O
V
T
D
D
E LISABETH D EDMAN (W ARWICK B USINESS S CHOOL )
T HANAMAS K UNGWAL (M ANCHESTER B USINESS S CHOOL )
A NDREW W S TARK (M ANCHESTER B USINESS S CHOOL )
August 2012

1
A BSTRACT
O MITTED V ARIABLES AND T ESTS OF D IVIDEND D ISPLACEMENT
Theory suggests that, under certain assumptions, corporate distributions should reduce market value on a one-to-one basis. This result is sometimes known as dividend displacement.
Nonetheless, dividend displacement tends to be rejected by empirical tests for total dividends and regular dividends, but not for share buybacks, in the UK. This is associated with regular dividends having a stronger relationship with future earnings than share buybacks (that is, more information content). We study a conjecture in the literature that the omission of lagged accounting variables in accounting-based market value models is the cause of the rejection of dividend displacement for regular or total dividends. Our results suggest that this is not so, although lagged accounting variables are value relevant. When various controls for omitted variables in models used in prior literature are employed, it is in only one case that the results of prior studies are overturned. This case is when lagged market value is added into the estimated equation. Theoretically, within a general study of controlling for omitted variables in accounting-based market valuation models, we argue that including lagged market value as an independent variable cannot control for the omission of lagged accounting variables, or other omitted variables, without producing additional model mis-specification problems which, in turn, introduces bias into statistical tests. Qualitative analysis of the various empirical results suggests that the results concerning dividend displacement when lagged market value is included in the model are induced by coefficient bias. As a consequence, we conclude that the results in previous literature are robust to the inclusion of lagged accounting variables, and the evidence against these results when lagged market value is included in the model is unreliable.

1 I NTRODUCTION
2
Miller and Modigliani (1961) theorize that, under certain assumptions, corporate distributions should reduce market value on a one-to-one basis. This result is sometimes known as dividend displacement. Key assumptions underlying the theory are that corporate distributions have no information content, there are no agency problems, and there are no tax advantages or disadvantages associated with corporate distributions.
1
Nonetheless, dividend displacement tends to be strongly rejected by empirical tests. These tests have focussed on dividends as the specific form of corporate distribution. Much empirical research in the UK has established a positive relationship between market value and dividends in cross-sectional regression analyses. Published papers reporting this result, using various model specifications and estimation approaches, include, for example, Rees
(1997), Akbar and Stark (2003), Poletti Hughes (2008), Dedman, Mouselli, Shen and Stark
(2009), Shah, Stark and Akbar (2009) and Akbar, Shah and Stark (2011).
2
In the UK, given the models of market value employed, the rejection of dividend displacement with respect to dividends appears to be robust to deflator choice (Akbar and Stark, 2003; Dedman, et al
.,
2009).
Dedman et al (2012) study whether different forms of corporate distributions have relationships of a different strength with market value and whether this is associated with predictably different relationships between different forms of corporate distribution and
1
Pope and Wang (2005) raise the theoretical possibility that the relationship between net corporate distributions and market value could be positive in accounting-based valuation models.
2
Poletti Hughes (2008) specifically analyses the impact of regular dividends on market value, as opposed to total dividends. Nonetheless, special dividends have been very rare in the UK and, as a consequence, there is little difference between regular and total dividends as a practical matter..

3 future earnings. They find that regular dividends have a significantly higher relationship with market value than do share buybacks, a less routine form of returning cash to shareholders in the UK. Consistent with prior literature, they reject dividend displacement for regular dividends. Generally, however, they cannot reject dividend displacement for share buybacks.
They also find that regular dividends have a stronger relationship with future earnings than share buybacks. This suggests that regular dividends carry more information about the future earnings relative to share buybacks.
3, 4
Goncharov and Veenman (2010) conjecture that lagged accounting variables are omitted variables in the estimated valuation models and, since these omitted variables are correlated with dividends, the coefficient of the latter captures these effects. Goncharov and Veenman
(2010) then argue that the omitted variables problem leaves the dividend displacement test mis-specified. Thus, when lagged accounting variables are omitted, the coefficient of dividends is artificially high. Rather than including lagged accounting variables in testing for dividend displacement, they use lagged market value to capture the effects of omitted lagged accounting variables. Including lagged market value in their estimations results in a failure to reject dividend displacement for dividends using US data (a similar result is found in Lo and
Lys, 2000).
This study aims to contribute to the ongoing debate as follows. First, we theoretically analyse the general issue of controlling for omitted variables in accounting-based market
3
In the USA, Hand and Landsman (2005) also find that dividends have a positive relationship with market value,
4 although prior work by Lo and Lys (2000) would suggest that the results in Hand and Landsman (2005) arise from the choice of deflator used in the estimations. Lo and Lys (2000) argue that opening market value is both the best deflator to use in value relevance studies and, further, its use results in dividend displacement being impossible to reject in the USA. Livnat (2000) rejects the assertion that opening market value is the best deflator, however. Hand and Landsman (2005) also study the extent to which dividends are associated with future abnormal earnings in association with their market value tests.
Renneboog and Trojanowski (2011) reject agency issues and tax explanations for payout policies in the UK. Hence, this leaves studying the information content of corporate distributions an attractive avenue forward in understanding the effects of corporate distributions.

4 value equations, and the impact of using controls such as including lagged market value or the ‘other information’ variable of Akbar and Stark (2003), the latter being a transformation of lagged market value in which it is orthogonalised with respect to lagged accounting data.
Second, using extensions of the model in Dedman et al
(2012), we investigate the impact of including lagged accounting variables on tests of dividend displacement using UK data. We study UK data because, unlike the USA, share buybacks have not displaced dividends as the main form of corporate distribution (Renneboog and Trojanowski, 2011, Dedman et al , 2012) and many firms still pay regular dividends. Third, as a more general aim, we investigate the value relevance of lagged accounting variables in cross-sectional valuation models. Fourth, we evaluate the performance of different methods of controlling for omitted variables.
Our results are as follows. First, we theoretically demonstrate that including lagged market value cannot control for the omission of lagged accounting variables, or other omitted variables, without producing additional model mis-specification problems which introduce bias into statistical tests. Further, in comparison with including the ‘other information’ variable of Akbar and Stark (2003), the lack of orthogonalisation in lagged market value could be a crucial source of model mis-specification and subsequent bias in tests of dividend displacement.
Second, in empirical tests extending the model of Dedman et al
(2012), we find that the introduction of lagged accounting variables adds to explanatory power to a limited extent, and does reduce the coefficients of regular dividends and share buybacks. As a consequence, lagged accounting variables do possess limited valuation relevance. Nonetheless, that value relevance does not alter conclusions with respect to the relative size of the coefficients of regular dividends and share buybacks (the former is greater than the latter) and dividend

5 displacement tests. As a consequence, the results remain similar to those in Dedman et al
(2012) in this regard. Nor does the addition of the Akbar and Stark (2003) ‘other information’ variable, or adding further lags of market value to models incorporating lagged accounting variables alter these conclusions.
The only model specification in which the null hypothesis of equality of the coefficients of regular dividends and share buybacks, and dividend displacement with respect to both forms of corporate distribution, cannot be rejected is when lagged market value is added into a model regressing market value on current accounting variables (consistent with the empiricism in Goncharov and Veenman, 2010). Qualitative analysis of the various empirical results suggests that this is likely caused by the lack of orthogonalisation. As a consequence, we conclude that these results are unreliable.
The remainder of this paper is structured as follows. Section two provides a theoretical analysis of controlling for omitted variables and an assessment of different methods of so doing. It also provides a description of the research design. Section three provides variable descriptions, describes the process of sample selection, and presents descriptive statistics.
Section four reports the main empirical findings. Section five provides additional analyses and robustness tests, while section six provides conclusions.
2 T HEORY AND R ESEARCH D ESIGN
We use linear information dynamics to initially illustrate, from a theoretical perspective, how multiple lags of accounting data can affect market value. Initially, posit a linear information dynamics system with the following characteristics. Let z t
be an n -vector defined by:

