Description
The paper aims to examine the value relevance of alternative accounting performance
measures in Australia. It also documents the relative and incremental value relevance of revenue
earnings and the longitudinal changes in such value relevance. Finally, the impact of certain
firm characteristics including firm life cycle on the value relevance of revenue and earnings
information is investigated.
Accounting Research Journal
Value relevance of alternative accounting performance measures: Australian evidence
Ahsan Habib
Article information:
To cite this document:
Ahsan Habib, (2010),"Value relevance of alternative accounting performance measures: Australian
evidence", Accounting Research J ournal, Vol. 23 Iss 2 pp. 190 - 212
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Value relevance of alternative
accounting performance
measures: Australian evidence
Ahsan Habib
Auckland University of Technology, Auckland, New Zealand
Abstract
Purpose – The paper aims to examine the value relevance of alternative accounting performance
measures in Australia. It also documents the relative and incremental value relevance of revenue
vis-a` -vis earnings and the longitudinal changes in such value relevance. Finally, the impact of certain
?rm characteristics including ?rm life cycle on the value relevance of revenue and earnings
information is investigated.
Design/methodology/approach – The paper utilises data on Australian listed companies from
1992 to 2005 on the level of and changes in seven alternative accounting performance measures.
Standard ordinary least square regression is conducted.
Findings – Results reveal that: the coef?cient estimates on all the performance measures are much
higher for large ?rms compared to their small ?rm counterpart; the explanatory power of incremental
revenue in explaining stock returns has declined signi?cantly over the sample period; and life cycle
analysis shows that the combined coef?cients for both revenue and earnings are signi?cant in the
growth and maturity stages of the ?rm life cycle.
Practical implications – When making equity valuation decisions investors consider ?rms’
fundamentals as re?ected in ?nancial statements. However, which line item is more important for
equity valuation is an important consideration. From a regulatory perspective, this stream of research
is quite relevant because standard setters will have evidence from an investor viewpoint about
whether certain line items, subtotals, and totals should be de?ned in standards and required to be
displayed in ?nancial statements.
Originality/value – The paper adds to the existing capital market research in Australia by
documenting differential persistence of alternative performance measures.
Keywords Australia, Accountancy, Performance measures, Asset valuation, Capital markets
Paper type Research paper
1. Introduction
This paper examines the value relevance of alternative accounting performance
measures in Australia. Barton et al. (2008) estimate and compare the value relevance of
a comprehensive set of performance measures commonly disclosed in ?nancial
statements across 46 countries including Australia during 1996-2005 and report that
the value relevance of the performance measures varies substantially across line items
in the income statement, as well as across countries. The authors report that operating
income has the strongest association with contemporaneous stock returns; while
subtotals at the ends of the income statement, such as sales and total comprehensive
income, have the weakest association with stock returns.
This paper revisits and expands Barton et al. (2008) in the Australian context for a
number of reasons. First, the Barton et al. (2008) study, like other international
comparative studies, has the advantage of aggregating a large number of ?rm-year
observations, which increases the statistical power of the tests. However, combining
The current issue and full text archive of this journal is available at
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Accounting Research Journal
Vol. 23 No. 2, 2010
pp. 190-212
qEmerald Group Publishing Limited
1030-9616
DOI 10.1108/10309611011073269
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results from such a large and diverse set of countries also masks country-speci?c
idiosyncracies. Furthermore, they assemble required data from the Global Vantage
database, which is not as comprehensive as local databases (e.g. the Huntley Aspect
database in Australia) because the former database does not cover many small and
medium-sized listed companies. This is particularly important in the present study
because there is likely to be signi?cant differences in the persistence of individual
performance measures between large and small ?rms. The present study uses
Australian data that is twice the sample size of Barton et al. (2008). Second, USA
evidence on the time-series behavior of the value-relevance of accounting information
(particularly earnings) has produced inconclusive evidence. Collins et al. (1997) and Lev
and Zarowin (1999) provide strong evidence that accounting earnings is losing
value-relevance in the USA, and investors are focusing on alternative performance
measures, like revenue, to evaluate company performance (Chandra and Ro, 2008)[1].
Brimble and Hodgson (2007, p. 602) investigate inter-temporal value relevance of
accounting information in Australia hypothesising that:
[. . .] ?rm conditions, competitive and economic structures, and business culture, vary
signi?cantly in a global sense. Hence, there is no compelling reason to assume that the US
results will also hold in Australia.
Brimble and Hodgson (2007) fail to ?nd any systematic decline in the value-relevance of
earnings but do not examine the inter-temporal value relevance of revenue. Therefore, an
empirical examination of the changing value relevance of revenue in Australia would be
of interest. Third, although Australia has been studied in the comparative
value-relevance research, extant studies use a single performance measure and a
rather small Australian sample compared to the present study. For example, Hung
(2001), Ali and Hwang (2000), and DeFond et al. (2007), among others, investigate the
impact of shareholder protection on the properties of accounting information in a
number of countries, including Australia. Results reveal that countries with strong
investor protection and enforcement regimes (Australia, for example) provide
accounting information that is more strongly associated with market returns.
This paper also addresses the role of ?rm-speci?c characteristics, like ?rm size, ?rm
pro?tability and ?rm life cycle, in moderating the association between market returns
and alternative ?rm performance measures. Speci?cally, this paper addresses four
research questions:
RQ1. Which of the performance measures shows the strongest association with
market returns, and how did the documented association vary over time?
RQ2. Howdoes ?rmsize affect value relevance of alternative performance measures?
RQ3. Is there any incremental value relevance of revenue vis-a` -vis earnings and have
there been any longitudinal changes in such value relevance?
RQ4. How do ?rm pro?tability and ?rm life cycle characteristics affect the
incremental value relevance of revenue information? In addressing these
questions, this paper considers seven alternative performance measures
namely, total revenue (TOTREV), earnings before interest tax depreciation and
amortisation (EBITDA), operating income proxied by earnings before interest
and tax (EBIT), earnings before tax (EBT), net pro?t after tax but before
Accounting
performance
measures
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abnormal items (NPATEXABN), bottom line net income (NPAT) and
operating cash ?ows (OCF).
Results reveal that both EBTand NPAThave the highest explanatory power while EBT
has the largest combined coef?cient (sum of the coef?cient of the level and changes in
EBT). Surprisingly, the combined coef?cient for TOTREV is the lowest among all the
coef?cients, possibly implying lower persistence. Relative value relevance of alternative
performance measures based on size classi?cation reveals that coef?cient estimates on
all the performance measures are much higher for large ?rms compared to their small
?rmcounterpart implying greater persistence for the former group. Larger ?rms tend to
produce more persistent earnings information via smoothing of income as these ?rms
have larger portfolios of accounting choices (Hodgson and Stevenson-Clarke, 2000).
However, with respect to the adjusted R
2
s, small ?rms exhibit higher explanatory
power compared to large ?rms. This decline in adjusted R
2
from small to large
companies could be explained by the fact that information pertaining to larger ?rms is
readily available well before its actual announcement and is impounded into security
prices instantaneously in an ef?cient market. Regarding incremental value relevance of
revenue, the ?ndings reveal that the combined ability of earnings and revenues to
explain stock returns has not diminished, but the explanatory power of incremental
revenue in explaining stock returns has declined signi?cantly over the sample period.
However, this result is primarily driven by young ?rms. Finally, life cycle analysis
shows that the combined coef?cients for both revenue and earnings are signi?cant in the
growth and maturity stages of the ?rm life cycle.
The results of this study will be of practical use to prospective investors and
accounting regulators. When making equity valuation decisions investors consider
?rms’ fundamentals as re?ected in ?nancial statements. However, which line item is
more important for equity valuation is an important consideration. Because of
differences in persistence among performance measures, investors need to know which
of the measures is most strongly associated with investors’ beliefs as re?ected in stock
market returns. From a regulatory perspective, this stream of research is quite relevant
because standard setters will have evidence from an investor viewpoint “[. . .] whether
certain line items, subtotals, and totals should be de?ned in standards and required to
be displayed in ?nancial statements” (Financial Accounting Standards Board, 2001).
The paper proceeds as follows. The next section provides a brief literature review.
Section 3 explains the research design and sample selection procedure. Section 4
provides substantive test results and the ?nal section concludes.
2. Related literature
Accounting earnings is the premier information item provided in the ?nancial
statements (Lev, 1989). Beginning with the seminal work of Ball and Brown (1968) and
Beaver (1968), the last four decades of accounting research have produced a substantial
volume of work showing that the market reacts positively to positive earnings news
(earnings are value relevant) (see Kothari, 2001, for a review). However, published
research on the value relevance of other line items in the income statement is rather
limited. Available USA evidence generally is consistent with the view that
disaggregated earnings provide more value-relevant information in the marketplace,
and improve out-of-sample forecasting (Lipe, 1986; Wild, 1992; Barth et al., 1992;
Fair?eld and Yohn, 1996). Lipe (1986), for example, reveals that the components
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of earnings (namely, gross pro?ts, general and administrative expense, depreciation
expense, interest expense, income taxes, and other items) increase the explanatory
power for security returns signi?cantly. Fair?eld and Yohn (1996) analyze accuracy
improvements in out-of-sample forecasts of one-year-ahead return-on-equity. They ?nd
forecast improvements from progressively disaggregating earnings into the
components of operating income, non-operating income plus taxes, special items and
nonrecurring items.
Revenue is the ?rst line item in the income statement which arises in the course of
ordinary business activities and, therefore, is likely to be more persistent than other
measures. Published research on the value relevance of revenue is sparse relative to
research on earnings. Although earnings is widely considered to be the primary
summary measure of operating performance for ?rms, revenue may contain
incremental information beyond earnings that is relevant to future earnings and
cash ?ows predictions but lost when aggregated into earnings with expenses. Several
early studies (Hopwood and McKeown, 1985; Hoskin et al., 1986; Wilson, 1986) ?nd no
evidence that revenue conveys information beyond earnings. Swaminathan and
Weintrop (1991), however, ?nd that revenue surprises explain excess returns around
earnings announcements after controlling for earnings surprises[2]. Ghosh et al. (2005)
report that, for a subsample of ?rms with sustained earnings increases and sustained
revenue growth are associated with higher earnings response coef?cients (ERCs),
increased earnings persistence and reduced susceptibility to earnings management.
Ertimur et al. (2003) reveal that the market reaction to revenue surprises is greater than
to expense surprises, especially for growth ?rms, and that the differential price
reaction is associated with relative persistence. Jegadeesh and Livnat (2006) ?nd that
past revenues are informative in explaining future earnings after controlling for past
earnings. They further document that stock prices and analysts’ forecasts adjust to the
information conveyed by revenue over an extended period – of approximately
six months – after quarterly earnings announcements. Chandra and Ro (2008) show
that revenue is useful both as a summary measure for valuation purposes (has value
relevance) and in conveying new information (has information content) to the market,
after controlling for earnings information. These results are not driven by technology
?rms, extreme earnings news or loss situations, nor by model misspeci?cation because
of nonlinearities. They also report that while the combined ability of revenue and
earnings to summarise contemporaneous value relevant information has remained
stable over time, the value relevance of new information conveyed by earnings has
declined whereas the ability of revenue to incrementally convey new value relevant
information has not diminished.
Value relevance of accounting information, however, is expected to be determined
by ?rm-speci?c characteristics like ?rm size, ?rm pro?tability, ?rm leverage, ?rm life
cycle, and many others. Firm size has been extensively researched in explaining value
relevance of accounting information (Atiase, 1985). Larger ?rms are characterised by a
richer information environment and higher analyst following compared to their small
?rm counterparts. Because of the availability of more information for large ?rms prior
to earnings announcements, earnings announcements of larger ?rms tend to generate
fewer surprises in the marketplace and have a lower association with stock returns.
