“Asymmetric Timely Loss Recognition, Earnings Smoothness and
Information Quality”
Logan B. Steele
lsteele@bus.wisc.edu
University of Wiscsonsin
Wisconsin 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Preliminary Draft – Please Do Not Cite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
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.
1
1
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
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.
2
to low powered or biased tests when evaluating the causes and consequences of smoother
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
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.
3
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)
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
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.
4
4
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
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
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.
5
“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
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,
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.
6
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
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
7
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
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
8
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
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
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.
9
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
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 riskaverse 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.
10
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
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
11
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
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
12
To examine the impact of ATLR on the smoothing proxy I employ the following
regression:
SMOOTH it   0  1  PR _ NRit   2  CSCORE it   3  PR _ NRit  CSCORE it 
 4  SIZEit   5  TURN it   6  LEVit   7  BTM it   8  AGEit   9  STD _ SALEit 
10  CYCLEit  11  GR _ SALEit  12  OP _ LEVit  13  DIVit  14  AVG _ CFit 
15  PR _ LOSS it   it
[1]
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
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.
10
13
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
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
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
14
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
(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 1  in equation [1].
Across all specifications PR_NR loads negatively and significantly, with β1 = -.1121 (PValue < .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  3  . 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
more recent. For instance, in a more recent study, Ettridge et al. 2012, the mean (median) of CSCORE is
.1632 (.1553).
15
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.
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   0 j  1  SMOOTH it   2  PR _ NRit   3  PR _ NRit  SMOOTH it 
 4  SIZEit   5  TURN it   6  AMIHUDit   7  PRCit   8  LEVit   9  BTM it 
10  AGEit  11  INSTit  12  N _ ESTit  13  STD _ SALEit  14  CYCLEit 
15  GR _ SALEit  16  OP _ LEVit  17  DIVit  18  AVG _ CFit 
19  PR _ LOSS it   it
[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
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.
16
proxy 1  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 1  .
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
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  3  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.
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.
17
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
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
18
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 (PValue < .01), which indicates that offsetting is associated with increased information
asymmetry.
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
19
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
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 Ushaped 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 (PValue < .05) and β1 = .225 (P-Value < .01) respectively. These results indicate that
16
Both of these differences are significant at the less than 1% level.
20
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
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.
Untabulated results indicate that the coefficient on SMOOTH*PR_NR (β3) is significantly different
across the two sub-samples, with a P-Value < .01.
17
21
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:
E _ RETim3,im15   0 j  1  B _ MKTRETit4,it   2  SIZEit  3  BTM it   4  SMOOTH it4,it 
5  PR _ NRit4,it   6  SMOOTH it4,it  PR _ NRit4,it   it
[4]
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 timeseries 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  4  .
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
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
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 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
23
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.
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
24
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
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
25
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
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
26
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
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   0  1  LNASSETSit   2  LEVit   3  BTM it 
 4  STD _ SALESit   5  PR _ LOSS it   6  CYCLEit   7  GR _ SALEit 
 8  OP _ LEVit  14  AVG _ CFit   it
[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]:
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.
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
SPREADit   0 j  1  IN _ SMOOTH it   2  DISC _ SMOOTH it   3  SIZEit 
 4  TURN it   5  AMIHUDit   6  PRC it   7  AGEit   8  INSTit 
 9  N _ ESTit  10  DIVit   it
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
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
28
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
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
29
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
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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Watts, R. L. “ATLR in Accounting Part I: Explanations and Implications.” Accounting
Horizons 17 (2003): 207-221.
