Who Smoothes Dividends?
Abstract
This paper examines the relation between dividend smoothing and asymmetric
information between managers and investors. We find that the extent of dividend
smoothing are negatively related to the measures of information asymmetry. Firms
with higher levels of asymmetric information have a higher propensity to smooth
their dividends. These results imply that a firm’s information environment affects
its dividend smoothing decision.
Lintner (1956) observed that firms tend to smooth their dividends. Managers are reluctant to cut (raise) dividends immediately following a decrease (increase) in earnings.
So, dividend changes appear to lag changes in earnings by a number of periods. Subsequent empirical work confirms Lintner’s findings (for example, Fama and Babiak (1968)
and Aivazian et al. (2006)). Despite of the well documented empirical evidence, some
questions remain, such as: what type of firms tend to smooth their dividend payments
and what affects the degree of dividend smoothing?
This paper tackles these questions by looking at how a firm’s information environment affects its decision to smooth dividends. Specifically, we examine how the asymmetric information between managers and investors affect dividend smoothing. Empirical studies suggest that dividend changes convey information and investors react to
dividend announcements (for example, Asquith and Mullins (1983), Healy and Palepu
(1988), Michaely et al. (1995)). Managers tend to use dividend changes to signal the
prospects of future earnings. However, the effectiveness of this information transmission
may depend on the firm’s information environment.
A smoothing dividend policy is more valuable for firms facing more severe asymmetric information between managers and investors, because a stable dividend policy
conveys more information than an erratic policy. The reason for this argument is that
investors infer earnings information from dividends. However, if a firm’s dividends are
very volatile, investors are less likely to interpret an increase in dividends as a permanent
increase in future earnings. The fluctuation of dividends reduce investors’ confidence in
their beliefs about future earnings. Hence, the dividend changes are less informative
1
under an erratic dividend policy. As the degree of information asymmetry increases,
a stable dividend policy becomes more effective in conveying credible information to
investors.
The most related paper is Li and Zhao (2007), which examines the relation between
information asymmetry and firms’ dividend policies. They find that firms with higher
degree of information asymmetry are less likely to pay or increase dividends. However,
their study does not include the impact on dividend smoothing. Aivazian et al. (2006)
relate the dividend smoothing with firms’ information environments. However, their
focus is to test how the asymmetric information between managers and creditors affects
firms’ dividend policy. Expanding upon previous research, this paper directly studies
the impact of asymmetric information between managers and investors on the extent of
dividend smoothing.
We use idiosyncratic risk, analysts’ earnings forecast error, and dispersion of analyst
forecasts as measures of the degree of information asymmetry between managers and
investors. Greater idiosyncratic risk, larger forecast error, and bigger forecast dispersion
indicate poorer information environment. Our findings confirm the conjecture that firms
with more severe asymmetric information problem have a greater incentive to smooth
their dividends. The contribution of this paper to the literature is that we demonstrate
that firms’ dividend smoothing decisions depend in part on the degree of information
asymmetry between managers and investors.
The paper is organized as follows. Section I reviews the literature. Section II presents
information asymmetry measures. Section III describes the samples and summary statis-
2
tics. Section IV studies the relationship between the quality of information environment
and dividend smoothing. Section V presents robustness checks, and Section VI concludes.
1
Literature Review
Miller and Modigliani (1961) (M&M) show that in perfect markets, dividends have
no impact on firm value. Shareholders are indifferent to receiving their cash flows as
dividends or capital gains, as long as the firms’ investment policy does not change. In
this way the firm’s dividend payout reflects their residual free cash flows. When this
free cash flow is positive, they pay dividends; when it is negative, they issue shares.
However, M&M also recognized that changes in dividends might convey information to
the market about firms’ future earnings, that is, there may be an “information content
of dividends”.
