings resulting from conservatism, then stock returns subsequent to the

announcement will be more positive for firms with more conservative earnings. This could be

the case, for example, if investors exhibit the functional fixation bias documented in Hand (1990)

and Sloan (1996). Assuming insiders understand the valuation implications of earnings and

other investors exhibit such functional fixation, then the insiders can take advantage of this

investor underreaction. Thus, we also predict that during the post-earnings announcement period

higher conditional conservatism is associated with more net purchases of shares by insiders.

3. Research Design

3.1 Measure of Asymmetric Earnings Timeliness

Most conditional conservatism studies base their measure of conservatism on Basu

(1997). The Basu (1997) measure is the incremental coefficient on negative annual equity return

from a regression, as specified by equation (1), of annual earnings on equity return that permits

the coefficients to differ for positive and negative return.

, (1) , 0 1 , 2 , 3 , , ,*i t i t i t i t i t i tX DR R DR R v

8

where is earnings deflated by beginning of year price, is annual stock return beginning

three months after the start of the fiscal year, is an indicator variable that equals one if is

negative and zero otherwise, and i and t refer to firm and year. Basu (1997) adopts the view that

return reflects the change in the firm’s economics and refers to positive and negative equity

returns as good and bad news. Basu (1997) finds that the incremental coefficient for negative

return is significantly positive and concludes that bad news is incorporated into earnings on a

more timely basis. Thus, the larger is the incremental coefficient on negative return, , the less

timely is earnings when the firm experiences good news.1 is referred to as the asymmetric

timeliness coefficient.2

Typically, the asymmetric timeliness coefficient is based on a cross-sectional regression

for a group of firms, which assumes that all firms in the group have the same coefficients at a

point in time if the regression is estimated annually, and across time if a panel of data is used.

However, as Khan and Watts (2009) notes, empirical research on conservatism requires a metric

that exhibits both cross-sectional and intertemporal variation in conservatism. Thus, to test

whether higher conservatism decreases the speed with which investor disagreement and

uncertainty resolve at earnings announcements, we need to modify the Basu (1997) measure to

obtain a measure of conditional conservatism that varies cross-sectionally and intertemporally.

Our measure of conditional conservatism, ATC, is based on the Basu (1997) asymmetric

1 Basu (1997) also measures conditional conservatism based on the ratio of the sum of the positive and negative return coefficients to the positive return coefficient, i.e., . This measure is also employed by Givoly and

Hayn (2000), Ryan and Zarowin (2003), and Beaver, Landsman, and Owens (2012), among others, although the incremental coefficient typically is the primary focus. We do not use the ratio measure because most of our sample years are post-1996, after which Ryan and Zarowin (2003) finds that the positive return coefficient is negative and insignificantly different from zero. Givoly and Hayn (2000) and Ryan and Zarowin (2003) offer as an explanation the greater presence of younger, less profitable, and high growth firms in post-Basu (1997) sample years. 2 Dietrich, Muller, and Riedl (2007), Givoly, Hayn, and Natarajan (2007), and Patatoukas and Thomas (2011) raise concerns about potential bias in the coefficient estimates from the Basu (1997) specification as a measure of conditional conservatism. To the extent that such biases exist, it will be more difficult to detect a relation between the measure and investor disagreement and uncertainty at earnings announcements.

X R

DR R

3

3

(2 3 ) / 2

9

timeliness coefficient, and is adapted from the two-step estimation approach of Barth,

Konchitchki, and Landsman (2013).3

Our measure for each firm-year is the sum of the asymmetric timeliness coefficients, ,

from that firm-year’s earnings-return relation given by equation (1) estimated in the two steps.4

The first we use to construct ATC is that from annual estimations of equation (1) by industry.

We use the industry classifications in Barth, Beaver, and Landsman (1998). By construction,

this component of ATC is the same for all firms for a given industry-year. There is a strong

industry component to the earnings-return relation as a result of accounting practices likely being

similar within industries (Barth, Beaver, Hand, and Landsman, 1999; 2005). However, as Barth,

Konchitchki, and Landsman (2013) notes, estimating the earnings-return relation by industry is

not likely to capture fully differences across firms in the earnings-return relation (Barth et al.,

2005).

The second we use to construct ATC is that from the annual estimations of equation

(1) by portfolio, where portfolio membership is based on the residuals from the industry

regressions. We use five portfolios for each year, where, for example, the first portfolio is

3 Khan and Watts (2009) develops a firm-year measure of conservatism, CSCORE, by expressing the asymmetric earnings timeliness coefficient as a linear function of firm size, the equity market-to-book ratio, and leverage. Although CSCORE is a firm-year measure, size, the market-to-book ratio, and leverage are key control variables in our tests of the relation between conservatism and trading volume and equity volatility at earnings announcements, EA_VOLM and EA_VOLA. Untabulated findings from estimations of equations (4) and (6) that include our estimate of CSCORE in place of ATC but exclude the three control variables reveal that our estimate of CSCORE has a significantly negative relation with EA_VOLM and EA_VOLA (coefficients = 0.10 and 0.96; t-statistics = 1.78 and 2.70). However, additional untabulated findings from estimations of equations (4), (6), (7), and (8) that include our estimate of CSCORE in place of ATC reveal the CSCORE coefficient is insignificantly different from zero in all four equations (coefficients = 0.01, 0.33, 0.01, and 0.34; t-statistics = 0.20, 0.68, 0.14, and .72). Although these results are not surprising in light of the fact that CSCORE is constructed from the three key control variables, the results reveal that CSCORE is not a viable measure of conditional conservatism for our tests. 4 When estimating equation (1) we eliminate firm-year observations that have both negative earnings and positive return. We do so because such observations do not fit the logic underlying the Basu (1997) measure of conditional conservatism. In particular, Figure 2 in Basu (1997, p.12), which depicts the hypothesized association between earnings and return under conservatism, presumes a positive relation between earnings and return. Of note is that Figure 2 includes observations for all sign combinations of earnings and return except observations that have negative earnings and positive return, i.e., those observations in Quadrant IV.