Firm pro?tability is also considered to be an important contextual factor that affects
the value relevance of accounting information differently. Extant research reports that
Accounting
performance
measures
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investors focus more on balance sheet information (e.g. equity book values) for the
equity valuation of ?rms reporting losses (Collins et al., 1999; Barth et al., 1998).
However, there is a paucity of research (Ghosh et al., 2005; Chandra and Ro, 2008 are
exceptions), and none in Australia, regarding investor valuation of revenue items when
?rms report negative earnings. Hayn (1995) calls for further research on the “degree of
substitution between earnings numbers and alternative accounting variables in loss
situations” (italics added). Finally, ?rm life cycle stages are used as important
?rm-speci?c characteristics that are likely to impact the value relevance of accounting
information differentially because of differences in persistence among ?rms at
different life cycle stages.
To sum up, available empirical evidence on the value-relevance of alternative
accounting performance measures comes primarily from the USA. This paper expands
this stream of research in Australia, where no evidence yet exists regarding the value
relevance of different line items in the income statement, except for net income
(Brimble and Hodgson, 2007). An additional contribution of this paper is to document
the relative and incremental value relevance of revenue information vis-a` -vis earnings
and the longitudinal changes in such value relevance. Finally, the impact of certain
?rm characteristics including ?rm life cycle on the value relevance of revenue versus
earnings information is investigated.
3. Research design and sample selection
The proxy for summary performance measure j’s value RELEVANCE is the adjusted
R
2
from the following regression:
RETURN
it
¼ g
0
þg
1
ðPerformance Measure jÞ
it
þg
2
ðDPerformance Measure jÞ
it
þg
3-13
YRDUM þg
14-31
INDUM þ1
it;
ð1Þ
where RETURN is ?rm i’s stock return for ?scal year t, adjusted for average market
return during the period[3], and performance measures j [ {TOTREV, EBITDA,
OPINC, EBT, NPATEXABN, NPAT, OCF} are de?ated by the lagged market value of
equity (MVE). The equation also includes change in performance measures as
explanatory variables following Easton and Harris (1991). A set of year and industry
dummy coef?cients are included in equation (1) to control for period and industry
effects. Larger values of RELEVANCE of the performance measure j imply that this
particular measure is more relevant for equity valuation.
To determine the incremental value relevance of revenue information vis-a` -vis
earnings, the following three regression equations are estimated:
RETURN
it
¼ a
0
þa
1
REVLEV
it
þa
2
REVGR
it
þa
3
EARLEV
it
þa
4
EARGR
it
þa
5-13
YRDUM þa
14-31
INDUM þ1
it
ð2Þ
where REVLEV and EARLEV are revenue and earnings levels, respectively, and
REVGR and EARGR are revenue and earnings growth, respectively. REVGR and
EARGR are calculated as the difference between current year revenue (earnings)
and last year revenue (earnings) divided by lagged MVE, respectively. EAR is de?ned
as operating earnings (EBIT). To compare the explanatory power that revenue and
earnings have for stock returns, a decomposition technique used in Easton (1985) is
employed. Total explanatory power (R
2
T
) is decomposed into three parts:
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(1) the incremental explanatory power of revenues (incremental REV);
(2) the incremental explanatory power of earnings (incremental EAR); and
(3) the explanatory power common to both revenues and earnings (incremental
COMMON).
Stated in equation form:
RETURN
it
¼ b
0
þb
1
REVLEV
it
þb
2
REVGR
it
þb
3-11
YRDUM
þb
12-29
INDUM þ1
it
ð3Þ
RETURN
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¼ z
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þz
1
EARLEV
it
þz
2
EARGR
it
þz
3-11
YRDUM
þz
12-29
INDUM þ1
it
ð4Þ
The coef?cients of determination from equations (2) to (4) are denoted as R
2
T,
R
2
REV
and R
2
EAR
, respectively. Then R
2
T
2 R
2
EAR
¼ INCREMENTAL REV and
R
2
T
2R
2
REV
¼ INCREMENTAL EAR. The remaining R
2
T
2 R
2
IncrREV
2 R
2
IncrEAR
equals the explanatory power common to both earnings and book values (COMMON).
The initial sample consists of 10,336 ?rm-year observations excluding ?nancial
institutions over a period of 1992 to 2005 and is retrieved from the Aspect Huntley
database. Missing MVE data reduces the sample by 1,927 ?rm-year observations.
MVE data is required to de?ate the performance measures. A further 57 observations
are lost due to missing market adjusted return data and, ?nally, missing performance
measures data reduces the sample size by a further 22 observations, resulting in a ?nal
usable sample of 8,330 ?rm-year observations. By comparison, Barton et al. (2008)
conduct their analysis using a sample size of 4,193 ?rm-year observations. There is wide
variation in the number of sample observations across the sample period, with 1992
having the lowest (136) while 2005 the highest (934) number of observations,
respectively. Table I presents descriptive statistics, industry composition of the selected
sample and correlation analysis among the variables. These statistics are computed
after winsorising the top and bottom 1 percent of the distribution of all the variables to
reduce the effect of outliers. Mean return is positively skewed (skewness ¼ 2.30), so is
TOTREV (skewness ¼ 3.45). However, NPATEXABN and NPAT are negatively
skewed (skewness ¼ 22.71 and 23.37, respectively), re?ecting large negative earnings
values. All the variables have high kurtosis (kurtosis coef?cients are in the range of
8.07-19.05). These statistics indicate that the distributions are not normally distributed
even after winsorising the variables. Panel B of Table I reports industry composition,
and clearly demonstrates that the Materials industry constitutes the largest number of
unique ?rms (338) and ?rm-year observations (30 percent of the total sample), followed
by the energy sector (8 percent of the total sample). The average life of the sample ?rms
is seven years (8,330/1,170). Panel C of Table I presents the correlation analysis. All the
reported correlations are statistically signi?cant at better than the 1 percent level. Some
of the performance measures are highly correlated among themselves, and therefore,
used separately in regression analysis to avoid the multicollinearity problem.
4. Substantive test results
4.1 Value relevance of alternative performance measures
Table II shows the pooled regression estimates of value-relevance of seven alternative
performance measures in terms of adjusted R
2
and coef?cient estimates (both level
Accounting
performance
measures
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1
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6
8
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o
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8
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7
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5
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6
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1
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7
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7
4
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1
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l
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2
8
0
3
.
3
6
M
a
t
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i
a
l
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3
8
2
,
5
0
3
3
0
.
0
5
M
e
d
i
a
3
7
2
9
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3
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4
8
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h
a
r
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a
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c
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l
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d
b
i
o
t
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c
h
n
o
l
o
g
y
5
6
3
2
1
3
.
8
5
(
c
o
n
t
i
n
u
e
d
)
Table I.
Descriptive statistics and
correlation analysis
ARJ
23,2
196
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
1
2
4
J
a
n
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a
r
y
2
0
1
6
(
P
T
)
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e
a
l
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s
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t
e
7
6
5
6
0
6
.
7
2
R
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t
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3
2
2
3
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2
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7
9
S
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1
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2
4
T
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4
1
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T
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:
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r
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l
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t
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l
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s
i
s
(
1
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(
2
)
(
3
)
(
4
)
(
5
)
(
6
)
(
7
)
(
8
)
(
9
)
(
1
0
)
(
1
1
)
(
1
2
)
(
1
3
)
(
1
4
)
(
1
5
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M
K
T
A
D
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1
)
1
.
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N
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:
T
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,
3
3
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c
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(
t
w
o
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t
a
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s
t
s
)
.
V
a
r
i
a
b
l
e
s
a
r
e
d
e
?
n
e
d
a
s
f
o
l
l
o
w
s
:
M
K
T
A
D
J
R
E
T
,
m
a
r
k
e
t
-
a
d
j
u
s
t
e
d
b
u
y
-
a
n
d
-
h
o
l
d
a
n
n
u
l
r
e
t
u
r
n
;
T
O
T
R
E
V
,
t
o
t
a
l
r
e
v
e
n
u
e
o
f
t
h
e
?
r
m
;
E
B
I
T
D
A
,
e
a
r
n
i
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g
s
b
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f
o
r
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t
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e
s
t
,
t
a
x
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,
d
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p
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c
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a
t
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o
n
,
a
n
d
a
m
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t
i
z
a
t
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o
n
;
O
P
I
N
C
,
o
p
e
r
a
t
i
n
g
i
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c
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m
e
;
E
B
T
,
e
a
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n
i
n
g
s
b
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f
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r
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t
a
x
;
N
P
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A
B
N
,
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;
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,
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t
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;
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C
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,
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a
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s
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;
a
l
l
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p
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n
d
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t
v
a
r
i
a
b
l
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a
v
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n
d
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?
a
t
e
d
b
y
l
a
g
g
e
d
M
V
E
Table I.
Accounting
performance
measures
197
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
as well as changes) based on equation (1). The regression also includes year and
industry dummies to control for the possible year and industry effects. Reported
results reveal that the value relevance of the EBIT and NPAT measures command the
highest explanatory power of 8.2 percent, while the OCF measure has the lowest
adjusted R
2
of 6.9 percent. The reported adjusted R
2
for the earnings measure is
comparable to that of Easton and Harris (1991) who ?nd an average R
2
of 7.8 percent
when security return is regressed on both the level of, and changes in, earnings. With
respect to the value relevance of revenue information, Chandra and Ro (2008) report an
adjusted R
2
of 4.3 percent when both the revenue and earnings level, and growth,
variables are included in the same regression whereas this study reports a much higher
adjusted R
2
when revenue variables alone are included in the regression estimation.
With respect to the coef?cient estimates, Table II shows that all the combined
coef?cients are positive, and statistically signi?cant at better than 1 percent level.
The combined coef?cients on EBT measure is the largest among all the performance
measures (a combined coef?cient of 0.37). The combined coef?cient estimate for the
revenue variable is very similar to that reported by Chandra and Ro (2008) (Figure 1).
4.2 Firm size and value relevance of alternative performance measures
This section provides evidence on how ?rm size affects the relative value relevance of
alternative performance measures. Firm size has been extensively used in capital
market research as a conditioning variable in explaining many ?nancial reporting
outcome effects. Firm-year observations are classi?ed into small, medium, and large
?rm size groups based on yearly distribution of market value of equities. Then
market-adjusted buy-and-hold annual stock returns are regressed on alternative
performance measures in each of the size categories, and the resulting coef?cients and
adjusted R
2
s are reported in Table III.
Coef?cient estimates on all the performance measures are much larger for large
?rms compared to their small ?rm counterparts implying greater persistence for the
former group. For example, the combined coef?cient estimates for the revenue variable
Performance measures Intercept g
1
g
2
[g
1
þ g
2
] Adjusted R
2
TOTREV 0.20
*
(3.28) 0.02
*
(6.10) 0.08
*
(6.69) 0.09
*
(8.18) 0.077
EBITDA 0.22
*
(3.45) 0.13
*
(3.77) 0.23
*
(6.40) 0.36
*
(9.00) 0.08
EBIT [OPINC] 0.22
*
(3.52) 0.13
*
(3.72) 0.21
*
(6.40) 0.34
*
(8.50) 0.08
EBT 0.24
*
(3.91) 0.13
*
(3.78) 0.24
*
(7.01) 0.37
*
(9.00) 0.082
NPATEXABN 0.25
*
(4.05) 0.12
*
(3.32) 0.23
*
(6.65) 0.34
*
(9.10) 0.079
NPAT 0.25
*
(4.07) 0.10
*
(4.00) 0.10
*
(5.33) 0.20
*
(7.81) 0.082
OCF 0.24
*
(3.69) 0.14
*
(3.73) 0.11
*
(2.94) 0.25
*
(6.23) 0.069
Observations 8,330
Industry dummies Included
Year dummies Included
Note: Sigini?cant at:
*
1 percent level
RETURN
it
¼ g
0
þg
1
ðPerformance Measure jÞ
it
þg
2
ðDPerformance Measure jÞ
it
þg
3ÿ13
YRDUM þg
14ÿ31
INDUM þ1
it
ð1Þ
Table II.