The information content of dividends reflects the existence of information asymmetries and thus the imperfect capital markets, that is, managers have more information
about their firms’ value than investors. Firms’ management may use dividends as a
signal to indicate the quality of their firms’ future earnings. High quality firms are perceived to be more capable of bearing the cost of the dividend than low-quality firms. It
is expensive for the poor quality firms to mimic the dividend policies of the good quality
firms, so dividend payments lead to a separating equilibrium. Positive dividend changes
signal good prospects in the sense of higher future earnings and negative changes suggest the opposite. This basic idea has been supported by dividend signalling models of
3
Bhattacharya (1979), John and Williams (1985), and Miller and Rock (1985). However,
despite the clear theoretical results, the empirical evidence is mixed. Watts (1973) finds
that changes in dividends forecast future earnings, though the evidence is statistically
weak, which is supported by Nissim and Ziv (2001). However, Benartzi et al. (1997) and
Grullon et al. (2005) find a strong correlation between dividend changes and past and
current earnings changes, but no positive relation between dividend changes and future
earning changes.
Allen and Michaely (2003) point out that the relation between dividend changes
and future earnings changes is a necessary, but not a sufficient condition for dividend
signalling. Dividends convey information, but the exact information content has not yet
been pinned down in the literature (Brav et al. (2005)).
Another implication of information signalling with dividends is that firms are reluctant to cut dividends because dividend decreases are followed by price drops. Pettit
(1972), Aharony and Swary (1980), and Grullon et al. (2002) confirm that stock prices
drop significantly following the announcements of dividend decreases. Consequently,
firms are motivated to maintain or smooth their dividends, except if earnings are thought
to be permanently impaired. In this way, managers only increase dividends when they
believe the increases in earnings are permanent. The survey studies of Lintner (1956)
and Brav et al. (2005) provide evidence of the prevalence of dividend smoothing.
In the classic study, Lintner (1956) interviewed 28 managers and observed that they
smoothed dividends. Firms tend to implement their dividend policies following an adaptive process. Managers typically set long-run target ratios of dividends to earnings and
4
adjust current dividends to the target gradually following an increase in earnings. Firms
are then very reluctant to cut dividends. Lintner suggested that dividend changes will
tend to follow the model:
Di,t − Di,t−1 = si (ki Ei,t − Di,t−1 ).
(1)
where D is the dividend per share, E is the underlying “permanent” earnings per share,
s is the speed of adjustment as a firm moves toward the target payout ratio, and k is
the target dividend payout ratio. A small value for the speed of adjustment suggests
a smooth dividend policy. Empirically, the adjustment model of equation (1) can be
estimated as
Di,t = bi Di,t−1 + ci Ei,t + ei,t .
(2)
where 1 − b is the speed of adjustment, and c/(1 − b) is the target payout ratio.
Empirical studies show that the Lintner model performs well. For example, Fama
and Babiak (1968) use data for 392 major industrial firms over the period 1946 through
1964 and confirm that Lintner model explains dividend changes for individual firms fairly
well. Benartzi et al. (1997) confirm that firms increase dividends when their earnings
increases are permanent. Firms with dividend increases are then less likely to experience
a drop in future earnings than those with no dividend changes.
However, we have yet to determine whether it is optimal for every firm to smooth its
dividends and what affects this dividend smoothing decision? Little research has looked
at this question. Aivazian et al. (2006) find that the decision to smooth dividends
depends in part on public debt market access as proxied by bond ratings. In their
5
work, dividend smoothing is optimal for firms raising debt in the public “uninformed”
bond market, but not for firms in the private informed bank market. In this way, the
dividend decision is related to information asymmetry between the managers and the
firm’s creditors.
Aivazian et al. (2006)’s findings indicate that a firm’s dividend policy is related to its
information environment. Earlier, Kwan (1981) shows that revised Lintner model helps
to identify the information content of dividends. More recently, Khang and King (2006)
use insider trading returns as a proxy for the information asymmetry between managers
and outside investors and find that the level of the dividend is negatively related to
this information asymmetry. However, none of the existing research has examined how
a firm’s information asymmetry between managers and investors affects the extent of
dividend smoothing.
Following Aivazian et al. (2006) and inspired by Khang and King (2006), we argue
that firms facing significant informational asymmetries are more likely to smooth their
dividends because it is harder for these firms to convey information about their future
prospects to outside investors. We do not argue that this is the only way of signalling
such information, but do suggest that a more stable dividend helps relieve information
asymmetries and therefore attract more investors. Note that we revert to Lintner’s
original study, where the speed of adjustment of firms is allowed to vary across firms. In
contrast to Aivazian et al. (2006), we include a more related analysis if this adjustment
varies across firms according to their underlying characteristics. In this paper, we test
the hypothesis that firms operating in a poorer information environment, with severer
6
asymmetries, is more likely to smooth their dividends.