6 z t
=
( z z
2, t
,..., z n -
1, t
, d t
) , (1) where the z i,t could include accounting items (e.g., components of profit, components of the balance sheet, cash flow components) and ‘other information’. d t
represents net shareholder cash flows.
Assume that z t
evolves according to the following stochastic process: z t
'
+
1 1 z t 2 z t
'
-
1
+ + W m +
1 z ' + E t +
1
, (2) where the
W i
' s
are n by n matrices and
E t +
1
is an n-vector of random variables. There could be restrictions imposed upon the
W i
' s
and
E t +
1
by accounting structures (e.g., clean surplus accounting), the notion that ‘other information’ is only forecast by its previous value, and dividend displacement.
It can easily be demonstrated that this description of linear information dynamics is consistent with a linear valuation function of the following form:
MV t
= b
1 z t
' + b
2 z t
'
-
1 b m +
1 z ' , (3) where b i
=
( b b
1 i 2
,...
b in
) and MV t
is the value of the equity of the firm at time t. In equilibrium, we have:

7 t +
1
)
+ E d t +
1
)
= MV t
(1
+ r ) , (4) where r is the cost of capital. Define the n-vector e n
=
(0, 0,...,1) . Then, the equilibrium pricing relationship, combined with the linear information dynamics system, implies:
(( b
1
+ e n
)
W + b
2
) z t
' +
(( b
1
+
(( b
1
+ e n
)
W
+ e n
)
W + m +
1
) z ' = b
3
) z t
'
-
1
( b
1 z t
' b
1
+ e n
)
W + b m +
1 z ' t m
)(1
+ r
) b m +
1
) z ' t m 1
(5) which, in turn, suggests that the b i
’s and the W i
' s
are connected by a set of simultaneous vector equations:
( b
1
+ e n
)
W +
1 b
2
= b
1
(1
+ r )
( b
1
+ e n
)
W +
2 b
3
= b
2
(1
+ r )
…..
( b
1
+ e n
)
W + b m +
1
= b m
(1
+ r
)
( b
1
+ e n
)
W m +
1
= b m +
1
(1
+ r
)
Thus, if the true model is:
MV t
= b
1 z t
' + b
2 z t
'
-
1 b m +
1 z '
, but we run regressions as if:
MV t
= b
1 z t
'
(6)
(7)
(8)

8 is the correct model, then there will be an omitted variables problem – the quantity b
2 z t
'
-
1 b m +
1 z ' (9) is omitted. Furthermore, this quantity is likely to be correlated with the independent variables in the model, because of the assumed LID system. As a consequence, there will be a correlated omitted variables problem, leading to potential coefficient bias in the estimated regression.
Will adding
MV t-1
into the regression control for the omitted variables in this theoretical structure, as suggested by Goncharov and Veenman (2010)? Assume that all the accounting variables in the system can be identified by the researcher. Given that MV t-1
is given by:
MV t -
1
= b
1 z t
'
-
1
+ b
2 z t
'
-
2 b m +
1 z ' t m 1
, (10) the answer is that MV t-1
is imperfectly correlated with the expression for the omitted variables, although it reflects some of the same information. Hence, it likely will act as a control, but only to a certain extent. Further there is still likely to be an omitted variables problem because
MV t-1
imperfectly captures the omitted variables, and their structure. As a consequence, this approach cannot be relied upon to give us safe tests on dividend displacement. Further, using
MV t-1
as a control for omitted lagged variables should be worse, in terms of explanatory power, than using the lagged variables themselves.

9
To illustrate another potential impact of introducing
MV t-1
into the regression, suppose the conjecture of Goncharov and Veenman (2010) is incorrect and lagged variables are not value relevant. What will happen if, in fact, equation (8) is the true model, we correctly identify all the variables contained in the vector z t
, and we include
MV t-1
in a regression of
MV t
on z t
?
Given that
MV t -
1
= b
1 z t
'
-
1
(11) and z t-1
forecasts z t
, MV t-1
will be correlated with the variables in z t
and, hence, biased tests of coefficients will result. Nonetheless, the inclusion of MV t-1 should not add to explanatory power in this case.
5
If, however, the researcher is unable to identify the full set of variables contained in z t
,
MV t-1
will also contain information relevant to the forecasting of these omitted variables and, as a consequence, its inclusion in the regression could not only lead to biased tests of those variables included in the regression but also add to explanatory power.
Finally, suppose that the researcher can identify all the variables in the system other than one
– ‘other information’. This is a particular example of the omitted variable problem. Let z n -
1, t
represent ‘other information’ at time t
, which we will refer to as
OI t-1
. A typical assumption concerning ‘other information’ is that it evolves according to the following process (for example, Ohlson, 1995, 2001):
OI t
= l OI t -
1
+ m t
(12)
5
This conclusion can be related to the literature on whether prices lead earnings – see, for example, Husseiney and
Walker (2009) for a recent literature review. That prices potentially lead earnings clearly is the case in the current analysis, along with all the variables in the LID system.

10
Thus, in this formulation, last year’s ‘other information’ is a noisy estimator of current ‘other information’. Given that:
MV t -
1`
= n i
2
=
1 b z
-
+ b n -
1
OI t -
1
+ b
-
1
, (13) if we can estimate b i
, i = 1 to n-2 , and n , a multiple of OI t-1
can be estimated as: b n -
1
OI t -
1
= MV t -
1`
n i
2
=
1 b z
,
-
1
b
-
1
(14)
This estimated multiple of last year’s ‘other information’ can then be used as the noisy estimate of current ‘other information’. In essence, it is a version of
MV t-1
that is orthogonalised with respect to the rest of the variables in z t-1
. This method of estimating
‘other information’ is operationalised in Akbar and Stark (2003) and Barth, Beaver, Hand and
Landsman (2005).
6
As a consequence, another possible effect of including
MV t-1
in a regression of
MV t
on z t
is that it will capture information helpful in predicting
OI t
.
Nonetheless, if there are more omitted variables (i.e., the researcher is unable to correctly identify all the other variables in the LID system, whether because of omitted lagged variables or omitted current variables), the proxy for OI t
. will also reflect these omitted variables, producing a variable that mixes a proxy for ‘other information’ with one that captures information on the previous year’s omitted variables that will help in predicting this year’s values for the omitted variables and, hence, will also help explain current market
6
Another way of estimating ‘other information’ involves using analysts’ forecasts (see, for example, Dechow, Hutton and Sloan, 1999, and Ohlson, 2001). In the UK, this approach involves a considerable loss of data, together with a biasing of the sample towards larger firms (Akbar and Stark, 2003).

11 value. How this conflated variable will perform relative to lagged market value in increasing explanatory power is not clear. Nonetheless, because it is at least orthogonalised with respect to the lagged values of the identified variables, it should bias tests on the coefficients of the current values of the identified variables less relative to including lagged market value in the regression equation.
In our empirical tests, our first purpose is to investigate whether lagged accounting variables are omitted from accounting-based valuation equations and, further, whether their inclusion leads to a failure to reject dividend irrelevance for corporate distributions. Our second purpose is to investigate the role played by including the proxy for ‘other information’ and various lags of market value as possible controls for omitted variables.
Initially, we build on recent research by Dedman et al
(2012), who estimate the relationship between market value, regular dividends and share buybacks. After controlling for book value, earnings, research and development expenditures, capital expenditures and capital contributions, their results suggests that regular dividends have a higher relationship with future earnings than share buybacks and, in associated fashion, have a higher relationship with market value than share buybacks. Dividend displacement can be rejected for regular dividends but not always for share buybacks. Their accounting-based market value model is as follows:
MV t
= a a
0 1
BV t
+ a
2
E t
+ a
3
CE t
+ a
4
RND t
+ a
5
RD t
+ a
6
SB t
+ a
7
CC t
+ e t
(15) where:

RND t
RD t
SB t
CC t
MV t
BV t
E t
= market value of the firm at time t;
= book value of the firm at time t;
= earnings of the firm for time t;
12
= research and development expense of the firm for time t;
= regular dividends of the firm for time t;
= share buybacks of the firm for time t; and
= capital contributions of the firm for time t.
Equation (15) is referred to subsequently as Model 1 and is our benchmark model.
To provide some comparable information to that in Goncharov and Veenman (2010), and despite the lack of unequivocal theoretical support for including lagged market value in the regression, we investigate the impact of lagged market value on the coefficients of regular dividends and share buybacks by running the following regression:
MV t
= a a
0 1
BV t
+ a
2
E t
+ a
3
CE t
+ a
4
RND t
+ a
5
RD t
+ a
6
SB t
+ a
7
CC t
+ a
8
LMV t
+ e t
(16) where:
LMV t
= market value of the firm at time t-1.
We refer to equation (16) as Model 2.