Pooled regression results
of the value relevance of
alternative accounting
performance measures
ARJ
23,2
198
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
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is 0.17 for large ?rms as compared to only 0.04 for small ?rms. Similarly, large (small)
?rms report combined coef?cients of 0.59 (0.11) for the NPAT variable, respectively.
Larger ?rms tend to produce more persistent earnings information via smoothing of
income as these ?rms have a larger portfolio of accounting choices (Hodgson and
Stevenson-Clarke, 2000). However, the opposite picture emerges with respect to the
adjusted R
2
s where performance measures of small ?rms exhibit higher explanatory
power with respect to security returns compared to large ?rms. This decline in the
adjusted R
2
from small to large ?rms may be explained by the fact information
pertaining to larger ?rms is readily available well before the actual announcement, and
is impounded into security prices. This is more applicable in Australia which is
characterised by a “continuous disclosure regime”.
4.3 Value relevance of revenue
This section provides empirical evidence on the ability of revenue to explain stock
returns beyond earnings. As mentioned in the literature review section, published
research on the value relevance of revenue is sparse relative to research on earnings,
and is based primarily on the USA market. Table IV presents the Australian evidence
on the relative and incremental value relevance of revenue vis-a` -vis earnings. Pooled
regression results with respect to equation (2) reveal that all four regression variables
representing earnings and revenue levels and growth, enter the regression equation
with positive coef?cients (columns 2-6). They are statistically signi?cant at better than
the 5 percent level and explain about 9 percent of the variation in market-adjusted stock
returns. Combined earnings coef?cients, however, are much higher than their revenue
counterparts (0.31 versus 0.09, respectively). Columns (7-9) report regression results of
equation (3). Both the REVLEV and REVGR variables are positive and statistically
signi?cant at better than 1 percent level. The regression result of equation (4) in
columns (10-12) reveals that the EARLEV and EARGR are also positive and
statistically signi?cant, and both the models explain about 8 percent of the variation
Figure 1.
Value-relevance of
alternative performance
measures, proxied by
adjusted R
2
from the
regression of
market-adjusted returns
on seven accounting
performance measures in
Australia is depicted on
the vertical axis
Value-relevance of performance measures
0.085
0.08
0.075
0.07
0.065
0.06
0.077
0.08 0.08
0.082
0.079
0.082
0.069
Adjusted R
2
TOTREV EBITDA OPINC EBT NPATEXABN NPAT OCF
Accounting
performance
measures
199
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Table III.
Firm size and the relative
value relevance of
alternative performance
measures
ARJ
23,2
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h
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n
R
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R
¼
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2
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¼
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T
A
L
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Table IV.
Incremental
value-relevance of
revenue and earnings
information
Accounting
performance
measures
201
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
in market returns. However, the pooled time-series cross-sectional regressions suffer
from a lack of independence in the residuals. This causes substantially understated
standard errors or in?ated t-statistics. To alleviate this problem, the average
coef?cients are divided by the average standard errors to calculate the t-statistics, in
accordance with the Fama and Macbeth (1973) approach. The reported results show
both REVLEV and EARLEV lose signi?cance in all speci?cations while REVGR and
EARGR continue to be statistically signi?cant.
Figure 2 shows the trend in combined and incremental value relevance of revenue
and earnings over 1992-2005. For the ?rst four years of the sample period the trend in
the incremental revenue R
2
and the combined value relevance R
2
track each other
closely. However, from 1996 onwards the incremental earnings R
2
starts to mirror the
changes in the combined value relevance R
2
.
With respect to the longitudinal changes in the value relevance of revenue, Chandra
and Ro (2008) report that while the combined ability of revenue and earnings to
summarise contemporaneous value relevant information has remained stable over
time, the new information conveyed by earnings has declined whereas the ability of
revenue to incrementally convey new information has not diminished. To estimate the
longitudinal changes in value relevance of earnings and revenue variables, regression
equations (2)-(4) are estimated cross-sectionally each year. Then a series of regression
models are estimated (equation 5) using the regression estimates from these equations:
EST
jy
¼ z
0j
þz
1j
YEAR
jy
þe
jy;
j ¼ 1; 2; . . .; 9 ð5Þ
where j denotes a particular parameter from equations (2) to (4). For example, the ?rst
parameter is REVLEV which is 14 yearly coef?cients from 1992 to 2005 from
equation (1).
Table Vpresents longitudinal changes in the value relevance of earnings and revenue
information in Australia from 1992 to 2005. When the combined R
2
from regression
equation (2) is regressed on the TIME variable, the coef?cient estimate on TIME
is 20.0031 implying that the combined ability of earnings and revenue to explain
market adjusted return has declined over the sample period but the decline is not
Figure 2.
Trend in the explanatory
power of the combined,
incremental revenue, and
incremental earnings with
respect to market-adjusted
returns over 1992-2005
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
–0.02
1 2 3 4 5 6 7 8 9 10 11 12 14 13
Year
A
d
j
u
s
t
e
d
R
2
R
2
_COMBINED Incr_REV Incr_EAR
ARJ
23,2
202
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
statistically signi?cant (t-statistic ¼ 21.51). Interestingly, the incremental explanatory
power of revenue (INCREMENTAL REV) has declined signi?cantly over the sample
period (coef?cient 20.0027, t-statistic ¼ 23.09, p-value , 0.001), and so has the
annual revenue coef?cients [(REVLEV þ REVGR) [coef?cient value 20.0067,
t-statistic ¼ 21.90, signi?cant at better than the 10 percent level]]. One possible
reason for such a ?nding may relate to the persistence of revenue reported in the income
statement. The sample for the current study contains both mature, established ?rms as
well as relatively young ?rms. Persistence of revenue reported by more mature ?rms is
likely to be higher compared to their younger ?rm counterparts because of the
signi?cant environmental uncertainties faced by the latter group of companies.
All regressions reported in Table Vare rerun for companies surviving for at least nine of
the fourteen sample years. This choice, though arbitrary, provides a sample of relatively
established ?rms. Untabulated results show that when INCREMENTAL REV is
regressed on TIME for this sub-sample, the coef?cient on TIME still remains negative
(20.0032). However, unlike that of the full sample, it becomes insigni?cant. Similarly,
when annual revenue coef?cients [REVLEV þ REVGR] are regressed on TIME, the
coef?cient on TIME is again insigni?cant though negative in sign. This additional
analysis con?rms the argument that the decline in revenue reported for Australian
companies is primarily due to the inclusion of many young and small companies in the
total sample.
Measures Intercept Coef?cient Adjusted R
2
Adjusted R
2
_Eq(2) (g
7
) 0.08
*
[4.00] 20.0031 [21.51] 0.003
INCR_ REV R
2
(g
8
) 0.04
*
[3.99] 20.0027
*
[23.09] 0.21
INCR_ EAR R
2
(g
9
) 0.04
* * *
[1.93] 20.0010 [20.56] 20.07
REVLEV (g
1
) 0.02 [1.62] 20.0014 [21.35] 20.0053
REVGR (g
2
) 0.13
*
[2.91] 20.0054 [21.35] 0.02
EARLEV (g
3
) 20.26 [22.02] 0.033
*
[3.12] 0.10
EARGR(g
4
) 0.67
* *
[2.19] 20.04 [21.44] 0.08
REVLEV þ REVGR (g
5
) 0.15
*
[3.65] 20.0067
* * *
[21.90] 0.08
EARLEV þ EARGR (g
6
) 0.41 [1.34] 20.0054 [20.20] 20.08
Notes: Signi?cance at:
*
1,
* *
5, and
* * *
10 per cent levels, respectively; coef?cients g
1
to g
6
are
derived from equation (2) and regressed on TIME which takes a value of 1 for 1992, 2 for 1993 and so
on to 2005. Coef?cient g
7
is the adjusted R
2
from equation (2) regressed on TIME. Coef?cients are
derived from a decomposition technique used in Easton (1985). Total explanatory power (R
2
T
) is
decomposed into three parts: 1 – the incremental explanatory power of revenues (incremental REV); 2
– the incremental explanatory power of earnings (incremental EAR); and 3 – the explanatory power
common to both revenues and earnings (incremental COMMON). The coef?cients of determination
from equations (2) to (4) are denoted as R
2
T,
R
2
REV
, and R
2
EAR
, respectively. Then R
2
T
2R
2
EAR
¼
INCREMENTAL REV and R
2
T
2R
2
REV
¼ INCREMENTAL EAR. To estimate the longitudinal
changes in value relevance of earnings and revenue variables, regression equations (2) –(4) are
estimated cross-sectionally each year. Then a series of regression models are estimated using the
regression estimates from these equations:
EST
jy
¼ z
oj
þz
1j
YEAR
jy
þe
jy;
j ¼ 1; 2; . . .; 9 ð5Þ
where j denotes a particular parameter from equations (2) to (4). For example, the ?rst parameter is
REVLEV which is 14 yearly coef?cients from 1992 to 2005 from equation (1)
Table V.
Longitudinal changes in
the value relevance of
revenue and earnings
Accounting
performance
measures
203
D
o
w
n
l
o
a
d
e
d
b
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P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
4.4 Firm pro?tability and the value relevance of revenue
Hayn (1995) shows that ?rms reporting negative earnings have smaller ERCs. She
argues that this is because shareholders always have the option to liquidate a ?rm, and
as a result, negative earnings are transitory in nature. She also calls for additional
research to examine the role of alternative accounting information in equity valuation
when companies report negative earnings. Subsequent research has demonstrated that
when earnings are negative and ?rms face ?nancial distress, investors place more
weight on book value than on earnings (Barth et al., 1998; Collins et al., 1999; Burgstahler
and Dichev, 1997). However, the role of revenue in the context of ?rm pro?tability has
recently become the subject of academic scrutiny (Ghosh et al., 2005; Chandra and
Ro, 2008) but remains unexplored in Australia. This is particularly important given that
about 48 percent of the sample observations represent loss ?rms which are quite
substantial, and there has been a steady increase in the number of ?rms reporting losses
over the sample year with the ratio monotonically increasing from 32 percent in 1994 to
56 percent in 2002.
Table VI presents the regression results of equations (2)-(4) for pro?t and loss
sub-samples. For both sub-samples, the combined revenue coef?cient is positive and
statistically signi?cant although it is marginally higher for loss sub-sample (0.07 versus
0.05). On the other hand, the combined earnings coef?cient of the pro?t sub-sample is
signi?cantly higher than that of its loss sub-sample counterparts (1.69 versus 0.03,
respectively) consistent with the ?ndings of earlier research. This result, therefore,
provides weak evidence of the hypothesis that investors focus more on revenue
information in valuing equity when ?rms report negative earnings.
4.5 Firm life cycle stages and the value relevance of revenue
Firm life cycle is comprised of distinct phases, like introduction, growth, maturity, and
decline, and ?rms progress through these phases as a result of strategic decision-making
and competitive environments. Anthony and Ramesh (1992) is one of the ?rst studies in
accounting to use the life cycle hypothesis in explaining stock market responses to two
accounting performance measures: sales growth and capital investment. Using dividend
payout, sales growth and ?rm size as the proxies for ?rm life cycle, they ?nd that
response coef?cients of unexpected sales growth and unexpected capital expenditures
decline monotonically fromthe growth to the stagnant stages. Black (1998) examines the
value relevance of earnings and the components of cash ?ows in each of four life cycle
stages: start up, growth, maturity, and decline. Black ?nds that neither net income nor
cash ?ow from operations (CFO) is value relevant in the start-up phase. CFO becomes a
signi?cant explanatory variable in each of the growth, maturity, and decline phase.
Net income is signi?cant only in the maturity stage. Dickinson (2007) develops and
validates a parsimonious proxy for ?rm life cycle based on the patterns of a ?rm’s
operating, investing and ?nancing cash ?ows.
