“Asymmetric Timely Loss Recognition, Earnings Smoothness and Information Quality”

Logan B. Steelelsteele@bus.wisc.edu

University of WiscsonsinWisconsin School of Business

975 University Ave.Madison, WI 53706

Preliminary Draft – Please Do Not Cite Suggestions Welcome

Abstract:

I investigate the role of asymmetric timely loss recognition (ATLR) in determining earnings smoothness and how this role affects inferences about the consequences of earnings smoothness. Because ATLR involves accruals reinforcing variation in cash flows when a loss is recognized, ATLR should result in less smooth earnings. Accordingly, variation in earnings smoothness can be driven by ATLR as well as the use of accruals to offset temporary fluctuations in cash flows (offsetting), making it unclear whether ATLR or offsetting drives any association between smoothness and a particular outcome variable. This is an important distinction as theoretical studies exploring the determinants and consequences of smoothing behavior focus on offsetting in particular. Motivated by mixed empirical evidence linking smoothness and information quality, as well as evidence that ATLR plays a distinct information role, I re-examine the impact of smoothness on information quality. In tests designed to disentangle the role of offsetting and ATLR in determining earnings smoothness, my results suggest that offsetting induced smoothness reduces information quality.

I owe a debt of gratitude to my dissertation committee Dan Bens (Chair), Dan Dhaliwal, and Mark Trombley for their thoughtful comments and guidance. I also thank Oliver Li, Monica Neamtiu, Grace Lee, Ronen Gal-Or, Fabio Gaertner, James Chyz, John Campbell, James Sinclair, Sarah Shaikh, David Weber, Sunny Yang, workshop participants at The University of Arizona and the University of Connecticut for their helpful comments and suggestions.

1. Introduction

I examine the influence of asymmetric timely loss recognition (ATLR) on the

variability of earnings and inferences about the consequences of income smoothing. After

controlling for the natural smoothing role of accruals1, a smoother income stream can

occur due to two primary factors, though only one is typically discussed in the smoothing

literature. First, a smoother income stream can result from managerial intervention in

accruals to offset temporary variation in cash flows (hereafter offsetting). This

mechanism has been a familiar aspect of the theoretical smoothing literature where the

manager’s choice set typically includes the use of accruals to offset temporary

fluctuations in performance. Second, and generally novel to the smoothing literature, a

smooth income stream can result from the absence of ATLR. A significant body of

literature has found that accruals are used to recognize economic losses in earnings in a

timelier manner than are gains. This ATLR role of accruals, which is the mechanism

underlying conditional accounting conservatism, results in accruals reinforcing variation

in operating cash flows (Ball & Shivakumar 2006). Therefore, a smoother income stream

can also result from the absence of recognizable economic losses, or when full/timely

loss recognition is avoided.

The observation that economic losses are reflected in earnings in a more timely

manner than gains is explained as a function of conditional accounting conservatism

(hereafter conservatism), which is a pervasive aspect of firms’ reporting strategy. Under

conservatism, firms experiencing an economic loss are likely to recognize that loss in

1 Because of accrual’s important role matching revenues and expenses, even absent reporting discretion accruals decrease the volatility of earnings relative to cash flows (Dechow & Dichev 2002 and Ball & Shivakumar 2006). Consequently, researchers take care to control for the normal level of smoothing caused by the matching role of accruals.

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earnings using an income decreasing accrual. For example, under lower of cost or market

rules, inventory must be written down when its market value falls below its book value,

but cannot be written up. Researchers have found conservative reporting to be present

across a variety of settings and time periods.2 Ball & Shivakumar 2006 document that the

impact of conservatism changes the correlation between accruals and cash flows from

strongly negative to near zero when economic news is bad. Thus, ATLR becomes a

driver of earnings variability when a firm experiences recognizable economic losses.

In addition to evidence that ATLR is an important driver of earnings variability

on average, there is also evidence of variation in the intensity of ATLR across firms.

Watts 2003a discusses how contracting concerns and litigation risk explain variation in

the demand for, and the provision of, a conservative reporting strategy. Additionally,

Lawrence et al. 2013 provides evidence that there is variation in the level of mandatory

conservatism across firms. When conservatism is more pronounced for a firm, economic

losses are more likely to be recognized fully and in a timely manner yielding more

pronounced ATLR and, therefore, a stronger relation between economic losses and

earnings variability should result.

Extant smoothing studies typically employ the variability of earnings relative to

cash flows, which is likely a function of both offsetting and ATLR, to proxy for

managerial intervention to smooth income. Because the causes and consequences of

ATLR and offsetting are likely different, their effect on the smoothing measure can lead

to low powered or biased tests when evaluating the causes and consequences of smoother

2 These findings are not without detractors, for instance Dietrich et al. 2007 provide evidence that the ATLR coefficient in Basu’s 1997 model is econometrically biased, while Givoly et al. 2007 provide evidence that the ATLR coefficient is affected by the nature of news occurring for the firm as is not persistent across time. It is important to note that my research design re-examining the consequences of income smoothing does not rely on the validity of the Basu 1997 model.

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income. Much of the smoothing literature has focused on the general question of whether

smoothing is a desirable reporting strategy, accordingly a variety of outcome variables

designed to proxy for information quality and the cost of capital have been employed

(Dechow et al. 2009). In studies on the consequences of smoothing, low powered tests

can result if ATLR and offsetting have countervailing effects on the outcome variable.

Also, if the research question specifically focuses on managers’ discretionary offsetting

activities, bias can result if ATLR is correlated with the outcome variable. This bias is

concerning as LaFond and Watts 2008 provide evidence that conservatism is positively

associated with information asymmetry,3 while Lara et al. 2011 find a negative

association between conservatism and the cost of equity capital after controlling for

endogeneity.

Further motivating a re-examination of the information consequences of

smoothing are the mixed results present in the literature (Jayaraman 2008, Dechow et al.

2009 and McGinnis 2010). Several studies of U.S. firms find that income smoothing is

positively associated with information quality (Sankar & Subramanyam 1996; Tucker &

Zarowin 2006), and hence is associated with a lower cost of capital (Francis et al. 2004).

However, McGinnis 2010 finds no significant association between income smoothing

and the cost of capital. Cross-country studies generally find that smoothing is negatively

associated with information quality (Leuz et al. 2003; Francis & Wang 2008; Lang et al.

2012). Finally, Jayaraman 2008 finds that the relationship between smoothing and

information quality is non-linear. I focus on re-evaluating results for two of the outcome

constructs from these studies; 1) information asymmetry (proxied by the bid-ask spread)

3 They argue that higher information asymmetry is associated with greater demand from market participants for more conservative earnings, and therefore firms provide more conservative earnings when information asymmetry is high.

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because of the information economics literature that links the informativeness of public

information with informed trading4, and 2) the cost of equity capital (proxied by realized

future stock returns) to examine the valuation consequences of smoother income.

I begin my empirical analysis by documenting the magnitude of the influence of

ATLR on the smoothing measure.5 Using negative stock returns to proxy for the presence

of recognizable economic losses, I find that moving from one negative return year to

three negative return years in the smoothing measurement window (5 years) decreases the

smoothing proxy by 25%. I employ the Khan & Watts CSCORE to proxy for firm’s

expected degree of conservatism, which should be associated with the

likelihood/magnitude of loss recognition given that an economic loss has occurred. I find

that moving from the bottom to top decile of CSCORE decreases the smoothing measure

by 10%. I also find a strong positive interactive effect between CSCORE and the

proportion of negative return years, consistent with more conservative firms providing

earnings that are strongly linked to negative economic news. Overall, these results show

that ATLR significantly affects the smoothing proxy.

To evaluate the consequences of smoother income due to offsetting I examine the

association between smoother income and information quality conditional on the absence

of economic losses. When economic losses are absent, and after controlling for the innate

level of earning’s smoothness, offsetting should be the dominant driver of earnings

smoothness. To explore this conditional effect I allow the coefficient on the smoothing

proxy to vary with the frequency of economic losses (i.e., the proportion of negative

stock return years occurring during the smoothing measurement window). By examining 4 See Grossman & Stiglitz 1980, Diamond 1985, Easley & O’Hara 2004, Baiman & Verrecchia 1996 and Brown & Hillegeist 2004.5 I follow the research design employed by Jayaraman 2008 where the difference between the volatility of cash flows and earnings over the period t-4 to t is used to proxy for smoothing.

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the main effect of the smoothing measure in this specification one can determine if

smoother income due to offsetting has a distinct information quality consequence.

I begin by replicating recent studies on the information quality consequences of

smoothing by conducting an analysis that is not conditioned on the presence of economic

losses. In these tests I find that information asymmetry and the cost of capital are not

significantly associated with the smoothness of earnings. However, when I condition the

effect of smoother income on the presence of economic losses, I find that the effect of

smoother income varies. When offsetting is the driver of earnings variability (i.e., when

there are less negative return years) smoother income is associated with increased

information asymmetry and generally a higher cost of capital.6 When ATLR should be

more prominent (i.e., when there are more negative return years) smoother income is

associated with decreased information asymmetry and a lower cost of capital.7 Together,

these countervailing effects help explain the absence of consistent findings in the

literature.

I also re-examine findings in Jayaraman 2008 that indicate managerial

intervention that distorts earnings variability, either to smooth or volatize earnings,

results in greater information asymmetry. Jayaraman splits his sample of firms into a

“Smooth” and a “Volatile” sub-sample predicting that smoother income will be positively

associated with information asymmetry in the “Smooth” sample but negatively related in

the “Volatile” sample. Because firms with economic losses generally have more volatile

earnings, this sample formation method is likely to result in sub-samples that vary

6 However, the association between smoothing and the cost of capital is insignificant in one specification based on the realized return test in Core et al. 2008. 7 Because the smoothing measure decreases in the presence of conservative reporting, this negative association is consistent with the findings in LaFond & Watts 2008, where conservatism and information asymmetry are positively associated.

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systematically with the prominence of economic losses (i.e., the “Volatile” sub-sample

will disproportionately include firms that experienced economic losses). My evidence

indicates that the differential impact of smoothing between the sub-samples is likely a

result of unintentionally sorting firms by the presence of economic losses, and therefore,

the prominence of ATLR in determining earnings smoothness.

I contribute to the literature by documenting the magnitude of the influence of

ATLR on the smoothness of earnings. I find that the presence of economic losses

combined with the conservatism role of accruals is an important determinant of the

smoothness of earnings.8 To the extent that ATLR is a mandatory attribute of reporting

(Lawrence et al. 2013) this answers a call by Dechow et al. 2009 for work identifying the

artificial component of earnings’ smoothness. A research design that interacts the

smoothing proxy with the proportion of negative return years should provide researchers

with a simple and effective tool to determine whether their inferences are a function of

the ATLR or offsetting component of earnings variability.

I also contribute to the literature by providing evidence on the consequences of

offsetting. Several theoretical studies conclude that managers communicate private

information about the permanent level of earnings by engaging in offsetting. However,

empirical findings are mixed regarding the total effect of earnings smoothness on

information quality. My evidence indicates that, upon taking into account the role of

ATLR in determining the smoothness of earnings, information quality is negatively

associated with offsetting. This finding should be of interest to investors, managers,

academics and regulators as there is a debate about the extent to which managers use

8 This is similar to the contribution provided by Ball & Shivakumar [2006], which finds that failing to incorporate accrual’s conservatism’s role in discretionary accruals models (such as Jones [1991], and Dechow & Dichev [2002]) reduces the ability of the researcher to identify “non-discretionary” accruals.

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accounting discretion to communicate private information, thus improving information

quality.

2. Literature Review and Hypothesis Development

In summarizing the state of research on the consequences of income smoothing,

Dechow et al. 2009 (p. 362) remark that:

“While the consequences studies do not provide a clear conclusion about

smoothness as a proxy for earnings quality, they do lead us to one conclusion: in order

to understand the consequences of smoothness in terms of decision usefulness, we will

need smoothness measures that better distinguish artificial smoothness from the

smoothness of fundamental performance.”

