Dividend Policy and Earnings: A Study of Short- and Long-Term Causality
Dr. Mukesh K. Chaudhry
Department of Finance and Legal Studies
Indiana University of Pennsylvania
Indiana, PA 15705 (USA)
E-Mail: chaudhry@iup.edu
*Dr. Robert J. Boldin
Department of Finance and Legal Studies
Indiana University of Pennsylvania
Indiana, PA 15705 (USA)
E-Mail: rboldin@iup.edu
*Contact person
Keywords: dividend policy, earnings per share, cointegration, causality
Dividend Policy and Earnings: A Study of Short- and Long-Term Causality
Abstract
This study examines whether the earnings per share (EPS) and the dividend per share (DPS)
exhibit a long-run equilibrium relationship. The data employed in this study consist of monthly
EPS and DPS for 28 of the DJIA companies obtained from Bloomberg over the past 10 years.
The companies under investigation have the EPS and the DPS available over the period studied.
Dividends are generally paid out of earnings. The amount and timing of the dividend paid is a
function of the respective company’s dividend policy. Therefore, the EPSt can be expressed in
terms of the DPSt as follows: EPSt = αDPSt where α is a non-negative constant. The equation
suggests that there is a linear relationship between the EPSt and the DPSt.
I. Introduction
This research examines the short- and long-term relationship between dividends and
earnings per share across large companies from different industries. Dividend policy, when
implemented by publicly listed corporations, constitutes a long-term managerial commitment to
shareholders (Faccio, Lang & Young, 2001). While dividends are typically paid from earnings,
the amount provided could come from any figure between net income after taxes but before
extraordinary items to comprehensive income. Cash dividends can be paid from past earnings as
long as there is a sufficient amount in the retained earnings account and adequate cash in the
cash accounts. For many shareholders, their quarterly dividend check is used as their primary
source of income. For this reason, management often aims to provide a smoother dividend
payment stream than that delivered by the pattern of earnings. This is evidenced by the fact that,
even in years of a loss, corporations still pay a dividend that is similar to that paid in the prior
profitable year.
Market reactions surrounding dividend changes are well documented. For example, Koch
and Sun (2004) found that when a dividend change is preceded by an earnings change of the
same direction, the market reaction will be in the same direction. The effect is apparent within
approximately three days of the dividend announcement. Charitou, Lambertides and Theodoulou
(2010) also investigated market reaction to dividend announcements. Their investigation,
however, focused on firms that had a loss. In their research design, they segmented their sample
into two cohorts; established firms and less-established firms. They found that market reaction is
significantly more negative for established firms that declare dividend reductions after record
earnings and dividend payments than for less-established firms.
DeAngelo, Deangelo, and Skinner (1992) documented that established firms that incur a
first-time loss but do not reduce dividends, manage to recover to positive profits. In contrast,
established firms that reduced dividends continued to incur losses. These findings indicate that
dividend policy might provide unofficial insight into the expected earnings pattern of the firm.
That is, if the dividend payment is not reduced in a loss year, then management is reasonably
certain that the firm will return to profit in the subsequent year. This may be due to new
management, which adopts the “big bath” approach. Significant write-downs and write-offs are
implemented so that the new management team can start with a clean slate. In contrast, if the loss
year is accompanied by a reduction in the dividend amount, this might indicate that the loss in
earnings is probably a longer-term problem.
There is supporting evidence showing that significant changes to the dividend paid
appears to be driven by changes in permanent earnings (Hsu, Wang, & Wu, 1998). Stock prices
tend to reflect market expectations of the future direction of earnings (Marsh & Merton, 1987).
Using stock price as a proxy for the firm’s intrinsic value, Marsh and Merton found that dividend
changes are significantly related to past stock price. Thus, managers are reluctant to reduce
dividends knowing that a reduction will most likely lead to a drop in the market price of the
stock. They are especially unwilling to reduce the dividend amount if they know in advance that
the earnings reduction is most likely temporary. Charitou, Lambertides, and Theodoulou (2011)
show that dividend reduction announcements by established firms are accompanied by larger
negative market reactions than for similar announcements by less-established firms. This finding
indicates that investors do take notice of dividend directions and show their viewpoint through
the change in the market price of the stock.
Nissim and Ziv (2001) focused on “the information content of dividends hypothesis.”
Using a modified regression model, they found that in each of the two years after the dividend
change, there was a positive association with earnings changes. To some extent, dividend
announcements could provide a more useful indicator of management’s view as to the future
earnings of the company than the earnings figure itself. This notion is supported by Healy and
Palepu’s (1988) finding that dividend changes signal a manager’s private information about
future earnings changes. They found that investors regard extreme changes in dividends as
indicators of management’s forecasts of future earnings change.
As an aside, between 1978 and 1999, the percentage of US industrial firms paying
dividends has declined from 66.5% to 20.8% (see Fama & French, 2001). It is worthy to note
that two stocks that received the most publicity over the past 10 years were Apple and Google;
they do not pay dividends. Any return that the shareholders of those companies earn is derived
from selling their stock at a gain.
This paper builds on prior research by hypothesizing that a change in dividend policy
signals to the market that management is expecting earnings to continue in the same direction for
the long-term. Several issues are addressed: First, what is regarded as a dividend policy change?