13
We then estimate a proxy for ‘other information’ using the approach described above, an orthogonalised version of lagged market value, and add it into equation (15).
7
This produces
Model 3, described in equation (17).
MV t
= a a
0 1
BV t
+ a
2
E t
+ a
3
CE t
+ a
4
RND t
+ a
5
RD t
+ a
6
SB t
+ a
7
CC t
+ a
9
OI t
+ e t
(17)
Comparing Models 1, 2 and 3 will help develop initial insight into the role being played by lagged market value.
In order to more directly test the assertion of Goncharov and Veenman (2010) that lagged accounting variables are omitted variables in the market value equation, we then add in lagged accounting variables to equation (15). It is not clear how many lags of data we should add in. Unfortunately, no research in the UK is available that can advise us on this issue. As a consequence, we make the choice to add in one and two lags of accounting data.
8
Hence, we first estimate equation (18). In this equation, the prefix L is used to denote a singlelagged variable. For example, LE t
= E t-1
. A single lag of the accounting variables is added into equation (15). In LID terms, it assumes that two lags of accounting data are included in the predictive equations for the accounting variables. This is Model 4.
MV t
= a a
0 1
BV t
+ a
11
LE t
+ a
12
+ a
2
E t
LCE t
+
+ a
3
CE t a
13
+ a
LRND t
4
RND t
+ a
14
+ a
LRD t
5
RD t
+
+ a
6
SB t a
15
LSB t
+
+ a
7
CC t a
16
LCC t
+
+ e a
10 t
LBV t
(18)
7
In constructing
OI
, we do so under the null hypotheses expressed in equations (23), (24) and (25). As a consequence, we amalgamate RD and SB to create Gross Shareholder Cash Flow ( GSCF ). As a consequence we annually regress lagged market value on the lagged values of
BV
,
E
,
CE
,
RND
,
GSCF
and
CC
. We estimate these regressions under the constrain that the negative of the coefficient of BV plus the coefficient of GSCF equals -1, as is required under
8 the null hypothesis of dividend displacement.
In the USA, Bar-Yosef, Callen and Segal (1996) and Dechow et al
(1998) suggest that there might be a need for two lags of accounting data.

14
As we add in lags of accounting information (and the reason for adding in lags of market value is to capture lagged accounting information), we would expect the impact of further lags market value to decrease. Hence, if one lag of accounting data is sufficient to capture the impact of omitted lagged accounting variables, we would expect that adding in a lagged, lagged market value would have no impact on explanatory power, although the coefficients on the accounting variables might change for similar reasons to those outlined above with respect to lagged market value. By way of contrast, if lagged, lagged market value in fact captures ‘other information’, and ‘other information’ follows the Markovian structure outlined above, then we would still expect lagged, lagged market value to have explanatory power, albeit diminished.
9
Further, and again as outlined above, it could be capturing the impact omitted variables other than ‘other information’. Hence, we add in lagged, lagged market value to equation (18) to produce equation (19) (with the prefix
LL
denoting two lag data):
MV t
= a a
0 1
+ a
11
LE t
BV t
+ a
12
+ a
2
E t
LCE t
+
+ a
3
CE t a
13
+ a
4
LRND t
+
RND t a
14
+ a
LRD t
5
RD t
+ a
15
+ a
6
LSB t
SB t
+
+ a
7
CC t a
16
LCC t
+
+ a
10
LBV t a
17
LLMV t
+ e t
(19)
This is Model 5.
9 Lagged, lagged market value will contain ‘other information’ of lag two which, because of the Markovian process followed by ‘other information’, will still be a noisy estimator of current ‘other information’. As a consequence, there will be an errors-in-variables problem that is likely to bias downwards the coefficient of lagged, lagged market value.

15
Further, in a similar fashion, we add a further lagged set of accounting data into equation (18) to produce equation (20) - Model 6:
MV t
= a a
1
BV t
+ a
2
E t
+ a
3
CE t
+ a
4
RND t
+ a
5
RD t
+ a
6
SB t
+ a
7
CC t
+ a
10
LBV t
+
+ a
16
+ a
LCC t
22
LLRD t
+ a a
+
11
18 a
LE t
+ a
12
LLBV t
23
LLSB t
+
LCE t a
19
+
LLE t a
13
+
LRND t a
+
20
LLCE t a
14
+
LRD t a
+ a
15
21
LLRND t
LSB t
+ a
24
LLCC t
+ e t
(20)
Finally, we extend Model 6 by adding in triple lagged market value, where the suffix LLL denotes data with three lags to produce equation (21) – Model 7:
MV t
= a a
0 1
BV t
+
+ a
10 a
16
LBV t
LCC t
+
+ a
+ a a
2
E t
11
LE t
+
+
18
LLBV t a
3
CE t a
12
+
LCE t a
19
+ a
+
LLE t
4
RND t
+ a
5
RD t a
13
+
LRND t a
+
20
LLCE t a
14
+
+ a a
21
6
LRD t
SB t
+ a
+
15 a
LLRND t
7
CC t
LSB t
+ a
22
LLRD t
+ a
23
LLSB t
+ a
24
LLCC t t
+ a
25
LLLMV t
+ e t
(21)
To test for whether the coefficients of the components of corporate distributions are different, as in Dedman et al (2012), we use F-tests. The underlying null hypothesis of the tests is that the coefficients of the components are equal. Hence, we test equation (22): a
5
= a
6
(22)
Using F-tests again, we also test for dividend displacement with respect to the two types of transfers to shareholders. To derive these tests, take, for example, our benchmark model in

16 equation (16). If we differentiate it with respect to RD t
, and dividend displacement holds, then the differential of market value ought to be
-1
. Further, bearing in mind that corporate distributions reduce book value on a one-to-one basis, the negative of the coefficient of book value plus the coefficient of regular dividends will then be equal to -1 . Therefore, we test the following restrictions in equations (15) to (21). For regular dividends, the restriction is: a a
5
= -
1 (23) and, for share buybacks, it is: a a
6
= -
1 (24)
Our overall testing strategy is as follows. We first estimate Models 1 to 3 on a common sample to investigate, first, the effect of adding in lagged market value to the market value equation of Dedman et al
(2012) and, second, the impact of adding in ‘other information’, and how that impact compares with adding in lagged market value. Second, we estimate
Models 1 to 5 on a common sample to investigate the impact of adding in one lag of accounting information and a further lag of market value. Third, we estimate Models 1 to 7 on a common sample to investigate the impact of further lags of accounting and market value data. We estimate the models after deflation of each of the equations by book value.
Coefficient standard errors are estimated using firm and year two-way clustering.