This paper adopts the life cycle methodology developed by Dickinson because the other
life cycle proxies inherentlyassume a uniformdistributionof life cycle stages (Anthonyand
Rameh 1992, for example). The combination of cash ?ow patterns used by Dickinson, on
the other hand, “represents the ?rm’s resource allocation and operational capabilities
interacted with the ?rm’s choice in strategy”. Four life cycle stages based on the ?rm’s
operating, investing, and ?nancing cash ?ows are developed. The introduction stage is
characterised by negative operating, negative investing, and positive ?nancing cash ?ows.
ARJ
23,2
204
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
V
a
r
i
a
b
l
e
s
L
o
s
s
?
r
m
s
P
r
o
?
t
?
r
m
s
I
N
T
E
R
C
E
P
T
(
a
0
)
0
.
0
8
(
0
.
6
2
)
0
.
1
0
(
0
.
7
5
)
0
.
0
9
(
0
.
5
0
)
0
.
0
9
(
1
.
5
3
)
0
.
2
3
*
(
3
.
1
1
)
0
.
0
8
(
1
.
2
9
)
R
E
V
L
E
V
(
a
1
)
0
.
0
1
(
1
.
5
7
)
0
.
0
2
*
*
*
(
1
.
9
2
)
–
2
0
.
0
0
6
9
(
2
1
.
5
4
)
0
.
0
2
*
(
4
.
9
9
)
–
R
E
V
G
R
(
a
2
)
0
.
0
5
*
(
2
.
8
3
)
0
.
0
4
*
*
(
2
.
3
5
)
–
0
.
0
6
*
(
3
.
5
7
)
0
.
0
9
*
(
8
.
0
1
)
–
E
A
R
L
E
V
(
a
3
)
2
0
.
1
0
*
*
(
2
2
.
4
5
)
–
2
0
.
1
0
*
(
2
2
.
6
3
)
1
.
6
3
*
(
1
0
.
4
5
)
–
1
.
6
8
*
(
1
0
.
9
3
)
E
A
R
G
R
(
a
4
)
0
.
1
3
*
(
3
.
6
7
)
–
0
.
1
2
*
(
3
.
3
6
)
0
.
0
6
(
0
.
7
1
)
–
0
.
0
5
(
0
.
6
8
)
A
d
j
.
R
2
0
.
1
0
0
.
0
9
0
.
0
9
0
.
1
7
0
.
0
8
0
.
1
7
(
a
1
þ
a
2
)
0
.
0
7
*
[
2
.
9
9
]
0
.
0
6
*
*
[
2
.
5
4
]
–
0
.
0
5
*
[
3
.
3
2
]
0
.
1
0
*
[
1
0
.
2
8
]
–
(
a
3
þ
a
4
)
0
.
0
3
[
0
.
7
5
]
–
0
.
0
2
[
0
.
3
5
]
1
.
6
9
*
[
1
0
.
5
6
]
–
1
.
7
3
*
[
1
1
.
5
3
]
O
b
s
e
r
v
a
t
i
o
n
s
4
,
0
0
3
4
,
0
0
3
4
,
0
0
3
4
,
3
2
7
4
,
3
2
7
4
,
3
2
7
Y
e
a
r
a
n
d
i
n
d
u
s
t
r
y
d
u
m
m
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s
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d
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N
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:
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t
:
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1
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5
,
a
n
d
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*
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1
0
p
e
r
c
e
n
t
l
e
v
e
l
s
,
r
e
s
p
e
c
t
i
v
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l
y
;
?
r
m
s
w
i
t
h
p
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s
i
t
i
v
e
(
n
e
g
a
t
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)
o
p
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r
a
t
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i
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c
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a
r
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p
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t
(
l
o
s
s
)
?
r
m
s
,
r
e
s
p
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c
t
i
v
e
l
y
.
T
o
d
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t
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r
m
i
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t
h
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m
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i
s
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a `
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v
i
s
e
a
r
n
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g
s
,
t
h
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f
o
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o
w
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n
g
t
h
r
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r
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s
s
i
o
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e
q
u
a
t
i
o
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s
a
r
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e
s
t
i
m
a
t
e
d
:
R
E
T
U
R
N
i
t
¼
a
0
þ
a
1
R
E
V
L
E
V
i
t
þ
a
2
R
E
V
G
R
i
t
þ
a
3
E
A
R
L
E
V
i
t
þ
a
4
E
A
R
G
R
i
t
þ
1
;
ð
2
Þ
R
E
T
U
R
N
i
t
¼
b
0
þ
b
1
R
E
V
L
E
V
i
t
þ
b
2
R
E
V
G
R
i
t
þ
1
;
ð
3
Þ
R
E
T
U
R
N
i
t
¼
z
0
þ
z
1
E
A
R
L
E
V
i
t
þ
z
2
E
A
R
G
R
i
t
þ
1
;
ð
4
Þ
w
h
e
r
e
R
E
V
L
E
V
a
n
d
E
A
R
L
E
V
a
r
e
r
e
v
e
n
u
e
a
n
d
e
a
r
n
i
n
g
s
l
e
v
e
l
s
,
r
e
s
p
e
c
t
i
v
e
l
y
,
a
n
d
R
E
V
G
R
a
n
d
E
A
R
G
R
a
r
e
r
e
v
e
n
u
e
a
n
d
e
a
r
n
i
n
g
s
g
r
o
w
t
h
,
r
e
s
p
e
c
t
i
v
e
l
y
.
L
a
t
t
e
r
t
w
o
v
a
r
i
a
b
l
e
s
a
r
e
c
a
l
c
u
l
a
t
e
s
a
s
t
h
e
d
i
f
f
e
r
e
n
c
e
b
e
t
w
e
e
n
c
u
r
r
e
n
t
y
e
a
r
r
e
v
e
n
u
e
(
e
a
r
n
i
n
g
s
)
a
n
d
l
a
s
t
y
e
a
r
r
e
v
e
n
u
e
(
e
a
r
n
i
n
g
s
)
d
i
v
i
d
e
d
b
y
l
a
g
g
e
d
M
V
E
.
E
A
R
i
s
d
e
?
n
e
d
a
s
o
p
e
r
a
t
i
n
g
e
a
r
n
i
n
g
s
(
E
B
I
T
)
.
T
o
c
o
m
p
a
r
e
t
h
e
e
x
p
l
a
n
a
t
o
r
y
p
o
w
e
r
t
h
a
t
r
e
v
e
n
u
e
a
n
d
e
a
r
n
i
n
g
s
h
a
v
e
f
o
r
s
t
o
c
k
r
e
t
u
r
n
,
a
d
e
c
o
m
p
o
s
i
t
i
o
n
t
e
c
h
n
i
q
u
e
u
s
e
d
i
n
E
a
s
t
o
n
(
1
9
8
5
)
i
s
e
m
p
l
o
y
e
d
.
T
o
t
a
l
e
x
p
l
a
n
a
t
o
r
y
p
o
w
e
r
(
R
2T
)
i
s
d
e
c
o
m
p
o
s
e
d
i
n
t
o
t
h
r
e
e
p
a
r
t
s
:
1
–
t
h
e
i
n
c
r
e
m
e
n
t
a
l
e
x
p
l
a
n
a
t
o
r
y
p
o
w
e
r
o
f
r
e
v
e
n
u
e
s
(
i
n
c
r
e
m
e
n
t
a
l
R
E
V
)
;
2
–
t
h
e
i
n
c
r
e
m
e
n
t
a
l
e
x
p
l
a
n
a
t
o
r
y
p
o
w
e
r
o
f
e
a
r
n
i
n
g
s
(
i
n
c
r
e
m
e
n
t
a
l
E
A
R
)
;
a
n
d
3
–
t
h
e
e
x
p
l
a
n
a
t
o
r
y
p
o
w
e
r
c
o
m
m
o
n
t
o
b
o
t
h
r
e
v
e
n
u
e
s
a
n
d
e
a
r
n
i
n
g
s
(
i
n
c
r
e
m
e
n
t
a
l
C
O
M
M
O
N
)
.
T
h
e
c
o
e
f
?
c
i
e
n
t
s
o
f
d
e
t
e
r
m
i
n
a
t
i
o
n
f
r
o
m
e
q
u
a
t
i
o
n
s
(
2
)
t
o
(
4
)
a
r
e
d
e
n
o
t
e
d
a
s
R
2T
,
R
2R
E
V
,
a
n
d
R
2E
A
R
,
r
e
s
p
e
c
t
i
v
e
l
y
.
T
h
e
n
R
2T
2
R
2E
A
R
¼
I
N
C
R
E
M
E
N
T
A
L
R
E
V
a
n
d
R
2T
2
R
2R
E
V
¼
I
N
C
R
E
M
E
N
T
A
L
E
A
R
Table VI.
Firm pro?tability and
incremental value
relevance of revenue
Accounting
performance
measures
205
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
The growth phase is characterised by positive operating, negative investing, and positive
?nancing cash ?ows. The mature stage re?ects positive operating, negative investing, and
negative ?nancing cash ?ows. Finally, the decline stage is characterised by negative
operating, positive investing, and positive/negative ?nancing cash ?ows patterns.
The regression results reported in Table VII reveal that combined revenue and
earnings coef?cients are statistically highly signi?cant only in the growth stage. Firms
with signi?cant growth opportunities are likely to have experienced positive unexpected
(abnormal) earnings in recent periods which are expected to persist. Additionally,
growth stage ?rms may be associated with positive net present value investment
opportunities, and unexpected revenue as well as earnings may be used as a proxy to
infer changes in market expectations about such opportunities (Charitou et al., 2001).
Firms in the growth stage continue to invest not only in ?nancial and tangible assets, but
also in organisational capital which allow a ?rm to earn temporary monopoly rents.
Growth ?rms, however, are more likely to manipulate reported information for the
purpose of avoiding a negative stock price reaction. For example, Skinner and Sloan
(2002, p. 299) ?nd that growth ?rms missing analysts’ forecasts by 0.5 percent of the
stock price suffer a signi?cantly negative abnormal return of 210 to 215 percent.
However, ?rms in the growth phase need to signal private information to the
marketplace, because it is relatively dif?cult for outsiders to monitor operating
performance of such ?rms owing to the unique nature of the assets these ?rms
possess (intangible-intensive industries). Therefore, managers could judiciously use
Variables Introduction Growth Maturity Decline
INTERCEPT (a
0
) 0.52
*
(2.95) 0.21
*
(2.64) 0.07 (1.34) 0.58
*
(2.79)
REVLEV (a
1
) 20.11
* * *
(21.75) 0.00078 (0.09) 0.16
*
(3.14) 0.0013 (0.09)
REVGR (a
2
) 0.02 (1.03) 0.18
*
(4.79) 0.02 (1.03) 0.04 (1.52)
EARLEV (a
3
) 20.04 (20.83) 0.16 (1.16) 0.49
*
(3.44) 0.06 (0.73)
EARGR (a
4
) 0.16
*
(2.76) 0.34
*
(2.82) 0.27
*
(2.36) 0.03 (0.61)
Adjusted R
2
0.08 0.13 0.15 0.11
(a
2þ
a
3
) 20.09 [20.50] 0.18
*
[4.86] 0.19
* * *
[1.81] 0.043 [1.41]
(a
4þ
a
5
) 0.12
* * *
[1.93] 0.50
*
[3.27] 0.76
*
[4.47] 0.09 [1.06]
Year and industry
dummies Included Included Included Included
Observations 2,461 1,692 2,113 848
Notes: Signi?cance at:
*
1,
* *
5, and
* * *
10 per cent levels, respectively; the table reports coef?cient
estimates and t-statistics from the following regression equation:
RETURN
it
¼ a
0
þa
1
REVLEV
it
þa
2
REVGR
it
þa
3
EARLEV
it
þa
4
EARGR
it
þ1; ð2Þ
Firm life cycle is operationalised following Dickinson (2007) model. She develops four life cycle stages
based on the patterns of a ?rm’s operating, investing and ?nancing cash ?ows. Introduction stage is
characterised by negative operating, negative investing, and positive ?nancing cash ?ows. Growth
phase is characterised by positive operating, negative investing, and positive ?nancing cash ?ows.