As a step in this direction, the purpose of this study is to 1) examine how ATLR

affects the smoothing proxy with the goal of more precisely identifying managerial

intervention to offset temporary cash flow fluctuations using accruals (offsetting), and 2)

provide evidence on the information quality consequences of offsetting in particular. In

order to identify discretionary offsetting behavior the researcher must identify how the

variability of fundamental performance translates into the variability of earnings. As a

starting point, smoothing studies typically compare the variance of earnings to that of

operating cash flows. This is a useful method because cash flows capture a large portion

of variation in fundamental performance and are relatively difficult to manipulate.

Moving beyond using the variance of cash flows as a benchmark, researchers have

become more sophisticated in parsing out other sources of variation in the volatility of

earnings. A key development in this regard is establishing how the various roles of

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accruals should be expected to influence the variance of earnings conditional on the

innate characteristics of the firm and its fundamental performance.

2.1 Accrual Roles and the Smoothness of Earnings

Accounting accruals play two key roles; matching revenues and expenses and

facilitating conditional accounting conservatism (Ball & Shivakumar 2005). By matching

revenues and expenses, accruals tend to smooth out transitory fluctuations in cash flows.

Working capital accruals such as inventory, accounts payable and accounts receivable

help dampen the inherently lumpy receipt and disbursement of cash across time.

Accruals’ role in smoothing out capital expenditures is also very important, spreading out

expenses over the useful life of productive assets. Overall, this matching role results in an

income number that contains less noise and is more useful to capital markets as well as

for contracting relative to cash flows (Dechow 1994). In order to parse out the innate

level of earnings’ smoothness resulting from the matching role of accruals researchers

have added controls for numerous innate firm characteristics in order to distill the

artificial level of earnings’ smoothness.

The conservatism role of accruals facilitates the recognition of unrealized

economic losses in earnings and is commonly thought to lead to the asymmetric

sensitivity of earnings to negative stock returns (ATLR). This role of accruals arises from

the demand for conservative accounting numbers by contracting parties to the firm (Watts

2003a) as well as being mandatory under GAAP (Lawrence et al. 2013). As opposed to

the matching role of accruals, ATLR results in a more volatile income stream relative to

cash flows. Accruals are likely to play an ATLR role when an economic loss drives the

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value of a particular asset or asset grouping below its book value.9 When this occurs, a

negative accrual is used to adjust the value of the asset downwards (i.e., an asset

impairment). Because economic losses are generally caused by a decrease in the cash

flows produced by an asset, ATLR results in a more positive correlation between accruals

and cash flows. Ceteris paribus, this positive correlation will increase the volatility of

earnings relative to cash flows.

In addition to a “mean” effect of ATLR on the measurement of smoothing that

occurs because firms engage in ATLR on average, there is also variation in the degree of

ATLR across firms. Numerous studies document variation in ATLR, including Ahmed et

al. 2002, Bushman & Piotroski 2006, LaFond & Watts 2008, Guay 2008, Lee 2009, Khan

& Watts 2009 and Lawrence et al. 2013. Variation in both the managers’ choice to

provide more/less ATLR and variation in the effect of mandatorily conservative

accounting rules can affect the degree of ATLR across firms. More conservative

earnings, and thus higher ATLR, can be achieved through an increased probability that an

existing economic loss is recognized in a timely manner, and by fully (rather than

partially) recognizing an existing economic loss. Because recognizing economic losses

generally increases earnings’ volatility, the negative association between economic losses

and the smoothness of earnings should be intensified for firms that exhibit higher ATLR.

2.2 Offsetting, Smoother Income and Information Quality

The theoretical literature on income smoothing generally supports the notion that

a smoother income stream can maximize both the manager’s and the investor’s wealth.

Thus, offsetting behavior can arise even in the absence of agency conflict and could

9 Conversely, if an economic event results in an increase in future liabilities (e.g., a contingent legal liability) a large negative accrual may be necessary.

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improve the firm’s information environment. Chaney & Lewis 1995 model reporting

incentives for a value-maximizing manager who is asymmetrically informed about the

firm’s permanent level of earnings. They find that the manager at a “high-value” firm

smoothes earnings towards its expected value by offsetting temporary fluctuations in cash

flows. Sankar and Subramanyam 2001 also model reporting incentives, but for a risk-

averse manager with private information regarding future earnings. They find that the

manager will smooth earnings to communicate their private information. Arya et al. 2003

argue that managers can offset the transient portion of earnings by engaging in income

smoothing and are thus able to communicate the permanent portion of earnings. Investors

recognize this and are able to arrive at an efficient estimate of the firm’s stock price.

Survey evidence provided by Graham et al. 2005 supports the notion that income

smoothing is an equilibrium reporting strategy, finding that approximately 97% of

corporate executives prefer to report smoother earnings, holding cash flows constant.

Studies on U.S. samples offer some support for the analytical results. Tucker &

Zarowin 2006 examine the effect of income smoothing on the ability of stock returns to

incorporate future earnings. They find that when earnings are smoothed to a greater

extent, stock returns incorporate more information about the level of future earnings.

Francis et al. 2004 provide evidence that income smoothing is associated with a lower

cost of equity capital. However, McGinnis 2010 finds no association between income

smoothing and the cost of capital.

Several empirical studies in international settings examine the effect of income

smoothing on information quality. LaFond et al. 2007 examine the effect of income

smoothing on information asymmetry using a two-stage research design. They find that

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discretionary income smoothing increases trading costs. In a similar paper Lang et al.

2012 also examine the effects of income smoothing in an international setting. They

establish a link between discretionary income smoothing and stock market illiquidity,

which is found to increase (decrease) the Cost of Capital (Tobin’s Q). Because of this

lack of consensus about the influence of smoother income (or offsetting in particular) on

information quality, I state my hypothesis in null form.

H1: Information quality exhibits no association with smoother income

when offsetting is prominent.

2.3 ATLR, Earnings Smoothness and Information Quality

A significant literature supports the view that volatizing earnings by recognizing

bad news contemporaneously is preferable to avoiding recognition when news is bad

(Watts 2003a, Ball & Shivakumar 2005, and Khan & Watts 2009). Consistent with more

volatile earnings (due to ATLR) communicating information to investors, there should be

a negative association between smoother income and information quality when there are

economic losses. However, LaFond & Watts 2008 provide evidence that managers

respond to greater information asymmetry by providing more ATLR, which should lead

to a negative association between smoother income and information quality. It is

important to note that LaFond & Watts 2008 findings are consistent with information

asymmetry leading to greater ATLR, not the other way around. Therefore, I expect that

information quality is positively associated with smoother income when economic losses

are prominent.

3. Research Design and Empirical Results

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3.1 Sample Selection and Variable Construction

My sample consists of all CompuStat firms with sufficient data successfully

matched to a continuous 12 months of return data on the Center for Research in

Securities Pricing (CRSP) database. I require at least 5 years of income and cash flows,

as well as non-missing data for the innate determinants of smoother income for each

observation to be included in the sample. In the realized return tests, I require 12 months

of return data beginning three months after fiscal year end. The sample period begins in

1988 and ends in 2011, because several variables require five years of data to measure,

my first valid observation occurs in 1992. I require at least 100 daily observations of

closing bid and ask data from CRSP to calculate firms’ bid/ask spread. Finally, I

winsorize all continuous variables at the top and bottom 1% of their distributions to

mitigate the effects of extreme values. This yields an initial sample of 33,896 firm-year

observations for tests associating the income smoothing proxy with the presence of

economic losses in the smoothing measurement window. The sample drops to of 25,895

firm-years when I require sufficient industry data to estimate the firm’s expected level of

conservatism.

3.2 The Effect of ATLR on the Income Smoothing Proxy

To examine the impact of ATLR on the smoothing proxy I employ the following

regression:

SMOOTH it=β0+β1×PRNRit+ β2×CSCOREit+ β3×PRNR

it×CSCOREit+

β4×SIZEit +β5×TURN it+ β6×LEV it +β7×BTM it+β8×AGEit +β9×STD SALEit+

β10×CYCLEit +β11×GR SALEit+β12×OPLEV

it+ β13×DIV it+β14×AVGCF

it+

β15×PRLOSSit+εit

[1]

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Income smoothing (SMOOTH) is measured as the standard deviation of cash

flows (OANCF) less the standard deviation of net income (NI) both deflated by average

total assets. The measurement window for SMOOTH is 5 years, beginning in year t-4 and

ending in year t.10 I proxy for the frequency of economic losses using the proportion of

negative return years (PR_NR). Economic losses are more likely to trigger the ATLR role

of accruals as the proportion of negative return years increases. In order to examine the

role that variation in ATLR across firms plays, I require a proxy for the expected level of

conditional conservatism. Expected conservatism (CSCORE), is measured over a 5 year

period in regressions conducted by four digit SIC code.11 See the variable description

under Table 1 for detail on the construction of CSCORE.

My control variables capture firm, industry, and macroeconomic fundamentals

that have been shown to cause variation in the variability of earnings. From LaFond et al.

2007 I add the standard deviation of sales over the previous five years (STD_SALE), the

length of the operating cycle in days (CYCLE), the average percentage growth rate in

sales over the previous five years (GR_SALE), the level of operating leverage (OP_LEV),

the average dividend payout ratio over the previous five years (DIV) and finally the

average level of cash flows deflated by lagged assets over the previous five years

(AVG_CF). From Jayaraman 2008 I include firm age (AGE). I add firm size (SIZE),

book-to-market (BTM) and leverage (LEV) as these variables have been found to be

associated with variation in conservatism as well as information asymmetry. From Lang

et al. 2012 I include the proportion of negative net income years (PR_LOSS) over the

10 Several studies employ the “backing-out method” where the correlation between pre-managed earnings and discretionary accruals is employed to measure smoothing. I do not use this method because of criticisms that the backing-out method is ineffective (Lim & Lustgarten 2002).11 I require at least 30 firm/year observations for each industry/year pool, with at-least 15 negative return years to calculate CSCORE.

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previous five years. Including the proportion of negative income years as a control could

result in over-controlling if offsetting activities result in fewer negative income years.

The significance level and direction of my results are not affected by the exclusion of

PR_LOSS.12

Table 1 provides descriptive statistics for SMOOTH, PR_NR and CSCORE as

well as the control variables. SMOOTH has a mean (median) value of .001 (.007)

indicating that earnings are less volatile than cash flows for firms in my sample, but to a

small degree. Because I interact the smoothing measure in some of the regression tests, I

employ a version ranked into deciles by year that is scaled to vary from zero to one.

PR_NR has a mean (median) value of .437 (.400) indicating that the average firm

experiences slightly more than two negative return years. CSCORE is positive on

average, with a mean (median) of .181 (.164) indicates that firms in my sample are on

average conservative.13 CSCORE is negative for at least 10% of firms, indicating that

there is considerable measurement error as conditional conservatism is mandatory under

GAAP.

Table 2 presents the correlation statistics for selected variables used in equation

[1] with Pearson correlation statistics presented on the top right, and Spearman

correlation statistics presented on the bottom left. The univariate results support a

negative association between economic losses and the smoothing proxy, as the Pearson

12 Further, including PR_LOSS as a control could be beneficial to the extent that the variable controls for the impact of conservative reporting on the smoothness measure. However, negative net income can occur due to reasons other than conservative reporting, and thus PR_LOSS is not likely to be a suitable control for variation in conservative reporting. 13 On average firms are more conservative in my sample as compared to those in Khan & Watts 2008 where the mean (median) of their CSCORE measure is .105 (.097). Because firms may be becoming more conservative over time the higher level of CSCORE in my sample may be due to my sample period being more recent. For instance, in a more recent study, Ettridge et al. 2012, the mean (median) of CSCORE is .1632 (.1553).

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(Spearman) correlation between PR_NR and SMOOTH is -.166 (-.166) and significant at

the <1% level. The univariate results also support a negative association between the

expected level of ATLR and the smoothing proxy, as the Pearson(Spearman) correlation

between CSCORE and SMOOTH is -.111 (-.100) and significant at the <1% level.