2
The Sample
Firm financial data are obtained from CRSP/COMPUSTAT merged database, stock and
market returns and dividend payments are collected from CRSP (Center for Research in
Security Prices), and analyst forecast data are from I/B/E/S. The sample contains firms
that pay regular cash dividends between January 1, 1986 and December 31, 2005.Firms
with a book value of total assets less than $10 million are excluded from the sample, as
are firms that have missing total assets (Compustat Item 6) or total sales data (Item 12).
Our final sample is a balanced panel of 484 firms with complete 20-year data resulting
in 9680 firm-year observations.1
Table 1 presents the descriptive statistics for firm characteristic variables. This study
focuses on firms with regular cash dividend payments. We collect regular monthly, quarterly, semi-annual and annual dividend in the CRSP monthly file (CRSP distribution
code 1212, 1222, 1232, 1239, 1242, 1252, 1262) and then aggregate them into annual
dividends (DPS).2 We use annual data due to the evidence that firms adjust dividends
on annual basis and using quarterly data will tend to overestimate the degree of smoothing. EPS is earnings per share (basic)-excluding extraordinary items (Compustat Annual
Item 58). On average, our sample firms pay $1.04 as dividends per share and their earnings are $2.29 per share. So, the average payout ratio is 45%, which is close to the
1
Our results also hold for the sample containing firms with more than 10-year consecutive dividend
payments. Allowing firms to have dividend omissions does not change our results, either.
2
We do not use Compustat dividend item (data item 26) because it includes non-regular dividend
payments, such as special dividends and liquidation dividends.
7
50% optimal payout of dividends in Lintner’s sample. The median payout ratio is 44%,
which is very close to the average value. Noticeably, DPS is far less volatile than EPS
because the standard deviation of DPS is 0.78 and that of EPS is 2.65, which confirms
the widespread existence of dividend smoothing.
Table 1 also provides summary data on several firm characteristic variables. EBIT DA/A
is earnings before interest, taxes, and depreciation (Item 13) divided by total assets (Item
6). We use it as a basic measure of operating profitability. The mean (median) profitability of these firms is 15% (14%) of total assets. The market-to-book ratio M/B
is defined as market equity divided by book equity, where book equity is stockholders’
equity (Item 216) plus minority interest (Item 38) and market equity is common shares
outstanding times stock price (Item 25×Item 199). The market-to-book ratio is closely
related with the firm’s Q ratio and reflect the firm’s investment opportunity. The average(median) market-to-book ratio is 2.48 (1.78). Investment is capital expenditures
(Item 128) normalized by net property, plant and equipment and represents how quickly
the firm is growing. The mean (median) investment rate is 18% (16%).
CASH/A is defined as cash and short-term investments (Item 1) divided by total
assets and reflects the firms’s liquidity. The average (median) cash ratio is 7% (4%).
Size is measured as the logarithm of net sales (Item 12), which has an average value
of 7.23 and median value of 7.28. P P E/A denotes net property, plant and equipment
(Item 8) divided by total assets. The mean (median) ratio of tangible assets to total
assets is 36% (31%). Book Leverage is book debt divided by invested capital. Book
debt is defined as debt in current liabilities (Item 34) plus long-term debt (Item 9).
8
Invested capital is book debt plus book equity. M arket Leverage is the ratio of book
debt to market value of assets (Item 25×Item 199 plus book debt). The average values
of book and market leverage ratios are 38% and 27% respectively, with medians of 40%
and 24%.
3
Measures of Information Asymmetry
3.1
Idiosyncratic Risk
Roll (1988) finds that idiosyncratic volatility is driven by private information rather than
by noise, suggesting that idiosyncratic risk is a good measure of information asymmetry.