3

3

3

10

comprised of the quintile of observations from each annual industry regression with the most

negative residuals.5 Thus, the portfolio regressions capture cross-sectional differences in the

earnings-return relation that are not captured fully by industry estimation. Also, the portfolios

are industry-neutral because each portfolio has the same industry composition. Thus, differences

in from the portfolio regressions cannot be attributed to differences in industry membership.6

Our asymmetric timeliness measure for firm i in year t, , is the sum of the s

from equation (1) pertaining to firm i’s industry and industry-neutral earnings-return regressions

in year t, which we label ATCI and ATCIN. Thus,

+ , (2)

where j and p denote industry and portfolio.

3.2 Association between Conservatism and Earnings Announcement Volume and Volatility

We base our tests for whether conservatism reduces the information content of earnings

at earnings announcements on two commonly employed earnings announcement information

content measures, equity trading volume and stock return volatility. Theoretical and empirical

studies suggest that the greater the information content in an earnings announcement, the greater

are volume and volatility (Beaver, 1968; Kim and Verrecchia, 1991a; Landsman and Maydew,

2002). Based on this literature, we interpret equity trading volume as measuring the extent to

which an announcement generates diversity in opinions across investors; the greater the

5 As Barth, Konchitchki, and Landsman (2013) notes in a different research context, selection of the optimal number of portfolios is an empirical matter reflecting a tradeoff between precision of estimation and forcing otherwise different groups of firms to have the same asymmetric timeliness of earnings. 6 Forming portfolios based on residuals from the industry regressions does not effectively group firms ex ante according to the magnitude of their earnings. First, although for our sample the untabulated cross-industry mean correlation between earnings and residuals from the annual industry-by-industry earnings-return regressions is 85%, the untabulated mean cross-portfolio correlation from the annual portfolio-by-portfolio regressions is only 34%. Second, untabulated findings from annual regressions based on portfolios explicitly ranked on earnings reveal little explanatory power; the mean (across 18 years) adjusted R2 is only 3%.

3

ATCi,t 3

ATCi,t ATCI j,t ATCIN p,t

11

information content of an announcement, the more likely investors will interpret the content

dissimilarly, and thus the more they will trade as a result of their dissimilar interpretations. Also

based on this literature, we interpret stock return volatility as measuring the extent to which an

announcement changes investors’ beliefs on average; the greater the content, the more investors’

beliefs are likely to change on average.

If an earnings announcement is informative in terms of resolving diversity of investor

opinions regarding the information in the announcement, then there will be a spike in trading

volume in the days immediately surrounding the announcement. However, if a firm’s earnings is

not fully transparent, then trading volume might remain high for an extended period of time until

investors resolve their disagreement regarding the information content of the announcement.

Accordingly, we calculate our first information content measure, EA_VOLM, as the ratio of

average daily trading volume during the four-day window surrounding the earnings

announcement, , to the average daily trading volume beginning the third trading day

after the announcement and extending 18 trading days, :

. (3)

is the average daily trading volume over the relevant window relative to earnings

announcement day zero.

To test whether conservatism has a negative effect on information content as reflected by

trading volume, we estimate the following regression:

. (4)

We predict the coefficient, , is negative if conservatism causes a delay in the time it

takes for resolution of investor disagreement.

(1,2)

(3,20)

EA_VOLM

i,tVOLM (1,2)

i,t/ VOLM (3,20)

i,t

VOLM

EA_VOLMi,t

0

1ATC

i,t

2Size

i,t

3BM

i,t

4Lev

i,t

i,t

ATC 1

12

We include as controls firm size, the natural logarithm of equity market value, Size; the

equity book-to-market ratio, BM; and financial leverage, the ratio of debt to total assets, Lev. We

include size as a control because to the extent that larger firms provide more disclosure, larger

firms’ earnings announcements are expected to provide less new information than those of

smaller firms (Atiase, 1985; Freeman, 1987). Thus, to the extent that larger firms have richer

information environments than smaller firms, larger firms’ earnings announcements are likely to

convey less news, which leads to the prediction that the Size coefficient, , is negative. We

include the equity book-to-market ratio as a proxy for investor attention, where a higher ratio

indicates the firm has less visibility (Peress, 2008). Thus, a higher equity book-to-market ratio

would suggest a slower investor reaction when earnings are announced, which leads to the

prediction that the BM coefficient, , is negative. Finally, because prior literature shows that

leverage is positively associated with conservatism (Watts, 2003; Khan and Watts, 2009), we

include leverage to test whether ATC has explanatory power incremental to leverage.

Similarly, if an earnings announcement is informative in terms of changing average

investor beliefs regarding the information in the announcement, then there will be a spike in

equity volatility in the days immediately surrounding the announcement. If a firm’s earnings are

not fully transparent, then volatility might remain high for an extended period of time until

average investor beliefs regarding the information content of the announcement change fully.

Accordingly, as with EA_VOLM, we calculate our second information content measure,

EA_VOLA, as the ratio of average daily equity volatility during the four-day window surrounding

the earnings announcement, , to average daily volatility beginning the third trading day

after the earnings announcement and extending 18 trading days, :

. (5)

2

3

(1,2)

(3,20)

EA_VOLA

i,tVOLA(1,2)

i,t/ VOLA(3,20)

i,t

13

is the average daily volatility, VOLA, over the relevant window relative to earnings

announcement day zero. Following prior literature (Beaver, 1968; Landsman and Maydew,

2002), we compute VOLA on any given trading day as the square of residual equity return for

that day. We calculate the daily residual return as the difference between realized return and

expected return based on the Fama and French (1993) three-factor model supplemented with the

Carhart (1997) momentum factor, time-varying factor loadings, risk-free interest rates, and risk

premia.7

To test whether conditional conservatism has a negative effect on information content as

reflected by equity volatility we estimate the following regression:8

. (6)

We predict the coefficient, , is negative if conservatism causes a delay in the time it

takes for average investor beliefs to change fully. Equation (6) uses the same controls as those in

equation (4). For both equations (4) and (6) and equations (7) through (10) below, we include

fixed effects for industry and earnings announcement month-year, and cluster standard errors by

firm and earnings announcement month-year.9

We also estimate versions of equations (4) and (6) that include additional control

variables, given by equations (7) and (8).