For example, is there a percentage benchmark (say, 10%) that the market regards as significant?
Second, how many years constitute the long-term? Is it three, five, seven or ten years? General
Motors continued to pay dividends for a number of years despite long-term losses which
eventually led to its filing for bankruptcy. Large US banks such as Bank of America and
Citibank have been in similar positions since 2008. How has the market reacted to those losses
and the reduction in dividends? This research addresses the above questions by investigating
corporations listed in the Dow Jones Industrial Average (DOW) index that have paid dividends
from 1992 to 2011. The paper is organized as follows: Part II explains the design of the empirical
tests and sample selection. Part III outlines the results using descriptive statistics. The last part
presents the conclusions.
II. Research Design and Data
Since dividends are generally paid out of current earnings, the two are related. The
dividend payment, however, depends on the dividend policy of the firm. Hence, the two
variables are related as follows:
EPSt = α DPSt
The value of α is dependent on the dividend policy of the firm. But, to determine the
long-term dividend policy followed by the firms, the time series behavior of different companies
that comprise the DOW were examined.
Most time-series are nonstationary and the use of cointegrated methodologies account
for this characteristic.1 Engle and Granger (1987) suggest that if a system of variables is
cointegrated, then economic forces interact to bind these variables together in a long-run
equilibrium relationship. They suggest that an error correction model (ECM) can represent the
cointegrated variables.2 In general, the ECM shows the dependence of this period’s price change
on the last period’s price change, thus providing a measure of how far the system is out of its
long-run equilibrium.
There are preliminaries a researcher has to observe before applying the methods of
cointegration. Specifically, before testing for cointegration between two or more series, it is
necessary to test whether the different time series are integrated to the same order.3 This is done
by applying conventional unit roots tests, described below.
Stationary (Unit Root) Tests for Individual Time Series
In general, most texts on stationarity of a time series (TSt) will probably begin with an
estimation of the following regression equation, if no linear trend is considered.

TS     TS
   TS

j
t j t
t
0
1 t 1
j 1
(1)
and by equation (2) when linear trend and a parameter for drift are considered:

TS     TS
  t    TS

j
t j t
t
0
1 t 1
2
j 1
(2)
where  represents differences (first difference unless otherwise noted),  0 represents the term
for drift in the series,  1 allows testing for a unit root, and  2 verifies the presence of a trend.
The error-correcting mechanism is represented by TS
t j
in the model. If the hypothesis
1  0 cannot be rejected, then the series is said to have a unit root and is nonstationary.
Conversely, if the hypothesis, 1  0 , is rejected, it is concluded that the series does not contain
a unit root and is stationary. Tests involving parameters  0 and  2 verify the presence of drift
and trend, respectively. Inclusion of the p lagged values ensures a white-noise series. The
number of lags is determined by a test of significances, such as the Akaike information criterion
(AIC) (Akaike, 1973).5 Importantly, the distribution of the ordinary t and F statistics computed
for the regressions do not have their expected distribution. Thus, in order to test various
hypotheses, critical values have been computed using Monte Carlo techniques and are tabulated
in various references (see, e.g., Davidson and MacKinnon, 1993). Tests for stationarity and
cointegration use the Philips-Perron (P&P) non-parametric testing procedure. The P&P
procedure is used since the crucial iid error assumption is not needed.6
Integration/Segmentation Tests
For these tests, the methodology developed by Johansen (1988) was used. This method is
preferred to alternatives since it enables testing for the presence of more than one cointegrating
vector. The description that follows draws from Johansen (1988, 1991, 1994) and Johansen and
Juselius (1990, 1991).
The Johansen method provides some distinct advantages. For example, identification of
the number of cointegrating vectors is possible with the Johansen test. Such inferences are based
on the number of significant eigenvalues. Also, many argue that the statistical properties and
power for Johansen’s test are generally higher than for alternative procedures. To check for
stationarity arising from a linear combination of variables, the following AR representation for a
vector VTS made up of n variables is used,
k
s 1
VTS  c    Q    VTS

i it
i
t i t
t
i 1
i 1
(3)
where VTS is at most I(1), Qit are seasonal dummies (i.e., a vector of non-stochastic variables)
and c is a constant. It is not necessary that all variables that make up VTS be I(1). To find
cointegration in the system, only two variables in the system need be I(1). If only two time
series are examined (bivariate representation), however, then both have to be I(1). If an errorcorrection term is appended, then:
s 1
k 1
VTSt  c   iQit    I VTSt i VTSt k t
i 1
i 1
(4)
which is basically a vector representation of equation (1) with seasonal dummies added. All
long-run information is contained in the levels terms, VTS t k , and short-run information in the
differences VTS t i . The above equation would have the same degree of integration on both
sides only if   0 (the series are not cointegrated) or VTS t k is (0), which infers
cointegration. In order to test for cointegration, the validity of H1(r), shown below, is tested as:
H (r) :    
1
(5)
where  is a matrix of cointegrating vectors and  represents a matrix of error correction
coefficients.