Mature stage re?ects positive operating, negative investing, and negative ?nancing cash ?ows.
Finally, decline stage is characterised by negative operating, positive investing and positive/negative
?nancing cash ?ows patterns. REVLEV and EARLEV are revenue and earnings levels, respectively,
and REVGR and EARGR are revenue and earnings growth, respectively, calculated as the difference
between current year revenue (earnings) and last year revenue (earnings) divided by lagged MVE.
EAR is de?ned as operating earnings (EBIT)
Table VII.
Firm life cycle stages and
incremental value
relevance of revenue
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the reporting ?exibilities offered by the generally accepted accounting principles
(GAAP) to increase the value-relevance of accounting information primarily via the use
of discretionary accruals (DACCR). Although these competing hypotheses have not been
tested in this paper, the sign of the coef?cients suggests an ef?cient use of DACCR by
managers of growth ?rms.
In the introduction stage of the life cycle the combined earnings coef?cients are
marginally signi?cant while that of revenue is insigni?cant implying that ?rms make
investments in the introduction phase with the expectation of generating more revenues
and earnings in the growth phase. The combined earnings coef?cient is highest in the
maturity stage (0.76) and this life cycle stage also results in the highest explanatory
power for market adjusted returns. Combined revenue coef?cient is positive and
statistically signi?cant at better than the 1 percent level in both the growth and maturity
stages of the ?rm life cycle (0.18 and 0.19, respectively). Consistent with predictions,
none of the earnings and revenue variables is statistically signi?cant inthe decline stage.
Anthony and Ramesh (1992) report the largest coef?cient on the sales growth variable
during the growth phase (a coef?cient of 0.21), but this monotonically decreases
to 20.0022 in the stagnant phase. Overall, evidence clearly suggests differential pricing
of earnings and revenue information in different life cycle stages.
4.6 Robustness checks
4.6.1. Test of non-linearity. The main results reported in the study assume a linear
association between market returns and the unexpected accounting performance
measures. However, prior capital market research has shown that the relationship
betweenunexpectedearnings andmarket returns assumes a non-linear S-shapedpattern
(Freeman and Tse, 1992). This is because tails of the unexpected earnings distributions
are dominated by transitory earnings which are dif?cult to forecast and therefore
generate lower marginal price response to unexpected earnings shock. To test for the
non-linear effect in the association between market return and unexpected performance
measures The following regression adapted from Chandra and Ro (2008) is estimated:
RETURN
it
¼ g
0
þg
1
ðPerformance Measure jÞ
it
þg
2
ðDPerformance Measure jÞ
it
þg
3
ðNLDPerformance Measure jÞ
it
þg
4-14
YRDUM
þg
15-32
INDUM þ1
it;
ð1aÞ
where NLDPerformance Measure j is the product of DPerformance Measure j and the
absolute value of DPerformance Measure j. If the association between market return and
performance measures is S-shaped then the coef?cient g
3
will be negative. Unreported
results indeed ?nd that the coef?cients are all negative and statistically signi?cant, with
the exception of the OCF variable, con?rming the non-linear relationship. To further
explore the non-linear association between market returns and unexpected revenue in
the presence of unexpected earnings, the following modi?ed non-linear regression is
estimated:
RETURN
it
¼ a
0
þa
1
REVLEV
it
þa
2
REVGR
it
þa
3
NLREVGR
it
þa
4
EARLEV
it
þa
5
EARGR
it
þa
6
NLEARGR
it
þa
7-15
YRDUM þa
16-33
INDUM þ1
it
ð2aÞ
Accounting
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Again, a non-linear relationshipwouldresult inthe a
3
anda
6
coef?cients beingnegative.
Unreported results ?nd evidence consistent with a non-linear relationship. In particular,
the coef?cients on a
3
and a
6
are 20.03 and 20.25, respectively, which are statistically
signi?cant at better than the 1 percent level.
4.6.2 Alternative return measure. As an alternative market measure to
MKTADJRET, buy-and-hold annual returns (BHRET) are used and the results are
almost identical to those previously reported. This is not surprising given that the
pair-wise correlation between the two market return measures is 0.98.
5. Concluding remarks
Financial statements provided by corporate managers contain an array of ?rm
performance measures for current and prospective investors to evaluate in investment
decision making. Market reaction to different performance line items could be different
because of differences in persistence among the measures, as well as differences in ?rm
characteristics that could make one performance measure superior to another. This
paper attempts to shed light on the relative superiority of alternative accounting
performance measures in Australia. Results reveal that EBITDA has the highest
explanatory power, followed by TOTREV number. With respect to the impact of ?rm
size, this study ?nds that for small and medium sized ?rms TOTREV has the highest
explanatory power, while the corresponding variable for large ?rms is OPINC.
Regarding the incremental value relevance of revenue in explaining contemporaneous
stock returns, this paper shows that the combined ability of earnings and revenue to
explain stock returns has not diminished, but the explanatory power of incremental
revenue has signi?cantly declined over the sample period. However, the latter result is
primarily driven by young ?rms. Further analysis reveals that the combined revenue
regression coef?cients are positive and statistically signi?cant for ?rms reporting
negative earnings, but the combined earnings coef?cients are not. For pro?t making
?rms, the combined earnings coef?cients are much higher than their revenue
counterparts. Finally, life cycle analysis shows that both the combined revenue and
earnings coef?cients are statistically signi?cant only in the growth stage, while both
earnings variables are signi?cant in the maturity stage.
The results of this study will be of practical use to prospective investors and
accounting regulators. When making equity valuation decisions investors consider
?rms’ fundamentals as re?ected in ?nancial statements. However, which line item is
more important for equity valuation is an important consideration. Because of
differences in persistence among performance measures, investors need to know which
of the measures is more strongly associated with investors’ beliefs as re?ected in stock
market returns. A recent comprehensive survey of chief ?nancial of?cers (CFO) by
Graham et al. (2005) shows the GAAP earnings number, especially the earnings per
share, is the key metric upon which the market focuses. This is mainly because
investors need a simple benchmark to evaluate a ?rm’s performance that reduces the
costs of information processing due to information overload (Graham et al., 2005, p. 21).
However, evidence from this study ?nds that investors do factor in ?rm-speci?c
characteristics in weighing alternative performance measures. The ?nding that there
has been no systematic decline in the value relevance of accounting earnings in
Australia will enhance the con?dence of accounting regulators and corporate
stakeholders regarding accounting information quality.
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Notes
1. Interestingly, increasing value-relevance of revenue information is reported at a time when
the allegations about revenue manipulation are also mounting. Anecdotal evidence shows
that a signi?cant proportion of earnings management cases take the form of revenue
manipulation. For example, in a recent report by the General Accounting Of?ce (GAO) on
?nancial restatements, revenue recognition-related restatements account for about
37 percent of the 919 restatements announced during 1997-2002 (GAO, 2003). Similarly,
Dechow and Schrand (2004) indicate that over 70 percent of the 294 Securities and Exchange
Commission Accounting and Auditing Enforcement Releases they examine involve
overstated revenue.
2. Research on the value relevance of revenue gained momentum during the dot-com bubble in
the late 1990s, when many ?rms – mainly in the technology sector – earning minimal pro?ts,
or incurring losses, had large market valuations based on sales growth. Trueman et al.
(2000, 2001) ?nd that earnings are unrelated to stock price on average for internet ?rms, but
that both ?nancial and non?nancial measures of revenue are value relevant. Davis (2002)
reports that revenue surprises are positively related to announcement-period abnormal
returns for a sample of internet ?rms.
3. It is important to note that returns and levels speci?cations do not address the same research
question. Returns speci?cation can be used to determine whether independent variables
re?ect information that over a speci?c period of time: the returns window; causes investors
to change their beliefs and, consequently, prices (i.e. used to capture the timeliness notion of
?nancial reporting). On the other hand, levels speci?cation can be used to determine whether
accounting variables re?ect information associated with information used to price shares
over all periods up to a speci?c point in time. The research design of this study re?ects the
timeliness notion of performance measures, and hence uses returns speci?cation. Beaver
(2002, pp. 461-2) cautions that changing the form of the variable may fundamentally change
the question addressed. He notes:
One chooses the levels design when the problem is to determine what accounting
numbers are re?ected in ?rm value, whereas one chooses the ?rst difference research
design when the problem is to explain changes in value over a speci?c period of time.
Hence, in the ?rst differences formulation, the issue of the timing of information is
important.
Gonedes and Dopuch (1974) argue that returns models are theoretically superior to price
models in the absence of well-developed theories of valuation. Lev and Ohlson (1982)
describe the two approaches as complementary. Landsman and Magliolo (1988) show that
levels models can dominate returns models, for example, when model parameters and
omitted variables are not inter-temporally constant.
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Accounting Research, Vol. 37, pp. 319-52.
Corresponding author
Ahsan Habib can be contacted at: [email protected]
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doc_339445107.pdf
The paper aims to examine the value relevance of alternative accounting performance
measures in Australia. It also documents the relative and incremental value relevance of revenue
earnings and the longitudinal changes in such value relevance. Finally, the impact of certain
firm characteristics including firm life cycle on the value relevance of revenue and earnings
information is investigated.
Accounting Research Journal
Value relevance of alternative accounting performance measures: Australian evidence
Ahsan Habib
Article information:
To cite this document:
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evidence", Accounting Research J ournal, Vol. 23 Iss 2 pp. 190 - 212
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Value relevance of alternative
accounting performance
measures: Australian evidence
Ahsan Habib
Auckland University of Technology, Auckland, New Zealand
Abstract
Purpose – The paper aims to examine the value relevance of alternative accounting performance
measures in Australia. It also documents the relative and incremental value relevance of revenue
vis-a` -vis earnings and the longitudinal changes in such value relevance. Finally, the impact of certain
?rm characteristics including ?rm life cycle on the value relevance of revenue and earnings
information is investigated.
Design/methodology/approach – The paper utilises data on Australian listed companies from
1992 to 2005 on the level of and changes in seven alternative accounting performance measures.
Standard ordinary least square regression is conducted.
Findings – Results reveal that: the coef?cient estimates on all the performance measures are much
higher for large ?rms compared to their small ?rm counterpart; the explanatory power of incremental
revenue in explaining stock returns has declined signi?cantly over the sample period; and life cycle
analysis shows that the combined coef?cients for both revenue and earnings are signi?cant in the
growth and maturity stages of the ?rm life cycle.
Practical implications – When making equity valuation decisions investors consider ?rms’
fundamentals as re?ected in ?nancial statements. However, which line item is more important for
equity valuation is an important consideration. From a regulatory perspective, this stream of research
is quite relevant because standard setters will have evidence from an investor viewpoint about
whether certain line items, subtotals, and totals should be de?ned in standards and required to be
displayed in ?nancial statements.
Originality/value – The paper adds to the existing capital market research in Australia by
documenting differential persistence of alternative performance measures.
Keywords Australia, Accountancy, Performance measures, Asset valuation, Capital markets
Paper type Research paper
1. Introduction
This paper examines the value relevance of alternative accounting performance
measures in Australia. Barton et al. (2008) estimate and compare the value relevance of
a comprehensive set of performance measures commonly disclosed in ?nancial
statements across 46 countries including Australia during 1996-2005 and report that
the value relevance of the performance measures varies substantially across line items
in the income statement, as well as across countries. The authors report that operating
income has the strongest association with contemporaneous stock returns; while
subtotals at the ends of the income statement, such as sales and total comprehensive
income, have the weakest association with stock returns.