We measure idiosyncratic risk as the standard deviation of the residual from the market
model:
Ri,t = α + βRm,t + ²i,t
(3)
where Ri,t is the individual firm return, Rm,t is the market returns (CRSP value-weighted
returns), and ²i,t is an error term. σ(²i,t ) is then the idiosyncratic risk, which reflects the
firm-specific uncertainty after removing market uncertainty.
We estimate the market model for all firms using monthly data and then calculate
the standard deviation of the residuals from the market model for each firm for each
year to obtain idiosyncratic volatility. As suggested in Krishnaswami and Subramaniam
(1999), the residual standard deviation captures the information asymmetry between
the managers and investors about firm-specific factors, assuming that both parties are
well-informed about the economy-wide factors. A higher residual volatility indicates
9
a higher level of information asymmetry between managers and investors about firmspecific information.
This idiosyncratic risk measure may not be a perfect measure of information asymmetry because it assumes that the market uncertainty is the only information shared
by the managers and the market. As pointed out in Dierkens (1991), residual volatility
may also include industry uncertainty that is more likely to be shared by the managers
and the investors. So, idiosyncratic risk may also capture industry-specific information.
Our study addresses this concern by comparing the idiosyncratic risk across different
industries and find that the variation of residual volatility is small. As a complement,
we also use analyst earnings forecast errors and the dispersion of analyst forecasts as
additional measures of information asymmetry.
3.2
Analyst Forecasts
Elton et al. (1984) provide evidence that analyst forecast errors are reasonable proxies
for the degree of information asymmetry about a firm. They show that mis-estimation of
firm-specific factors, rather than mis-estimation of economy or industry factors, accounts
for nearly 84% of analyst forecast errors. We define analyst forecast error as the absolute
value of the difference between mean earnings forecasts and actual earnings, divided by
the absolute value of actual earnings. Mean earnings forecasts in the last month of the
fiscal year are used because O’Brien (1988) has shown that forecasts in the last month
of the fiscal year have less “optimism bias”.3
3
O’Brien (1988) shows that analysts are overly optimistic at the beginning of the fiscal year. Thus,
forecast errors at the beginning of the fiscal year may include this “optimism bias”. O’Brien (1988)
shows that earnings forecasts in the last month of fiscal year are the most accurate.
10
The dispersion of analyst earnings forecasts measures the uncertainty about a firm’s
future earnings. Forecast dispersion is defined as the standard deviation of analyst earnings forecasts for the last month of the current fiscal year divided by the absolute value
of the mean earnings forecast.4 This variable represents a consensus among financial
analysts about the future performance of the firm. Brown and Han (1992) argue that as
the amount of information asymmetry decreases, there is likely to be a higher analyst
consensus. So, disagreement among analysts suggests the lack of available information
about the firm.
3.3
Descriptive Statistics of Information Asymmetry Measures
Panel A of Table 2 reports the summary statistics of the information asymmetry variables. The mean (median) idiosyncratic risk is 7% (6%), the mean (median) analyst
earnings forecast error is 6% (2%) of actual earnings, and the mean (median) analyst
forecast dispersion is 4% (3%)of mean earnings forecast.
Panel B presents the pair-wise correlation between the three information asymmetry
measures. These asymmetric information measures are significantly positively correlated. For firms with higher idiosyncratic risk, analyst forecast errors are bigger and so
is disagreement among analysts. The correlation between the idiosyncratic risk measure
and the two analyst based measures is relatively weak, although they are significant.
Our analysis focus more on the idiosyncratic risk measure because this measure does
not require firms to have more than two analyst coverage, which allows us to use the
maximum available data. Tests using analyst based measures are complements to the
4
Dispersion of analyst earnings forecast is available when there are more than two analyst forecasts.
11
study.
4
Measures of Dividend Smoothing
The first dividend smoothing measure we used is the speed of adjustment from the Lintner model. It indicates the speed at which firms adjust toward their target payout ratios.
Speed of adjustment measures the degree of dividend stickiness. A slow speed of adjustment suggests a smoothing dividend policy. One way to estimate the Lintner model is
using panel data techniques which account for the individual firm effects. However, panel
data estimation forces the slope coefficient on the lagged dividend and current earnings
per share to be the same for all firms. As a result, this implies the same equilibrium target payout ratio and speed of adjustment across firms. This assumption does not seem
reasonable, especially when we study dividend smoothing under different informational
environments. We would expect firms with different information asymmetry to smooth
toward different equilibrium payout ratios at different speeds. Consequently, we cannot
rely on panel data techniques in this study.