EA_VOLMit

0

1ATC

i ,t

2Size

i ,t

3BM

i ,t

4Lev

i ,t

5MOM

i ,t

6DISP

i ,t

7FE

i ,t

8EA_ RET

i ,t

9NEG

i ,t

10TURN _ Pre

i ,t

11ASQRET

i ,t

it

(7)

7 To calculate EA_VOLA (EA_VOLM) we require a minimum of three non-missing returns (volume) in the four-day announcement window, and 15 in the 18 day post-announcement window. We also calculated EA_VOLM and EA_VOLA extending the earnings announcement from day 14 to day +1. This allows for preannouncement of earnings news (Skinner, 1997). Untabulated findings reveal inferences identical to those based on tabulated findings. 8 For ease of exposition, we use the same notation for coefficients and error terms in equations (4) and (6), as well as in equations (7) through (10). In all likelihood they differ. 9 Throughout we use a five percent significance level under a one-sided alternative when we have a signed prediction and under a two-sided alternative otherwise.

VOLA

EA_VOLAi,t

0

1ATC

i,t

2Size

i,t

3BM

i,t

4Lev

i,t

i,t

ATC 1

14

EA_VOLAit

0

1ATC

i ,t

2Size

i ,t

3BM

i ,t

4Lev

i ,t

5MOM

i ,t

6DISP

i ,t

7FE

i ,t

8EA_ RET

i ,t

9NEG

i ,t

10TURN _ Pre

i ,t

11ASQRET

i ,t

it

(8)

MOM is pre-announcement period price momentum, which equals the firm’s equity return for

the first ten months of the current fiscal year. We include MOM because prior research

establishes a positive (negative) relation between momentum and trading volume (equity

volatility) (Lee and Swaminathan, 2000; Stivers and Sun, 2010), and therefore predict is

positive (negative) in equation (7) (equation (8)). Analyst dispersion, DISP, and forecast error,

FE, are indications of uncertainty in advance of the earnings announcement (Barron, Kim, Lim,

and Stevens, 1998; Barron and Stuerke, 1998; Zhang, 2006). We define DISP as the standard

deviation of analyst earnings forecasts of the current year’s earnings immediately preceding the

earnings announcement. To construct DISP, we require at least three forecasts. We define FE as

the absolute value of the difference between the mean analyst forecast of the current year’s

earnings and actual earnings divided by beginning-of-year stock price. When constructing DISP

and FE we exclude forecasts made more than 120 days before the earnings announcement. If the

earnings announcement removes pre-announcement uncertainty, then the investor response may

be heightened just after the announcement. If the uncertainty persists, investor response may be

dampened. Thus we have no sign predictions for the DISP and FE coefficients, and .

We include the earnings announcement return, EA_RET, as a control for the signed

magnitude of news at the earnings announcement. We define EA_RET as the Fama-French plus

momentum factor-adjusted returns over the window relative to the earnings

announcement. We include an indicator variable, NEG, which equals one if the announced

earnings is negative and zero otherwise, because trading volume and equity volatility at the

earnings announcement could differ depending on the sign of earnings. We have no predictions

5

6

7

(1,2)

15

for the signs of 8and

9. and ASQRET are the average daily turnover and

average squared daily excess return during the pre-announcement period, which we define as the

60 days before and ending 2 days before the earnings announcement.10 We include TURN_Pre

as a control for pre-announcement trading volume, and because prior research establishes a

positive relation between volume and volatility (Admati and Pfleiderer, 1988; Kim and

Verrecchia, 1991b; Jones, Kaul, and Lipson, 1994). Therefore, we predict 10 is positive in both

equations. We include ASQRET as a control for pre-announcement return volatility and predict

11 is negative in both equations.

3.3 Effects of Reduced Information Content

3.3.1 Price Drift

Finding that conditional conservatism is associated with a delay in the time it takes for

resolution of investor disagreement and for average investor beliefs to change fully can be

interpreted as indicating that ATC creates a market friction at earnings announcements. That is,

higher ATC requires investors to spend more time interpreting the information in earnings

announcements. Because information processing is costly, we expect the price adjustments to

announcements of conservative earnings also to be delayed. If investors exhibit the functional

fixation bias documented in Hand (1990) and Sloan (1996), then stock returns following

announcements of conservative earnings may be predictable. Specifically, if earnings are

conservative and investors are unable to adjust fully for the negative bias in earnings resulting

from conservatism, then stock returns subsequent to the announcement will be more positive for

firms with more conservative earnings. Thus, we predict subsequent stock returns to be

10 We compute daily excess returns by subtracting from the realized daily return the expected daily return, which is based on the Fama-French plus momentum factor-adjusted return model.

TURN _ Pre

16

positively related with ATC.

To test whether this is the case, we conduct two tests. First, we form five portfolios

based on the magnitude of ATC. For each portfolio, we calculate the 18-day post-announcement

buy-hold excess return, ABRET_Post, as the excess return compounded over the period starting

three days after the earnings announcement and ending 18 days after the earnings

announcement.11 We compute excess return by subtracting the expected return based on the

Fama and French (1993) three-factor model supplemented with the momentum factor from the

raw post-announcement return. We predict higher (lower) ATC portfolios earn larger (smaller)

post-announcement returns. Specifically, we test whether the mean ABRET_Post for

announcements in the top quintile portfolio is greater than that in the bottom quintile portfolio.

Second, following Core, Guay, and Verdi (2008), we estimate the following annual cross-

sectional regression and aggregate coefficients using the Fama and MacBeth (1973) approach:

. (9)

Because we predict higher ATC is associated with larger post-announcement returns, we predict

that the ATC coefficient, , is positive. Equation (9) includes as controls fundamental risk

characteristics identified in prior research to ensure that our inferences regarding the association

between ABRET_Post and ATC are not attributable to the correlation between ATC and the

characteristics. The characteristics are Size, BM, Beta, the CAPM beta, and MOM. We have no

predictions for signs of their coefficients.