The hypothesis H1(r) implies that the process VTSt is stationary, VTSt is
nonstationary, and   VTSt is stationary (Johansen, 1991). The Johansen method yields the
Trace and the  max statistics that enable determination of the number of cointegrating vectors.
Description of Data
Quarterly data for earnings per share (EPS) and dividends per share (DPS) were obtained
from Bloomberg for the DOW 30 stocks. The data for two companies could not be utilized due
to lack of data. Hence, the remaining 28 companies were used in the analysis. The sample spans
the period March 31, 1992 until September 30, 2011. One important issue regarding the data
requires some discussion. The use of quarterly data, and the lesser frequency of observations
versus the use of weekly or daily data, may create misgivings about the power of unit-root and
cointegration tests against an alternative hypothesis, such as a trend stationary model. Hakkio
and Rush (1991), from their Monte Carlo simulations, however, showed that the frequency of
information plays a minor role; rather, it is the length of data series or span, which is more
important in discerning whether the time series are cointegrated or not. This finding is also
supported by Shiller and Perron (1985) and Perron (1989) who found that changing the
frequency of observations, while keeping the sample length fixed, is not helpful when testing for
cointegration because it is mainly a long-run phenomenon.
III. Results
The list of companies included in our analysis along with their ticker symbol and SIC Code
have been provided in the Appendix. Descriptive statistics for dividend payout ratio of the firms
in our sample are shown in Table I. From the mean values it can be seen that most of the firms
displayed in Table I that firms pay a significant amount of dividends from earnings. For
instance, mean value of dividend payout ratio ranges from 75% for Pfizer Inc. (PFE) to 19% for
International Business Machines (IBM). The period under examination was very challenging for
these companies as they faced a recession in 2000/2001 and followed by the financial crisis in
2008 in conjunction with the European crisis which in-turn significantly impacted their earnings.
Despite the negative conditions, these firms managed to pay consistently high dividends. From
the descriptive statistics it is evident that companies such as, General Electric (GE), Johnson and
Johnson (JNJ), J.P. Morgan Chase and Company (JPM), Kraft Foods Inc. (KFT), 3 M Company
(MMM), Merck and Company (MRK), Pfizer Inc. (PFE), Procter and Gamble Co. (PG), AT &T
(T), Verizon (VZ), and Exxon Mobil (XOM) repeatedly paid very high dividends from their
earnings over long periods. Even when these firms suffered losses or had low earnings, they still
paid dividends. This can be seen from the maximum and minimum value of the dividend payout
ratio which clearly indicates that almost all firms in the sample continue to pay dividends even
when they have low earnings or suffer losses for some of the time periods in the sample. This is
indicative of the fact that the dividend policy decisions by the firms is a long-term criteria and
has only a minimal effect on the basis of short-term fluctuation in earnings.
To obtain a deeper understanding of the dividend policy of the firms, the time series behavior
of both dividends and earnings per share were examined. In order to eliminate autocorrelations in
the time-series for both EPS and DPS, the appropriate lag length was found using the Akaike
information criterion (AIC). The lag length is selected by minimizing the AIC over different
choices for the length of the lag. The values of AIC are formulated by computing the value of
the equation T log (RSS) +2 K, where K is the number of regressors, T is the number of
observations and RSS is the residual sum of squares.
Tests for Stationarity of Each Time Series Using the ADF and P&P Test
The tests of stationarity are shown in Table II using both Augmented Dickey Fuller (ADF)
and Philips-Perron (P&P) Test. The time series were tested for a unit root using the ADF and
P&P tests. These tests suggest that most of the time series for EPS and DPS are nonstationary.
Stationary time series for EPS include, Alcoa (AA), Boeing (BA), Du-Pont (DD), KFT, MRK,
PFE, T, TRV, and VZ whereas, stationary time series for DPS include Caterpillar (CAT), KFT,
PG, XOM.
While it is reassuring to note the non-rejection of nonstationarity, this is not
altogether surprising since many other studies find nonstationary in time series (Phillips and
Perron, 1988; Brenner and Kroner, 1995; and, Doukas and Rahman, 1987).
Johansen Bivariate Tests for Cointegration Rank for EPS and DPS
The results for systems (composed of the EPS and DPS for each company) using Johansen’s
method are presented in Table III. Tests using the Trace statistic are reported in Table III along
with the critical and the ‘p’ values. These are basically likelihood ratio tests, where the null
hypothesis is Lr+1 =Lr+2=…….=Lp=0, indicating that the system has p-r unit roots, where r is the
number of cointegrating vectors. The rank is then determined using a sequential approach
starting with the hypothesis of p unit roots.
If this is rejected then the next hypothesis
L2=L3=…….=Lp=0 is tested and so on. From Table III it is evident that there is a close
relationship between EPS and DPS as all bivariate systems are cointegrated. Hence, the EPS and
DPS are closely related to each other in a long-term equilibrium relationship.