In this paper, we follow the classic Fama and Babiak (1968) estimation technique,
allowing the slope coefficients differ across firms. As a result, we estimate different
equilibrium payout ratios and speeds of adjustment for different firms. In this way, we
can test the impact of information asymmetries on dividend policy across different firms.
We estimate the Lintner model separately for each firm and present the distribution
of estimated parameters in Table 3.5 The mean (median) speed of adjustment is 23%
5
A test of autocorrelation across time shows that we cannot reject the hypothesis that there is no
serial correlation among disturbance.
12
(18%) and the mean (median) target payout ratio is 40% (36%), slightly less than the
sample average of 50% in Lintner’s study. The reason for the difference may due to the
different study periods. Lintner (1956) estimated his model for 28 firms in the period
of 1918 to 1941. Our sample covers 484 firm between 1986 and 2005. Fama and French
(2001) document firms do not pay dividends as much as they used to.
Dividends appear to be relatively insensitive to temporary earnings shocks. Firms
only distribute $0.09 of $1 additional earnings to shareholders through an increase in
dividends. The average (median) adjusted R2 is 0.97 (0.98), indicating a good performance of the Lintner partial adjustment model. More importantly, there is a wide range
of target payout ratios: the first quartile average is 20% while the third is 54%. With this
wide variation in the coefficients of the Lintner model, forcing the speed of adjustment
to be the same across firms with the use of panel data techniques seems problematic.
The second dividend smoothing measure is intend to take into account the volatility
of earnings. We define the alternative measure, called Smoothing, as the ratio of the
standard deviation of DPS to the standard deviation of EPS. It measures the volatility
of DPS relative to EPS. A lower value of Smoothing indicates a more stable dividend
policy.
13
5
Impact of Information Asymmetry on Dividend
Smoothing
5.1
Speed of Adjustment
To test the hypothesis that firms with more informational asymmetry smooth their dividends more, we regress the speed of adjustment on the information asymmetry measures.
We estimate the following model:
si = a + bInf oi + ui .
(4)
where s is the speed of adjustment and Inf o is a measure of information asymmetry
including idiosyncratic risk, analyst forecast error and dispersion of analyst forecasts.
We estimate the speed of adjustment for each firm from the Lintner model and measure
each firm’s idiosyncratic risk as the standard deviation of the residuals from the market
model. The median values of analyst forecast error and dispersion of analyst forecasts
for each firm are used in the regression.
Table 4 shows that there is a negative relationship between the speed of adjustment
and each measure of information asymmetry. The impact of information asymmetry on
speed of adjustment is significant across all three measures. Firms with a higher degree of
information asymmetry, as measured by higher idiosyncratic risk, larger analyst forecast
error, or bigger dispersion of analyst forecasts, adjust toward their target payout ratios
more slowly. In other words, a lower speed of adjustment means less sensitivity to
transitory earnings shocks and more dividend smoothing. These results suggest that
firms with more severe asymmetric information problems smooth their dividends over
longer periods of time, that is, they follow a more stable dividend policy.
14
Since size and industry are important in the dividend decision, we also control for
the potential influence of size and industry. We decompose our sample into seven industry groups based on SIC codes: Resource, Manufacturing, Transportation and Public
Utility, Wholesale & Retail, Finance, Insurance and Real Estate, Service, Public Administration and Others.6 After controlling for firm size and the industry dummies, the
information asymmetry measures still have a significantly negative impact on the speed
of adjustment.
Another concern is that the R2 is relatively low. We do not argue that information
asymmetry is the only factor that affects firms’ dividend smoothing decisions. However,
a firm’s information environment does appear to have a significant impact on its speed
adjusting toward the target payout ratio. In next subsection, we use an alternative
measure of dividend smoothing and the R2 is much higher.