34
Table 1
Descriptive Statistics
Descriptive Statistics for Variables Employed in the Regression Analyses (N=33,869)
Mean
Std
Min
P10
Q1
Median
Q3
P90
Max
RAW_SPREAD
3.046
3.932
0.019
0.168
0.634
1.690
3.845
7.506
31.525
SPREAD
1.085
0.743
-0.022
0.151
0.485
0.986
1.576
2.137
3.482
SMOOTH
0.001
0.058
-0.326
-0.062
-0.017
0.007
0.029
0.058
0.190
PR_NR
0.437
0.219
0.000
0.200
0.200
0.400
0.600
0.800
1.000
CSCORE (N=25,895)
0.181
0.304
-1.112
-0.153
0.009
0.164
0.347
0.554
1.419
SIZE
5.491
2.256
0.736
2.592
3.777
5.380
7.040
8.564
11.247
TURN
1.565
0.759
0.108
0.602
0.996
1.503
2.070
2.614
3.924
AMIHUD
9.601
1.556
6.404
7.744
8.386
9.320
10.766
11.879
13.968
PRC
0.188
0.314
0.008
0.022
0.037
0.076
0.194
0.457
3.205
LEV
0.207
0.177
0.000
0.000
0.038
0.186
0.329
0.456
0.746
BTM
0.685
0.602
0.043
0.184
0.312
0.522
0.851
1.355
6.056
AGE
2.361
0.300
1.946
1.946
2.079
2.303
2.565
2.773
2.996
INST
0.094
0.124
0.000
0.000
0.000
0.022
0.167
0.290
0.979
N_EST
0.960
1.083
0.000
0.000
0.000
0.693
1.792
2.639
3.892
STD_SALE
0.269
0.343
0.018
0.052
0.088
0.162
0.301
0.545
2.386
CYCLE
4.715
0.726
2.110
3.855
4.335
4.788
5.174
5.529
6.985
GR_SALE
0.161
0.234
-0.201
-0.030
0.030
0.101
0.216
0.408
1.461
OP_LEV
0.567
0.380
0.029
0.142
0.268
0.484
0.794
1.113
1.967
DIV
0.008
0.015
0.000
0.000
0.000
0.000
0.013
0.029
0.098
AVG_CF
0.106
0.012
0.200
0.130
0.171
0.742
-0.304
-1.966
-0.969
-0.035
-0.146
-0.461
0.041
-0.010
-0.207
0.095
0.042
0.071
0.161
0.089
0.406
0.254
0.143
0.902
0.802
0.422
8.383
DNI
RET
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 =  3 j   7 j  *BTM it  11 j * LEVit  15 j * SIZEit from equation [1] below):
DNI it   0 j   1 j  Rit   2 j  NRit   3 j  Rit  NRit 
 4 j * BTM it   5 j  Rit  BTM it   6 j  NRit  BTM it   7 j  Rit  NRit  BTM it 
 8 j * LEVit   9 j  Rit  LEVit   10 j  NRit  LEVit   11 j  Rit  NRit  LEVit 
 12 j * SIZE it   13 j  Rit  SIZE it   14 j  NRit  SIZE it   15 j  Rit  NRit  SIZE it   it
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 10 6). 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 SPREAD
2 SMOOTH
3 PR_NR
4 CSCORE
5 SIZE
6 LEV
7 BTM
8 STD_SALE
9 OP_LEV
10 AVG_CF
Table 2
Descriptive Statistics
Correlation Statistics for Variables Employed in the Write-Down Analysis
1
2
3
4
5
6
7
8
0.000
0.311
-0.021
-0.732
0.101
0.390
0.066
0.936
<.0001
0.001
<.0001
<.0001
<.0001
<.0001
0.011
-0.166
-0.111
0.044
0.025
0.062
-0.211
0.041
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
0.289
-0.166
0.048
-0.399
0.061
0.350
0.159
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
-0.039
-0.100
0.056
-0.047
-0.172
-0.127
0.095
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
-0.748
0.019
-0.399
-0.058
0.032
-0.439
-0.145
<.0001
0.000
<.0001
<.0001
<.0001
<.0001
<.0001
0.077
0.007
0.039
-0.211
0.066
0.109
-0.021
<.0001
0.208
<.0001
<.0001
<.0001
<.0001
<.0001
0.377
0.084
0.340
-0.180
-0.457
0.107
-0.028
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
0.081
-0.160
0.203
0.119
-0.201
-0.049
-0.018
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
0.001
0.067
-0.032
-0.022
-0.161
0.047
0.245
0.099
-0.223
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
-0.054
0.185
-0.219
-0.194
0.105
0.220
0.228
-0.302
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
9
0.065
<.0001
-0.005
0.395
-0.012
0.034
-0.124
<.0001
0.041
<.0001
0.212
<.0001
0.064
<.0001
-0.136
<.0001
10
-0.050
<.0001
0.221
<.0001
-0.186
<.0001
-0.160
<.0001
0.074
<.0001
0.205
<.0001
0.169
<.0001
-0.285
<.0001
0.354
<.0001
0.418
<.0001
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
Table 3
DETERMINANTS OF SMOOTHING
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
Intercept
ß0
Prediction
?