Table 3 presents results from an OLS regression based on equation [1]. I expect

that the smoothing proxy will decrease as the proportion of negative return years

increases. Therefore, I expect a negative coefficient on PR_NR(β1) in equation [1].

Across all specifications PR_NR loads negatively and significantly, with β1 = -.1121 (P-

Value < .01) in Column 4 where the full model results are presented.

I expect that the negative association between the proportion of negative return

years and the smoothing proxy will be intensified for firms that engage in more ATLR.

Therefore, I predict a negative loading for the coefficient on the interaction term

PR_NR*CSCORE (β3 ) . In Column 2 of Table 3 I only include CSCORE as an

independent variable, in Column 3 I also include an interaction between PR_NR and

CSCORE, finally in Column 4 I include all of the control variables. In Column 2, the

main effect of CSCORE is negative and significant with β3 = -.050 (P-Value < .01),

consistent with the idea that, on average, more ATLR increase the variability of earnings.

However, in Column 3 the main effect of CSCORE is positive and significant with β3

= .031 (P-Value < .05). Caution should be taken in interpreting this result as this

coefficient only applies to firms which did not have a negative return year (PR_NR = 0).

In the full results presented in Column 4, the interaction between PR_NR and CSCORE

loads negatively and significantly, with β3 = -.097 (P-Value < .01). Also, the main effect

on CSCORE is insignificant in Column 4.

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3.3 The Association between Smoother Income and Information Asymmetry

To examine the association between income smoothing and information

asymmetry I employ the following regression:

SPREADit=β0 j+β1×SMOOTH it +β2×PRNRit+β3×PRNR

it×SMOOTH it+

β4×SIZEit +β5×TURN it+ β6×AMIHUD it+β7×PRCit +β8×LEV it +β9×BTM it+β10×AGEit +β11×INST it+β12×N EST it

+ β13×STDSALE it+β14×CYCLE it+

β15×GRSALEit+β16×OPLEV

it+β17×DIV it +β18×AVGCF

it+

β19×PRLOSSit+εit

[3]Both SMOOTH and PR_NR as well as the control variables are defined as before.

I calculate SPREAD as the log of the average daily closing bid-ask spread expressed as a

percentage of price from CRSP.14 In equation [3] I allow the coefficient on the smoothing

proxy to vary with the proportion of negative return years. By including the interaction

term, the relationship between smoother income and information asymmetry can be

measured conditional on the prominence of ATLR. A positive loading on the smoothing

proxy(β1) in equation [3] would indicate that smoother income results in higher

information asymmetry when managers engage in offsetting. I do not sign my prediction

in H1 regarding the association between smoothing and information quality when

offsetting is prominent(β1) .

In addition to controls for the innate level of earnings smoothness, I include

controls related to information asymmetry. From Jayaraman 2008 I include share

turnover (TURN), a measure for market depth (AMIHUD), the inverse of year end stock

price (PRICE), the percent of institutional ownership (INST), and the log of analysts

following (N_EST). Because I include a comprehensive set of control variables (in terms

14 Chung & Zhang [2009] validate the bid-ask spread calculated using CRSP daily data finding that it is highly correlated with that calculated using trade-by-trade data available on TAQ.

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of those employed in the literature) I interpret the coefficient on smoothing as the effect

of discretionary offsetting.15 I also include industry and year fixed effects. I expect that

information quality will be positively associated with income smoothing when ATLR is

prominent. Therefore, I expect the coefficient on the interaction between smoothing and

the proportion of negative return years (β3 ) to be negative.

Table 1 provides descriptive statistics for variables used to estimate equation [3].

Because the raw percent bid/ask spread is highly right skewed, (RAW_SPREAD) has a

mean (median) of 3.046% (1.690%), I use the natural log version in my regressions. The

natural log version of the bid/ask spread (SPREAD) has a more well-behaved distribution,

with a mean (median) of 1.085 (.986). The remaining innate determinants of income

smoothing and the bid/ask spread have unremarkable means and medians.

Table 2 presents the correlation statistics for selected variables used in equation

[3] with Pearson correlation statistics presented on the top right, and Spearman

correlation statistics presented on the bottom left. SMOOTH is only marginally correlated

with SPREAD, with only the Spearman coefficient of .011 being significant at the 5%

level. On the other hand, the correlation between SPREAD and PR_NR is very strong,

with a Pearson (Spearman) correlation of .311 (.298). This is consistent with firms

experiencing more negative annual stock returns in recent years having greater

information asymmetry. Surprisingly, given the findings in LaFond & Watts 2008,

CSCORE and SPREAD exhibit a negative correlation with a Pearson (Spearman)

correlation of -.021 (-.039). This is likely due to the construction of CSCORE, which is

15 As is typical with studies attempting to distill the discretionary component of an earnings property, my study is open to the criticism that the effect I document could be generated by a failure to fully control for the innate component of smoother income. Although I cannot rule this issue out, the intent of this study is to move in the direction of improvement by addressing the role of ATLR in earnings variability.

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based on the firm’s 2-digit SIC industry level of conservatism as well as a linear function

of the firm’s size, leverage and book-to-market ratio. That is, CSCORE does not capture

firm-year specific variation in conservatism, but rather the expected level. Therefore, the

correlation between CSCORE and SPREAD should be interpreted with caution.

Table 4 presents results from an OLS regression based on equation [3]. In Column

1 of Table 4 I only include SMOOTH and control variables in the model, which allows

for the estimation of the unconditional association between income smoothing and

information asymmetry. Not surprisingly given the mixed findings in the literature, I find

no significant association between SMOOTH and SPREAD. In Column 2 I include

PR_NR, but do not yet interact PR_NR with SMOOTH. I find that the coefficient on

PR_NR is positive and significant with β2 = .1951 (P-Value < .01), indicating that

information asymmetry is higher when more economic losses were incurred by the firm

in the recent past. However, the coefficient on SMOOTH remains insignificant in Column

2, indicating that a failure to control for past economic losses alone does not contribute

materially to the attenuation of the coefficient on SMOOTH. In H1, I provide an unsigned

hypothesis regarding the association between income smoothing and information quality

when offsetting is prominent. Offsetting, rather than ATLR, should drive earnings

variability in the absence of economic losses. Because the main effect of SMOOTH in

Column 3 reflects the impact of smoothing when there are zero negative return years, β1

should provide evidence on the impact of offsetting on SPREAD. I find that the

coefficient on the main effect of SMOOTH is positive and significant with β1 = .047 (P-

Value < .01), which indicates that offsetting is associated with increased information

asymmetry.

18

To interpret the sign of the coefficient on the interaction term (β3) it is important

to keep in mind the following; 1) when both SMOOTH and PR_NR are high it indicates

that the firm is providing a low level of ATLR, and 2) ATLR and information asymmetry

have been shown to be positively associated. Thus, firms that have high SMOOTH and

PR_NR should have lower information asymmetry, suggesting that the coefficient on β3

should be negative. In Column 4 of Table 4 I include the interaction term as well as all of

the control variables. I find that the coefficient on the interaction term is negative and

significant with β3 = -.098 (P-Value < .01). This negative loading indicates that,

conditional on economic losses occurring, smoother earnings are associated with

decreased information asymmetry. This result is consistent with the positive association

between ATLR and information asymmetry documented by LaFond and Watts 2008.

Jayaraman 2008 hypothesizes that managerial intervention that causes the

variability of earnings to deviate from the variability of cash flows, whether higher or

lower, result in greater information asymmetry. In addition to being intuitively appealing,

this U-shape relationship could explain an insignificant full-sample relationship between

smoothing and information asymmetry. In a smooth (i.e., SMOOTH >= 1) sub-sample

Jayaraman finds that increased earnings smoothness results in increased information

asymmetry, whereas in a volatile (i.e., SMOOTH < 1) sub-sample increased smoothness

results in decreased information asymmetry. One issue, as it relates to my study, is that

the smooth and volatile sub-samples will include a disproportionate number of economic

losses as well as different ATLR levels. For example, the average PR_NR of firms in the

smooth (volatile) subsample is .405 (.485), while the average CSCORE of firms in the

smooth (volatile) subsample is .197 (.264).16 Based on these systematic difference across

16 Both of these differences are significant at the less than 1% level.

19

the samples it is likely that offsetting will be a more important component of earnings

variability in the smooth sub-sample whereas ATLR will be more prominent in the

volatile sub-sample.

Table 5 presents results from an OLS regression based on equation [3] where the

sample is split into a smooth (SMOOTH >= 1) and volatile (SMOOTH < 1) sub-sample.

In the first two columns, I replicate Jayaraman’s results in my sample. I find a similar U-

shaped relationship between SMOOTH and SPREAD with SMOOTH loading positively

in the “smooth” sub-sample (presented on Column 1) and negatively in the “volatile”

sub-sample (presented on Column 2). In Columns 3 and 4 I allow the coefficient on

SMOOTH to vary with PR_NR. In this specification the main effect of SMOOTH is

positive and significant in both the smooth and volatile sub-samples, with β1 = .050 (P-

Value < .05) and β1 = .225 (P-Value < .01) respectively. These results indicate that

offsetting is associated with higher information asymmetry regardless of sub-sample. The

interaction term SMOOTH*PR_NR only loads significantly in the volatile sub-sample,

with β3 = -.5467 (P-Value < .01).17 I speculate that the different coefficient loading on the

interaction term SMOOTH*PR_NR across the samples occurs because the volatile sample

includes firms for which ATLR is a more dominant aspect of earnings variability (e.g.,

firms with more economic losses and higher CSCORE). Overall, my evidence suggests it

is premature to infer that managerial intervention into the variability of earnings, whether

upwards or downwards, increases information asymmetry.

3.4 The Association between Income Smoothing and Realized Stock Returns

17 Untabulated results indicate that the coefficient on SMOOTH*PR_NR (β3) is significantly different across the two sub-samples, with a P-Value < .01.

20

I employ realized stock returns to proxy for the cost of capital. Realized returns

are, tautologically, a function of only information surprises and expected returns

(Campbell 1991). To compensate investors for holding riskier securities, expected returns

must be higher as risk increases. These expected returns form the basis for the cost of

capital as they represent the discount rate investors apply to the future expected payoffs

resulting from their ownership of the firm (i.e., stock appreciation and dividends).

Assuming a general rational pricing framework, information surprises should be zero in

expectation and unpredictable by nature. Because of this, average realized returns, for

firms or portfolios, proxy for the cost of capital. Despite controversy in the literature

regarding the influence of information quality or information asymmetry on the cost of

capital, I employ the cost of capital in my analyses to aid in comparison to the prior

literature on income smoothing (Francis et al. 2004 and McGinnis 2010). Francis et al.

2004 finds that income smoothing is associated with a lower implied cost of capital.

However, McGinnis 2010 employs realized return based tests and finds no consistent

relationship between income smoothing and the cost of capital. Further, McGinnis 2010

provides evidence that the findings in Francis et al. 2004 were a spurious result of bias in

the estimation of the cost of capital.

To examine the impact of income smoothing on the cost of capital I employ the

following regression based on McGinnis 2010:

ERETim+3 , im+15

=β0 j+β1×BMKTRETit−4, it

+β2×SIZE it+β3×BTM it+ β4×SMOOTH it−4 ,it+

β5×PRNR it−4 ,it+β6×SMOOTH it−4 ,it×PRNRit−4, it

+εit [4]

21

The variables SMOOTH, PR_NR , SIZE and BTM are as previously defined. I add

the firm’s excess monthly stock return, E_RET, which is defined as the raw stock return

for firm i in month m less the risk free rate. I also add a control for the firm’s sensitivity

to the market return, B_MKTRET, which is the coefficient loading for firm i in a time-

series regression of firm excess returns (E_RET) on the CRSP value-weighted return

(MKTRF) conducted by firm.18 In equation [4] I allow the coefficient on income

smoothing to vary with the proportion of negative return years. I do not sign my

prediction in H1 regarding the association between smoother income and information

quality when offsetting is prominent (β4 ) .