3.3.2 Insider Trading

If stock returns during the post-earnings announcement period are positively related with

ATC, then insiders potentially can take advantage of investor underreaction by purchasing the

11 To construct ABRET_Post, we require 15 non-missing return observations.

0 1 2 3 4 5_ i i i i i i iABRET Post a a ATC a Beta a Size a BM a MOM e

a1

17

firm’s shares. To test whether this is the case, as with price drift, we conduct two tests. First, we

form five portfolios based on the magnitude of net insider purchases, Insider_Purch, the number

of shares purchased minus the number of shares sold by all of the firm’s insiders (Lakonishok

and Lee, 2001) during the same 18-day post earnings announcement window that we use for the

price drift tests, divided by the firm’s shares outstanding as of the end of the fiscal year

(Jagolinzer, Larcker, Ormazabal, and Taylor, 2014). Following prior research (Lakonishok and

Lee, 2001; Jagolinzer, et al., 2014), we obtain insider stock transaction information reported on

SEC Form 4, which reports changes in insider ownership, from the Thomson Reuters Insider

Filing database. Insiders comprise a firm’s officers and directors, and any beneficial owners of

more than ten percent of a class of the firm’s equity securities.

We predict higher (lower) ATC portfolios are associated with larger (smaller) post-

announcement purchases of stock by insiders. Specifically, we test whether the mean

Insider_Purch for announcements in the top quintile portfolio is greater than that in the bottom

quintile portfolio.

Second, we estimate the following equation:

Insider _ Purchi

0

1ATC

i

2Size

i

3MOM

i

4EA_ RET

i e

i (10)

Because we predict higher ATC is associated with larger post-announcement insider purchases,

we predict that the ATC coefficient, 1, is positive. Equation (10) includes Size, BM, MOM, and

EA_RET as controls for determinants of insider purchases identified in prior research (Rozeff

and Zaman, 1998; Lakonishok and Lee, 2001), and industry and earnings announcement month-

year fixed effects. We base t-statistics on standard errors clustered by firm and earnings

announcement month-year.

18

4. Sample and Descriptive Statistics

We first identify a sample to construct ATC comprised of firms with fiscal year ends from

1994 to 2011. We refer to this sample as the ATC sample. When constructing the ATC sample,

we have four objectives. The first is to minimize the data restrictions we impose to mitigate

potential sample selection bias. Therefore, we only require the ATC sample firms to have data

available necessary to estimate equation (1). The second is to minimize the effects of extreme

observations on the estimates of ATC. Therefore, we eliminate observations whose earnings and

return are in the largest 99% and smallest 1% of the sample each year and with stock price less

$5 at fiscal year end (Basu, 1997; Khan and Watts, 2009). The third is to ensure there is

sufficient power to develop reliable estimates. Therefore, we require ten observations in each

industry-year. Finally, to ensure that the observations we use fit the logic underlying the Basu

(1997) measure of conditional conservatism, we eliminate observations with both negative

earnings and positive return (see footnotes 4 and 12). These four data requirements yield a

sample of 56,515 observations that we use to estimate equation (1). We obtain all of our data

from Compustat, CRSP, and I/B/E/S.

The sample we use as the basis for our tests comprises all ATC sample firm-year

observations with data necessary to estimate equations (4) and (6), and for which equity book

value is positive (Khan and Watts, 2009). These data requirements yield a sample of 39,539

annual earnings announcement observations. Requiring non-missing data for the additional

control variables in equations (7) and (8) results in a sample of 23,771 annual earnings

announcement observations. To eliminate the effects of outliers on our inferences, we winsorize

all continuous variables at the 1 and 99 percentile of all sample observations.

19

Table 1 presents descriptive statistics relating to the ATC sample and estimation of

equation (1). Panel A presents overall distributional statistics combining all industries and years.

Panel A reveals ATC averages 0.28, with a standard deviation of 0.10. ATC’s two components,

ATCI and ATCIN, each contributes equally, with means of 0.14 and 0.15. In addition, means of

X, R, and DR are 0.03, 0.04, and 0.51. Thus, although the average firm-year observation has

positive earnings and return, slightly more than half of the firm-year observations have negative

return.

Panels B and C present regression summary statistics from equation (1) estimated by

industry and by portfolio based on residuals from the industry estimations. All statistics are

based on 18 annual regressions, except for those relating to Other in panel B.12 Panel B reveals

that, on average, the incremental slope coefficient for negative return, 3, which is ATCI, is

0.13, and ranges from 0.06 for firms in Insurance and Real Estate to 0.18 for firms in Extractive

Industries and Transportation. In addition, the mean t-statistic for each industry exceeds 1.65 for

all but three industries, Chemicals, Insurance and Real Estate, and Other. On average, the slope

coefficient for positive return, 2, is 0.03, and ranges from 0.01 for firms in Computers,

Extractive Industries, and Other to 0.07 for firms in Insurance and Real Estate. In contrast to 3,

the mean 2 t-statistic for each industry exceeds 1.65 for only two industries, Durable

12 Consistent with hypothesized positive relation between earnings and return underlying the Basu (1997) measure of conditional conservatism and the absence of observations with negative earnings and positive return in Basu (1997), Figure 2, untabulated statistics reveal that these observations are the only earnings and return sign combination for which the correlation between earnings and return is negative. In particular, the Pearson correlation coefficients between earnings and return for the negative/positive, positive/positive, positive/negative, negative/negative, and combinations of earnings/return are 0.09, 0.11, 0.20, and 0.16. Additional untabulated statistics reveal that inclusion of the 4,529 negative/positive observations has a noticeable effect on mean ATC by biasing downward the coefficient on positive return in equation (1), thereby biasing upward the incremental coefficient for negative return. Mean ATC based on estimations including these observations is 7% higher than mean ATC we employ in our tests. This positive bias in the incremental coefficient for negative return obtains broadly across our industry and portfolio estimations, i.e., for ATCI and ATCIN. In addition, regression explanatory power is, on average, substantially smaller than that associated with estimations of equation (1) we use to construct ATC.

20

Manufacturers and Food. The magnitude of the difference between 3 and

2 and the general

insignificance of 2 is consistent with findings in prior research (Givoly and Hayn, 2000; Ryan

and Zarowin, 2003).

Turning to the portfolio regressions, panel C reveals that, on average, the incremental

slope coefficient for negative return, 3, which is ATCIN, is 0.14, and ranges from 0.10 to 0.20

across portfolios. In addition, the mean t-statistic for each industry exceeds 1.65 for all

portfolios. The slope coefficient for positive return, 2, is 0.02, and ranges from 0.01 to 0.03.