Error Correction Model for EPS and DPS
From Table IV, the closeness of the relationships between EPS and DPS using the error
correction methodology is shown. In addition, using the lagged values of change in EPS and
DPS one can establish the lead and lags between the two variables. For AA for example,
coefficients for the error correction cointegration equation are significant and negative indicating
mean reversion of the long-term dividend policy. Also, from the lagged differenced values it is
evident that ΔEPS(-1) leads in a significant way the ΔDPS(-1) indicating that changes in EPS
affects the level of dividends paid by this company. For American Express (AXP), the error
correction coefficients for both ΔEPS and ΔDPS are significant. The fact that ΔEPS does not
lead or lag ΔDPS for both lags in a significant way may indicate that AXP’s dividend policy may
be independent of the level of earnings in the short-term for this company. For both BA and
Bank of America (BAC), the coefficients for the error correction equation are significant. Also,
similar to AA, earnings lead dividends per share with a lag of two periods.
CAT exhibits a mean reverting error correction cointegrating relationship. But, there is no
clear cut causality that exists in the short-term between ΔEPS and ΔDPS. This may imply that
the change in earnings may not affect the amount of dividend payment by this firm in the shortrun. There is a long-term relationship between dividends and earnings for CAT, however, as
discussed previously. Similar to CAT, Chevron Corporation (CVX), DD, GE, Hewlett Packard
(HPQ), JPM, KFT, MRK, PFE, T, TRV, and VZ do not indicate any short-term relationship
between earnings and dividends, but do exhibit long-term mean-reverting behavior between
ΔEPS and ΔDPS. This may imply that these companies, in the short-run, may not follow
dividend payments based on current earnings but may rely on the earnings from the previous
years. As pointed out in several studies, many firms are reluctant to reduce the dividend
payments even when they incur losses as the investors consider a dividend reduction as a
negative signal about the future prospects of the company in the long-term. Several firms also
smooth out the dividend payments on the basis of their long-term earnings potential rather than
short-term profits.
This finding is similar to the findings by Charitou, Lambertides, and
Thedoulou (2010, 2011), Healy and Palepu (1988), Hsu, Wang, and Wu (1998) and Marsh and
Merton (1987).
On the other hand, Disney (DIS), Home Depot (HD), McDonald Corporation (MCD),
and United Technologies Corporation (UTX), the earnings lead the dividends with lags. These
companies therefore, display contemporaneous dividend policy directly related to earnings in the
short-term. It should be noted that these companies are very well established and have consistent
earnings.
The consistent dividend payers, however, have declined in recent times as the
percentage of US industrial firms paying dividends has shrunk from 66.5% to 20.8% from 1978
to1999 (Fama and French, 2001). After the financial crisis of 2007/08 the amount of dividend
payment has gone up slightly as compared to 1999.
Bi-directional causality exists for companies such as IBM, JNJ, MMM, PG, Walmart
(WMT) and XOM. This indicates that the earnings affect dividend policy as well as dividend
policy also has an impact on the earnings of these companies. It may also mean that if companies
retain a larger amount and make investments in projects that generate large net present values for
the company, the future growth of earnings is likely to increase. These are very progressive
companies with large investments globally. Hence, these companies follow a dividend policy
based on the opportunities to grow in the global markets.
IV. Conclusions
V. Bibliography
Appendix
TICKER
SIC
NAME
AA
AXP
BA
BAC
CAT
CVX
DD
DIS
GE
HD
HPQ
IBM
INTC
JNJ
JPM
KFT
KO
MCD
MMM
MRK
PFE
PG
T
TRV
UTX
VZ
WMT
XOM
3334
6199
3721
6021
3531
2911
3087
7990
3621
5211
3571
3570
3674
2834
6021
2000
2080
5812
3699
2834
2834
2844
4813
6712
3724
4813
5331
1311
Alcoa Inc.
American Express Co.
Boeing Co.
Bank Of America Co.
Caterpillar, Inc.
Chevron Corporation
Du Pont
Walt Disney Co.
General Electric Co.
Home Depot Inc.
Hewlett-Packard Co.
International Business Machines
Intel Corporation
Johnson and Johnson
JP Morgan Chase and Co.
Kraft Foods Inc.
Coca Cola Company
McDonald’s Corporation
3 M Company
Merck and Company
Pfizer Inc.
Procter and Gamble Co.
AT&T Inc.
The Travelers Companies
United Technologies Corporation
Verizon Communications
Wal-Mart Stores
Exxon Mobil Corporation
Table I
Descriptive Statistics for Dividends Payout
Ratio (DPS/EPS) for DOW Companies
Company
Mean
Median
Max
Min
Company
Mean
Median
Max
Min
AA
0.28
0.29
1.29
-0.88
KFT
0.54
0.53
2.42
0.00
AXP
0.28
0.20
2.00
-0.56
KO
0.24
0.45
1.70
-8.50
BA
0.23
0.28
2.80
-3.50
MCD
0.23
0.13
1.45
-0.87
BAC
0.71
0.42
12.8
-0.67
MMM
0.47
0.49
1.25
-1.96
CAT
0.25
0.25
2.97
-7.52
MRK
0.61
0.47
3.80
-2.24
CVX
0.47
0.38
5.83
-1.97
PFE
0.75
0.44
9.33
-3.00
DD
0.22
0.43
2.69
-15.75
PG
0.42
0.39
0.98
-0.15
DIS
0.35
0
10.5
-2.26
T
0.57
0.61
2.39
-4.50
GE
0.54
0.45
1.85
0.33
TRV
0.28
0.26
1.38
-0.52
HD
0.12
0.14
1.13
-7.50
UTX
0.33
0.29
2.00
-0.28
HPQ
0.36
0.19
4.00
-0.24
VZ
0.54
0.68
3.21
-7.18
IBM
0.19
0.15
5.00
-2.08
WMT
0.21
0.19
0.37
0.18
INTC
0.26
0.06
3.50
0.00
XOM
0.45
0.43
1.03
0.13
JNJ
0.42
0.36
3.57
-0.91
JPM
0.48
0.37
5.43
-1.89
Table II
Augmented Dickey-Fuller (ADF) and Phillips Perron (PP) tests for unit root in the of the
quarterly DPS and the EPS for twenty eight companies for the
time period March, 1992 through September 2011.