17
As one robustness check, we repeat our tests using opening market value as a deflator, as in
Lo and Lys (2000). We also do various sub-sample analyses, including focussing only on profitable firms, profitable firms that make some type of corporate distribution, and profitable firms that pay regular dividends.
3 D
ATA AND
S
AMPLES
The initial population of firms for this study consists of all UK non-financial firms listed on the London Stock Exchange from 1994 to 2010. We are restricted to starting our sample period at 1994 because of data availability with respect to our measure of regular dividends.
Data on the split between regular and special dividends is predictably only available for UK firms from 1993 onwards. Given the additional need to calculate our measure of ‘other information’ on lagged data, this constrains any of our samples to start at 1994. The data includes both ‘live’ and ‘dead’ firms to mitigate possible survivorship bias. Accounting and market data are collected from Datastream and Worldscope.
Initially, annual cross-sections are constructed for each of calendar years 1994-2010. To be included in an annual cross-section, a firm-year observation must meet the criteria that:
(i) the financial year for that firm must end during the calendar year for the annual crosssection;
(ii) firms must not have an irregular length fiscal year – those that are over a seven day tolerance around one year are excluded;
(iii) the firm must have only a single code in Datastream, suggesting that the firm has only a single listed security; and they must be available for that calendar year;

18
(iv) the firm must not have (unlisted) preference shares in excess of 10% of total shareholders’ equity;
(v) the firm must not be classified as either financial or unclassified using the industry classification in Datastream;
(vi) the currency of the firm’s financial statements is pound sterling;
(vii) all accounting and market data must be available; and
(viii) closing book value must be positive because of its role as a deflator
The definitions for the variables used in this paper are as follows:
(i) Market Value (MV t
) – the market value for year t , is measured six months after its balance sheet date during that calendar year. For a firm whose financial year ends on
December 31, 1992, its market value will be measured on June 30, 1993, or the nearest trading day. The reason for measuring market value six months after the balance sheet date is that, until recently, all UK listed firms have six months to prepare and release their annual accounts. However, after January 2007, the market value for year t , is measured four months after its balance sheet date during that calendar year.
(Datastream item MV);
(ii) Book Value (BV t
) - book value for year t is measured as the sum of preferred stock and common shareholders’ equity for the financial year ending in year t
(Worldscope item -
WC03995 - total shareholder’s equity);
(iii) Earnings (E t
) - earnings represents income after all operating and non-operating income and expense, reserves, income taxes, minority interest and extraordinary items for the financial year ending in year
t
(Worldscope item - WC01651 - Net income before preferred dividends);

19
(iv) Capital Expenditures (CE t
) - capital expenditures for year
t
are measured as funds used to acquire fixed assets other than those associated with acquisitions at year t
(Worldscope item - WC04601 - Capital Expenditures – Additions to Fixed Assets);
(v) Research and Development Expense (RND t
)
- research and development expenditures for year t are measured as all costs which are recognised in the income statement at year t . They relate to the creation and development of new processes, applications and products with commercial possibilities (Worldscope item -WC01201-Research and
Development Expense);
(vi) Regular Dividends (RD t
)
- dividends declared at year t
represents the total value of the common dividends declared for the year t
. For most countries outside of the U.S. and
Canada it includes the interim paid, if any, plus the proposed final dividend declared after the year end. If not reported separately, it is the dividend charged to retained earnings. Dividends declared combines between regular and special dividends
(Worldscope item –WC18192 - Dividends Provided for or Paid). We exclude firmyears which pay special dividends;
(viii) Share Buybacks (SB t
) – share buybacks for year t represents funds used to decrease the outstanding shares of common stock. (Worldscope item - WC04751 –
Common/Preferred Redeemed, Retired, Converted etc). It is assumed that the buying back of preference shares is small;
(ix) Capital Contributions (CC t
) - capital contributions for year
t
are measured as the amount the firms receives from the sale of common and/or preferred stock at year t .
(Worldscope item - WC04251 - Net Proceeds from Sales/Issue of Common and
Preferred). Again, it is assumed that amounts of preference shares issued will be small; and
(x)
‘Other Information’ (OI t
) – other information for year t is estimated as the regression

20 error term from a regression of time t-1 market value on the accounting variables described above, multiplied by the deflator for the observation (as in Akbar and Stark,
2003).
For the first sample, used to estimate Models 1 to 3, each firm-year needs to have current and lagged data for both market value and accounting data. For each annual cross-section, we construct the deflated variables, using book value as the deflator. For each annual crosssection, a firm-year is deleted from it if one or more of the deflated variables are in the top or bottom 1% of values for that variable, other than for capital expenditures, research and development expense, regular dividends, share buybacks and capital contributions, for which the deletion rule only covers the top 1% of the observations. We refer to this sample as the base sample.
We provide some descriptive statistics for this sample. Table 1 shows how many firms are left in each annual cross-section after the deletion of extreme observations.
__________________________________
Insert Table 1
__________________________________
Table 2 describes the pattern of firm-year observations with respect to the occurrence of regular dividends, and share buybacks this sample. The table suggests that although the number and proportion of share buybacks grows after year 2000, such a method of distributing resources to shareholders is not yet totally common in the UK. Further, nearly a majority of firms are still paying a regular dividend to shareholders in 2010 although,

21 perhaps not surprisingly, the proportion is much higher for profit-making firms than for loss-making firms. Finally, very few firms engage in share buybacks on their own. Far more often, regular dividends and share buybacks both occur during a year for those firms engaging in share buybacks. Despite the sample selection criteria in this paper being more onerous than those in Dedman et al (2012), the data in Table 1 is similar to that reported by those authors.
__________________________________
Insert Table 2
__________________________________
Table 3 provides information relating to the characteristics of the sample and describes the deflated variables used in the regression models for the trimmed samples. In general, all variables indicate a degree of skewness.
__________________________________
Insert Table 3
__________________________________
Table 4 provides the correlations between variables. The result suggests that extreme multicollinearity problems are unlikely to exist in the estimated regressions. Although there is a fairly high correlation between LMV and OI, these two variables do not ever enter into the same regression equation. LMV has a reasonable degree of predictive power for RND and
RD. Lesser degrees of predictive power exist for CC and CE. OI is not highly correlated with any of the other variables.

22
__________________________________
Insert Table 4
__________________________________
To construct the other samples, we place further restrictions on sample selection. In order to test Models 1 to 5 on a common sample, we require all of the data requirements for the base sample, together with a requirement that lagged, lagged market value is available. After trimming, this results in a sample size of 11752 observations. In order to test Model 1 to 7, a further lag of accounting and market value data is required. After trimming, this results in a sample size of 9476 observations. For reasons of space, we do not report the characteristics of these samples.
4 R
ESULTS
Tables 5 reports the results of estimating Models 1 to 3 on the base sample. In Model 1, the results for the control variables largely confirm those found in prior UK studies (e.g., Rees,
1997, Akbar and Stark, 2003, Dedman et al
, 2009, Shah et al
, 2009, Akbar et al
, 2011). The coefficient of book value is positive and significant. The coefficient of earnings is relatively small and positive but, in our case, is strongly significant. The coefficient of capital expenditures is positive and significant, as is that for research and development expense. The sizes of these coefficients are similar to those found in previous studies. Further, as in
Dedman et al (2012), the coefficient of regular dividends is significantly higher than that for

23 share buybacks, and dividend displacement can be rejected for regular dividends but not for share buybacks.
__________________________________
Insert Table 5
__________________________________
In Model 2, the addition of lagged market value has a strong effect on explanatory power.
Further, the addition has a major effect on the coefficients of a number of variables relative to those estimated in Model 1. In particular, the coefficients of all variables other than earnings fall substantially, with that for earnings falling to a small extent. Further, the null hypothesis that the coefficient of regular dividends equals that for share buybacks cannot be rejected at the 5% level (the p-value is .08), although dividend displacement is again rejected for regular dividends but not for share buybacks. Overall, these results are qualitatively similar to those in Goncharov and Veenman (2010) other than that dividend displacement can be rejected for regular dividends.
Turning to Model 3, the inclusion of our proxy for ‘other information’ also substantially increases explanatory power, although not to the extent of the inclusion of lagged market value. Its inclusion also alters coefficient estimates but, unlike with lagged market value, some coefficients increase (e.g., book value and earnings). The coefficient of regular dividends does fall, but nowhere to the extent that occurs with the inclusion of lagged market value. The inclusion of the proxy for ‘other information’ leaves the results of hypothesis testing with respect to the relative sizes of the coefficients of regular dividends and share buybacks and dividend displacement unchanged from Model 1.