Table 6 provides descriptive statistics for variables used to estimate equation [4].

The firm’s monthly excess stock return, E_RET, has a mean (median) of .012 (-.001)

during the sample period. This equates to a 15.4% annual risk premium for the market,

corresponding with the general bull-market in the mid to late 1990s occurring during the

sample period. Firm’s sensitivity to the market return, B_MKTRET, has a mean (median)

of 1.046 (.933) during the sample period.

Table 7 presents results from an OLS regression based on equation [4]. In Column

1 of Table 7 I provide the base model, excluding SMOOTH. As expected, B_MKTRET

loads positively, consistent with firms that are more sensitive to the market return

earnings a higher realized return. Consistent with the “size effect” (Fama & French 1993)

SIZE loads negatively, and consistent with the “value effect” (Fama & French 1993)

BTM loads positively. In Column 2 I add SMOOTH, which allows for the estimation of

the unconditional effect of smoother income on realized stock returns. Not surprisingly

18 I estimate B_MKTRET using monthly return data from the current and previous four years and each firm must have at least 18 monthly return observations to be included in the sample.

22

given the mixed findings in the literature, I find no significant unconditional association

between SMOOTH and E_RET.

In Column 3 I include SMOOTH, PR_NR, and the interaction between PR_NR

and SMOOTH. Offsetting, rather than ATLR, should impact earnings variability in the

absence of economic losses. Because the main effect of SMOOTH in Column 3 reflects

the impact of smoothing when there are zero negative return years, β4 should provide

evidence on the impact of offsetting on realized returns. I find that the coefficient on the

main effect of SMOOTH is positive and significant with β4 = .006 (P-Value < .01), which

indicates that offsetting results in increased future realized returns. Assuming that higher

future realized returns reflect lower information quality, this result indicates that

offsetting reduces information quality and increases the cost of capital. I find that the

coefficient on PR_NR is positive and significant with β5 = .018 (P-Value < .01),

indicating that realized stock returns are higher when more economic losses were

incurred by the firm in the recent past. This result is not surprising as firms experiencing

poor past performance are likely to be more risky. The interaction between PR_NR and

CSCORE should capture the impact of the ATLR driven portion of earnings variability

on realized returns, resulting in a negative coefficient on β6. As expected, the coefficient

on the interaction term is negative and significant with β6 = -.014 (P-Value < .01). This

negative loading indicates that, conditional on economic losses occurring, smoother

earnings are associated with increased information quality. Because smoother earnings in

the presence of economic losses indicates that firms are engaging in less ATLR, this

result is consistent with the positive association between ATLR and information

asymmetry documented by LaFond and Watts 2008.

23

3.5 Additional Analyses

On Tables 8 and 9 I present results from portfolio based tests of the association

between smoother earnings and future realized stock returns. One issue with analyses

using firm specific stock returns, as presented on Table 7, is that they can be subject to

noise, which could jeopardize statistical power (Botosan & Plumlee 2005). Because of

this, I follow McGinnis 2010 by constructing a factor mimicking portfolio return, VMS,

which reflects the monthly hedge portfolio return of investing in the bottom three deciles

of the smoothing measure and shorting the top three deciles of the smoothing measure. If

smoother income, unconditional on the presence of economic losses, results in a lower

cost of capital (i.e., higher realized returns) then the hedge portfolio return, VMS, would

be positive. Descriptive statistics on Table 7 indicate that VMS is positive with a mean

of .001, which is significantly greater than zero (P-Value < .01).

I also estimate two separate smoothing hedge portfolio returns that are conditional

on the presence of bad economic news. To do so, I first split the sample of firms into

those with zero or one negative return years over years t-4 to t (the good news sample)

and a sample of firms with four or five negative return years (the bad news sample). I

then estimated a VMS hedge return for each of these subsamples, VMS_GN for the good

news sample and VMS_BN for the bad news sample. By doing so, VMS_GN should

reflect the hedge portfolio return related to offsetting while VMS_BN should reflect the

hedge portfolio return related to the impact of ATLR on earnings smoothness. VMS_GN

should be positive if the offsetting component of smoother income increases information

quality, while VMS_GN should be negative if the offsetting component of smoother

income decreases information quality. VMS_BN should be positive if ATLR is associated

24

with lower information quality. Descriptive statistics on Table 7 indicate that VMS_GN is

negative with a mean of -.002, which is significantly less than zero (P-Value < .01)

consistent with offsetting resulting in decreased information quality. VMS_BN is positive

with a mean of .002, which is significantly greater than zero (P-Value < .01) consistent

with ATLR being negatively associated with information quality.

On Table 8, Column 1 I present the results of a time-series regression run by firm

of future excess stock returns, E_RET, on VMS and the three factor mimicking portfolio

returns (Fama & French 1996) that control for other sources of variation in realized

returns. Consistent with McGinnis 2010, I find that VMS loads positively and

significantly. On Table 9, Column 1 I present the results of a time-series regression run

by firm of future excess stock returns, E_RET, on VMS_GN, VMS_BN and the three

factor mimicking portfolio returns (Fama & French 1993). The coefficient on both

VMS_GN and VMS_BN are positive and significant. Because the mean value of VMS_GN

is negative, these results suggest that the offsetting component of smoother earnings is

associated with higher realized stock returns. However, Core et al. 2008 and McGinnis

2010 argue that the testing method I describe above results in highly inflated t-stats.

Thus, these firm-level realized return results should be interpreted with caution.

To resolve issues with inflated T-stats I again follow McGinnis 2010 by using a

technique described by Core et al. 2008, where a two-stage cross-sectional regression

technique is employed. In the first stage I estimate the sensitivity of 25 portfolios formed

by independent sorts of BTM and SIZE to VMS and the three Fama & French factors. See

Column 2 of Tables 8 and 9 for the results of this regression, as well as a more detailed

description of the process in the table notes. In the second stage, I regress portfolio level

25

monthly excess returns on the factor loadings estimated in the first stage. This method

allows the researcher to identify actual risk factors from a set of potential risk factors

(e.g., income smoothing or beta). If smoothing is actually a risk factor, then portfolios

whose returns co-vary more strongly with the returns to a smoothing factor hedge return

(i.e., VMS) should command a higher risk premium and have higher realized returns.

Results presented in Column 3 of Table 8 show that there is no significant relationship

between the sensitivity of a given portfolio to the unconditional smoothing hedge return

(B_VMS) and realized portfolio stock returns.

On Column 2 of Table 9 I report first-stage results where the smoothing hedge

return is conditioned on the number of economic losses. This regression allows me to

estimate the sensitivity of the 25 portfolio returns to VMS_GN and VMS_BN. I then use

these sensitivities (B_VMS_GN and B_VMS_BN) in the second stage regression, with

results displayed in Column 3. The coefficient on B_VMS_GN is insignificant, which

does not support the notion that offsetting is related to realized stock returns and the cost

of capital.19 As opposed to the insignificant loading on B_VMS_GN, the loading on

B_VMS_BN is positive and significant with δm4 = .034 (P-Value < .05). Because

B_VMS_BN represents the portfolio’s sensitivity to the bad news VMS portfolio return,

the positive loading suggests that the component of earnings variability driven by ATLR

is associated with a higher cost of capital. Caution in assessing causality should be taken,

as LaFond & Watts 2008 finds that demand for conservative accounting can drive the

provision of conservative accounting. It is possible that investors at firms facing greater

19 It is important to note that results presented on Table 4 & 5 indicating a positive association between discretionary offsetting and information asymmetry do not necessarily imply an association between offsetting and realized stock returns. For instance, Leuz et al. 2007 provide analytical results suggesting that the average level of information possessed by investors, not information asymmetry, affects the cost of capital. Also, Fama 1991 and Core et al. 2008 suggest that “information risk” is diversifiable and should not affect the cost of capital.

26

systematic risk, and therefore a higher cost of capital, would demand more conservative

reporting.20

In untabulated tests I follow the two-stage research design employed by Lang et

al. 2012. In the first stage, I estimate the discretionary (the error term) and innate (fitted

value) portions of income smootheness using the following regression, which includes

industry and year fixed effects:

SMOOTH it=β0+β1×LNASSETSit+β2×LEV it+β3×BTM it+β4×STDSALES

it+β5×PRLOSS

it+β6×CYCLEit+ β7×GRSALE

it+

β8×OPLEV it+β14×AVGCFit

+εit

[5]

In the second stage I regress the SPREAD on the discretionary and innate portions

of SMOOTH, including all of the remaining controls from equation [3]:

SPREADit=β0 j+β1×INSMOOTHit+β2×DISC SMOOTH

it+ β3×SIZEit +

β4×TURN it+β5×AMIHUDit +β6×PRC it+β7×AGEit +β8×INST it +β9×N EST it

+β10×DIV it+εit

4. Conclusion

In this paper, I examine the influence of asymmetric timely loss recognition

(ATLR) on the smoothness of earnings and inferences about the consequences of income

smoothing. In addition to managerial intervention to offset temporary fluctuations in cash

flows (offsetting), the ATLR role of accruals can also impact earnings variability. ATLR

generally leads to accruals reinforcing variation in operating cash flows, and therefore,

increases earnings variability. Much of the smoothing literature has focused on the

general question of whether smoothing is a desirable reporting strategy, accordingly a

20 Controlling for B_MKTRET in the regression may not fully control for variation in systematic risk as it is well known that market sensitivities (e.g., Beta) are measured with error (Blume 1975).

27

variety of dependent variables designed to proxy for information quality have been

examined. Existing studies on the consequences of income smoothing do not attempt to

disentangle the effect of ATLR and offsetting on earnings smoothness. These studies

provide decidedly mixed results regarding whether smoothing improves the information

environment (Dechow et al. 2009). Given the strong information role played by ATLR

(Watts 2003a, LaFond and Watts 2008) as well as the mandatory nature of ATLR in the

presence of economic losses (Lawrence et al. 2013) it is unclear whether an association

between the smoothing proxy and an outcome variable is due to the impact of offsetting

or ATLR. Further, insignificant results could be obtained if the ATLR and offsetting

components of earnings smoothness have countervailing effects on an outcome variable,

even when ATLR and offsetting individually effect the outcome variable.

My results indicate that the smoothing measure is decreasing in the presence of

economic losses even after controlling for common determinants of smoothness. Moving

from one negative return year to three negative return years in the measurement window

decreases the smoothing measure by 25%. Also, I find that the negative relationship

between the smoothing proxy and economic losses increases in magnitude when firms

exhibit stronger ATLR. I employ the Khan & Watts CSCORE to proxy for firm’s

expected degree of ATLR. For the average firm, I find that moving from the bottom to

top decile of CSCORE decreases the smoothing measure by 10%.

In an analysis of the information quality consequences of smoother income when

I do not condition on the presence of economic losses, I find that both proxies for

information quality are not significantly associated with smoothing. However, when I

interact the smoothing proxy with the proportion of negative return years I find that the

28

effect of smoother income is conditional. My results indicate that when offsetting is a

more prominent component of smoothness (i.e., when there are no negative return years),

the association between smoother income and information quality is negative. When the

ATLR role of accruals is more prominent (i.e., when there are more negative return

years) the association between smoother income and information quality is positive.

Because the smoothing measure decreases in the presence of ATLR, this negative

association is consistent with the reasoning in LaFond & Watts 2008 who infer that

ATLR and information asymmetry are positively associated.

Using a spilt sample Jayaraman 2008 documents a U-shaped relationship between

smoother income and information asymmetry. Jayaraman infers that this U-shaped

pattern indicates that managerial intervention resulting in earnings variability that

deviates from cash flow variability, whether higher or lower, result in lower reporting

quality. My evidence indicates that the conditional association between smoother income

and information asymmetry documented by Jayaraman 2008 is a result of unintentionally

sorting firms by the prominence of ATLR when forming the split sample. Thus, it may be

premature to conclude that managerial intervention that distorts earnings variability

results in lower reporting quality.