EPS
Ticker:
AA
AXP
BA
BAC
CAT
CVX
DD
DIS
GE
HD
HPQ
IBM
INTC
JNJ
JPM
KFT
KO
MCD
MMM
MRK
PFE
PG
T
TRV
UTX
VZ
WMT
XOM
ADF
-6.40***
-2.45
-7.88***
0.45
0.27
-1.88
-11.49***
-0.54
-1.86
-1.09
-0.10
-1.39
-2.49
-0.20
-2.92*
-5.97***
1.21
0.02
-1.18
-6.65***
-6.87***
-0.39
-7.63***
-5.08***
-0.31
-4.47***
4.31***
-1.21
DPS
PP
-6.41***
-1.55
-7.96***
0.45
-11.23***
-1.88
-11.42***
-3.03**
-3.03**
-3.24**
-2.21
-3.28**
-2.44
-3.09**
-2.99*
-5.97***
-4.81***
-2.52
-1.41
-6.62***
-6.79***
-0.32
-7.63***
-5.13***
-2.57
-7.64***
-2.90*
-1.19
ADF
-2.39
-0.33
1.05
-2.06
-6.54***
2.45
-1.69
-10.45***
-1.73
0.53
-10.39***
1.80
-8.73***
4.05***
-2.93*
-4.26***
3.80***
-6.00***
0.24
-2.54
-1.24
7.55***
0.12
-2.75*
5.25***
0.19
7.36***
4.30***
Critical Value
(10%)
PP
-2.12
-0.22
-0.51
-1.34
-6.97***
1.23
-1.64
-1.77
-1.72
1.25
-0.79
1.29
0.76
-0.82
-1.89
-3.15**
1.23
0.06
1.33
-2.17
-1.24
3.65***
-0.12
-1.79
1.74
0.23
2.71*
3.01***
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
-2.59
Critical values are taken from MacKinnon (1991). *,**,*** implies significance at 10%, 5%, and 1% levels
respectively.
Table III
Long-Run Cointegrating Relationship Between Dividends and Earnings:
Johansen’s Methodology
Group
DPS and EPS
r=
Eigen Value
Trace
Statistic
Critical
Value (5%)
Probability
0
1
0.324
0.065
34.41***
3.02
15.49
3.84
0.00
0.12
AXP
0
1
0.190
0.001
15.89**
0.07
15.49
3.84
0.04
0.80
BA
0
1
0.319
0.018
30.22***
1.35
15.49
3.84
0.00
0.24
BAC
0
1
0.269
0.065
17.95**
2.02
15.49
2.85
0.02
0.12
CAT
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
CVX
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
DD
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
DIS
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
GE
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
HD
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
HPQ
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
IBM
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
INTC
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
JNJ
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
JPM
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
Ticker
AA
Table III Continued
Group
DPS and EPS
r=
Eigen Value
Trace
Statistic
Critical
Value (5%)
Probability
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
KO
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
MCD
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
MMM
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
MRK
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
PFE
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
PG
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
T
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
TRV
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
UTX
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
VZ
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
WMT
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
XOM
0
1
0.324
0.065
34.41***
2.02
15.49
2.85
0.00
0.12
Ticker
KFT
Table IV
Estimates of the Parameters from the Error Correction Model for
Dividends versus Earnings per Share
Model for EPSt
Regressor
Ticker: AA
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
Coefficient
0.09263
1.00000
-3.21517***
SE
0.92723
Error Correction
Coint Equation1
ΔEPS
-0.28236 **
(-2.06)
ΔDPS
0.05595***
(5.23)
ΔEPS(-1)
-0.12722
(-0.86)
-0.81439
(-0.71)
-1.59281
(-1.25)
0.24455
(0.19)
-0.05262***
(-4.57)
-0.00312
(-0.35)
-0.41158***
(-4.14)
-0.20121**
(-2.04)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
Ticker: AXP
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
-0.10007
1.00000
-3.58970***
1.13260
Error Correction
Coint Equation1
ΔEPS
-0.21280***
(-3.09)
ΔDPS
0.01604**
(2.02)
ΔEPS(-1)
-0.04777
-0.00988
(-0.43)
(-0.77)
0.39540***
-0.00928
(3.75)
(-0.76)
-0.87374
-0.50320***
(-0.88)
(-4.39)
-1.03562
-0.23023**
(-1.04)
(-2.01)