5.2
Smoothing
To see how dividend smoothing varies with idiosyncratic risk and firm characteristic
variables, we sort firms into quintile based on the levels of idiosyncratic risk. Idiosyncratic risk increases from the first to the fifth quintile. Firms in the first quintile have
the least degree of information asymmetry, while those in the fifth quintile have the
poorest information environment. Table 5 reports the means of characteristic variables
for firms with different levels of idiosyncratic risk. From Table 5, we can see a monotonic
6
Firms with SIC<2000 are classified as Resource Industry, 2000<SIC<3000 as Manufacturing, 4000<SIC<5000 as Transportation and Public Utility, 5000<SIC<6000 as Wholesale & Retail, 6000<SIC<7000 as Finance, Insurance and Real Estate, 7000<SIC<9000 as Service, and
9000<SIC<10000 as Public Administration and Others.
15
decline in DPS, EPS and Smoothing moving from the first quintile to the last quintile,
indicating that firms with higher idiosyncratic risk pay less dividends, have less earnings
per share, and adopt a more smoothing dividend policy. Firms in the fifth quintile also
spend more on investment, are smaller, tend to have more cash, less tangible assets, and
use less debt than those in the first quintile, while they have equal operation profitability.
We then directly test the impact of information asymmetry on the second dividend
smoothing measure by estimating the following model:
Smoothingi = a + bInf oi + ui .
(5)
where Smoothing is ratio of the standard deviation of DPS to the standard deviation
of and Inf o a measure of information asymmetry including idiosyncratic risk, analyst
forecast, and dispersion of analyst forecasts. Firm level data are used to study the
effect on Smoothing because time-series observations of each firm are used to calculate
forecast error and dispersion of analyst forecasts. We estimate the speed of the smoothing
measure and correspondingly idiosyncratic risk is the 20-year average.
Table 6 shows that the coefficients on the information asymmetry measures are significantly negative. The coefficients remain negative after controlling for size and industry effects. Information asymmetry appear to affect firms’ dividend decisions substantially. These results confirm that firms with higher degree of information asymmetry pay
smooth their dividends more. Overall, firms with more severe information asymmetric
problem appear to follow more conservative dividend policies.
16
6
Robustness Checks
The above results are based on an idiosyncratic risk measure estimated from the market
model. As a robustness check, we examine our results using idiosyncratic risk estimated
as the residual from the Fama and French (1992) three-factor model.
ri,t = α + βM KT M KTt + βSM B SM Bt + βHM L HM Lt + ²i,t
(6)
where r is excess return over risk free rate, M KT is market excess return, SM B is
the return on small minus big capitalization stocks (size premium), HM L measure the
return on high minus low book-to-market stocks (value premium). σ(²i,t ) is idiosyncratic
risk.
As shown in Table 7, our results are robust to the alternative way of measuring
idiosyncratic risk. In the table (not presented), our results also hold when we estimate
idiosyncratic risk from the four-factor model that includes a momentum factor (winner
minus loser hedge portfolio returns) in addition to Fama-French three factors.7 Firms
with higher idiosyncratic risk pay less dividends, adjust more slowly toward their target
payout ratio, and adopt a more stable dividend policy.
7
Conclusions
This paper finds that a firm’s dividend policy depends on its information environment.
Informational asymmetry, as measured by idiosyncratic risk, analyst forecast error, and
dispersion of analyst forecasts, is negatively related to dividend smoothing. These find7
Data on SMB, HML, and UMD are obtained from WRDS’s Fama French, Momentum, and Liquidity
data sets.
17
ings are consistent with the hypothesis that firms with higher degree of information
asymmetry between managers and investors tend to smooth their dividends more.