This paper revisits and expands Barton et al. (2008) in the Australian context for a
number of reasons. First, the Barton et al. (2008) study, like other international
comparative studies, has the advantage of aggregating a large number of ?rm-year
observations, which increases the statistical power of the tests. However, combining
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1030-9616.htm
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Accounting Research Journal
Vol. 23 No. 2, 2010
pp. 190-212
qEmerald Group Publishing Limited
1030-9616
DOI 10.1108/10309611011073269
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results from such a large and diverse set of countries also masks country-speci?c
idiosyncracies. Furthermore, they assemble required data from the Global Vantage
database, which is not as comprehensive as local databases (e.g. the Huntley Aspect
database in Australia) because the former database does not cover many small and
medium-sized listed companies. This is particularly important in the present study
because there is likely to be signi?cant differences in the persistence of individual
performance measures between large and small ?rms. The present study uses
Australian data that is twice the sample size of Barton et al. (2008). Second, USA
evidence on the time-series behavior of the value-relevance of accounting information
(particularly earnings) has produced inconclusive evidence. Collins et al. (1997) and Lev
and Zarowin (1999) provide strong evidence that accounting earnings is losing
value-relevance in the USA, and investors are focusing on alternative performance
measures, like revenue, to evaluate company performance (Chandra and Ro, 2008)[1].
Brimble and Hodgson (2007, p. 602) investigate inter-temporal value relevance of
accounting information in Australia hypothesising that:
[. . .] ?rm conditions, competitive and economic structures, and business culture, vary
signi?cantly in a global sense. Hence, there is no compelling reason to assume that the US
results will also hold in Australia.
Brimble and Hodgson (2007) fail to ?nd any systematic decline in the value-relevance of
earnings but do not examine the inter-temporal value relevance of revenue. Therefore, an
empirical examination of the changing value relevance of revenue in Australia would be
of interest. Third, although Australia has been studied in the comparative
value-relevance research, extant studies use a single performance measure and a
rather small Australian sample compared to the present study. For example, Hung
(2001), Ali and Hwang (2000), and DeFond et al. (2007), among others, investigate the
impact of shareholder protection on the properties of accounting information in a
number of countries, including Australia. Results reveal that countries with strong
investor protection and enforcement regimes (Australia, for example) provide
accounting information that is more strongly associated with market returns.
This paper also addresses the role of ?rm-speci?c characteristics, like ?rm size, ?rm
pro?tability and ?rm life cycle, in moderating the association between market returns
and alternative ?rm performance measures. Speci?cally, this paper addresses four
research questions:
RQ1. Which of the performance measures shows the strongest association with
market returns, and how did the documented association vary over time?
RQ2. Howdoes ?rmsize affect value relevance of alternative performance measures?
RQ3. Is there any incremental value relevance of revenue vis-a` -vis earnings and have
there been any longitudinal changes in such value relevance?
RQ4. How do ?rm pro?tability and ?rm life cycle characteristics affect the
incremental value relevance of revenue information? In addressing these
questions, this paper considers seven alternative performance measures
namely, total revenue (TOTREV), earnings before interest tax depreciation and
amortisation (EBITDA), operating income proxied by earnings before interest
and tax (EBIT), earnings before tax (EBT), net pro?t after tax but before
Accounting
performance
measures
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abnormal items (NPATEXABN), bottom line net income (NPAT) and
operating cash ?ows (OCF).
Results reveal that both EBTand NPAThave the highest explanatory power while EBT
has the largest combined coef?cient (sum of the coef?cient of the level and changes in
EBT). Surprisingly, the combined coef?cient for TOTREV is the lowest among all the
coef?cients, possibly implying lower persistence. Relative value relevance of alternative
performance measures based on size classi?cation reveals that coef?cient estimates on
all the performance measures are much higher for large ?rms compared to their small
?rmcounterpart implying greater persistence for the former group. Larger ?rms tend to
produce more persistent earnings information via smoothing of income as these ?rms
have larger portfolios of accounting choices (Hodgson and Stevenson-Clarke, 2000).
However, with respect to the adjusted R
2
s, small ?rms exhibit higher explanatory
power compared to large ?rms. This decline in adjusted R
2
from small to large
companies could be explained by the fact that information pertaining to larger ?rms is
readily available well before its actual announcement and is impounded into security
prices instantaneously in an ef?cient market. Regarding incremental value relevance of
revenue, the ?ndings reveal that the combined ability of earnings and revenues to
explain stock returns has not diminished, but the explanatory power of incremental
revenue in explaining stock returns has declined signi?cantly over the sample period.
However, this result is primarily driven by young ?rms. Finally, life cycle analysis
shows that the combined coef?cients for both revenue and earnings are signi?cant in the
growth and maturity stages of the ?rm life cycle.
The results of this study will be of practical use to prospective investors and
accounting regulators. When making equity valuation decisions investors consider
?rms’ fundamentals as re?ected in ?nancial statements. However, which line item is
more important for equity valuation is an important consideration. Because of
differences in persistence among performance measures, investors need to know which
of the measures is most strongly associated with investors’ beliefs as re?ected in stock
market returns. From a regulatory perspective, this stream of research is quite relevant
because standard setters will have evidence from an investor viewpoint “[. . .] whether
certain line items, subtotals, and totals should be de?ned in standards and required to
be displayed in ?nancial statements” (Financial Accounting Standards Board, 2001).
The paper proceeds as follows. The next section provides a brief literature review.
Section 3 explains the research design and sample selection procedure. Section 4
provides substantive test results and the ?nal section concludes.
2. Related literature
Accounting earnings is the premier information item provided in the ?nancial
statements (Lev, 1989). Beginning with the seminal work of Ball and Brown (1968) and
Beaver (1968), the last four decades of accounting research have produced a substantial
volume of work showing that the market reacts positively to positive earnings news
(earnings are value relevant) (see Kothari, 2001, for a review). However, published
research on the value relevance of other line items in the income statement is rather
limited. Available USA evidence generally is consistent with the view that
disaggregated earnings provide more value-relevant information in the marketplace,
and improve out-of-sample forecasting (Lipe, 1986; Wild, 1992; Barth et al., 1992;
Fair?eld and Yohn, 1996). Lipe (1986), for example, reveals that the components
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of earnings (namely, gross pro?ts, general and administrative expense, depreciation
expense, interest expense, income taxes, and other items) increase the explanatory
power for security returns signi?cantly. Fair?eld and Yohn (1996) analyze accuracy
improvements in out-of-sample forecasts of one-year-ahead return-on-equity. They ?nd
forecast improvements from progressively disaggregating earnings into the
components of operating income, non-operating income plus taxes, special items and
nonrecurring items.
Revenue is the ?rst line item in the income statement which arises in the course of
ordinary business activities and, therefore, is likely to be more persistent than other
measures. Published research on the value relevance of revenue is sparse relative to
research on earnings. Although earnings is widely considered to be the primary
summary measure of operating performance for ?rms, revenue may contain
incremental information beyond earnings that is relevant to future earnings and
cash ?ows predictions but lost when aggregated into earnings with expenses. Several
early studies (Hopwood and McKeown, 1985; Hoskin et al., 1986; Wilson, 1986) ?nd no
evidence that revenue conveys information beyond earnings. Swaminathan and
Weintrop (1991), however, ?nd that revenue surprises explain excess returns around
earnings announcements after controlling for earnings surprises[2]. Ghosh et al. (2005)
report that, for a subsample of ?rms with sustained earnings increases and sustained
revenue growth are associated with higher earnings response coef?cients (ERCs),
increased earnings persistence and reduced susceptibility to earnings management.
Ertimur et al. (2003) reveal that the market reaction to revenue surprises is greater than
to expense surprises, especially for growth ?rms, and that the differential price
reaction is associated with relative persistence. Jegadeesh and Livnat (2006) ?nd that
past revenues are informative in explaining future earnings after controlling for past
earnings. They further document that stock prices and analysts’ forecasts adjust to the
information conveyed by revenue over an extended period – of approximately
six months – after quarterly earnings announcements. Chandra and Ro (2008) show
that revenue is useful both as a summary measure for valuation purposes (has value
relevance) and in conveying new information (has information content) to the market,
after controlling for earnings information. These results are not driven by technology
?rms, extreme earnings news or loss situations, nor by model misspeci?cation because
of nonlinearities. They also report that while the combined ability of revenue and
earnings to summarise contemporaneous value relevant information has remained
stable over time, the value relevance of new information conveyed by earnings has
declined whereas the ability of revenue to incrementally convey new value relevant
information has not diminished.
Value relevance of accounting information, however, is expected to be determined
by ?rm-speci?c characteristics like ?rm size, ?rm pro?tability, ?rm leverage, ?rm life
cycle, and many others. Firm size has been extensively researched in explaining value
relevance of accounting information (Atiase, 1985). Larger ?rms are characterised by a
richer information environment and higher analyst following compared to their small
?rm counterparts. Because of the availability of more information for large ?rms prior
to earnings announcements, earnings announcements of larger ?rms tend to generate
fewer surprises in the marketplace and have a lower association with stock returns.
Firm pro?tability is also considered to be an important contextual factor that affects
the value relevance of accounting information differently. Extant research reports that
Accounting
performance
measures
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investors focus more on balance sheet information (e.g. equity book values) for the
equity valuation of ?rms reporting losses (Collins et al., 1999; Barth et al., 1998).
However, there is a paucity of research (Ghosh et al., 2005; Chandra and Ro, 2008 are
exceptions), and none in Australia, regarding investor valuation of revenue items when
?rms report negative earnings. Hayn (1995) calls for further research on the “degree of
substitution between earnings numbers and alternative accounting variables in loss
situations” (italics added). Finally, ?rm life cycle stages are used as important
?rm-speci?c characteristics that are likely to impact the value relevance of accounting
information differentially because of differences in persistence among ?rms at
different life cycle stages.
To sum up, available empirical evidence on the value-relevance of alternative
accounting performance measures comes primarily from the USA. This paper expands
this stream of research in Australia, where no evidence yet exists regarding the value
relevance of different line items in the income statement, except for net income
(Brimble and Hodgson, 2007). An additional contribution of this paper is to document
the relative and incremental value relevance of revenue information vis-a` -vis earnings
and the longitudinal changes in such value relevance. Finally, the impact of certain
?rm characteristics including ?rm life cycle on the value relevance of revenue versus
earnings information is investigated.
3. Research design and sample selection
The proxy for summary performance measure j’s value RELEVANCE is the adjusted
R
2
from the following regression:
RETURN
it
¼ g
0
þg
1
ðPerformance Measure jÞ
it
þg
2
ðDPerformance Measure jÞ
it
þg
3-13
YRDUM þg
14-31
INDUM þ1
it;
ð1Þ
where RETURN is ?rm i’s stock return for ?scal year t, adjusted for average market
return during the period[3], and performance measures j [ {TOTREV, EBITDA,
OPINC, EBT, NPATEXABN, NPAT, OCF} are de?ated by the lagged market value of
equity (MVE). The equation also includes change in performance measures as
explanatory variables following Easton and Harris (1991). A set of year and industry
dummy coef?cients are included in equation (1) to control for period and industry
effects. Larger values of RELEVANCE of the performance measure j imply that this
particular measure is more relevant for equity valuation.
To determine the incremental value relevance of revenue information vis-a` -vis
earnings, the following three regression equations are estimated:
RETURN
it
¼ a
0
þa
1
REVLEV
it
þa
2
REVGR
it
þa
3
EARLEV
it
þa
4
EARGR
it
þa
5-13
YRDUM þa
14-31
INDUM þ1
it
ð2Þ
where REVLEV and EARLEV are revenue and earnings levels, respectively, and
REVGR and EARGR are revenue and earnings growth, respectively. REVGR and
EARGR are calculated as the difference between current year revenue (earnings)
and last year revenue (earnings) divided by lagged MVE, respectively. EAR is de?ned
as operating earnings (EBIT). To compare the explanatory power that revenue and
earnings have for stock returns, a decomposition technique used in Easton (1985) is
employed. Total explanatory power (R
2
T
) is decomposed into three parts:
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(1) the incremental explanatory power of revenues (incremental REV);
(2) the incremental explanatory power of earnings (incremental EAR); and
(3) the explanatory power common to both revenues and earnings (incremental
COMMON).