I contribute to the academic literature by documenting the influence of ATLR on

the smoothness of earnings. Future research on the causes and consequences of a

reporting strategy that smoothes income should incorporate the effect of ATLR in its

research design. A research design that interacts the smoothing proxy with the proportion

of negative return years should provide researchers with a simple tool to determine

whether their inferences are a function of the ATLR role of accruals or offsetting. My

29

evidence shows that upon taking into account the role of ATLR in determining the

smoothness of earnings, information quality is negatively associated with offsetting. This

finding should be of interest to investors, managers, academics and regulators as there is

a debate about to what extent managers use accounting discretion to smooth earnings

thereby communicating private information and improving information quality.

30

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34

Mean Std Min P10 Q1 Median Q3 P90 MaxRAW_SPREAD 3.046 3.932 0.019 0.168 0.634 1.690 3.845 7.506 31.525SPREAD 1.085 0.743 -0.022 0.151 0.485 0.986 1.576 2.137 3.482SMOOTH 0.001 0.058 -0.326 -0.062 -0.017 0.007 0.029 0.058 0.190PR_NR 0.437 0.219 0.000 0.200 0.200 0.400 0.600 0.800 1.000CSCORE (N=25,895) 0.181 0.304 -1.112 -0.153 0.009 0.164 0.347 0.554 1.419SIZE 5.491 2.256 0.736 2.592 3.777 5.380 7.040 8.564 11.247TURN 1.565 0.759 0.108 0.602 0.996 1.503 2.070 2.614 3.924AMIHUD 9.601 1.556 6.404 7.744 8.386 9.320 10.766 11.879 13.968PRC 0.188 0.314 0.008 0.022 0.037 0.076 0.194 0.457 3.205LEV 0.207 0.177 0.000 0.000 0.038 0.186 0.329 0.456 0.746BTM 0.685 0.602 0.043 0.184 0.312 0.522 0.851 1.355 6.056AGE 2.361 0.300 1.946 1.946 2.079 2.303 2.565 2.773 2.996INST 0.094 0.124 0.000 0.000 0.000 0.022 0.167 0.290 0.979N_EST 0.960 1.083 0.000 0.000 0.000 0.693 1.792 2.639 3.892STD_SALE 0.269 0.343 0.018 0.052 0.088 0.162 0.301 0.545 2.386CYCLE 4.715 0.726 2.110 3.855 4.335 4.788 5.174 5.529 6.985GR_SALE 0.161 0.234 -0.201 -0.030 0.030 0.101 0.216 0.408 1.461OP_LEV 0.567 0.380 0.029 0.142 0.268 0.484 0.794 1.113 1.967DIV 0.008 0.015 0.000 0.000 0.000 0.000 0.013 0.029 0.098AVG_CF 0.106 0.130 -0.304 -0.035 0.041 0.095 0.161 0.254 0.802DNI 0.012 0.171 -1.966 -0.146 -0.010 0.042 0.089 0.143 0.422RET 0.200 0.742 -0.969 -0.461 -0.207 0.071 0.406 0.902 8.383

Table 1Descriptive Statistics

Descriptive Statistics for Variables Employed in the Regression Analyses (N=33,869)

Description:The table above presents descriptive statistics for variables used in Tables 3 through 5. I truncate all continuous variables at the top and bottom 1%, with the exception of variables censored at zero for which I truncated only the top 1%. The sample period begins in 1988 and ends in 2011, because several variables require five years of data to measure, my first valid observation occurs in 1992.Variable Descriptions:RAW_SPREADit is the average of the day ending percent bid-ask spread from CRSP measured over the 250 trading days following the end of the last fiscal year in the smoothing measurement window. SPREADit is the natural log of the average of the day ending percent bid-ask spread from CRSP measured over the 250 trading days following the end of the last fiscal year in the smoothing measurement window. SMOOTHit is measured as the standard deviation of operating cash flows (OANCF) deflated by total assets (AT) less the standard deviation of net income (NI) also deflated by total assets. PR_NRit is the proportion of negative return years occurring over the current and previous four years. CSCOREit, is measured over a 5 year period in regressions conducted by four digit SIC code. I require at least 30 firm/year observations for each industry/year pool, with at-least 15 negative return years to calculate CSCORE. I run the Basu [1997] model by firm, using the incremental coefficient on negative returns as well as the coefficients on negative returns interacted with firm’s book to market ratio, leverage and market value as my measure of

conservatism (CSCORE = β3 j

¿̂+ β7 j

¿̂׿ BTMit

+ β11 j

^¿∗LEV

it+β

15 j

^¿∗SIZE

it¿

¿

¿

¿ from equation [1] below):DNI it=β0 j+β1 j×Rit+ β2 j×NRit+β3 j×R it×NRit+β4 j∗BTM it+β5 j×Rit×BTM it+β6 j×NRit×BTM it+β7 j×Rit×NRit×BTM it +β8 j∗LEV it+β9 j×Rit×LEV it+ β10 j×NRit×LEV it +β11 j×Rit×NRit×LEV it +β12 j∗SIZE it+ β13 j×R it×SIZE it+ β14 j×NRit×SIZE it+β15 j×R it×NRit×SIZE it+εit

SIZEit is the natural log of the firm’s market value of equity (MVE) which is defined as PRCC_F*CSHO. TURNit is measured in the last year of the smoothing measurement window and is calculated as the average volume expressed as a percentage of shares outstanding using CRSP data. AMIHUDit is measured in the

35

last year of the smoothing measurement window and is calculated as the average absolute daily return divided by the market value of equity using CRSP data (scaled up by 106). PRICEit is measured in the last year of the smoothing measurement window and is calculated as the inverse of the ending stock price (PRCC_F). LEVit is BVD/(BVD+MVE) with BVD defined as the current and long-term portion of debt; (DLTT)+(DLC). BTMit is BVE/MVE with BVE defined as (SEQ). AGEit is the number of years that the firm has been present on Compustat. INSTit is the proportion of shares owned by institutional investors per the Thompson Financial Institutional Ownership database. N_ESTit is the log of one plus the number of equity analysts following the firm as reported by I/B/E/. STD_SALEit is the standard deviation of sales (SALE) over the current and previous four years. CYCLEit is defined as the logged operating cycle where operating cycle is defined as log ((365*(((ARt-1+ARt)/2)/SALEt)) + (365*(((INVt-1+INVt)/2)/COGSt))) where AR is defined as (RECT), Inv is defined as (INVT), and COGS is defined as (COGS). GR_SALEit is defined as the average of yearly sales growth deflated by average total assets with sales defined as (SALE). OPLEVit is defined as total fixed assets (PPENT) deflated by average total assets (AT). DIVit is defined as the average dividend yield over the current and previous four years, which is calculated as dividends (DVC) divided by the market value of equity. AVG_CFit is defined as the average cash flows from operations (OANCF) deflated by total assets ([ATt+ATt-1]/2). RETit is the compounded annual stock return from CRSP for the 12 month period beginning four months after the beginning of the fiscal year. DNIit is net income before extraordinary items (IB) deflated by MVEt-1.

36

1 2 3 4 5 6 7 8 9 101 SPREAD 0.000 0.311 -0.021 -0.732 0.101 0.390 0.066 0.065 -0.050

0.936 <.0001 0.001 <.0001 <.0001 <.0001 <.0001 <.0001 <.00012 SMOOTH 0.011 -0.166 -0.111 0.044 0.025 0.062 -0.211 -0.005 0.221

0.041 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.395 <.00013 PR_NR 0.289 -0.166 0.048 -0.399 0.061 0.350 0.159 -0.012 -0.186

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.034 <.00014 CSCORE -0.039 -0.100 0.056 -0.047 -0.172 -0.127 0.095 -0.124 -0.160

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.00015 SIZE -0.748 0.019 -0.399 -0.058 0.032 -0.439 -0.145 0.041 0.074

<.0001 0.000 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.00016 LEV 0.077 0.007 0.039 -0.211 0.066 0.109 -0.021 0.212 0.205

<.0001 0.208 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.00017 BTM 0.377 0.084 0.340 -0.180 -0.457 0.107 -0.028 0.064 0.169

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.00018 STD_SALE 0.081 -0.160 0.203 0.119 -0.201 -0.049 -0.018 -0.136 -0.285

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.001 <.0001 <.00019 OP_LEV 0.067 -0.032 -0.022 -0.161 0.047 0.245 0.099 -0.223 0.354

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.000110 AVG_CF -0.054 0.185 -0.219 -0.194 0.105 0.220 0.228 -0.302 0.418

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

Table 2Descriptive Statistics

Correlation Statistics for Variables Employed in the Write-Down Analysis

Description:The table above presents correlation statistics for selected variables used in Tables 3 through 5. Pearson correlation coefficients are presented on the top right while Spearman correlation coefficients are presented on the bottom left. P-values are provided below the correlation coefficient. I truncate all continuous variables at the top and bottom 1%, with the exception of variables censored at zero for which I truncated only the top 1%. The sample period begins in and ends in. The sample period begins in 1988 and ends in 2011, because several variables require five years of data to measure, my first valid observation occurs in 1992. Variable Descriptions:SPREADit is the average of the day ending bid-ask spread from CRSP measured over the 250 trading days following the end of the last fiscal year in the smoothing measurement window. SMOOTHit is measured as the standard deviation of operating cash flows (OANCF) deflated by total assets (AT) less the standard deviation of net income (NI) also deflated by total assets. PR_NRit is the proportion of negative return years occurring over the current and previous four years. CSCOREit, is defined under Table 1. SIZEit is the natural log of the firm’s market value of equity (MVE) which is defined as PRCC_F*CSHO. LEVit is BVD/(BVD+MVE) with BVD defined as the current and long-term portion of debt; (DLTT)+(DLC). BTMit is BVE/MVE with BVE defined as (SEQ). STD_SALEit is the standard deviation of sales (SALE) over the current and previous four years. OPLEVit is defined as total fixed assets (PPENT) deflated by average total assets (AT). AVG_CFit is defined as the average cash flows from operations (OANCF) deflated by total assets ([ATt+ATt-1]/2).

37

SMOOTH it-4,t = ß 0 + ß 1 PR_ NRit + ß 2 CSCORE it + ß 3 PR_NR it *CSCORE it + ß 4 SIZE it + ß 5 TURN it + ß 6 LEV it + ß 7 BTM it + ß 8 AGE it + ß 9 STD_SALE it + ß 10 CYCLE it + ß 11 GR_SALES it + ß 12 OP_LEV it + ß 13 DIV it + ß 14 AVG_CF it + ε it

Prediction 2 3 4Intercept ß0 ? 0.6049 *** 0.5413 *** 0.5987 *** 0.4722 ***

(0.0216) (0.0229) (0.0237) (0.0398) PR_NR it-4,t ß1 - -0.2327 *** -0.1569 *** -0.1121 ***

(0.0078) (0.0165) (0.0171) CSCORE it-4,t ß2 - -0.0498 *** 0.0310 ** 0.0079

(0.0066) (0.0136) (0.0133) PR_NR it-4,t *CSCORE it-4,t ß3 - -0.1735 *** -0.0964 ***

(0.0284) (0.0280) SIZE it ß4 ? -0.0066 ***

(0.0012) TURN it ß5 ? -0.0302 ***

(0.0031) LEV it ß6 ? -0.1072 ***

(0.0123) BTM it ß7 ? 0.0110 ***

(0.0042) AGE it ß8 ? 0.0605 ***

(0.0088) STD_SALE it ß9 + -0.2029 ***

(0.0108) CYCLE it ß10 + 0.0119 ***

(0.0038) GR_SALE it ß11 ? 0.3008 ***

(0.0160) OP_LEV it ß12 + -0.0727 ***

(0.0072) DIV it ß13 ? 0.4600 ***

(0.1468) AVG_CF it ß14 ? 0.3682 ***

(0.0189)