18
Table 1:
Summary Statistics
This table presents the summary statistics of firm characteristic variables. Dividend per share (DPS)
is aggregated from regular monthly, quarterly, semi-annual and annual dividend in CRSP monthly
file (CRSP distribution code 1212, 1222, 1232, 1239, 1242, 1252, 1262). EPS is earnings per share
(basic)-excluding extraordinary items (Compustat Annual Item 58). EBIT DA/A is earnings before
interest, taxes, and depreciation (Item 13) divided by total assets (Item 6). Market-to-book ratio M/B
is defined as book equity divided by market equity, where book equity is stockholders’ equity (Item
216) plus minority interest (Item 38) and market equity is common shares outstanding times stock
price (Item 25×Item 199). Investment is capital expenditures (Item 128) normalized by net property,
plant and equipment. CASH/A is defined as cash and short-term investments (Item 1) divided by total
assets. Size is the logarithm of net sales (Item 12). P P E/A denotes net property, plant and equipment
(Item 8) divided by total assets. Book Leverage is book debt divided by invested capital. Book debt
is defined as debt in current liabilities (Item 34) plus long-term debt (Item 9). Invested capital is book
debt plus book equity. M arket Leverage is the ratio of book debt to market value of assets (Item
25×Item 199 plus book debt)
Variable
DPS
EPS
EBITDA/A
M/B
Investment
Cash/A
Size
PPE/A
Book leverage
Market leverage
Mean
1.04
2.29
0.15
2.48
0.18
0.07
7.23
0.36
0.38
0.27
Standard
Deviation
0.78
2.65
0.10
11.38
0.14
0.10
1.82
0.26
0.90
0.21
Median
0.88
1.98
0.14
1.78
0.16
0.04
7.28
0.31
0.40
0.24
19
25th
Percentile
0.50
1.23
0.10
1.30
0.11
0.01
6.09
0.16
0.21
0.10
75th
Percentile
1.40
2.96
0.18
2.73
0.23
0.10
8.51
0.57
0.53
0.41
Table 2:
Information Asymmetry Measures
Panel A of this table presents the descriptive statistics of measures of information asymmetry and Panel
B shows the pairwise correlation of these measures. Idiosyncratic risk is measured as the standard
deviation of residual from the market model:
Ri,t = α + βRm,t + ²i,t
where Ri,t is individual firm return, Rm,t is market returns (CRSP value-weighted returns), and ²i,t
is an error term. σ(²i,t ) is idiosyncratic risk. Analyst forecast error is the absolute value of the
difference between mean earnings forecasts in the last month of the current fiscal year and actual
earnings, divided by the absolute value of actual earnings. Dispersion of analyst forecast is defined as
the standard deviation of analyst earnings forecasts for the last month of the current fiscal year divided
by the absolute value of the mean earnings forecast. Analyst forecast data are obtained from I/B/E/S
Panel A
Variable
Idiosyncratic Risk
Forecast Error
Forecast Dispersion
Idiosyncratic Risk
p value
Forecast Error
p value
Forecast Dispersion
p value
Mean
0.07
0.06
0.04
Idiosyncratic
Risk
1.00
0.07
[0.00]
0.07
[0.00]
Median
0.06
0.02
0.02
Panel B
Forecast
Error
Standard
Deviation
0.03
0.15
0.07
Forecast
Dispersion
1.00
0.44
[0.00]
20
1.00
25th
Percentile
0.05
0.01
0.01
75th
Percentile
0.08
0.06
0.04
Table 3:
Lintner Model of Dividend Policy
This table presents the summary statistics of the speed of adjustment coefficients and the target payout
ratios. We estimate Lintner model for each firm:
Di,t = bi Di,t−1 + ci Ei,t + ei,t .
where D is dividend per share, E is earnings per share, 1 − b is the speed of adjustment, and c/(1 − b)
is the target payout ratio.
Variable
Speed
Target
Adj R2
Mean
0.23
0.40
0.97
Median
0.18
0.36
0.98
Standard
Deviation
0.20
0.57
0.04
21
25th
Percentile
0.07
0.20
0.96
75th
Percentile
0.35
0.54
0.99
Table 4:
Impact of Information Asymmetry on Speed of Adjustment
This table presents the effect of the information asymmetry proxies on the speed of adjustment. We
estimate the following model:
si = a + bInf oi + ui .
where s is the speed of adjustment and Inf o is the measure of information asymmetry: idiosyncratic
risk, analyst forecast error and dispersion of analyst forecasts. The speed of adjustment for each
firm is estimated from the Lintner model. Each firm’s idiosyncratic risk is measured as the standard
deviation of the residual from the market model. Analyst forecast error is as the absolute value of the
difference between the mean earnings forecasts and actual earnings, divided by the absolute value of
actual earnings. Dispersion of analyst forecast is defined as the standard deviation of analyst earnings
forecasts for the last month of the current fiscal year divided by the absolute value of the mean earnings
forecast. The median values of the forecast error and dispersion of analyst forecast are used in the
regression. The absolute value of the robust t-statistics are in the brackets. ∗ ∗ ∗ denotes 1% significant
level, ∗∗ denotes 5% significant level, and ∗ denotes 10% significant level.