PR_NR it-4,t
ß1
-
CSCORE it-4,t
ß2
-
PR_NR it-4,t *CSCORE it-4,t
ß3
-
SIZE it
ß4
?
TURN it
ß5
?
LEV it
ß6
?
BTM it
ß7
?
AGE it
ß8
?
STD_SALE it
ß9
+
CYCLE it
ß 10
+
GR_SALE it
ß 11
?
OP_LEV it
ß 12
+
DIV it
ß 13
?
AVG_CF it
ß 14
?
1
0.6049 ***
(0.0216)
SPECIFICATION:
2
3
0.5413 ***
(0.0229)
0.5987 ***
(0.0237)
-0.0498 ***
(0.0066)
-0.1569 ***
(0.0165)
0.0310 **
(0.0136)
-0.1735 ***
(0.0284)
-0.2327 ***
(0.0078)
Number of Observations
33,896
25,895
Adjusted R-Square
0.077
0.057
*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level
4
0.4722 ***
(0.0398)
-0.1121
(0.0171)
0.0079
(0.0133)
-0.0964
(0.0280)
-0.0066
(0.0012)
-0.0302
(0.0031)
-0.1072
(0.0123)
0.0110
(0.0042)
0.0605
(0.0088)
***
***
***
***
***
***
***
-0.2029 ***
(0.0108)
0.0119 ***
(0.0038)
0.3008 ***
(0.0160)
-0.0727 ***
(0.0072)
0.4600 ***
(0.1468)
0.3682 ***
(0.0189)
25,895
0.083
25,895
0.131
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
38
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).
39
Table 4
BID/ASK SPREAD REGRESSED ON SMOOTHING & CONTROLS
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
Intercept
ß0
Prediction
?
SMOOTH it-4,t
ß1
?
PR_NR it-4,t
ß2
+
PR_NR it-4,t *SMOOTH it-4,t
ß3
-
SIZE it
ß4
-
TURN it
ß5
?
AMIHUD it
ß6
+
PRC it
ß7
+
LEV it
ß8
+
BTM it
ß9
+
AGE it
ß 10
-
INST it
ß 11
-
N_EST it
ß 12
-
STD_SALE it
ß 13
+
CYCLE it
ß 14
+
GR_SALE it
ß 15
-
OP_LEV it
ß 16
+
DIV it
ß 17
-
AVG_CF it
ß 18
-
SPECIFICATION:
2
1
-1.0181 ***
(0.0565)
-0.0086
(0.0061)
-0.1086
(0.0016)
0.0638
(0.0053)
0.1850
(0.0034)
0.1667
(0.0091)
0.2743
(0.0116)
0.1068
(0.0043)
0.0902
(0.0078)
-0.2755
(0.0157)
0.0090
(0.0021)
0.1077
(0.0099)
0.0207
(0.0034)
-0.1921
(0.0142)
0.0362
(0.0067)
-0.7027
(0.1411)
-0.2893
(0.0176)
3
-1.1127 ***
(0.0561)
0.0016
(0.0061)
0.1951 ***
(0.0100)
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
-0.1044
(0.0016)
0.0577
(0.0053)
0.1837
(0.0033)
0.1597
(0.0090)
0.2499
(0.0116)
0.0904
(0.0044)
0.0991
(0.0078)
-0.2641
(0.0157)
0.0078
(0.0021)
0.0803
(0.0098)
0.0211
(0.0034)
-0.1448
(0.0142)
0.0291
(0.0067)
-0.5743
(0.1401)
-0.2320
(0.0178)
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
-1.1390 ***
(0.0564)
0.0472
(0.0132)
0.2443
(0.0171)
-0.0983
(0.0271)
-0.1042
(0.0016)
0.0576
(0.0053)
0.1837
(0.0033)
0.1588
(0.0090)
0.2504
(0.0116)
0.0909
(0.0044)
0.0990
(0.0078)
-0.2642
(0.0157)
0.0078
(0.0021)
0.0807
(0.0098)
0.0213
(0.0034)
-0.1454
(0.0142)
0.0288
(0.0067)
-0.5633
(0.1402)
-0.2292
(0.0178)
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
Number of Observations
Adjusted R-Square
*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level
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 10 6). 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
Table 5
BID/ASK SPREAD REGRESSED ON SMOOTHING & CONTROLS IN A SPLIT SAMPLE
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
.