Number of Observations 33,896 25,895 25,895 25,895 0.077 0.057 0.083 0.131

*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level

Table 3DETERMINANTS OF SMOOTHING

SPECIFICATION:1

Adjusted R-Square

Description:This is a regression of income smoothing on measures that proxy for the presence of economic losses and the estimated level of asymmetric timely loss recognition, estimated using equation [1]. Coefficient standard errors are shown in parentheses below the coefficient loading. Standard errors are Huber-White heteroskedastic robust and are clustered by firm. Fixed effects are included (FF48 and Year). I truncate all continuous variables at the top and bottom 1%, with the exception of variables censored at zero for which I truncated only the top 1%. Variable Descriptions:SMOOTHit is measured as the standard deviation of operating cash flows (OANCF) deflated by total assets (AT) less the standard deviation of net income (NI) also deflated by total assets. PR_NRit is the proportion of negative return years occurring over the current and previous four years. CSCOREit, is defined under Table 1. SIZEit is the natural log of the firm’s market value of equity (MVE) which is defined as PRCC_F*CSHO. LEVit is BVD/(BVD+MVE) with BVD defined as the current and long-term portion of debt; (DLTT)+(DLC). BTMit is BVE/MVE with BVE defined as (SEQ). AGEit is the number of years that the firm has been present on Compustat. STD_SALEit is the standard deviation of sales (SALE) over the current and previous four years. CYCLEit is defined as the logged operating cycle where operating cycle is defined as log ((365*(((ARt-1+ARt)/2)/SALEt)) + (365*(((INVt-1+INVt)/2)/COGSt))) where AR is defined as

38

(RECT), Inv is defined as (INVT), and COGS is defined as (COGS). GR_SALEit is defined as the average of yearly sales growth deflated by average total assets with sales defined as (SALE). OPLEVit is defined as total fixed assets (PPENT) deflated by average total assets (AT). DIVit is defined as the average dividend yield over the current and previous four years, which is calculated as dividends (DVC) divided by the market value of equity. AVG_CFit is defined as the average cash flows from operations (OANCF) deflated by total assets ([ATt+ATt-1]/2).

39

SPREAD it = ß 0 + ß 1 SMOOTH it + ß 2 PR_NR it + ß 3 PR_NR it *SMOOTH it + ß 4 SIZE it + ß 5 TURN it + ß 6 AMIHUD it + ß 7 PRC it + ß 8 LEV it + ß 9 BTM it + ß 10 AGE it + ß 11 INST it + ß 12 N_EST it + ß 13 STD_SALE it + ß 14 CYCLE it + ß 15 GR_SALE it + ß 16 OP_LEV it + ß 17 DIV it + ß 18 AVG_CF it + ε it

Prediction 2 3Intercept ß0 ? -1.0181 *** -1.1127 *** -1.1390 ***

(0.0565) (0.0561) (0.0564) SMOOTH it-4,t ß1 ? -0.0086 0.0016 0.0472 ***

(0.0061) (0.0061) (0.0132) PR_NR it-4,t ß2 + 0.1951 *** 0.2443 ***

(0.0100) (0.0171) PR_NR it-4,t *SMOOTH it-4,t ß3 - -0.0983 ***

(0.0271) SIZE it ß4 - -0.1086 *** -0.1044 *** -0.1042 ***

(0.0016) (0.0016) (0.0016) TURN it ß5 ? 0.0638 *** 0.0577 *** 0.0576 ***

(0.0053) (0.0053) (0.0053) AMIHUD it ß6 + 0.1850 *** 0.1837 *** 0.1837 ***

(0.0034) (0.0033) (0.0033) PRC it ß7 + 0.1667 *** 0.1597 *** 0.1588 ***

(0.0091) (0.0090) (0.0090) LEV it ß8 + 0.2743 *** 0.2499 *** 0.2504 ***

(0.0116) (0.0116) (0.0116) BTM it ß9 + 0.1068 *** 0.0904 *** 0.0909 ***

(0.0043) (0.0044) (0.0044) AGE it ß10 - 0.0902 *** 0.0991 *** 0.0990 ***

(0.0078) (0.0078) (0.0078) INST it ß11 - -0.2755 *** -0.2641 *** -0.2642 ***

(0.0157) (0.0157) (0.0157) N_EST it ß12 - 0.0090 *** 0.0078 *** 0.0078 ***

(0.0021) (0.0021) (0.0021) STD_SALE it ß13 + 0.1077 *** 0.0803 *** 0.0807 ***

(0.0099) (0.0098) (0.0098) CYCLE it ß14 + 0.0207 *** 0.0211 *** 0.0213 ***

(0.0034) (0.0034) (0.0034) GR_SALE it ß15 - -0.1921 *** -0.1448 *** -0.1454 ***

(0.0142) (0.0142) (0.0142) OP_LEV it ß16 + 0.0362 *** 0.0291 *** 0.0288 ***

(0.0067) (0.0067) (0.0067) DIV it ß17 - -0.7027 *** -0.5743 *** -0.5633 ***

(0.1411) (0.1401) (0.1402) AVG_CF it ß18 - -0.2893 *** -0.2320 *** -0.2292 ***

(0.0176) (0.0178) (0.0178)

Number of Observations

*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level

Table 4BID/ASK SPREAD REGRESSED ON SMOOTHING & CONTROLS

SPECIFICATION:1

Adjusted R-Square

Description:This is a regression of the bid/ask spread on income smoothing estimated using equation [3]. Coefficient standard errors are shown in parentheses below the coefficient loading. Standard errors are Huber-White heteroskedastic robust and are clustered by firm. Fixed effects are included (FF48 and Year). I truncate all continuous variables at the top and bottom 1%, with the exception of variables censored at zero for which I truncated only the top 1%.

40

Variable Descriptions:SPREADit is the average of the day ending bid-ask spread from CRSP measured over the 250 trading days following the end of the last fiscal year in the smoothing measurement window. SMOOTHit is measured as the standard deviation of operating cash flows (OANCF) deflated by total assets (AT) less the standard deviation of net income (NI) also deflated by total assets. PR_NRit is the proportion of negative return years occurring over the current and previous four years. CSCOREit, is defined under Table 1. SIZEit is the natural log of the firm’s market value of equity (MVE) which is defined as PRCC_F*CSHO. TURNit is measured in the last year of the smoothing measurement window and is calculated as the average volume expressed as a percentage of shares outstanding using CRSP data. AMIHUDit is measured in the last year of the smoothing measurement window and is calculated as the average absolute daily return divided by the market value of equity using CRSP data (scaled up by 106). PRICEit is measured in the last year of the smoothing measurement window and is calculated as the inverse of the ending stock price (PRCC_F). LEVit is BVD/(BVD+MVE) with BVD defined as the current and long-term portion of debt; (DLTT)+(DLC). BTMit is BVE/MVE with BVE defined as (SEQ). AGEit is the number of years that the firm has been present on Compustat. INSTit is the proportion of shares owned by institutional investors per the Thompson Financial Institutional Ownership database. N_ESTit is the log of one plus the number of equity analysts following the firm as reported by I/B/E/. STD_SALEit is the standard deviation of sales (SALE) over the current and previous four years. CYCLEit is defined as the logged operating cycle where operating cycle is defined as log ((365*(((ARt-1+ARt)/2)/SALEt)) + (365*(((INVt-1+INVt)/2)/COGSt))) where AR is defined as (RECT), Inv is defined as (INVT), and COGS is defined as (COGS). GR_SALEit is defined as the average of yearly sales growth deflated by average total assets with sales defined as (SALE). OPLEVit is defined as total fixed assets (PPENT) deflated by average total assets (AT). DIVit is defined as the average dividend yield over the current and previous four years, which is calculated as dividends (DVC) divided by the market value of equity. AVG_CFit is defined as the average cash flows from operations (OANCF) deflated by total assets ([ATt+ATt-1]/2). RETit is the compounded annual stock return from CRSP for the 12 month period beginning four months after the beginning of the fiscal year. DNIit is net income before extraordinary items (IB) deflated by MVEt-1.

41

SPREAD it = ß 0 + ß 1 SMOOTH it + ß 2 PR_NR it + ß 3 PR_NR it *SMOOTH it + ß 4 SIZE it + ß 5 TURN it + ß 6 AMIHUD it + ß 7 PRC it + ß 8 LEV it + ß 9 BTM it + ß 10 AGE it + ß 11 INST it + ß 12 N_EST it + ß 13 STD_SALE it + ß 14 CYCLE it + ß 15 GR_SALE it + ß 16 OP_LEV it + ß 17 DIV it + ß 18 AVG_CF it + ε it.

Prediction VOLATILE VOLATILEIntercept ß0 ? -1.1532 *** -0.8160 *** -1.2372 *** -0.9961 ***

(0.0727) (0.0911) (0.0740) (0.0913) SMOOTH it-4,t ß1 ? 0.0338 *** -0.0637 *** 0.0495 ** 0.2254 ***

(0.0130) (0.0244) (0.0247) (0.0530) PR_NR it-4,t ß2 + 0.1884 *** 0.3184 ***

(0.0415) (0.0256) PR_NR it-4,t *SMOOTH it-4,t ß3 - -0.0302 -0.5467 ***

(0.0559) (0.1012) SIZE it ß4 - -0.1005 *** -0.1163 *** -0.0970 *** -0.1119 ***

(0.0021) (0.0026) (0.0021) (0.0026) TURN it ß5 ? 0.0736 *** 0.0435 *** 0.0675 *** 0.0405 ***

(0.0067) (0.0087) (0.0067) (0.0086) AMIHUD it ß6 + 0.1924 *** 0.1718 *** 0.1914 *** 0.1713 ***

(0.0042) (0.0056) (0.0042) (0.0055) PRC it ß7 + 0.2101 *** 0.1354 *** 0.2022 *** 0.1253 ***

(0.0145) (0.0118) (0.0143) (0.0117) LEV it ß8 + 0.2613 *** 0.2895 *** 0.2414 *** 0.2628 ***

(0.0149) (0.0183) (0.0149) (0.0183) BTM it ß9 + 0.1201 *** 0.0943 *** 0.1044 *** 0.0802 ***

(0.0058) (0.0065) (0.0059) (0.0066) AGE it ß10 - 0.0866 *** 0.0874 *** 0.0953 *** 0.0951 ***

(0.0104) (0.0121) (0.0103) (0.0120) INST it ß11 - -0.2555 *** -0.3147 *** -0.2460 *** -0.2972 ***

(0.0195) (0.0268) (0.0195) (0.0268) N_EST it ß12 - 0.0127 *** 0.0053 0.0117 *** 0.0033

(0.0027) (0.0036) (0.0027) (0.0036) STD_SALE it ß13 + 0.1380 *** 0.0718 *** 0.1094 *** 0.0474 ***

(0.0139) (0.0143) (0.0140) (0.0142) CYCLE it ß14 + 0.0130 *** 0.0290 *** 0.0128 *** 0.0301 ***

(0.0045) (0.0053) (0.0045) (0.0053) GR_SALE it ß15 - -0.2225 *** -0.1558 *** -0.1749 *** -0.1106 ***

(0.0200) (0.0201) (0.0202) (0.0201) OP_LEV it ß16 + 0.0135 0.0611 *** 0.0081 0.0501 ***

(0.0087) (0.0106) (0.0086) (0.0105) DIV it ß17 - -0.4625 *** -0.9491 *** -0.3857 ** -0.8048 ***

(0.1784) (0.2330) (0.1773) (0.2311) AVG_CF it ß18 - -0.2838 *** -0.3015 *** -0.2348 *** -0.2274 ***

(0.0229) (0.0281) (0.0232) (0.0285)

Number of Observations 20,166 13,703 20,166 13,703 0.817 0.818 0.819 0.821

*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% levelAdjusted R-Square

Table 5BID/ASK SPREAD REGRESSED ON SMOOTHING & CONTROLS IN A SPLIT SAMPLE

REGIME:SMOOTH SMOOTH

Description:This is a regression of the bid/ask spread on income smoothing estimated using equation [3]. In this table I split the sample into a “smooth” (SMOOTH >= 1) and “volatile” (SMOOTH < 1) subsample. Coefficient standard errors are shown in parentheses below the coefficient loading. Standard errors are Huber-White heteroskedastic robust and are clustered by firm. Fixed effects are included (FF48 and Year). I truncate all continuous variables at the top and bottom 1%, with the exception of variables censored at zero for which I truncated only the top 1%.