The mean 2 t-statistic exceeds 1.65 for four of the five portfolios, although the t-statistics are

substantially smaller than those for 3. The overall mean R2 for the portfolio regressions, 0.68,

is substantially larger than that for the industry regressions in panel B, 0.25.

The statistics in Table 1 indicate that there is little relation between earnings and return

when return is positive, and a strong positive relation when return is negative. In addition,

untabulated findings based on the industry and portfolio findings in panels B and C indicate there

is a significantly negative relation between the mean 3 and mean

2; the correlations are 0.58

and 0.91. Taken together, these findings reveal that observing a high ATC is indicative of a

weak relation between earnings and return when news is good.

Table 2 presents descriptive statistics for the variables used in estimating equations (4),

(6), (7), and (8). The statistics for ATC, EA_VOLM, EA_VOLA, Size, BM, and Lev are based on

39,539 observations, and the statistics for MOM, DISP, FE, EA_RET, NEG, TURN_Pre, and

ASQRET, are based on 23,771 observations. Most notably, Table 2 reveals that mean (median)

ATC is 0.28 (0.29), which is approximately twice the OLS estimate of the asymmetric timeliness

coefficient in Beaver, Landsman, and Owens (2012), 0.15, which uses a sample period that

21

overlaps with ours. Thus, the two-step approach that sums the asymmetric timeliness

coefficients from two regressions yields a mean estimate that is about double a benchmark value.

In addition, untabulated statistics reveal that ATC exhibits intertemporal variation; the Pearson

correlations between ATC and its first and second lags are 0.07 and 0.06. Means (medians) for

EA_VOLM and EA_VOLA are 1.60 (1.36) and 3.71 (1.67), which indicates that for both

measures the average daily market reaction during the four-day announcement window is

substantially larger than that during the 18-day post-announcement window.

Table 3 presents sample Pearson (Spearman) correlations above (below) the diagonal.

Most notably, Table 3 reveals that ATC is significantly negatively correlated with EA_VOLM and

EA_VOLA—the Pearson (Spearman) correlations between ATC and EA_VOLM and EA_VOLA

are 0.06 (0.08) and 0.07 (0.07). In addition, many of the control variables are significantly

correlated with ATC, EA_VOLM, and EA_VOLA.

5. Results

5.1 Regressions of EA_VOLM and EA_VOLA with ATC

Table 4, panel A, presents regression summary statistics for the EA_VOLM and

EA_VOLA estimating equations (4) and (6). The key finding is that, consistent with predictions,

the ATC coefficient is significantly negative for both equations. In particular, panel A reveals

that the ATC coefficients (t-statistics) are 0.23 and 0.85 (3.23 and 2.04) in the EA_VOLM

and EA_VOLA equations. These findings are consistent with conservatism being associated with

a delay in the time it takes for resolution of investor disagreement as reflected in trading volume,

and a delay in the time it takes for average investor beliefs to change fully as reflected in equity

volatility. In addition, findings relating to Size and BM are consistent with predictions, except

for Size in the EA_VOLA equation. In particular, for the EA_VOLM equation, the Size, BM, and

22

Lev coefficients are significantly negative (coefficients = 0.05, 0.01, and 0.09; t-statistics =

8.94, 6.64, and 2.13). For the EA_VOLA equation, the BM and Lev coefficients are

significantly negative (coefficients = 0.03 and 0.46; t-statistics = 8.53 and 2.05); contrary to

predictions, the Size coefficient is significantly positive (coefficient = 0.19; t-statistic = 7.68).

Table 4, panel B, presents regression summary statistics for the EA_VOLM and

EA_VOLA estimating equations (7) and (8) that include additional control variables. As in panel

A, panel B reveals that the ATC coefficient is significantly negative for both equations. The ATC

coefficients (t-statistics) are 0.10 and 1.38 (1.78 and 2.70) in the EA_VOLM and EA_VOLA

equations. Thus, our inferences regarding the association between ATC and EA_VOLM and

EA_VOLA are unchanged by inclusion of the additional control variables.13

Regarding the additional control variables, neither of the MOM coefficients is

significantly different from zero (t-statistics = 0.55 and 0.68). Although we have no prediction

for sign of the DISP coefficient, it is significantly negative in both estimations (t-statistics =

.52 and 4.31). Also, the coefficients on FE and TURN_Pre are significantly positive in both

estimations (FE t-statistics = 2.39 and 3.25; TURN_Pre t-statistics = 11.65 and 3.38). The

EA_RET coefficient is significantly negative in the EA_VOLM estimation and significantly

positive in the EA_VOLA estimation (t-statistics = 2.08 and 3.84). The NEG coefficient is

significantly negative in both estimations (t-statistics = 3.26 and .10). Finally, the ASQRET

coefficient is insignificantly positive in the EA_VOLM estimation and significantly negative in

the EA_VOLA estimation (t-statistics = 0.71 and 4.53).

13 Data requirements for the additional control variables reduce the number of observations by half. Nonetheless, untabulated findings from estimating equations (4) and (6) on the smaller sample reveal the same inferences for the ATC and three control variable coefficients as revealed in Table 4, panel A. Most importantly, the ATC coefficients (t-statistics) are 0.19 and 1.73 (3.27 and 3.48) in the EA_VOLM and EA_VOLA estimations.

23

5.2 Effects of Reduced Information Content

5.2.1 Price Drift

Table 5, panels A and B, presents the results from the portfolio and regression tests of an

association between ATC and post-announcement returns described in section 3.3.1. The

findings in panel A relating to the portfolio tests reveal that the mean post-announcement return

in the top quintile portfolio, 0.0050, is significantly positive (t-statistic = 4.22), and that for the

bottom quintile portfolio, 0.0008, is insignificantly different from zero (t-statistic = 0.71). In

addition, the difference between the two, 0.0042, is significantly positive (t-statistic = 2.57).

These findings are evidence of significantly greater positive post-earnings announcement price

drift for firms with more conservative earnings.