24
Overall, and ignoring the possible role of omitted lagged accounting variables, we can reach the following conclusions. The increase in explanatory power arising from the inclusion of lagged market value suggests that it captures the effect of omitted variables. That the increase in explanatory power is higher than when a proxy for ‘other information’ is added in suggests that the omitted variable is not just ‘other information’. Nonetheless, as pointed out earlier, the changes in the coefficients arising from the inclusion of lagged market value is also consistent with lagged market value, as expected, containing information about the future values of the accounting variables.
__________________________________
Insert Table 6
__________________________________
Turning now to examining the effect of including lagged accounting variables, we can first look at the results for Models 1 to 3 on this sample. The results are qualitatively very similar to those found for the base sample except that now we cannot reject the null hypothesis of dividend displacement when lagged market value is added into the model. Otherwise, again the inclusion of either lagged market value, or the ‘other information’ proxy, increases explanatory power substantially, with the increase higher for lagged market value than for
‘other information’.
The impact of lagged accounting variables does add to explanatory power, but at a relatively low level. Certainly, the increase is much lower than for the inclusion of either lagged market value or ‘other information’. Nonetheless, four of the lagged accounting variables are

25 significant at the 5% level. The introduction of the lagged accounting variables does have some effect on the coefficients of regular dividends and share buybacks. Nonetheless, and of importance, we can reject the null hypotheses that the coefficient of regular dividends is equal to that for share buybacks and that dividend displacement applies to regular dividends.
We cannot reject the null hypothesis that dividend displacement applies to share buybacks.
When we add in lagged, lagged, market value into Model 4 to produce Model 5, it attracts a significant coefficient. Nonetheless, the combination of lagged accounting variables and lagged, lagged market value produces a lower explanatory power that merely adding in lagged market value or our ‘other information’ proxy. Further, again, we can reject the null hypotheses that the coefficient of regular dividends is equal to that for share buybacks and that dividend displacement applies to regular dividends, and we cannot reject the null hypothesis that dividend displacement applies to share buybacks.
__________________________________
Insert Table 7
__________________________________
Finally, Table 7 reports on the estimations of Models 1 to 7 on a common sample. The conclusions reached for Models 1 to 5 from the results in Table 6 can be repeated on this sample. The impact of adding in a further lag of accounting variables in Model 6 produces a barely discernible increase in explanatory power relative to only including one lag of accounting variables. Some of the coefficients of the lagged, lagged accounting variables are significant, and their inclusion causes coefficients for the lagged accounting variables to

26 change in size and significance. This suggests a degree of collinearity between the lagged and lagged, lagged accounting variables.
In Model 7, when market value lagged by three years is added in, it does produce a significant increase explanatory power relative to Model 6. Further, it tends to decrease the estimated coefficients of the lagged, lagged, accounting variables. Nonetheless, the explanatory power of a model with two lags of accounting variables and three lag market value is lower than that arising from a model with one lag of accounting data and market value lagged by two years.
Finally, the results with respect to the relative sizes of the coefficients of regular dividends and share buybacks and dividend displacement are exactly the same as with Models 1, 3, 4 and 5. Hence, we can reject the null hypotheses that the coefficient of regular dividends is equal to that for share buybacks and that dividend displacement applies to regular dividends, and we cannot reject the null hypothesis that dividend displacement applies to share buybacks.
What conclusions can be drawn overall from these results? First, there is some evidence that lagged accounting variables are omitted variables. Nonetheless, the increases in explanatory power from adding in one lag and two lag accounting variables are modest. Further, their inclusion neither alters conclusions as to the relative sizes of the coefficients of regular dividends and share buybacks nor tests of dividend displacement with respect to the two forms of corporate distribution. As a consequence, unless there are significant, if surprising, effects arising from the inclusion of further lags of accounting information, it is safe to say that the assertion of Goncharov and Veenman (2010), that the reason dividend displacement

27 is rejected for dividends is because of the omission of these variables, appears not to be consistent with the data.
Second, the impact of adding the proxy for ‘other information’ into the regressions is a significant increase in explanatory power, as in previous empirical work (see Akbar and
Stark, 2003, Dedman et al , 2009, Akbar et al , 2011). This increase is much more than is added by, for example, the inclusion of lagged accounting variables in the estimations. As pointed out above, the proxy could also be capturing omitted variables other than ‘other information’ such as, for example, lagged accounting variables. Nonetheless, as with the inclusion of lagged accounting variables into the regressions, the use of the proxy does not alter conclusions with respect to the relative sizes of the coefficients of regular dividends and share buybacks nor tests of dividend displacement with respect to the two forms of corporate distribution.
Third, the impact of including various lags of market value is mixed. The inclusion of lagged market value always produces the regression with the highest explanatory power, whichever sample is used. Further, in these regressions, the results are more like those in Goncharov and Veenman (2010). We cannot reject the null hypothesis that the coefficients of regular dividends and share buybacks are equal and, now, dividend displacement cannot be rejected in two out the three estimations for regular dividends.
As pointed out above, the inclusion of lagged market value can have the effect of controlling for omitted variables, whether lagged accounting variables or ‘other information’ or other variables. Nonetheless, because it is not orthogonalised with respect to the accounting variables in the model, there is the risk that its inclusion will bias tests on the significance of

28 the accounting variables or combinations thereof. If orthogonalisation were not an important issue, we would expect that there would be little difference between the estimated coefficients for the current accounting variables when adding the proxy for ‘other information’ to Model 1 and those resulting from the addition of lagged market value. That this is not the case suggests that the lack of orthogonalisation is indeed biasing tests. This suggests that the results from estimating Model 2 might well suffer from significant bias.
Further, we can observe that, as we add in higher and higher lags of market value, the increase in explanatory power lessens. Also, the effects on the coefficients of the current accounting variables are far less dramatic. Further, the impact of the various lags of market value appears to be strongest, in broad terms, on the coefficients for the accounting variables one year after. This suggests that market value does lead the accounting variables in the model, but the effect dies down quite quickly (e.g., mainly after a year). This again is consistent with the arguments above about the bias induced by the addition of lagged market value into Model 1.
With respect to dividend displacement and the equality of the coefficients of regular dividends and share buybacks, we get essentially the same results for all models other than
Model 2. Given the arguments above, we do not regard the results from estimating Model 2 as sufficiently reliable to overturn our conclusions with respect to these tests.
5 R OBUSTNESS C HECKS
We run a number of robustness checks. First, we estimate Models 1 through 7 using opening market value as the deflator (as in Lo and Lys, 2000). Overall, the conclusions with respect

29 to the relative size of the coefficients of regular dividends and share buybacks and dividend displacement are broadly consistent with the results reported above. The only difference is that dividend displacement is rejected a few times for share buybacks.
Jiang and Stark (2012) find that the coefficient of regular dividends can be significant for loss-making firms, firms which might be more prone to use dividends to signal future prospects relative to profitable firms. We then examine whether our results are an artefact of including loss-making firms in our samples by studying profit-making firms separately.
Therefore, we re-estimate Models 1 to 7 on profit-making firms alone. We do so by taking the each sample and splitting it into those firms that currently make losses and those that currently make profits for any given year. We re-estimate using both deflators. Again, the conclusions with respect to the relative size of the coefficients of regular dividends and share buybacks and dividend displacement are broadly consistent with the results reported in the previous section.
Next, because of the number of firms paying neither regular dividends nor making share buybacks, we investigate the impact of re-estimating Models 1 to 7 on firms with either positive regular dividends or positive share buybacks or both. Again, we use both deflators.
Our conclusions are unchanged. Finally, we concentrate on firms paying only regular dividends and dividend displacement tests. Our results with respect to dividend displacement for regular dividends are unchanged.