42

Variable Descriptions:SPREADit is the average of the day ending bid-ask spread from CRSP measured over the 250 trading days following the end of the last fiscal year in the smoothing measurement window. SMOOTHit is measured as the standard deviation of operating cash flows (OANCF) deflated by total assets (AT) less the standard deviation of net income (NI) also deflated by total assets. PR_NRit is the proportion of negative return years occurring over the current and previous four years. CSCOREit, is defined under Table 1. SIZEit is the natural log of the firm’s market value of equity (MVE) which is defined as PRCC_F*CSHO. TURNit is measured in the last year of the smoothing measurement window and is calculated as the average volume expressed as a percentage of shares outstanding using CRSP data. AMIHUDit is measured in the last year of the smoothing measurement window and is calculated as the average absolute daily return divided by the market value of equity using CRSP data (scaled up by 106). PRICEit is measured in the last year of the smoothing measurement window and is calculated as the inverse of the ending stock price (PRCC_F). LEVit is BVD/(BVD+MVE) with BVD defined as the current and long-term portion of debt; (DLTT)+(DLC). BTMit is BVE/MVE with BVE defined as (SEQ). AGEit is the number of years that the firm has been present on Compustat. INSTit is the proportion of shares owned by institutional investors per the Thompson Financial Institutional Ownership database. N_ESTit is the log of one plus the number of equity analysts following the firm as reported by I/B/E/. STD_SALEit is the standard deviation of sales (SALE) over the current and previous four years. CYCLEit is defined as the logged operating cycle where operating cycle is defined as log ((365*(((ARt-1+ARt)/2)/SALEt)) + (365*(((INVt-1+INVt)/2)/COGSt))) where AR is defined as (RECT), Inv is defined as (INVT), and COGS is defined as (COGS). GR_SALEit is defined as the average of yearly sales growth deflated by average total assets with sales defined as (SALE). OPLEVit is defined as total fixed assets (PPENT) deflated by average total assets (AT). DIVit is defined as the average dividend yield over the current and previous four years, which is calculated as dividends (DVC) divided by the market value of equity. AVG_CFit is defined as the average cash flows from operations (OANCF) deflated by total assets ([ATt+ATt-1]/2). RETit is the compounded annual stock return from CRSP for the 12 month period beginning four months after the beginning of the fiscal year. DNIit is net income before extraordinary items (IB) deflated by MVEt-1.

43

Mean Std Min P10 Q1 Median Q3 P90 Max ObsE_RETim 0.012 0.177 -0.983 -0.154 -0.069 -0.001 0.075 0.176 10.340 394,523 P25_RETpm 0.011 0.057 -0.336 -0.053 -0.021 0.013 0.044 0.074 0.498 4,262 VMSm 0.001 0.025 -0.058 -0.026 -0.012 0.000 0.011 0.025 0.088 384,616 VMS_GNm -0.002 0.024 -0.082 -0.029 -0.018 0.000 0.011 0.025 0.187 384,042 VMS_BNm 0.002 0.049 -0.160 -0.062 -0.028 -0.006 0.033 0.066 0.170 384,042 MKTRETm 0.006 0.042 -0.162 -0.044 -0.021 0.013 0.035 0.059 0.082 394,523 SMBm 0.002 0.039 -0.166 -0.039 -0.022 -0.002 0.026 0.044 0.221 394,523 HMLm 0.004 0.035 -0.129 -0.037 -0.014 0.004 0.020 0.042 0.139 394,523 B_MKTRETit-4,it 1.046 0.814 -5.226 0.195 0.524 0.933 1.439 2.079 10.446 394,523 B_VMSp 0.435 0.529 -0.263 -0.016 0.025 0.230 0.826 1.326 1.364 25 B_VMS_GNp 0.138 0.205 -0.142 -0.046 -0.016 0.104 0.179 0.472 0.564 25 B_VMS_BNp 0.119 0.092 0.018 0.057 0.065 0.088 0.117 0.252 0.409 25 B_MKTRETp 0.998 0.084 0.842 0.860 0.961 1.004 1.045 1.114 1.161 25 B_SMBp 0.537 0.196 0.059 0.264 0.425 0.606 0.665 0.740 0.809 25 B_HMLp 0.469 0.343 -0.253 -0.111 0.380 0.541 0.711 0.813 1.003 25

Table 6Descriptive Statistics

Descriptive Statistics for Realized Return Regressions

Description:The table above presents descriptive statistics for variables used in Tables 7 through 9. I truncate all continuous variables at the top and bottom 1%, with the exception of variables censored at zero for which I truncated only the top 1%. The sample period begins in 1988 and ends in 2011, because several variables require five years of data to measure, my first valid observation occurs in 1992. See Appendix A for variable definitions.Variable Descriptions:E_RETim is defined as the raw stock return for firm i in month m less the risk free rate. The monthly stock return is provided by CRSP while the risk free rate is available in the Fama & French Liquidity Factors dataset. P25_RETpm is defined as value weighted excess stock return for firms in portfolio p in month m. The monthly stock return is provided by CRSP while the risk free rate is available in the Fama & French Liquidity Factors dataset. VMSm is the monthly factor-mimicking portfolio return calculated by subtracting the value-weighted return of stocks in the lowest three deciles of earnings smoothness (SMOOTHit) from the value-weighted return on stocks in the highest three deciles of earnings smoothness. VMS_GNm is the monthly factor-mimicking portfolio return calculated by subtracting the value-weighted return of stocks in the lowest three deciles of earnings smoothness (SMOOTHit) from the value-weighted return on stocks in the highest three deciles of earnings smoothness for firms in the good news sample. Firms are included in the good news sample if they have one or less negative return years in the earnings smoothing measurement window (years t-4 to t). VMS_BNm is the monthly factor-mimicking portfolio return calculated by subtracting the value-weighted return of stocks in the lowest three deciles of earnings smoothness (SMOOTHit) from the value-weighted return on stocks in the highest three deciles of earnings smoothness for firms in the bad news sample. Firms are included in the bad news sample if they have four or more negative return years in the earnings smoothing measurement window (years t-4 to t). MKTRFm is the monthly excess return on the CRSP value-weighted portfolio provided by the Fama & French Liquidity Factors dataset. SMBm is the monthly small minus large firm size factor mimicking portfolio return provided by the Fama & French Liquidity Factors dataset. HMLm is the monthly high minus low book-to-market factor mimicking portfolio return provided by the Fama & French Liquidity Factors dataset. B_MKTRETit-4,it is the coefficient loading for firm i in a time-series regression of firm excess returns (E_RETim) on the CRSP value-weighted return (MKTRFm) conducted by firm. I estimate this regression using monthly return data from the current and previous four years. Firms must have at least 18 monthly return observations to be included in the sample. B_VMSp is the coefficient loading on the smoothing factor (VMSm) for portfolio p. The coefficient loading is calculated in a time-series regression, conducted by portfolio, of portfolio excess value-weighted monthly returns (P25_RETpm) on the three Fama-French

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factors (MKTRFm, SMBm, HMLm) plus the smoothing factor (VMSm). I form 25 portfolios by independently sorting all firms in the sample by beginning of period book to market (BTMit) and market value (SIZEit). B_VMS_GNp is the coefficient loading on the good news sample smoothing factor (VMS_GNm) for portfolio p. The coefficient loading is calculated in a time-series regression, conducted by portfolio, of portfolio excess value-weighted monthly returns (P25_RETpm) on the three Fama-French factors (MKTRFm, SMBm,

HMLm) plus the good and bad news smoothing factors (VMS_GNm & VMS_BNm). I form 25 portfolios by independently sorting all firms in the sample by beginning of period book to market (BTMit) and market value (SIZEit). B_VMS_BNp is the coefficient loading on the bad news sample smoothing factor (VMS_BNm) for portfolio p. The coefficient loading is calculated in a time-series regression, conducted by portfolio, of portfolio excess value-weighted monthly returns (P25_RETpm) on the three Fama-French factors (MKTRFm,

SMBm, HMLm) plus the good and bad news smoothing factors (VMS_GNm & VMS_BNm). I form 25 portfolios by independently sorting all firms in the sample by beginning of period book to market (BTMit) and market value (SIZEit). B_MKTRETp is the coefficient loading on the market factor (MKTRFm) for portfolio p. The coefficient loading is calculated in a time-series regression, conducted by portfolio, of portfolio excess value-weighted monthly returns (P25_RETpm) on the three Fama-French factors (MKTRFm,

SMBm, HMLm) plus the smoothing factor (VMSm). I form 25 portfolios by independently sorting all firms in the sample by beginning of period book to market (BTMit) and market value (SIZEit). B_SMBp is the coefficient loading on the small-minus-big factor (SMBm) for portfolio p. The coefficient loading is calculated in a time-series regression, conducted by portfolio, of portfolio excess value-weighted monthly returns (P25_RETpm) on the three Fama-French factors (MKTRFm, SMBm, HMLm) plus the smoothing factor (VMSm). I form 25 portfolios by independently sorting all firms in the sample by beginning of period book to market (BTMit) and market value (SIZEit). B_HMLp is the coefficient loading on the high-minus-low factor (HMLm) for portfolio p. The coefficient loading is calculated in a time-series regression, conducted by portfolio, of portfolio excess value-weighted monthly returns (P25_RETpm) on the three Fama-French factors (MKTRFm, SMBm, HMLm) plus the smoothing factor (VMSm). I form 25 portfolios by independently sorting all firms in the sample by beginning of period book to market (BTMit) and market value (SIZEit).

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E_RET im+3,im+15 = ß 0 + ß 1 B_MKTRET it-4,it + ß 2 SIZE it + ß 3 BTM it + ß 4 SMOOTH it-4,it + ß 5 PR_NR it-4,it + ß 6 SMOOTH it-4,it *PR_NR it-4,it + ε it

Prediction 2 3Intercept ß0 ? 0.0113 *** 0.0118 *** 0.0025

(0.0013) (0.0014) (0.0021) B_MKTRET it-4,t ß1 + 0.0013 *** 0.0012 *** 0.0008 **

(0.0004) (0.0004) (0.0004) SIZE it ß2 - -0.0012 *** -0.0012 *** -0.0008 ***

(0.0001) (0.0001) (0.0001) BTM it ß3 + 0.0089 *** 0.0089 *** 0.0081 ***

(0.0008) (0.0008) (0.0008) SMOOTH it-4,t ß4 + -0.0009 0.0064 ***

(0.0010) (0.0024) PR_NR it-4,t ß5 ? 0.0175 ***

(0.0034) PR_NR it-4,t *SMOOTH it-4,t ß6 - -0.0135 **

(0.0054)

Number of Observations 394,523 394,523 394,523 0.001 0.001 0.002

*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level

Table 7Future Firm-level Stock Returns Regressed on Smoothing

SPECIFICATION:1

Adjusted R-Square

Description:This is a regression of monthly stock returns on income smoothing estimated using equation [4]. Coefficient standard errors are shown in parentheses below the coefficient loading. Standard errors are Huber-White heteroskedastic robust and are clustered by firm. Variable Descriptions:E_RETim is defined as the raw stock return for firm i in month m less the risk free rate. The monthly stock return is provided by CRSP while the risk free rate is available in the Fama & French Liquidity Factors dataset. B_MKTRETit-4,it is the coefficient loading for firm i in a time-series regression of firm excess returns (E_RETim) on the CRSP value-weighted return (MKTRFm) conducted by firm. I estimate this regression using monthly return data from the current and previous four years. Firms must have at least 18 monthly return observations to be included in the sample. SMOOTHit is measured as the standard deviation of operating cash flows (OANCF) deflated by total assets (AT) less the standard deviation of net income (NI) also deflated by total assets. PR_NRit is the proportion of negative return years occurring over the current and previous four years. SIZEit is the natural log of the firm’s market value of equity (MVE) which is defined as PRCC_F*CSHO. BTMit is BVE/MVE with BVE defined as (SEQ).