Idiosyncratic Risk
Speed of Adjustment
-3.06***
[4.59]
-3.39***
[6.23]
Forecast Error
-0.48***
[2.91]
Dispersion
-0.52***
[3.03]
-0.50*
[1.84]
Size
Industry dummy
N
R2
No
484
0.05
No
423
0.02
No
413
0.02
22
0.00
[0.10]
Yes
484
0.07
0.00
[0.09]
Yes
423
0.07
-0.51**
[2.16]
0.01
[0.93]
Yes
413
0.06
Mean characteristics of firms with different levels of idiosyncratic risk
Table 5:
This table reports the means of the firm characteristic variables for firms with different levels of idiosyncratic risk. To get idiosyncratic risk, we estimate the market model in each calendar year for
each firms on CRSP monthly stock return database, and then calculate the standard deviation of the
residuals from the market model for each year. Firms are sorted into quintile based on their mean level
of idiosyncratic risk.
Quintile DPS EPS Smoothing M/B Investment Cash/A Size PPE/A EBITDA/A Book L
1
1.45 2.80
0.42
2.00
0.14
0.05 7.64 0.43
0.14
0.44
2
1.38 2.59
0.40
2.80
0.15
0.06 7.56 0.42
0.15
0.42
3
1.01 2.30
0.29
3.09
0.18
0.08 7.25 0.33
0.14
0.35
4
0.91 2.23
0.26
2.42
0.19
0.09 7.13 0.31
0.16
0.34
5
0.47 1.53
0.17
2.16
0.23
0.09 6.58 0.32
0.15
0.32
23
Table 6:
Impact of Information Asymmetry on Smoothing
This table presents the effect of an information asymmetry proxy on a firm’s dividend policy. We
estimate the following model:
Smoothingi = a + bIdioRiski + ui .
where Smoothing is the smoothing measure respectively, and IdioRisk is idiosyncratic risk. The
absolute value of the robust t-statistics are in the brackets. ∗ ∗ ∗ denotes 1% significant level, ∗∗ denotes
5% significant level, and ∗ denotes 10% significant level.
Idiosyncratic Risk
Smoothing
-5.29***
[8.05]
-6.08***
[10.92]
Forecast Error
-0.06**
[2.48]
-0.07***
[2.94]
Dispersion
-0.22*
[1.72]
Size
Industry dummy
N
R2
No
484
0.14
No
423
0.01
No
413
0.01
24
-0.03***
[3.58]
Yes
484
0.23
-0.02**
[2.25]
Yes
423
0.16
-0.17
[1.38]
-0.02**
[2.11]
Yes
413
0.15
Impact of Information Asymmetry on Dividend Smoothing:
Alternative Measure
Table 7:
This table presents the effect of an information asymmetry proxy on a firm’s dividend policy. The
following model is estimated:
yi = a + bIdioRiski + ui .
where y is dividend per share, the smoothing measure, and the speed of adjustment respectively, and
IdioRisk is idiosyncratic risk estimated from a Fama-French three-factor model:
ri,t = α + βM KT M KTt + βSM B SM Bt + +βHM L HM Lt + ²i,t
where r is excess return over risk free rate, M KT is market excess return, SM B is size premium, HM L
measure value premium. σ(²i,t ) is idiosyncratic risk. The absolute value of the robust t-statistics are
in the brackets. ∗ ∗ ∗ denotes 1% significant level, ∗∗ denotes 5% significant level, and ∗ denotes 10%
significant level.
Idiosyncratic Risk
Constant
N
R2
Smoothing
-5.98***
[9.59]
0.73***
[14.54]
446
0.12
25
Speed
-2.90***
[5.39]
0.45***
[9.99]
446
0.04
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