6 C ONCLUSIONS
30
In this paper, we provide an examination of a conjecture arising from Goncharov and
Veenman (2010), who argue that failing to control for lagged accounting value can induce a positive relation between price and dividends, contrary to the concept of dividend displacement. They do not test their conjecture directly. Instead, they argue that adding in lagged market value to a regression of market value on corporate distributions and other control variables will control for the omitted lagged accounting variables.
First, we theorise about the role played by lagged market value in regressions of market value on accounting variables, including corporate distributions. We demonstrate that including lagged market value cannot control for the omission of lagged accounting variables, or other omitted variables, without producing additional model mis-specification problems which introduces bias into statistical tests. Further, we compare the inclusion of lagged market value with the ‘other information’ variable of Akbar and Stark (2003), which takes the form of a lagged market value orthogonalised with respect to lagged accounting variables. From a theoretical perspective, we conclude that the lack of orthogonalisation in lagged market value could be a crucial source of model mis-specification.
Second, in empirical tests using the model of Dedman et al (2012), who study the relative sizes of different forms of corporate distributions (regular dividends and share buybacks), we find that the introduction of lagged accounting variables, although adding to explanatory power to a limited extent, does not alter conclusions with respect to the relative size of the coefficients of regular dividends and share buybacks (the former is greater than the latter) and dividend displacement tests. These results remain similar to those in Dedman et al (2012).

31
These results are inconsistent with the conjecture of Goncharov and Veenman (2010). Nor does the addition of the Akbar and Stark (2003) ‘other information’ variable, or adding further lags of market value to models incorporating lagged accounting variables alter these conclusions.
The only model specification in which the null hypotheses of equality of coefficients of regular dividends and share buybacks and dividend displacement with respect to both forms of corporate distribution cannot be rejected is when lagged market value is added into a model regressing market value on current accounting variables (consistent with the empiricism in Goncharov and Veenman, 2010). Qualitative analysis of the various empirical results suggests that this is caused by the lack of orthogonalisation. As a consequence, we conclude that these results are unreliable.
Thus, we conclude that the results in Dedman et al
(2012) are robust to specifications in which lagged accounting variables are considered as potential omitted variables the coefficient of regular dividends is significantly higher than that for share buybacks, dividend displacement can be rejected for regular dividends but, more often than not, not for share buybacks. Nonetheless, lagged accounting variables do add to explanatory power and could play a role as important control variables in other value relevance studies, even if not in this one. These conclusions are robust to the use of an alternative deflator and a number of different sub-sample analyses.
The analyses here, and in Dedman et al
(2012), notwithstanding the sub-sample analyses performed here, do not rule out the conjecture of Rees and Valentincic (2009) that regular dividends in particular might have different information content about future prospects and,

32 hence, a different relationship with market value depending on the degree of information asymmetry between firms and the stock market. We leave this line of inquiry for future research.

R
EFERENCES
33
Akbar, S. and A.W. Stark (2003), ‘Deflators, Net Shareholder Cash Flows, Dividends, Capital
Contributions and Estimated Models of Corporate Valuation’, Journal of Business
Finance and Accounting
, Vol. 30, pp.1211-33.
Akbar, S., S.Z.A. Shah and A.W. Stark (2011) ‘The Value Relevance of Major Media
Advertising Expenditures: Some U.K. Evidence’,
International Review of Financial
Analysis
, Vol. 20, pp. 311-319.
Bar-Yosef, S., J.L. Callen, and J. Livnat (1996), ‘Modelling Dividends, Earnings and Book
Value Equity: An Empirical Investigation of the Ohlson Valuation Dynamics’, Review of Accounting Studies
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Barth, M.E., W.H. Beaver, J.R.M. Hand and W.R. Landsman (2005), ‘Accruals, Accountingbased Valuation Models, and the Prediction of Equity Values’, Journal of Accounting
Auditing and Finance
, Vol. 20, pp.311-346.
Dedman, E., S. Mouselli, Y. Shen, and A.W. Stark (2009), ‘Accounting, Intangible Assets,
Stock Market Activity, and Measurement and Disclosure Policy - Views From the UK’,
Abacus
, Vol. 45, pp.312-341.
Dedman, E., T. Kungwal, and A.W. Stark (2012), ‘Investigating the Positive Association between Regular Dividends, Share Buybacks and Market Value’, Working Paper
Dechow, P.M., A.P. Hutton, and R.G. Sloan (1999), ‘An Empirical Assessment of The
Residual Income Valuation Model’, Journal of Accounting and Economics , Vol. 26, pp.1-34.
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Dividends in Equity Valuation’, Available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1072804.
Hand, J.R.M., and W.R. Landsman (2005), ‘The Pricing of Dividends in Equity Valuation’,
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Husseiney, K. and M. Walker (2009), ‘The effects of voluntary disclosure and dividend propensity on prices leading earnings’, Accounting and Business Research , Vol. 39, pp.37-55.

34
Jiang, W. and A.W. Stark (2012), ‘Dividends, research and development expenditures, and the value relevance of book value for UK loss-making firms’, Available at http://ssrn.com/abstract=1734287.
Livnat, J. (2000), ‘Discussion: The Ohlson Model: Contribution to Valuation Theory,
Limitation and Empirical Applications’,
Journal of Accounting, Auditing and Finance
,
Vol. 15, pp. 368-370.
Lo, K. and T. Lys (2000), ‘The Ohlson Model: Contribution to Valuation Theory, Limitations, and Empirical Applications’,
Journal of Accounting, Auditing and Finance
, Vol. 15, pp.
337-367.
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, Vol. 34, pp. 411-433.
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Empirical Perspective’,
Contemporary Accounting Research
, Vol. 18, pp. 107-120.
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Advertising Expenditures: Some U.K. Evidence’, International Journal of Accounting ,
Vol. 44, pp. 187-206.

35

36
2000
2001
2002
2003
2004
2005
2006
2007
Year
1994
1995
1996
1997
1998
1999
2008
2009
2010
Pooled
T ABLE 1
Numbers of Annual Firm-Year Observations in the Base Sample
(13830 Observations)
BV as deflator
742
760
742
856
852
808
777
838
861
813
791
848
922
826
835
789
770
13830

37
T ABLE 2
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Total
Year Firms RD
1994
651 607
Profit Firms
SB
1
Both None Firms
14 29 91
1995
Annual Dividend Observations in the Base Sample (13830 Observations)
687 641 0 15 31 73
RD
52
41
Loss Firms
SB
2
1
Both
0
0
None
37
31
1996
1997
1998
650
730
698
598
583
522
0
0
2
29
39
67
23
108
107
92
126
154
49
37
49
4
1
0
3
1
5
36
87
100
642
593
537
498
509
545
569
585
548
536
469
527
9974
456
433
375
361
354
352
334
294
267
231
213
222
6843
1
8
4
3
5
7
17
11
22
22
22
24
149
65
64
61
54
61
73
91
123
131
158
103
121
1269
120
88
97
80
89
113
127
157
128
125
131
160
1713
166
184
301
363
304
246
279
337
278
299
320
243
3856
42
49
83
84
63
54
34
29
24
38
48
23
799
2
3
4
7
12
3
12
12
6
11
13
9
102
4
3
12
9
5
3
6
4
5
9
15
3
87
118
129
202
263
224
186
227
292
243
241
244
208
2868
Notes:
RD = Regular Dividends; and SB = Share Buybacks.