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COLUMN 1: E_RET im = ß i0 + ß i1 MKTRET m + ß i2 SMB m + ß i3 HML m + ß i4 VMS m + ε im COLUMN 2: P25_E_RET pm = λ0 + λ p1 MKTRETm + λ p2 SMB m + λ p3 HML m + λ p4 VMS m + ε im COLUMN 3: P25_E_RET pm = δm0 + δ m1 B_MKTRET p + δ m2 B_SMB p + δ m3 B_HML p + δ m4 B_VMS p + ε im

Intercept ßi0 ? 0.0020 *** Intercept λp0 ? 0.0011 ** Intercept δm0 ? 0.0206 **(0.0288) (0.0029) (0.0184)

MKTRETm ßi1 + 0.9405 *** MKTRETm λp1 + 0.9984 *** B_MKTRET p δm1 + -0.0174(1.0668) (0.0838) 0.0163

SMB m ßi2 + 0.6650 *** SMB m λp2 + 0.5372 *** B_SMB p δm2 + 0.0186 *(1.2279) (0.1956) (0.0046)

HMLm ßi3 + 0.4065 *** HML m λp3 + 0.4689 *** B_HML p δm3 + -0.0028(1.4842) (0.3432) (0.0051)

VMSm ßi4 + 0.6721 *** VMS m λp4 + 0.4349 *** B_VMS p δm4 + 0.0062(2.6609) (0.5292) (0.0005)

Number of Observations 4,911 25 174 0.211 0.774 0.514

*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level

Prediction Prediction Prediction

Table 8In-sample Test Based on McGinnis 2010

Adjusted R-Square

COLUMN 1 COLUMN 2 COLUMN 3

Description:This is a regression of monthly stock returns on the smoothing factor mimicking portfolio return (Column 1 and 2), or the portfolio level sensitivity to the smoothing factor mimicking portfolio return (Column 3). Coefficient standard errors are shown in parentheses below the coefficient loading. Standard errors are Huber-White heteroskedastic robust and are clustered by firm. Variable Descriptions:E_RETim is defined as the raw stock return for firm i in month m less the risk free rate. The monthly stock return is provided by CRSP while the risk free rate is available in the Fama & French Liquidity Factors dataset. P25_RETpm is defined as value weighted excess stock return for firms in portfolio p in month m. The monthly stock return is provided by CRSP while the risk free rate is available in the Fama & French Liquidity Factors dataset. VMSm is the monthly factor-mimicking portfolio return calculated by subtracting the value-weighted return of stocks in the lowest three deciles of earnings smoothness (SMOOTHit) from the value-weighted return on stocks in the highest three deciles of earnings smoothness. MKTRFm is the monthly excess return on the CRSP value-weighted portfolio provided by the Fama & French Liquidity Factors dataset. SMBm is the monthly small minus large firm size factor mimicking portfolio return provided by the Fama & French Liquidity Factors dataset HMLm is the monthly high minus low book-to-market factor mimicking portfolio return provided by the Fama & French Liquidity Factors dataset. B_MKTRETit-4,it is the coefficient loading for firm i in a time-series regression of firm excess returns (E_RETim) on the CRSP value-weighted return (MKTRFm) conducted by firm. I estimate this regression using monthly return data from the current and previous four years. Firms must have at least 18 monthly return observations to be included in the sample. B_VMSp is the coefficient loading on the smoothing factor (VMSm) for portfolio p. The coefficient loading is calculated in a time-series regression, conducted by portfolio, of portfolio excess value-weighted monthly returns (P25_RETpm) on the three Fama-French factors (MKTRFm, SMBm, HMLm) plus the smoothing factor (VMSm). I form 25 portfolios by independently sorting all firms in the sample by beginning of period book to market (BTMit) and market value (SIZEit). B_MKTRETp is the coefficient loading on the market factor (MKTRFm) for portfolio p. The coefficient loading is calculated in a time-series regression, conducted by portfolio, of portfolio excess value-weighted monthly returns (P25_RETpm) on the three Fama-French factors (MKTRFm, SMBm, HMLm) plus the smoothing factor (VMSm). I form 25 portfolios by independently sorting all firms in the sample by beginning of period book to market (BTMit) and market value (SIZEit). B_SMBp is the coefficient loading on the small-minus-big factor (SMBm) for portfolio p. The coefficient loading is calculated in a time-series regression, conducted by portfolio, of portfolio excess value-weighted monthly returns (P25_RETpm) on the three Fama-French factors (MKTRFm, SMBm, HMLm) plus the smoothing factor (VMSm). I form 25 portfolios by independently sorting all firms in the sample by beginning of period book to market (BTMit) and market

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value (SIZEit). B_HMLp is the coefficient loading on the high-minus-low factor (HMLm) for portfolio p. The coefficient loading is calculated in a time-series regression, conducted by portfolio, of portfolio excess value-weighted monthly returns (P25_RETpm) on the three Fama-French factors (MKTRFm, SMBm, HMLm) plus the smoothing factor (VMSm). I form 25 portfolios by independently sorting all firms in the sample by beginning of period book to market (BTMit) and market value (SIZEit).

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COLUMN 1: E_RET im = ß 0 + ß i1 MKTRET m + ß i2 SMB m + ß i3 HML m + ß i4 VMS_GN m + ß i5 VMS_BN m + ε im COLUMN 2: P25_E_RET pm = λ0 + λ p1 MKTRET m + λ p2 SMB m + λ p3 HML m + λ p4 VMS_GN m + λ p5 VMS_BN m + ε im COLUMN 3: P25_E_RET pm = δm0 + δ m1 B_MKTRET p + δ m2 B_SMB p + δ m3 B_HML p + δ m4 B_VMS_GN p + δ m5 B_VMS_BN p + ε im

Intercept ßi0 ? 0.0023 *** Intercept λp0 ? 0.0030 *** Intercept δm0 ? 0.0307 **(0.0279) (0.0042) (0.2092)

MKTRET m ßi1 + 1.0024 *** MKTRET m λp1 + 1.0081 *** B_MKTRET p δm1 + -0.0294 *(1.0413) (0.0903) (0.2299)

SMB m ßi2 + 0.7590 *** SMB m λp2 + 0.6671 *** B_SMB p δm2 + 0.0166 *(1.2579) (0.2302) (0.1185)

HML m ßi3 + 0.2927 *** HML m λp3 + 0.4136 *** B_HMLp δm3 + -0.0016(1.4574) (0.3059) (0.1277)

VMS_GN m ßi4 + 0.1669 *** VMS_GN m λp4 + 0.1380 *** B_VMS_GN p δm4 + 0.0018(1.5466) (0.2047) (0.2493)

VMS_BN m ßi5 + 0.1247 *** VMS_BN m λp5 + 0.1192 *** B_VMS_BN p δm5 + 0.0336 **(0.7843) (0.0921) (0.1975)

Number of Observations 4,911 25 174 0.225 0.092 0.521

*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% levelAdjusted R-Square

Prediction Prediction Prediction

Table 9In-sample Test Based on McGinnis 2010 with Smoothing Hedge Returns Calculated Conditional on Economic News

COLUMN 1 COLUMN 2 COLUMN 3

Description:This is a regression of monthly stock returns on the smoothing factor mimicking portfolio return (Column 1 and 2), or the portfolio level sensitivity to the smoothing factor mimicking portfolio return (Column 3). Coefficient standard errors are shown in parentheses below the coefficient loading. Standard errors are Huber-White heteroskedastic robust and are clustered by firm. Variable Descriptions:E_RETim is defined as the raw stock return for firm i in month m less the risk free rate. The monthly stock return is provided by CRSP while the risk free rate is available in the Fama & French Liquidity Factors dataset. P25_RETpm is defined as value weighted excess stock return for firms in portfolio p in month m. The monthly stock return is provided by CRSP while the risk free rate is available in the Fama & French Liquidity Factors dataset. VMS_GNm is the monthly factor-mimicking portfolio return calculated by subtracting the value-weighted return of stocks in the lowest three deciles of earnings smoothness (SMOOTHit) from the value-weighted return on stocks in the highest three deciles of earnings smoothness for firms in the good news sample. Firms are included in the good news sample if they have one or less negative return years in the earnings smoothing measurement window (years t-4 to t). VMS_BNm is the monthly factor-mimicking portfolio return calculated by subtracting the value-weighted return of stocks in the lowest three deciles of earnings smoothness (SMOOTHit) from the value-weighted return on stocks in the highest three deciles of earnings smoothness for firms in the bad news sample. Firms are included in the bad news sample if they have four or more negative return years in the earnings smoothing measurement window (years t-4 to t). MKTRFm is the monthly excess return on the CRSP value-weighted portfolio provided by the Fama & French Liquidity Factors dataset. SMBm is the monthly small minus large firm size factor mimicking portfolio return provided by the Fama & French Liquidity Factors dataset HMLm is the monthly high minus low book-to-market factor mimicking portfolio return provided by the Fama & French Liquidity Factors dataset. B_MKTRETit-4,it is the coefficient loading for firm i in a time-series regression of firm excess returns (E_RETim) on the CRSP value-weighted return (MKTRFm) conducted by firm. I estimate this regression using monthly return data from the current and previous four years. Firms must have at least 18 monthly return observations to be included in the sample. B_VMS_GNp is the coefficient loading on the good news sample smoothing factor (VMS_GNm) for portfolio p. The coefficient loading is calculated in a time-series regression, conducted by portfolio, of portfolio excess value-weighted monthly returns (P25_RETpm) on the three Fama-French factors (MKTRFm, SMBm, HMLm) plus the good and bad news smoothing factors (VMS_GNm & VMS_BNm). I form 25 portfolios by independently sorting all firms in the sample by beginning of period book to market (BTMit) and market value (SIZEit). B_VMS_BNp is the

49

coefficient loading on the bad news sample smoothing factor (VMS_BNm) for portfolio p. The coefficient loading is calculated in a time-series regression, conducted by portfolio, of portfolio excess value-weighted monthly returns (P25_RETpm) on the three Fama-French factors (MKTRFm, SMBm, HMLm) plus the good and bad news smoothing factors (VMS_GNm & VMS_BNm). I form 25 portfolios by independently sorting all firms in the sample by beginning of period book to market (BTMit) and market value (SIZEit). B_MKTRETp is the coefficient loading on the market factor (MKTRFm) for portfolio p. The coefficient loading is calculated in a time-series regression, conducted by portfolio, of portfolio excess value-weighted monthly returns (P25_RETpm) on the three Fama-French factors (MKTRFm, SMBm, HMLm) plus the smoothing factor (VMSm). I form 25 portfolios by independently sorting all firms in the sample by beginning of period book to market (BTMit) and market value (SIZEit). B_SMBp is the coefficient loading on the small-minus-big factor (SMBm) for portfolio p. The coefficient loading is calculated in a time-series regression, conducted by portfolio, of portfolio excess value-weighted monthly returns (P25_RETpm) on the three Fama-French factors (MKTRFm, SMBm, HMLm) plus the smoothing factor (VMSm). I form 25 portfolios by independently sorting all firms in the sample by beginning of period book to market (BTMit) and market value (SIZEit). B_HMLp is the coefficient loading on the high-minus-low factor (HMLm) for portfolio p. The coefficient loading is calculated in a time-series regression, conducted by portfolio, of portfolio excess value-weighted monthly returns (P25_RETpm) on the three Fama-French factors (MKTRFm, SMBm, HMLm) plus the smoothing factor (VMSm). I form 25 portfolios by independently sorting all firms in the sample by beginning of period book to market (BTMit) and market value (SIZEit).

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