***,**,* represent significance at 1%, 5% and 10% levels.
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
(t-values are in parentheses and
Table IV Continued………
Model for EPSt
Regressor
Ticker: BA
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
Coefficient
-0.26269
1.00000
-1.35911**
SE
0.58856
Error Correction
Coint Equation1
ΔEPS
-0.0.99091***
(-4.74)
ΔDPS
0.00856**
(2.13)
ΔEPS(-1)
-0.12768
(-0.16)
0.03255
(0.27)
8.79246
(-1.25)
5.31067
(0.85)
-0.004955*
(-1.51)
-0.00286
(-1.26)
-0.16597
(-1.40)
-0.18428*
(-1.53)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
Ticker: BAC
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
0.06427
1.00000
-2.04811***
0.2786
Error Correction
Coint Equation1
ΔEPS
0.27159**
(2.02)
ΔDPS
0.11367***
(4.74)
ΔEPS(-1)
-0.56752***
(-3.90)
-0.86320***
(-6.72)
-0.75057
(-1.14)
0.60073
(0.91)
-0.02904
(-1.12)
-0.07369***
(-3.22)
0.51730 ***
(4.38)
-0.14646
(-1.26)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
(t-values are in parentheses and
***,**,* represent significance at 1%, 5% and 10% levels.
Table IV Continued………
Model for EPSt
Regressor
Ticker: CAT
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
Coefficient
-0.10748
1.00000
-2.71415***
SE
0.64276
Error Correction
Coint Equation1
ΔEPS
-0.218666 ***
(-2.59)
ΔDPS
0.13636***
(4.01)
ΔEPS(-1)
-0.06446
(-0.46)
0.02924
(0.41)
-0.46351*
(-1.57)
0.04109
(0.14)
-0.17585***
(-3.12)
0.04340 *
(1.51)
-1.12586***
(-9.48)
-0.20121***
(-3.48)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
Ticker: CVX
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
0.60589
1.00000
-4.25379***
0.74611
Error Correction
Coint Equation1
ΔEPS
-0.34498**
(-2.40)
ΔDPS
0.01477*
(1.79)
ΔEPS(-1)
-0.00536
(-0.04)
-0.09349
(-0.73)
-0.91547
(-0.44)
-2.08454
(-0.99)
-0.00286
(-0.35)
0.00050
(0.07)
-0.56543***
(-4.71)
-0.26579**
(-2.20)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
(t-values are in parentheses and
***,**,* represent significance at 1%, 5% and 10% levels.
Table IV Continued………
Model for EPSt
Regressor
Ticker: DD
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
Coefficient
0.05502
1.00000
-2.06192
SE
1.79546
Error Correction
Coint Equation1
ΔEPS
-0.1.10650***
(-4.60)
ΔDPS
0.00105
(0.53)
ΔEPS(-1)
-0.16117
(-0.87)
-0.20474*
(-1.88)
-5.75288
(-0.39)
11.8349
(0.81)
-0.00054
(-0.36)
-0.00018
(-0.20)
-0.11046
(-0.92)
-0.10731
(-0.90)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
Ticker: DIS
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
0.21206
1.00000
-8.37803***
0.62047
Error Correction
Coint Equation1
ΔEPS
-0.02181
(-0.46)
ΔDPS
0.31688***
(10.86)
ΔEPS(-1)
-0.67768***
(-5.86)
-0.1145
(-0.93)
-0.24743
(-0.92)
0.13986
(0.75)
-0.30823***
(-4.27)
-0.02247
(-0.29)
1.16282 ***
(6.94)
0.53148***
(4.60)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
(t-values are in parentheses and
***,**,* represent significance at 1%, 5% and 10% levels.
Table IV Continued………
Model for EPSt
Regressor
Ticker: GE
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
Coefficient
-0.04304
1.00000
-1.65166***
SE
0.07587
Error Correction
Coint Equation1
ΔEPS
-0.10787
(-0.43)
ΔEPS(-1)
-0.56746**
(-2.30)
-0.03830
(-0.22)
-0.18236
(-0.50)
0.03446
(0.09)
-0.22208***
(-3.36)
-0.04958
(-1.09)
0.07419
(0.76)
0.05914
(0.63)
-0.23962
1.00000
-1.09588***
0.38893
Error Correction
Coint Equation1
ΔEPS
-0.04731
(-0.58)
ΔDPS
0.04063 ***
(4.29)
ΔEPS(-1)
-0.13118*
(-1.57)
-0.80318***
(-9.86)
0.29365
(030)
-0.76723
(-0.77)
-0.01967**
(-2.01)
-0.03718***
(-3.88)
-0.70362***
(-6.17)
-0.05232
(-0.45)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
Ticker: HD
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
(t-values are in parentheses and
ΔDPS
0.37677 ***
(5.65)