Stated in equation form:
RETURN
it
¼ b
0
þb
1
REVLEV
it
þb
2
REVGR
it
þb
3-11
YRDUM
þb
12-29
INDUM þ1
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ð3Þ
RETURN
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¼ z
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1
EARLEV
it
þz
2
EARGR
it
þz
3-11
YRDUM
þz
12-29
INDUM þ1
it
ð4Þ
The coef?cients of determination from equations (2) to (4) are denoted as R
2
T,
R
2
REV
and R
2
EAR
, respectively. Then R
2
T
2 R
2
EAR
¼ INCREMENTAL REV and
R
2
T
2R
2
REV
¼ INCREMENTAL EAR. The remaining R
2
T
2 R
2
IncrREV
2 R
2
IncrEAR
equals the explanatory power common to both earnings and book values (COMMON).
The initial sample consists of 10,336 ?rm-year observations excluding ?nancial
institutions over a period of 1992 to 2005 and is retrieved from the Aspect Huntley
database. Missing MVE data reduces the sample by 1,927 ?rm-year observations.
MVE data is required to de?ate the performance measures. A further 57 observations
are lost due to missing market adjusted return data and, ?nally, missing performance
measures data reduces the sample size by a further 22 observations, resulting in a ?nal
usable sample of 8,330 ?rm-year observations. By comparison, Barton et al. (2008)
conduct their analysis using a sample size of 4,193 ?rm-year observations. There is wide
variation in the number of sample observations across the sample period, with 1992
having the lowest (136) while 2005 the highest (934) number of observations,
respectively. Table I presents descriptive statistics, industry composition of the selected
sample and correlation analysis among the variables. These statistics are computed
after winsorising the top and bottom 1 percent of the distribution of all the variables to
reduce the effect of outliers. Mean return is positively skewed (skewness ¼ 2.30), so is
TOTREV (skewness ¼ 3.45). However, NPATEXABN and NPAT are negatively
skewed (skewness ¼ 22.71 and 23.37, respectively), re?ecting large negative earnings
values. All the variables have high kurtosis (kurtosis coef?cients are in the range of
8.07-19.05). These statistics indicate that the distributions are not normally distributed
even after winsorising the variables. Panel B of Table I reports industry composition,
and clearly demonstrates that the Materials industry constitutes the largest number of
unique ?rms (338) and ?rm-year observations (30 percent of the total sample), followed
by the energy sector (8 percent of the total sample). The average life of the sample ?rms
is seven years (8,330/1,170). Panel C of Table I presents the correlation analysis. All the
reported correlations are statistically signi?cant at better than the 1 percent level. Some
of the performance measures are highly correlated among themselves, and therefore,
used separately in regression analysis to avoid the multicollinearity problem.
4. Substantive test results
4.1 Value relevance of alternative performance measures
Table II shows the pooled regression estimates of value-relevance of seven alternative
performance measures in terms of adjusted R
2
and coef?cient estimates (both level
Accounting
performance
measures
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6
8
C
o
m
m
e
r
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l
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n
d
c
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s
u
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r
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c
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s
9
8
6
3
1
7
.
5
8
C
o
n
s
u
m
e
r
d
u
r
a
b
l
e
s
1
6
1
1
5
1
.
3
8
E
n
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r
g
y
9
0
7
1
8
8
.
6
2
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7
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4
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4
1
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2
8
0
3
.
3
6
M
a
t
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i
a
l
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3
8
2
,
5
0
3
3
0
.
0
5
M
e
d
i
a
3
7
2
9
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3
.
4
8
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h
a
r
m
a
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l
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n
d
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i
o
t
e
c
h
n
o
l
o
g
y
5
6
3
2
1
3
.
8
5
(
c
o
n
t
i
n
u
e
d
)
Table I.
Descriptive statistics and
correlation analysis
ARJ
23,2
196
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
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R
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A
t
2
1
:
1
1
2
4
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a
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r
y
2
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1
6
(
P
T
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i
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(
1
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2
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(
3
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(
4
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(
5
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(
6
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(
7
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8
)
(
9
)
(
1
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(
1
1
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(
1
2
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3
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4
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m
a
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a
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u
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;
T
O
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O
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;
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B
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d
M
V
E
Table I.
Accounting
performance
measures
197
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
as well as changes) based on equation (1). The regression also includes year and
industry dummies to control for the possible year and industry effects. Reported
results reveal that the value relevance of the EBIT and NPAT measures command the
highest explanatory power of 8.2 percent, while the OCF measure has the lowest
adjusted R
2
of 6.9 percent. The reported adjusted R
2
for the earnings measure is
comparable to that of Easton and Harris (1991) who ?nd an average R
2
of 7.8 percent
when security return is regressed on both the level of, and changes in, earnings. With
respect to the value relevance of revenue information, Chandra and Ro (2008) report an
adjusted R
2
of 4.3 percent when both the revenue and earnings level, and growth,
variables are included in the same regression whereas this study reports a much higher
adjusted R
2
when revenue variables alone are included in the regression estimation.
With respect to the coef?cient estimates, Table II shows that all the combined
coef?cients are positive, and statistically signi?cant at better than 1 percent level.
The combined coef?cients on EBT measure is the largest among all the performance
measures (a combined coef?cient of 0.37). The combined coef?cient estimate for the
revenue variable is very similar to that reported by Chandra and Ro (2008) (Figure 1).
4.2 Firm size and value relevance of alternative performance measures
This section provides evidence on how ?rm size affects the relative value relevance of
alternative performance measures. Firm size has been extensively used in capital
market research as a conditioning variable in explaining many ?nancial reporting
outcome effects. Firm-year observations are classi?ed into small, medium, and large
?rm size groups based on yearly distribution of market value of equities. Then
market-adjusted buy-and-hold annual stock returns are regressed on alternative
performance measures in each of the size categories, and the resulting coef?cients and
adjusted R
2
s are reported in Table III.
Coef?cient estimates on all the performance measures are much larger for large
?rms compared to their small ?rm counterparts implying greater persistence for the
former group. For example, the combined coef?cient estimates for the revenue variable
Performance measures Intercept g
1
g
2
[g
1
þ g
2
] Adjusted R
2
TOTREV 0.20
*
(3.28) 0.02
*
(6.10) 0.08
*
(6.69) 0.09
*
(8.18) 0.077
EBITDA 0.22
*
(3.45) 0.13
*
(3.77) 0.23
*
(6.40) 0.36
*
(9.00) 0.08
EBIT [OPINC] 0.22
*
(3.52) 0.13
*
(3.72) 0.21
*
(6.40) 0.34
*
(8.50) 0.08
EBT 0.24
*
(3.91) 0.13
*
(3.78) 0.24
*
(7.01) 0.37
*
(9.00) 0.082
NPATEXABN 0.25
*
(4.05) 0.12
*
(3.32) 0.23
*
(6.65) 0.34
*
(9.10) 0.079
NPAT 0.25
*
(4.07) 0.10
*
(4.00) 0.10
*
(5.33) 0.20
*
(7.81) 0.082
OCF 0.24
*
(3.69) 0.14
*
(3.73) 0.11
*
(2.94) 0.25
*
(6.23) 0.069
Observations 8,330
Industry dummies Included
Year dummies Included
Note: Sigini?cant at:
*
1 percent level
RETURN
it
¼ g
0
þg
1
ðPerformance Measure jÞ
it
þg
2
ðDPerformance Measure jÞ
it
þg
3ÿ13
YRDUM þg
14ÿ31
INDUM þ1
it
ð1Þ
Table II.
Pooled regression results
of the value relevance of
alternative accounting
performance measures
ARJ
23,2
198
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
is 0.17 for large ?rms as compared to only 0.04 for small ?rms. Similarly, large (small)
?rms report combined coef?cients of 0.59 (0.11) for the NPAT variable, respectively.
Larger ?rms tend to produce more persistent earnings information via smoothing of
income as these ?rms have a larger portfolio of accounting choices (Hodgson and
Stevenson-Clarke, 2000). However, the opposite picture emerges with respect to the
adjusted R
2
s where performance measures of small ?rms exhibit higher explanatory
power with respect to security returns compared to large ?rms. This decline in the
adjusted R
2
from small to large ?rms may be explained by the fact information
pertaining to larger ?rms is readily available well before the actual announcement, and
is impounded into security prices. This is more applicable in Australia which is
characterised by a “continuous disclosure regime”.
4.3 Value relevance of revenue
This section provides empirical evidence on the ability of revenue to explain stock
returns beyond earnings. As mentioned in the literature review section, published
research on the value relevance of revenue is sparse relative to research on earnings,
and is based primarily on the USA market. Table IV presents the Australian evidence
on the relative and incremental value relevance of revenue vis-a` -vis earnings. Pooled
regression results with respect to equation (2) reveal that all four regression variables
representing earnings and revenue levels and growth, enter the regression equation
with positive coef?cients (columns 2-6). They are statistically signi?cant at better than
the 5 percent level and explain about 9 percent of the variation in market-adjusted stock
returns. Combined earnings coef?cients, however, are much higher than their revenue
counterparts (0.31 versus 0.09, respectively). Columns (7-9) report regression results of
equation (3). Both the REVLEV and REVGR variables are positive and statistically
signi?cant at better than 1 percent level. The regression result of equation (4) in
columns (10-12) reveals that the EARLEV and EARGR are also positive and
statistically signi?cant, and both the models explain about 8 percent of the variation
Figure 1.
Value-relevance of
alternative performance
measures, proxied by
adjusted R
2
from the
regression of
market-adjusted returns
on seven accounting
performance measures in
Australia is depicted on
the vertical axis
Value-relevance of performance measures
0.085
0.08
0.075
0.07
0.065
0.06
0.077
0.08 0.08
0.082
0.079
0.082
0.069
Adjusted R
2
TOTREV EBITDA OPINC EBT NPATEXABN NPAT OCF
Accounting
performance
measures
199
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
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1
1
2
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J
a
n
u
a
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y
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m
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3
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9
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6
4
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1
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8
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3
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4
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5
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7
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5
5
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0
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1
6
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2
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7
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n
2
,
7
7
3
2
,
7
7
4
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7
7
4
I
n
d
u
s
t
r
y
d
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m
m
y
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e
s
Y
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s
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Y
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a
r
d
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m
m
y
Y
e
s
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e
s
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e
s
N
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t
e
s
:
S
i
g
n
i
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c
a
n
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e
a
t
:
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1
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5
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a
n
d
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*
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1
0
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e
r
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t
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s
p
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t
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.
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m
a
l
l
?
r
m
g
r
o
u
p
c
o
n
t
a
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n
s
2
,
7
7
3
?
r
m
-
y
e
a
r
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b
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r
v
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t
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r
m
g
r
o
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p
s
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t
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7
4
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m
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r
o
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1
9
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.
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a
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R
2
f
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h
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w
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g
r
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g
r
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s
s
i
o
n
:
R
E
T
U
R
N
i
t
¼
g
0
þ
g
1
ð
P
e
r
f
o
r
m
a
n
c
e
M
e
a
s
u
r
e
j
Þ
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t
þ
g
2
ð
D
P
e
r
f
o
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m
a
n
c
e
M
e
a
s
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r
e
j
Þ
i
t
þ
g
3
ÿ
1
3
Y
R
D
U
M
þ
g
1
4
ÿ
3
1
I
N
D
U
M
þ
1
i
t
;
ð
1
Þ
R
E
T
U
R
N
i
t
¼
g
0
þ
g
1
ð
P
e
r
f
o
r
m
a
n
c
e
M
e
a
s
u
r
e
j
Þ
i
t
þ
g
2
ð
D
P
e
r
f
o
r
m
a
n
c
e
M
e
a
s
u
r
e
j
Þ
i
t
þ
1
i
t
;
ð
2
Þ
w
h
e
r
e
R
E
T
U
R
N
i
s
?
r
m
i
’
s
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t
o
c
k
r
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t
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n
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o
r
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s
c
a
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y
e
a
r
t
,
a
d
j
u
s
t
e
d
f
o
r
a
v
e
r
a
g
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m
a
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r
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r
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d
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f
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r
m
a
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m
e
a
s
u
r
e
j
[
{
T
O
T
R
E
V
,
E
B
I
T
D
A
,
O
P
I
N
C
,
E
B
T
,
N
P
A
T
E
X
A
B
N
,
N
P
A
T
,
O
C
F
}
.