The findings in panel B relating to estimation of equation (9) reveal the same inferences

as those based on the findings in panel A by providing evidence of significantly greater positive

post-earnings announcement price drift for firms with more conservative earnings. In particular,

the mean ATC coefficient is significantly positive (coefficient = 0.03; t-statistic = 1.89).

Regarding the control variables, only the coefficient on Size is significantly different from zero

(coefficient = 0.001; t-statistic = 2.15).

5.2.2 Insider Trading

Table 6, panels A and B, presents the results from the portfolio and regression tests of an

association between ATC and insider trading described in section 3.3.2. The findings in panel A

relating to the portfolio tests reveal that the mean net stock purchases by insiders, Insider_Purch,

in the top quintile portfolio is 0.83 and that for the bottom quintile portfolio is 1.02. In

addition, the difference between the two, 0.20, is significantly positive (t-statistic = 2.70). These

findings are evidence of significantly more insider net purchases for firms with more

24

conservative earnings.

The findings in panel B relating to estimation of equation (10) reveal the same inferences

as those based on the findings in panel A. In particular, the mean ATC coefficient is significantly

positive (coefficient = 0.92; t-statistic = 3.17). Regarding the control variables, the coefficients

on BM, MOM, and EA_RET are significantly different from zero (t-statistics = 2.57, 13.78, and

10.15).

6. Conclusion

We find that higher conditional conservatism decreases the speed with which equity

investor disagreement and uncertainty resolve at earnings announcements. These findings

indicate that a potential cost of conditional conservatism is lower information content of

earnings, which impedes equityholders’ ability to discern the valuation implications of earnings

when they are announced. We also find that subsequent to earnings announcements firms with

higher levels of conditional conservatism have positive stock returns and higher levels of stock

purchases by insiders.

We first test whether there is a negative relation between a measure of conditional

conservatism that varies cross-sectionally and intertemporally and two measures of information

content of earnings at earnings announcements. Our measure of conditional conservatism is

based on the Basu (1997) asymmetric timeliness coefficient, and uses a two-step estimation

approach. Our two information content measures are the ratios of average daily volume and

volatility during the annual earnings announcement window to average daily volume and

volatility during a post-announcement window. Our tests also include controls for known

determinants of equity trading volume and equity return volatility, including size, the equity

book-to-market ratio, and leverage.

25

We also test an implication for stock prices of the delayed reactions to earnings

announcements, namely whether there are positive stock returns subsequent to the

announcements for firms with higher levels of conditional conservatism, and find that there are.

This finding suggests that insiders can take advantage of the investor underreaction by

purchasing the firm’s shares in the post-announcement period, and we find evidence that they do.

Taken together, the findings in this study provide evidence that more conservative

earnings have lower information content. Although conservative accounting can provide

benefits, it can also entail costs associated with the attendant lower information content of

earnings.

26

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30

Table 1 Descriptive statistics relating to constructing measure of conditional conservatism, ATC

Panel A: Observations pooled across years and industries (N =56,515)

Mean Median

Std Dev

ATC 0.28 0.29 0.10 ATCI 0.14 0.14 0.08 ATCIN 0.15 0.14 0.05 X 0.03 0.05 0.09 R 0.04 −0.01 0.50 DR 0.51 1.00 0.50

31

Table 1 (continued) Descriptive statistics relating to constructing measure of conditional conservatism

Panel B: Summary statistics from Basu (1997) equation estimated by industry by year.

Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Adj.R2 Mean

Industry Mean Std Mean Std Mean Std Mean Std Mean Std Mean Std Mean Std Nobs

Chemicals 0.00 0.04 0.15 1.27 0.04 0.03 1.31 1.12 0.09 0.18 1.62 1.86 0.20 0.12 83.33

Computers −0.02 0.03 −1.67 1.79 0.01 0.01 0.72 0.77 0.14 0.06 4.30 1.98 0.24 0.08 420.83

Durable Manuf. −0.01 0.02 −1.87 2.04 0.02 0.02 1.90 1.69 0.13 0.06 5.55 2.95 0.25 0.07 723.72

Extractive Indust. −0.02 0.04 −1.26 1.27 0.01 0.05 0.77 1.48 0.18 0.08 2.17 1.13 0.25 0.10 136.28

Financial Instit. −0.01 0.05 −0.48 1.20 0.03 0.06 0.75 0.75 0.16 0.14 2.44 2.14 0.26 0.13 106.11

Food 0.00 0.03 −0.01 1.27 0.04 0.03 1.68 1.92 0.11 0.08 1.68 1.05 0.25 0.11 84.39 Insurance, real estate −0.02 0.04 −0.80 1.17 0.07 0.06 1.61 1.07 0.06 0.15 1.15 2.03 0.24 0.13 113.56 Mining, construction −0.01 0.03 −0.30 1.00 0.02 0.06 1.07 1.42 0.17 0.12 2.14 1.62 0.27 0.17 83.28

Pharmaceuticals −0.05 0.03 −2.39 1.39 0.02 0.03 0.43 0.45 0.12 0.07 2.07 1.28 0.35 0.09 138.56

Retail 0.00 0.02 −0.57 1.29 0.02 0.01 1.54 1.28 0.14 0.06 4.53 2.21 0.24 0.06 370.00

Services −0.02 0.02 −1.47 1.69 0.02 0.02 1.01 0.99 0.12 0.05 3.86 2.25 0.25 0.09 345.83 Textiles, printing, publishing 0.00 0.04 −0.31 1.28 0.04 0.03 1.50 1.29 0.13 0.09 2.30 1.84 0.26 0.10 150.78

Transportation −0.01 0.03 −0.96 1.67 0.02 0.02 0.91 1.84 0.18 0.08 3.16 1.09 0.25 0.10 221.61

Utilities 0.00 0.02 −0.08 1.04 0.03 0.04 1.36 1.86 0.16 0.08 2.79 1.71 0.24 0.11 155.44

Other −0.02 0.01 −0.47 0.20 0.01 0.02 0.08 0.33 0.09 0.01 0.83 0.14 0.04 0.05 54.00

Mean −0.01 0.03 −0.86 1.56 0.03 0.04 1.17 1.38 0.13 0.10 2.82 2.20 0.25 0.11 222.50

, 0 1 , 2 , 3 , , ,*i t i t i t i t i t i tX DR R DR R v

1

2

3

32

Table 1 (continued) Descriptive statistics relating to constructing measure of conditional conservatism

Panel C: Summary statistics from Basu (1997) equation estimated by portfolio by year.

Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Adj.R2 Mean

Portfolio Mean Std Mean Std Mean Std Mean Std Mean Std Mean Std Mean Std Nobs

1 −0.08 0.03 −9.74 5.16 0.01 0.01 0.97 1.02 0.20 0.08 8.24 3.51 0.60 0.09 622.72

2 −0.01 0.02 −3.17 5.35 0.02 0.01 4.56 4.19 0.16 0.03 20.12 8.24 0.73 0.11 630.33

3 −0.01 0.02 −4.77 6.42 0.02 0.02 5.63 4.43 0.13 0.03 19.37 8.49 0.75 0.10 630.89

4 −0.01 0.02 −5.69 5.41 0.02 0.01 5.67 4.09 0.12 0.03 16.18 7.54 0.75 0.10 630.33

5 −0.02 0.02 −4.04 2.53 0.03 0.02 3.93 2.69 0.10 0.04 8.13 4.82 0.56 0.14 625.44

Mean −0.03 0.03 −5.48 5.53 0.02 0.02 4.15 3.86 0.14 0.06 14.41 8.50 0.68 0.14 627.94 X is earnings before discontinued operations and extraordinary items for firm i in year t, deflated by lagged price. R is annual return for firm i, beginning three months after the start of fiscal year. DR is an indicator variable for observations with negative return. In panels B and C, means and standard deviations are across years. In panel C, portfolio 1 (5) comprises observations with the most negative (positive) residuals from each industry-year regressions in panel B. Sample of Compustat firms from 1994 through 2011.

, 0 1 , 2 , 3 , , ,*i t i t i t i t i t i tX DR R DR R v

1

2

3

33

Table 2 Descriptive statistics for variables used in the tests

Variable Mean Median Std Dev

ATC 0.28 0.29 0.10 EA_VOLM 1.60 1.36 1.08 EA_VOLA 3.71 1.67 5.90 Size 13.24 13.20 1.94 BM 1.79 0.54 6.73 Lev 0.21 0.20 0.18 MOM 0.08 0.05 0.38 DISP 0.25 0.10 0.52 FE 0.00 0.00 0.01 EA_RET 0.00 0.00 0.08 NEG 0.10 0.00 0.30 TURN_Pre 0.79 0.58 0.68 ASQRET 0.07 0.04 0.10

Table 2 presents descriptive statistics for the variables used in our tests. ATC is the measure of conditional conservatism; EA_VOLM (EA_VOLA) is the ratio of average daily trading volume (return volatility) during the earnings announcement window to the average daily trading volume (return volatility) in the post-announcement window; Size is the log of the market value of equity; BM is the ratio of the book value of equity to the market value of equity; Lev is the ratio of long-term debt to total assets; MOM is the firm’s equity return for the first ten months of the year; DISP is the standard deviation of analyst earnings forecasts of current earnings immediately preceding the earnings announcement divided by beginning of year stock price; FE is the absolute value of the difference between the mean analyst forecast of the current year’s earnings and actual earnings divided by beginning-of-year stock price; EA_RET is the four-day signed earnings announcement return; NEG is an indicator variable that equals one if the announced earnings are negative and zero otherwise; TURN_Pre is the average daily turnover during the 60 days preceding the earnings announcement window; ASQRET is average squared daily excess return during the 60 days preceding the earnings announcement window. The statistics for ATC, EA_VOLM, EA_VOLA, Size, BM, and Lev are based on 39,539 observations, and the statistics for MOM, DISP, FE, EA_RET, NEG, TURN_Pre, and ASQRET are based on 23,771 observations. All continuous variables are winsorized at the 1 and 99 percentiles. The sample comprises annual earnings announcements relating to fiscal years ending 1994 to 2011.

34

Table 3 Correlation matrix

1 2 3 4 5 6 7 8 9 10 11 12 13

1 ATC 0.06 0.07 0.04 0.03 0.05 0.12 0.06 0.07 0.00 0.18 0.00 0.172 EA_VOLM 0.08 0.47 0.07 0.05 0.13 0.02 0.03 0.00 0.02 0.00 0.17 0.043 EA_VOLA 0.07 0.54 0.04 0.05 0.10 0.02 0.06 0.02 0.06 0.04 0.09 0.074 Size 0.03 0.01 0.08 0.19 0.06 0.17 0.23 0.19 0.01 0.22 0.07 0.315 BM 0.06 0.12 0.09 0.37 0.04 0.04 0.16 0.07 0.00 0.02 0.02 0.006 Lev 0.05 0.15 0.11 0.10 0.16 0.09 0.11 0.07 0.00 0.05 0.13 0.047 MOM 0.10 0.02 0.02 0.20 0.32 0.07 0.10 0.09 0.02 0.32 0.05 0.188 DISP 0.05 0.09 0.10 0.28 0.40 0.16 0.10 0.64 0.03 0.29 0.05 0.209 FE 0.03 0.00 0.00 0.28 0.31 0.09 0.07 0.61 0.03 0.28 0.05 0.2010 EA_RET 0.00 0.03 0.07 0.02 0.01 0.01 0.03 0.07 0.00 0.06 0.01 0.0111 NEG 0.15 0.02 0.04 0.23 0.14 0.03 0.33 0.25 0.22 0.06 0.11 0.3412 TURN_Pre 0.02 0.28 0.19 0.13 0.09 0.17 0.01 0.01 0.04 0.00 0.10 0.3213 ASQRET 0.16 0.05 0.07 0.43 0.08 0.12 0.22 0.17 0.16 0.01 0.30 0.32

Table 3 presents sample Pearson (Spearman) correlations above (below) the diagonal. Correlations that are significantly different from zero at the p < 0.05 level are in bold. Variables are defined in Table 2. The correlations are reported for 23,771 observations. The sample comprises annual earnings announcements relating to fiscal years ending 1994 to 2011.