***,**,* represent significance at 1%, 5% and 10% levels.
Table IV Continued………
Model for EPSt
Regressor
Ticker: HPQ
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
Coefficient
SE
0.13818
1.00000
-7.14974*
3.89941
Error Correction
Coint Equation1
ΔEPS
-0.03040
(-0.45)
ΔDPS
0.04144***
(2.81)
ΔEPS(-1)
-0.17289
(-1.43)
-0.46185***
(-3.91)
0.37049
(0.60)
-0.51822
(-0.97)
-0.02215
(-0.83)
0.00572
(0.21)
-0.74086***
(-5.47)
0.02182
(0.18)
-0.54337
1.00000
-2.42055***
0.42263
Error Correction
Coint Equation1
ΔEPS
-0.68979***
(-5.60)
ΔDPS
0.03522***
(6.46)
ΔEPS(-1)
-0.10011
(-0.70)
0.31580***
(2.60)
11.3263***
(4.37)
11.1606***
(4.54)
-0.03001***
(-4.73)
-0.00656
(-1.22)
-0.37022***
(-3.22)
-0.11696
(-1.07)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
Ticker: IBM
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
(t-values are in parentheses and
***,**,* represent significance at 1%, 5% and 10% levels.
Table IV Continued………
Model for EPSt
Regressor
Ticker: INTC
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
Coefficient
SE
-0.17533
1.00000
-1.05203**
0.48773
Error Correction
Coint Equation1
ΔEPS
-0.21353 **
(-2.41)
ΔDPS
0.11946***
(3.15)
ΔEPS(-1)
0.03523
(0.28)
0.11188
(0.93)
-0.93291***
(-3.67)
-0.63228**
(-2.42)
0.07323
(0.02)
-0.00088
(-0.35)
-1.19204***
(-10.98)
-0.33485***
(-3.00)
-0.26992
1.00000
-1.37747***
0.13002
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
Ticker: JNJ
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
Error Correction
Coint Equation1
ΔEPS
0.22194**
(2.01)
ΔEPS(-1)
-1.00620***
(-9.33)
-0.39328***
(-3.18)
-2.78467*
(-1.57)
-11.3680***
(-9.79)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
(t-values are in parentheses and
ΔDPS
0.04287***
(6.81)
0.01573***
(2.55)
-0.00571
(-0.80)
-0.36998***
(-3.66)
-0.48121***
(-7.25)
***,**,* represent significance at 1%, 5% and 10% levels.
Table IV Continued………
Model for EPSt
Regressor
Ticker: JPM
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
Coefficient
SE
-0.81771
1.00000
0.50469
1.09814
Error Correction
Coint Equation1
ΔEPS
-0.34246 **
(-2.16)
ΔDPS
0.04916*
(1.75)
ΔEPS(-1)
-0.05872
(-0.34)
-0.02211
(-0.14)
0.69838
(0.77)
-1.12276
(-1.08)
-0.01578
(-0.51)
0.00772
(0.28)
-0.10459
(-0.65)
-0.07235
(-0.39)
-0.48007
1.00000
0.05189
0.44105
Error Correction
Coint Equation1
ΔEPS
-1.14863***
(-3.92)
ΔDPS
- 0.01969
(-1.26)
ΔEPS(-1)
0.13185
(0.56)
0.09244
(0.55)
-4.48729
(-1.42)
-1.30377
(-0.98)
0.01440
(1.14)
0.00828
(0.92)
-0.26995*
(-1.60)
-0.10811*
(-1.52)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
Ticker: KFT
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
(t-values are in parentheses and
***,**,* represent significance at 1%, 5% and 10% levels.
Table IV Continued………
Model for EPSt
Regressor
Ticker: KO
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
Coefficient
SE
-0.35946
1.00000
-0.45392*
0.24801
Error Correction
Coint Equation1
ΔEPS
-0.24485
(-1.47)
ΔDPS
0.03302 ***
(6.81)
ΔEPS(-1)
-0.57342***
(-2.96)
-0.29646
(-1.49)
8.61242***
(2.90)
6.35806**
(1.99)
-0.03725***
(-6.62)
-0.04208***
(-7.31)
-0.53955***
(-6.26)
-0.24534***
(-2.65)
-0.17682
1.00000
-1.74372***
0.07853
Error Correction
Coint Equation1
ΔEPS
-0.37247**
(-2.02)
ΔDPS
1.06304***
(5.98)
ΔEPS(-1)
-0.34479**
(-2.41)
-0.60829***
(-5.61)
-0.10815
(-0.45)
0.13522
(0.97)
-0.26156**
(-1.90)
-0.34420***
(-3.29)
0.66663 ***
(2.58)
0.34858 ***
(2.58)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
Ticker: MCD
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
(t-values are in parentheses and
***,**,* represent significance at 1%, 5% and 10% levels.
Table IV Continued………
Model for EPSt
Regressor
Ticker: MMM
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
Coefficient
SE
0.34412
1.00000
-3.25414***
0.26216
Error Correction
Coint Equation1
ΔEPS
-0.63401***
(-3.96)
ΔDPS
0.01198
(1.39)
ΔEPS(-1)
0.01267
(0.09)
0.02523
(0.20)
0.47703
(0.22)
3.30108*
(1.51)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
Ticker: MRK
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
-0.01483*
(-1.90)
-0.00038
(-0.06)
-0.20679*
(-1.78)
-0.23839**
(-2.03)
0.00132
1.00000
-1.91686***
0.48240
Error Correction
Coint Equation1
ΔEPS
-0.87511***
(-4.39)
ΔDPS
0.00632
(1.23)
ΔEPS(-1)
0.03485
(0.21)
-0.01765
(-0.14)
2.23995
(0.48)
1.58511
(0.34)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
(t-values are in parentheses and
-0.00323
(-0.75)
-0.00209
(-0.64)
-0.16572
(-1.39)
-0.10906
(-0.91)