REGIME:
Prediction
SMOOTH
VOLATILE
SMOOTH
VOLATILE
Intercept
?
ß0
-1.1532 ***
-0.8160 ***
-1.2372 ***
-0.9961 ***
(0.0727)
SMOOTH it-4,t
ß1
?
PR_NR it-4,t
ß2
+
PR_NR it-4,t *SMOOTH it-4,t
ß3
-
SIZE it
ß4
-
TURN it
ß5
?
AMIHUD it
ß6
+
PRC it
ß7
+
LEV it
ß8
+
BTM it
ß9
+
AGE it
ß 10
-
INST it
ß 11
-
N_EST it
ß 12
-
STD_SALE it
ß 13
+
CYCLE it
ß 14
+
GR_SALE it
ß 15
-
OP_LEV it
ß 16
+
DIV it
ß 17
-
AVG_CF it
ß 18
-
(0.0911)
0.0338 ***
(0.0130)
-0.1005
(0.0021)
0.0736
(0.0067)
0.1924
(0.0042)
0.2101
(0.0145)
0.2613
(0.0149)
0.1201
(0.0058)
0.0866
(0.0104)
-0.2555
(0.0195)
0.0127
(0.0027)
0.1380
(0.0139)
0.0130
(0.0045)
-0.2225
(0.0200)
0.0135
(0.0087)
-0.4625
(0.1784)
-0.2838
(0.0229)
***
***
***
***
***
***
***
***
***
***
***
***
***
***
(0.0740)
-0.0637 ***
(0.0244)
-0.1163
(0.0026)
0.0435
(0.0087)
0.1718
(0.0056)
0.1354
(0.0118)
0.2895
(0.0183)
0.0943
(0.0065)
0.0874
(0.0121)
-0.3147
(0.0268)
0.0053
(0.0036)
0.0718
(0.0143)
0.0290
(0.0053)
-0.1558
(0.0201)
0.0611
(0.0106)
-0.9491
(0.2330)
-0.3015
(0.0281)
***
***
***
***
***
***
***
***
***
***
***
***
***
***
Number of Observations
20,166
13,703
Adjusted R-Square
0.817
0.818
*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level
0.0495
(0.0247)
0.1884
(0.0415)
-0.0302
(0.0559)
-0.0970
(0.0021)
0.0675
(0.0067)
0.1914
(0.0042)
0.2022
(0.0143)
0.2414
(0.0149)
0.1044
(0.0059)
0.0953
(0.0103)
-0.2460
(0.0195)
0.0117
(0.0027)
0.1094
(0.0140)
0.0128
(0.0045)
-0.1749
(0.0202)
0.0081
(0.0086)
-0.3857
(0.1773)
-0.2348
(0.0232)
20,166
0.819
(0.0913)
**
***
***
***
***
***
***
***
***
***
***
***
***
***
**
***
0.2254
(0.0530)
0.3184
(0.0256)
-0.5467
(0.1012)
-0.1119
(0.0026)
0.0405
(0.0086)
0.1713
(0.0055)
0.1253
(0.0117)
0.2628
(0.0183)
0.0802
(0.0066)
0.0951
(0.0120)
-0.2972
(0.0268)
0.0033
(0.0036)
0.0474
(0.0142)
0.0301
(0.0053)
-0.1106
(0.0201)
0.0501
(0.0105)
-0.8048
(0.2311)
-0.2274
(0.0285)
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
13,703
0.821
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 10 6). 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
Table 6
Descriptive Statistics
Descriptive Statistics for Realized Return Regressions
Std
Min
P10
Q1
Median
Q3
P90
Max
Obs
E_RETim
0.012
0.177
-0.983
-0.154
-0.069
-0.001
0.075
0.176
P25_RETpm
0.011
0.057
-0.336
-0.053
-0.021
0.013
0.044
0.074
0.498
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
10.340 394,523
4,262
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-tomarket 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
44
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).