38
T
ABLE
3
E
CE
RND
RD
Sample Statistics for the Base Sample - Book Value as Deflator
(13830 Observations)
Deflated Mean Median Std. Min Max
0.00
0.13
0.03
0.04
0.09
0.08
0.00
0.03
0.42
0.15
0.09
0.05
-7.89
0.00
0.00
0.00
1.60
1.72
1.42
0.54
SB
CC
LMV
OI
0.01
0.08
2.35
-0.04
0.00
0.00
1.75
-0.39
0.03
0.22
2.04
1.90
0.00
0.00
0.00
-5.00
0.98
3.32
25.31
31.98
Notes:
MV = Market Value; BV = Book Value; E = Earnings; CE = Capital Expenditures; RND = Research and Development Expense; RD = Regular Dividends; SB = Share Buybacks; CC = Capital Contributions; and OI = ‘Other Information’.

39
T
ABLE
4
E
CE
RND
RD
SB
CC
LMV
OI
Correlations Between the Independent Variables for the Base Sample
Book Value as Deflator (13830 Observations)
E
1
0.06
-0.21
0.36
0.07
-0.28
-0.01
-0.27
CE
1
-0.08
0.20
0.00
0.00
0.19
0.09
RND
1
-0.07
0.01
0.11
0.25
0.12
RD
1
0.14
-0.15
0.36
0.11
SB
1
-0.04
0.06
0.03
CC
1
0.19
0.11
LMV
1
0.73
OI
1
Notes:
MV = Market Value; BV = Book Value; E = Earnings; CE = Capital Expenditures; RND = Research and
Development Expense; RD = Regular Dividends; SB = Share Buybacks; CC = Capital Contributions; and OI =
‘Other Information’.

40
T
ABLE
5
Estimates of Models 1 - 3 on All Available Firm Observations for 1994-2010
(13830 Firm-Years) – Book Value as Deflator
Variables
Constant
BV
E
CE
RND
RD
SB
CC
LMV
OI
R-square
Model 1
Coefficients
(p > |t|)
2477.24
(0.00)
0.90
(0.00)
0.78
(0.00)
1.93
(0.00)
7.50
(0.00)
16.81
(0.00)
-0.33
(0.83)
2.98
(0.00)
0.21
Model 2
Coefficients
(p > |t|)
841.69
(0.00)
0.09
(0.30)
0.72
(0.00)
0.16
(0.50)
1.85
(0.00)
1.79
(0.19)
-1.44
(0.20)
1.32
(0.00)
0.94
(0.00)
0.46
Model 3
Coefficients
(p > |t|)
2042.52
(0.00)
1.38
(0.00)
1.77
(0.00)
1.26
(0.00)
6.43
(0.00)
10.89
(0.00)
-1.68
(0.18)
2.72
(0.00)
0.73
(0.00)
0.38
F-tests
5
= a a a a a a
6
- + = -
1 5
1
- + = -
1 6
1
77.40
(0.00)
264.20
(0.00)
0.02
(0.88)
3.15
(0.08)
4.04
(0.04)
0.23
(0.63)
62.61
(0.00)
102.34
(0.00)
2.81
(0.09)

41
Notes:
Model 1:
MV t
= a a
1
BV t
+ a
2
E t
+ a
3
CE t
+ a
4
RND t
+ a
5
RD t
+ a
6
SB t
+ a
7
CC t
+ a
7
CC t
+ e t
Model 2:
MV t
= a a
0 1
BV t
+ a
2
E t
+ a
3
CE t
+ a
4
RND t
+ a
5
RD t
+ a
6
SB t
+ a
8
LMV t
+ e t
Model 3:
MV t
= a a
0 1
BV t
+ a
2
E t
+ a
3
CE t
+ a
4
RND t
+ a
5
RD t
+ a
6
SB t
+ a
7
CC t
+ a
9
OI t
+ e t
MV = Market Value; BV = Book Value; E = Earnings; CE = Capital Expenditures; RND = Research and
Development Expense; RD = Regular Dividends; SB = Share Buybacks; CC = Capital Contributions; OI =
‘Other Information’; and the prefix L denotes one lag variables.

42
T
ABLE
6
Estimates of Models 1 - 5 on All Available Firm Observations for 1995-2010
(11752 Firm-Years) – Book Value as Deflator
Constant
OI
LBV
LE
LCE
LRND
LRD
LSB
RD
SB
CC
LMV
BV
E
CE
RND
LCC
LLMV
R-Square
Model 2 Model 1 Model 3 Model 4 Model 5
Coefficient Coefficient Coefficient Coefficient Coefficient
(P>|t|) (P>|t|) (P>|t|) (P>|t|) (P>|t|)
2054.17 688.75 1831.72 1886.25 1262.76
(0.00)
0.87
(0.00)
0.81
(0.00)
1.72
(0.00)
7.45
(0.00)
16.99
(0.00)
-0.62
(0.70)
3.57
(0.00)
0.21
(0.00)
0.07
(0.44)
0.72
(0.00)
0.14
(0.40)
1.89
(0.01)
1.95
(0.24)
-1.60
(0.20)
1.67
(0.00)
0.94
(0.00)
0.46
(0.00)
1.33
(0.00)
1.75
(0.00)
1.26
(0.00)
6.34
(0.00)
11.43
(0.00)
-1.87
(0.17)
3.16
(0.00)
0.74
(0.00)
0.38
(0.00)
0.95
(0.00)
0.87
(0.01)
2.07
(0.00)
6.12
(0.00)
19.69
(0.00)
-0.20
(0.88)
3.27
(0.00)
-0.12
(0.52)
-0.15
(0.01)
-0.64
(0.00)
1.01
(0.40)
-2.37
(0.07)
-0.41
(0.66)
1.14
(0.00)
0.22
(0.00)
0.74
(0.00)
1.00
(0.00)
2.17
(0.00)
4.18
(0.00)
17.64
(0.00)
-0.53
(0.68)
2.83
(0.00)
-0.25
(0.19)
-0.08
(0.36)
-1.56
(0.00)
-0.30
(0.82)
-8.08
(0.00)
-1.01
(0.32)
0.36
(0.22)
0.46
(0.00)
0.29

43 a
- +
=
5
5 a
6
= -
1 a a
1
84.24
(0.00)
243.94
(0.00) a a a a a a a
10
1 6
0.10
(0.75)
= = = = = = =
11 12 13 14 15 16
0
F-tests
2.91
(0.09)
3.22
(0.07)
0.30
(0.58)
70.70
(0.00)
119.46
(0.00)
2.69
(0.10)
81.83
(0.00)
135.05
(0.00)
0.01
(0.91)
7.03
71.42
(0.00)
111.66
(0.00)
0.05
(0.83)
(0.00)
Notes:
Model 1:
MV t
= a a
1
BV t
+ a
2
E t
+ a
3
CE t
+ a
4
RND t
+ a
5
RD t
+ a
6
SB t
+ a
7
CC t
+ e t
Model 2:
MV t
= a a
1
BV t
+ a
2
E t
+ a
3
CE t
+ a
4
RND t
+ a
5
RD t
+ a
6
SB t
+ a
7
CC t
+ a
8
LMV + e t
Model 3:
MV t
= a a
0 1
BV t
+ a
2
E t
+ a
3
CE t
+ a
4
RND t
+ a
5
RD t
+ a
6
SB t
+ a
7
CC t
+ a
9
OI t
+ e t
Model 4:
MV t
= a a
0 1
+ a
11
LE t
BV t
+ a
12
+ a
2
E t
LCE t
+
+ a
3
CE t
+ a a
13
LRND t
4
RND t
+ a
14
+ a
5
LRD t
+
RD t a
15
+ a
6
SB t
LSB t
+
+ a
16 a
7
CC t
LCC t
+
+ a
10
LBV t e t
Model 5:
MV t
= a a
0 1
+ a
11
LE t
BV t
+ a
12
+ a
2
E t
LCE t
+
+ a
3
CE t
+ a a
13
LRND t
4
RND t
+ a
14
+ a
5
LRD t
+
RD t a
15
+ a
6
SB t
LSB t
+
+ a
16 a
7
CC t
LCC t
+
+ a
10
LBV t a
17
LLMV t
+ e t
MV = Market Value; BV = Book Value; E = Earnings; CE =Capital Expenditures; RND = Research and
Development Expense; RD = Regular Dividends; SB = Share Buybacks; CC = Capital Contributions; OI =
‘Other Information’; the prefix L denotes one lag variables; and the prefix LL denotes two lag variables.