***,**,* represent significance at 1%, 5% and 10% levels.
Table IV Continued………
Model for EPSt
Regressor
Ticker: PFE
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
Coefficient
SE
-0.07956
1.00000
-1.21193***
0.21558
Error Correction
Coint Equation1
ΔEPS
-0.98308 ***
(-4.59)
ΔDPS
0.042991*
(1.74)
ΔEPS(-1)
0.03748
(0.21)
0.04115
(0.33)
-0.98143
(-0.95)
0.55811
(0.53)
-0.03677*
(-1.83)
-0.01485
(-1.03)
-0.03977
(-0.33)
-0.03003
(-0.25)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
Ticker: PG
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
-0.26489
1.00000
-1.19042***
0.22525
Error Correction
Coint Equation1
ΔEPS
-0.39378***
(-4.24)
ΔDPS
0.02718***
(4.88)
ΔEPS(-1)
-0.40253***
(-4.29)
-0.39345***
(-3.97)
5.21015***
(2.77)
11.1871***
(6.80)
-0.04056***
(-7.21)
-0.02132***
(-3.58)
-0.46254***
(-4.09)
-0.24446**
(-2.47)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
(t-values are in parentheses and
***,**,* represent significance at 1%, 5% and 10% levels.
Table IV Continued………
Model for EPSt
Regressor
Ticker: T
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
Coefficient
-0.21999
1.00000
-0.76531
SE
0.52926
Error Correction
Coint Equation1
ΔEPS
-0.99820***
(-26.97)
ΔDPS
0.00279
(0.42)
ΔEPS(-1)
0.00795
(0.06)
0.00538
(0.07)
-2.36669
(-0.87)
-0.50948
(-0.19)
0.00188
(0.35)
0.000406
(1.08)
0.16091
(1.28)
-0.28533**
(-2.26)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
Ticker: TRV
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
-0.07621
1.00000
-2.59975***
4.25334
Error Correction
Coint Equation1
ΔEPS
-0.48003***
(-3.32)
ΔDPS
0.00369
(0.60)
ΔEPS(-1)
-0.07133
(-0.49)
-0.0922
(-0.72)
-1.46537
(-0.53)
0.15521
(0.06)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
(t-values are in parentheses and
-0.00582
(-0.94)
-0.00102
(-0.19)
-0.58721***
(-4.97)
-0.28361**
(-2.42)
***,**,* represent significance at 1%, 5% and 10% levels.
Table IV Continued………
Model for EPSt
Regressor
Ticker: UTX
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
Coefficient
-0.22722
1.00000
-2.11859***
SE
0.33210
Error Correction
Coint Equation1
ΔEPS
-0.22502*
(-1.69)
ΔDPS
0.03994***
(3.87)
ΔEPS(-1)
-0.33182**
(-2.47)
-0.44003***
(-3.90)
-0.43777
(-0.28)
0.39640
(0.26)
-0.02488**
(-2.39)
-0.01433*
(-1.63)
-0.32275***
(-2.72)
-0.23137*
(-1.93)
-0.06465
1.00000
-1.05295
2.14194
Error Correction
Coint Equation1
ΔEPS
-0.73358***
(-4.25)
ΔDPS
-0.00056
(-0.29)
ΔEPS(-1)
-0.14258
(-0.90)
0.09064
(0.76)
-11.3827
(-1.06)
-0.11924
(-0.01)
0.00071
(0.40)
0.00085
(0.63)
-0.15042
(-1.26)
-0.10351
(-0.87)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
Ticker: VZ
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
(t-values are in parentheses and
***,**,* represent significance at 1%, 5% and 10% levels.
Table IV Continued………
Model for EPSt
Regressor
Ticker: WMT
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
Coefficient
SE
0.19865
1.00000
-6.09809***
0.53490
Error Correction
Coint Equation1
ΔEPS
-0.26068***
(-4.81)
ΔDPS
- 0.00457
(-1.45)
ΔEPS(-1)
-1.01037***
(-9.48)
-0.32576*
(-1.84)
-9.54996***
(-5.00)
-6.65585***
(-3.27)
0.07780***
(12.58)
0.04011***
(3.91)
0.17826*
(1.61)
-0.01042
(-0.09)
-0.17005
1.00000
-2.59725***
0.93605
Error Correction
Coint Equation1
ΔEPS
-0.13928*
(-1.86)
ΔDPS
0.01162***
(4.79)
ΔEPS(-1)
0.02240
(0.18)
-0.06413
(-0.52)
7.34860**
(2.11)
0.20499
(0.06)
-0.00631*
(-1.59)
0.00044
(0.10)
-0.40434***
(-3.58)
-0.42014***
(-3.66)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
Ticker: XOM
Cointegrating Equation
Constant
EPS(-1)
DPS(-1)
ΔEPS(-2)
ΔDPS(-1)
ΔDPS(-2)
(t-values are in parentheses and
***,**,* represent significance at 1%, 5% and 10% levels.