45
E_RET im+3,im+15
Table 7
Future Firm-level Stock Returns Regressed on Smoothing
= 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
0.0113 ***
(0.0013)
0.0013 ***
(0.0004)
SPECIFICATION:
2
0.0118 ***
(0.0014)
0.0012 ***
(0.0004)
-0.0012 ***
(0.0001)
0.0089 ***
(0.0008)
-0.0012 ***
(0.0001)
0.0089 ***
(0.0008)
Intercept
ß0
Prediction
?
1
B_MKTRET it-4,t
ß1
+
SIZE it
ß2
-
BTM it
ß3
+
SMOOTH it-4,t
ß4
+
PR_NR it-4,t
ß5
?
0.0175 ***
(0.0034)
PR_NR it-4,t *SMOOTH it-4,t
ß6
-
-0.0135 **
(0.0054)
-0.0009
(0.0010)
Number of Observations
394,523
394,523
Adjusted R-Square
0.001
0.001
*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level
3
0.0025
(0.0021)
0.0008 **
(0.0004)
-0.0008 ***
(0.0001)
0.0081 ***
(0.0008)
0.0064 ***
(0.0024)
394,523
0.002
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).
46
COLUMN 1: E_RET im = ß i0
Table 8
In-sample Test Based on McGinnis 2010
+ 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 MKTRET m + 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
COLUMN 1
Prediction
?
ß i0
MKTRET m
ß i1
+
SMB m
ß i2
+
HML m
ß i3
+
VMS m
ßi4
+
COLUMN 2
Prediction
?
λp0
***
Intercept
***
MKTRET m
λp1
+
***
SMB m
λp2
+
***
HML m
λp3
+
0.6721 ***
(2.6609)
VMS m
λp4
+
0.0020
(0.0288)
0.9405
(1.0668)
0.6650
(1.2279)
0.4065
(1.4842)
COLUMN 3
Prediction
?
δm0
**
Intercept
***
B_MKTRET p δm1
+
***
B_SMB p
δm2
+
***
B_HML p
δm3
+
0.4349 ***
(0.5292)
B_VMS p
δm4
+
0.0011
(0.0029)
0.9984
(0.0838)
0.5372
(0.1956)
0.4689
(0.3432)
Number of Observations
4,911
25
Adjusted R-Square
0.211
0.774
*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level
0.0206 **
(0.0184)
-0.0174
0.0163
0.0186 *
(0.0046)
-0.0028
(0.0051)
0.0062
(0.0005)
174
0.514
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-tomarket 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
47
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).
48
Table 9
In-sample Test Based on McGinnis 2010 with Smoothing Hedge Returns Calculated Conditional on Economic News
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
COLUMN 1
Prediction
?
ß i0
0.0023 ***
(0.0279)
1.0024 ***
(1.0413)
0.7590 ***
(1.2579)
0.2927 ***
(1.4574)
Intercept
COLUMN 2
Prediction
?
λp0
MKTRET m
ß i1
+
MKTRET m
λp1
+
SMB m
ß i2
+
SMB m
λp2
+
HML m
ß i3
+
HML m
λp3
+
VMS_GN m ßi4
+
0.1669 ***
(1.5466)
VMS_GN m λp4
+
VMS_BN m ßi5
+
0.1247 ***
(0.7843)
VMS_BN m λp5
+
0.0030 ***
(0.0042)
1.0081 ***
(0.0903)
0.6671 ***
(0.2302)
0.4136 ***
(0.3059)
Intercept
COLUMN 3
Prediction
?
δm0
0.0307 **
(0.2092)
-0.0294 *
(0.2299)
0.0166 *
(0.1185)
-0.0016
(0.1277)
B_MKTRET p δm1
+
B_SMB p
δm2
+
B_HML p
δm3
+
0.1380 ***
(0.2047)
B_VMS_GN p δm4
+
0.0018
(0.2493)
0.1192 ***
(0.0921)
B_VMS_BN p δm5
+
0.0336 **
(0.1975)
Number of Observations
4,911
25
Adjusted R-Square
0.225
0.092
*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level
174
0.521
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
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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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