44
CC
LMV
OI
LBV
LE
Constant
BV
E
CE
RND
RD
SB
LCE
LRND
LRD
LSB
LCC
LLMV
LLBV
LLE
T ABLE 7
Estimates of Models 1 - 7 on All Available Firm Observations for 1996-2010
(9476 Firm-Years) – Book Value as Deflator
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Coefficients Coefficients Coefficients Coefficients Coefficients Coefficients Coefficient
(P>|t|)
1859.85
(0.00)
1.27
(0.00)
0.74
(0.05)
1.66
(0.00)
4.15
(0.00)
18.27
(0.00)
0.37
(0.78)
2.69
(0.00)
-0.59
(0.10)
0.08
(0.66)
-0.28
(P>|t|)
1418.87
(0.00)
1.01
(0.00)
0.85
(0.01)
1.75
(0.00)
1.92
(0.04)
16.66
(0.00)
0.00
(1.00)
2.32
(0.00)
-0.53
(0.04)
-0.18
(0.05)
-1.25
(P>|t|)
2057.15
(0.00)
1.24
(0.00)
0.67
(0.06)
1.70
(0.00)
4.12
(0.00)
18.81
(0.00)
0.42
(0.75)
2.83
(0.00)
-0.39
(0.19)
-0.19
(0.04)
-0.54
(P>|t|)
1949.28
(0.00)
1.42
(0.00)
1.64
(0.00)
1.05
(0.00)
5.21
(0.00)
10.47
(0.00)
-1.77
(0.17)
2.86
(0.00)
0.78
(0.00)
(0.97)
0.70
(0.25)
0.33
(0.84)
-1.49
(0.22)
1.26
(P>|t|)
904.61
(0.00)
0.14
(0.04)
0.64
(0.00)
0.01
(0.00)
0.96
(0.00)
(P>|t|)
2217.54
(0.00)
0.92
(0.00)
0.84
(0.00)
1.39
(0.00)
6.11
(0.00)
15.88
(0.00)
-0.24
(0.88)
3.39
(0.00)
1497.32
(0.00)
1.09
(0.00)
1.00
(0.01)
1.88
(0.00)
3.96
(0.00)
17.15
(0.00)
0.12
(0.92)
2.60
(0.00)
-0.51
(0.17)
0.18
(0.29)
-0.31
(0.01)
1.73
(0.22)
-2.03
(0.10)
-0.44
(0.57)
1.51
(0.00)
(0.00)
0.39
(0.76)
-8.73
(0.00)
-0.98
(0.31)
0.62
(0.09)
0.51
(0.00)
(0.22)
1.53
(0.51)
1.09
(0.42)
-0.68
(0.34)
1.59
(0.00)
0.18
(0.42)
-0.29
(0.25)
0.09
(0.97)
-0.46
(0.79)
-0.79
(0.32)
1.33
(0.00)
0.05
(0.83)
-0.29

45
LLCE
LLRND
LLRD
LLSB
LLCC
LLLMV
R-Square 0.19 a
- +
=
5
5 a
6
= -
1 a a
70.83
(0.00)
247.27
(0.00)
- + =-
1
1 0.01
(0.92) a a a a a a a
10
6
=
11
=
12
=
13
=
14
=
15
=
16
=
0 a
18
= a
19
= a
20
= a
21
= a
22
= a
23
= a
24
=
0
0.79
(0.37)
0.59
(0.44)
0.27
(0.60)
0.50 0.40
65.82
F-tests
(0.00)
91.91
(0.00)
2.87
(0.09)
73.77
(0.00)
140.77
(0.00)
0.02
(0.89)
4.82
(0.00)
0.21 0.31
62.49
(0.00)
126.16
(0.00)
0.00
(0.99)
Notes:
Model 1:
MV t
= a a
0 1
BV t
+ a
2
E t
+ a
3
CE t
+ a
4
RND t
+ a
5
RD t
+ a
6
SB t
+ a
7
CC t
+ e t
Model 2:
MV t
= a
0
+ a
1
BV t
+ a
2
E t
+ a
3
CE t
+ a
4
RND t
+ a
5
RD t
+ a
6
SB t
+ a
7
CC t
+ a
8
LMV t
+ e t
Model 3:
MV t
= a a
0 1
BV t
+ a
2
E t
+ a
3
CE t
+ a
4
RND t
+ a
5
RD t
+ a
6
SB t
+ a
7
CC t
+ a
9
OI t
+ e t
Model 4:
MV t
= a a
0 1
+ a
11
LE t
BV t
+ a
12
+ a
2
E t
LCE t
+
+ a
3
CE t
+ a a
13
LRND t
4
RND t
+ a
14
+ a
5
LRD t
+
RD t a
15
+ a
6
SB t
LSB t
+
+ a
16 a
7
CC t
LCC t
+
+ a
10
LBV t e t
Model 5:
MV t
= a a
0 1
+ a
11
LE t
BV t
+ a
12
+ a
2
E t
LCE t
+
+ a
3
CE t
+ a a
13
LRND t
4
RND t
+ a
14
+ a
5
LRD t
+
RD t a
15
+ a
6
SB t
LSB t
+
+ a
16 a
7
CC t
LCC t
+
+ a
10
LBV t a
17
LLMV t
+ e t
(0.03)
-0.34
(0.20)
-0.19
(0.91)
-2.79
(0.02)
0.83
(0.27)
0.51
(0.01)
0.21
72.29
(0.00)
138.33
(0.00)
0.01
(0.94)
28.94
(0.00)
75.27
(0.00)
144.67
(0.00)
0.00
(0.98)
(0.09)
-0.97
(0.01)
-0.78
(0.65)
-5.41
(0.00)
0.51
(0.43)
0.02
(0.91)
0.29
(0.00)
0.26

46
Model 6:
MV t
=
+
+ a a
0 a
10 a
16
1
BV t
LBV t
LCC t
+
+
+ a
11 a a
18
2
E t
LE t
+
+
LLBV t a
3
CE t a
12
+ a
19
+
LCE t a
+
LLE t
4
RND t
+ a
5
RD t a
13
+
LRND t a
+
20
LLCE t a
14
+
+ a
6
SB t
LRD t a
+ a
+
15 a
21
LLRND t
7
CC t
LSB t
+ a
22
LLRD t
+ a
23
LLSB t
+ a
24
LLCC t
+ e t
Model 7:
MV t
=
+
+ a a
0 a
10 a
16
1
BV t
LBV t
LCC t
+
+
+ a
11 a a
18
2
E t
LE t
+
+
LLBV t a
3
CE t a
12
+ a
19
+
LCE t a
+
LLE t
4
RND t
+ a
5
RD t a
13
+
LRND t a
+
20
LLCE t a
14
+
+ a
6
SB t
LRD t a
+ a
+
15 a
21
LLRND t
7
CC t
LSB t
+ a
22
LLRD t
+ a
23
LLSB t
+ a
24
LLCC tt
+ a
25
LLLMV t
+ e t
MV = Market Value; BV = Book Value; E = Earnings; CE = Capital Expenditures; RND = Research and
Development Expense; RD = Regular Dividends; SB = Share Buybacks; CC = Capital Contributions; OI =
‘Other Information’; the prefix L denotes one lag variables; the prefix LL denotes two lag variables; and the prefix LLL denotes three lag variables.