35

Table 4 Information content of earnings and conditional conservatism

Panel A: (1)

(2)

(1) (2) Prediction EA_VOLM EA_VOLA

ATC 0.23** 0.85*

(3.23) (2.04) Size 0.05** 0.19**

(8.94) (7.68) BM 0.01** 0.03**

(6.64) (8.53) Lev 0.09* 0.46*

(2.13) (2.05)

Observations 39,539 39,539 Adjusted R-squared 0.05 0.07

EA_VOLMi,t

0

1ATC

i,t

2Size

i,t

3BM

i,t

4Lev

i,t

i,t

EA_VOLAi,t

0

1ATC

i,t

2Size

i,t

3BM

i,t

4Lev

i,t

i,t

36

Table 4 (continued) Information content of earnings and conditional conservatism

Panel B:

EA_VOLMit

0

1ATC

i ,t

2Size

i ,t

3BM

i ,t

4Lev

i ,t

5MOM

i ,t

6DISP

i ,t

7FE

i ,t

8EA_ RET

i ,t

9NEG

i ,t

10TURN _ Pre

i ,t

11ASQRET

i ,t

it

(1)

EA_VOLAit

0

1ATC

i ,t

2Size

i ,t

3BM

i ,t

4Lev

i ,t

5MOM

i ,t

6DISP

i ,t

7FE

i ,t

8EA_ RET

i ,t

9NEG

i ,t

10TURN _ Pre

i ,t

11ASQRET

i ,t

it

(2)

(1) (2) Prediction EA_VOLM EA_VOLA

ATC 0.10* 1.38**

(1.78) (2.70) Size 0.05** 0.05

(8.99) (1.37) BM 0.01** 0.05**

(4.59) (8.53) Lev 0.18** 1.04**

(4.02) (3.16) MOM +/ 0.01 0.09

(0.55) (0.68) DISP 0.05* 0.44**

(2.52) (4.31) FE 1.35* 11.49**

(2.39) (3.25) EA_RET 0.30* 4.48**

(2.08) (3.84) NEG 0.09** 0.39*

(3.26) (2.10) TURN_Pre + 0.18** 0.30**

(11.65) (3.38) ASQRET 0.06 2.55**

(0.71) (4.53)

Observations 23,771 23,771 Adjusted R-squared 0.10 0.10

37

Table 4, panel A, presents regression summary statistics for the EA_VOLM and EA_VOLA estimating equations (4) and (6), and panel B presents regression summary statistics for the EA_VOLM and EA_VOLA estimating equations (7) and (8). All variables are defined in Table 2. The t-statistics based on standard errors clustered by firm and earnings announcement month-year are in parentheses. All regressions include untabulated industry and earnings announcement month-year fixed effects. ** and * indicate significance at the 1% and 5% levels; significance levels reflect a one-sided alternative when we have a signed prediction and a two-sided alternative otherwise. The sample comprises annual earnings announcements relating to fiscal years ending 1994 to 2011.

38

Table 5 Post-announcement returns and conservative earnings

Panel A: By ATC portfolios

N Mean

ABRET_Post t-statistic q1 7,910 0.0008 0.71 q2 to q4 23,716 0.0034 5.43** q5 7,913 0.0050 4.22** Difference (q5 q1) 0.0042 2.57**

Panel B: Fama-MacBeth (1973) statistics from annual regressions of

Coefficient Prediction Mean t-statistic

ATC + 0.027 1.89* Beta 0.003 0.92 Size 0.001 2.15* BM 0.000 1.25 MOM 0.004 1.44 N 18

Table 5, panel A, presents mean post-earnings announcement period returns, ABRET_Post, for portfolios based on the quintile rank of ATC. Observations in the first quintile, q1, comprise the first portfolio, observations in the second through fourth quintiles, q2 to q4, comprise the second portfolio, and the observations in the fifth quintile, q5, comprise the third portfolio. The t-statistic is associated with the test of whether mean ABRET_Post is positive. Panel B presents Fama-MacBeth (1973) summary statistics from annual regressions. ABRET_Post is excess return compounded over the period starting three days after the earnings announcement and ending 20 days after the earnings announcement. We compute excess returns by subtracting the expected return based on the Fama and French (1993) three-factor model supplemented with the momentum factor from the raw post-announcement return. Beta is the CAPM beta. All other variables are defined in Table 2. ** and * indicate significance at the 1% and 5% levels; significance levels reflect a one-sided alternative when we have a signed prediction and a two-sided alternative otherwise. The sample comprises annual earnings announcements relating to fiscal years ending 1994 to 2011.

0 1 2 3 4 5_ i i i i i i iABRET Post a a ATC a Beta a Size a BM a MOM e

39

Table 6 Insider trading and conservative earnings

Panel A: By ATC portfolios

N Mean

Insider_Purch t-statistic q1 5,671 1.02 q2 to q4 17,304 1.02 q5 5,576 0.83 Difference (q5 q1) 0.20 2.70**

Panel B: Regression summary statistics from estimating: Insider _ Purch

i

0

1ATC

i

2Size

i

3MOM

i

4EA_ RET

i e

i

Coefficient Prediction Mean t-statistic

ATC + 0.92 3.17** Size 0.02 0.80 BM 0.18 2.57* MOM 1.45 13.78** EA_RET 4.31 10.15** N 28,551

Table 6, panel A, presents mean net insider purchases, Insider_Purch, for portfolios based on the quintile rank of ATC. Observations in the first quintile, q1, comprise the first portfolio, observations in the second through fourth quintiles, q2 to q4, comprise the second portfolio, and the observations in the fifth quintile, q5, comprise the third portfolio. The t-statistic is associated with the test of whether the difference in mean Insider_Purch is different from zero. Panel B presents regression statistics from the regression of Insider_Purch on ATC and control variables. The regression includes untabulated industry and earnings announcement month-year fixed effects, and the t-statistics are based on standard errors clustered by firm and earnings announcement month-year. Insider_Purch is the number of shares purchased minus the number of shares sold by all of the firm’s insiders during the 18-day post earnings announcement window, divided by shares outstanding as of the end of the fiscal year. All other variables are defined in Table 2. ** and * indicate significance at the 1% and 5% levels; significance levels reflect a one-sided alternative when we have a signed prediction and a two-sided alternative otherwise. The sample comprises annual earnings announcements relating to fiscal years ending 1994 to 2011.