L
a
r
g
e
r
v
a
l
u
e
s
o
f
R
E
L
E
V
A
N
C
E
i
m
p
l
y
t
h
a
t
p
e
r
f
o
r
m
a
n
c
e
m
e
a
s
u
r
e
j
i
s
m
o
r
e
r
e
l
e
v
a
n
t
f
o
r
e
q
u
i
t
y
v
a
l
u
a
t
i
o
n
Table III.
Firm size and the relative
value relevance of
alternative performance
measures
ARJ
23,2
200
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
C
o
m
b
i
n
e
d
m
o
d
e
l
R
e
v
e
n
u
e
m
o
d
e
l
E
a
r
n
i
n
g
s
m
o
d
e
l
(
6
-
1
2
)
(
6
-
9
)
Y
e
a
r
a
1
a
2
a
3
a
4
R
2
b
1
b
2
R
2
z
1
z
2
R
2
I
n
c
r
_
R
E
V
I
n
c
r
_
E
A
R
(
1
)
(
2
)
(
3
)
(
4
)
(
5
)
(
6
)
(
7
)
(
8
)
(
9
)
(
1
0
)
(
1
1
)
(
1
2
)
(
1
3
)
(
1
4
)
1
9
9
2
0
.
0
2
9
2
*
*
*
0
.
1
1
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2
0
.
4
1
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2
4
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0
8
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0
2
0
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1
1
*
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0
.
0
4
2
0
.
3
7
*
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0
.
3
1
0
.
0
4
0
.
0
4
0
.
0
4
1
9
9
3
2
0
.
0
3
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*
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0
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1
3
*
*
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0
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0
0
4
6
0
.
1
5
0
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0
0
9
1
2
0
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0
3
0
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1
5
0
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0
1
0
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0
0
0
0
4
0
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2
3
0
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0
0
8
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0
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2
2
0
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0
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1
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9
4
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2
1
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2
0
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0
8
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5
9
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1
0
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0
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2
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2
5
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2
0
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0
5
0
.
6
9
*
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0
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0
2
0
.
0
8
0
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0
1
1
9
9
5
0
.
0
3
0
.
2
3
*
2
0
.
8
4
*
*
*
0
.
8
5
*
*
0
.
0
4
0
.
0
4
*
*
0
.
1
4
*
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0
.
0
3
2
0
.
0
9
0
.
5
7
*
*
*
0
.
0
0
3
2
0
.
0
3
6
8
0
.
0
1
1
9
9
6
0
.
0
4
*
0
.
0
0
9
8
2
0
.
0
6
1
.
6
6
*
0
.
1
5
0
.
0
4
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2
0
.
0
0
9
0
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0
2
3
2
0
.
0
4
1
.
6
4
*
0
.
1
3
0
.
0
2
0
.
1
2
7
1
9
9
7
0
.
0
2
*
*
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2
0
.
0
0
5
0
.
4
8
*
0
.
2
0
0
.
0
3
0
.
0
3
*
*
0
.
0
3
0
.
0
1
0
.
4
8
*
0
.
2
8
0
.
0
3
0
.
0
0
0
.
0
2
1
9
9
8
0
.
0
1
*
*
*
0
.
0
0
4
3
0
.
3
8
*
0
.
3
0
*
*
0
.
1
0
0
.
0
3
*
0
.
0
1
0
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0
5
0
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4
4
*
0
.
3
5
*
0
.
0
9
0
.
0
1
0
.
0
5
1
9
9
9
2
0
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0
0
5
4
0
.
1
3
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2
0
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2
0
0
.
2
9
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0
.
0
2
2
0
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1
0
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1
4
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.
0
1
2
0
.
1
3
0
.
3
4
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0
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0
0
8
8
0
.
0
1
1
0
.
0
1
2
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2
0
.
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2
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7
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2
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2
3
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2
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2
2
0
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0
1
0
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0
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3
8
2
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2
1
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0
.
4
1
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.
0
1
0
.
0
1
0
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0
1
6
2
0
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1
0
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0
3
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0
.
0
4
0
.
4
1
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0
.
1
0
0
.
1
3
0
.
0
4
*
0
.
0
3
0
.
0
4
0
.
4
3
*
0
.
0
9
0
.
1
0
0
.
0
3
0
.
0
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2
0
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9
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.
1
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1
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v
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t
-
s
t
a
t
(
0
.
9
1
)
(
2
.
1
0
)
(
2
0
.
0
6
6
)
(
2
.
3
3
)
(
0
.
6
9
)
(
2
.
3
0
)
(
0
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5
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(
2
.
6
8
)
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o
o
l
e
d
0
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9
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1
(
4
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)
(
5
.
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)
(
2
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6
1
)
(
5
.
4
8
)
(
5
.
2
2
)
(
6
.
5
0
)
(
3
.
5
9
)
(
5
.
6
4
)
N
o
t
e
s
:
S
i
g
n
i
?
c
a
n
c
e
a
t
:
*
1
,
*
*
5
,
a
n
d
*
*
*
1
0
p
e
r
c
e
n
t
l
e
v
e
l
s
,
r
e
s
p
e
c
t
i
v
e
l
y
;
t
o
d
e
t
e
r
m
i
n
e
t
h
e
i
n
c
r
e
m
e
n
t
a
l
v
a
l
u
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r
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l
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v
a
n
c
e
o
f
r
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v
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n
u
e
i
n
f
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r
m
a
t
i
o
n
v
i
s
-
a `
-
v
i
s
e
a
r
n
i
n
g
s
,
t
h
e
f
o
l
l
o
w
i
n
g
t
h
r
e
e
r
e
g
r
e
s
s
i
o
n
e
q
u
a
t
i
o
n
s
a
r
e
e
s
t
i
m
a
t
e
d
:
R
E
T
U
R
N
i
t
¼
a
0
þ
a
1
R
E
V
L
E
V
i
t
þ
a
2
R
E
V
G
R
i
t
þ
a
3
E
A
R
L
E
V
i
t
þ
a
4
E
A
R
G
R
i
t
þ
1
i
t
;
ð
2
Þ
R
E
T
U
R
N
i
t
¼
b
0
þ
b
1
R
E
V
L
E
V
i
t
þ
b
2
R
E
V
G
R
i
t
þ
1
i
t
;
ð
3
Þ
R
E
T
U
R
N
i
t
¼
z
0
þ
z
1
E
A
R
L
E
V
i
t
þ
z
2
E
A
R
G
R
i
t
þ
1
i
t
;
ð
4
Þ
w
h
e
r
e
R
E
V
L
E
V
a
n
d
E
A
R
L
E
V
a
r
e
r
e
v
e
n
u
e
a
n
d
e
a
r
n
i
n
g
s
l
e
v
e
l
s
,
r
e
s
p
e
c
t
i
v
e
l
y
,
a
n
d
R
E
V
G
R
a
n
d
E
A
R
G
R
a
r
e
r
e
v
e
n
u
e
a
n
d
e
a
r
n
i
n
g
s
g
r
o
w
t
h
,
r
e
s
p
e
c
t
i
v
e
l
y
.
L
a
t
t
e
r
t
w
o
v
a
r
i
a
b
l
e
s
a
r
e
c
a
l
c
u
l
a
t
e
s
a
s
t
h
e
d
i
f
f
e
r
e
n
c
e
b
e
t
w
e
e
n
c
u
r
r
e
n
t
y
e
a
r
r
e
v
e
n
u
e
(
e
a
r
n
i
n
g
s
)
a
n
d
l
a
s
t
y
e
a
r
r
e
v
e
n
u
e
(
e
a
r
n
i
n
g
s
)
d
i
v
i
d
e
d
b
y
l
a
g
g
e
d
M
V
E
.
E
A
R
i
s
d
e
?
n
e
d
a
s
o
p
e
r
a
t
i
n
g
e
a
r
n
i
n
g
s
(
E
B
I
T
)
.
T
o
c
o
m
p
a
r
e
t
h
e
e
x
p
l
a
n
a
t
o
r
y
p
o
w
e
r
t
h
a
t
r
e
v
e
n
u
e
a
n
d
e
a
r
n
i
n
g
s
h
a
v
e
f
o
r
s
t
o
c
k
r
e
t
u
r
n
,
a
d
e
c
o
m
p
o
s
i
t
i
o
n
t
e
c
h
n
i
q
u
e
u
s
e
d
i
n
E
a
s
t
o
n
(
1
9
8
5
)
i
s
e
m
p
l
o
y
e
d
.
T
o
t
a
l
e
x
p
l
a
n
a
t
o
r
y
p
o
w
e
r
(
R
2T
)
i
s
d
e
c
o
m
p
o
s
e
d
i
n
t
o
t
h
r
e
e
p
a
r
t
s
:
1
–
t
h
e
i
n
c
r
e
m
e
n
t
a
l
e
x
p
l
a
n
a
t
o
r
y
p
o
w
e
r
o
f
r
e
v
e
n
u
e
s
(
i
n
c
r
e
m
e
n
t
a
l
R
E
V
)
;
2
–
t
h
e
i
n
c
r
e
m
e
n
t
a
l
e
x
p
l
a
n
a
t
o
r
y
p
o
w
e
r
o
f
e
a
r
n
i
n
g
s
(
i
n
c
r
e
m
e
n
t
a
l
E
A
R
)
;
3
–
a
n
d
t
h
e
e
x
p
l
a
n
a
t
o
r
y
p
o
w
e
r
c
o
m
m
o
n
t
o
b
o
t
h
r
e
v
e
n
u
e
s
a
n
d
e
a
r
n
i
n
g
s
(
i
n
c
r
e
m
e
n
t
a
l
C
O
M
M
O
N
)
.
T
h
e
c
o
e
f
?
c
i
e
n
t
s
o
f
d
e
t
e
r
m
i
n
a
t
i
o
n
f
r
o
m
e
q
u
a
t
i
o
n
s
(
2
)
t
o
(
4
)
a
r
e
d
e
n
o
t
e
d
a
s
R
2T
,
R
2R
E
V
a
n
d
R
2E
A
R
,
r
e
s
p
e
c
t
i
v
e
l
y
.
T
h
e
n
R
2T
2
R
2E
A
R
¼
I
N
C
R
E
M
E
N
T
A
L
R
E
V
a
n
d
R
2T
2
R
2R
E
V
¼
I
N
C
R
E
M
E
N
T
A
L
E
A
R
Table IV.
Incremental
value-relevance of
revenue and earnings
information
Accounting
performance
measures
201
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
in market returns. However, the pooled time-series cross-sectional regressions suffer
from a lack of independence in the residuals. This causes substantially understated
standard errors or in?ated t-statistics. To alleviate this problem, the average
coef?cients are divided by the average standard errors to calculate the t-statistics, in
accordance with the Fama and Macbeth (1973) approach. The reported results show
both REVLEV and EARLEV lose signi?cance in all speci?cations while REVGR and
EARGR continue to be statistically signi?cant.
Figure 2 shows the trend in combined and incremental value relevance of revenue
and earnings over 1992-2005. For the ?rst four years of the sample period the trend in
the incremental revenue R
2
and the combined value relevance R
2
track each other
closely. However, from 1996 onwards the incremental earnings R
2
starts to mirror the
changes in the combined value relevance R
2
.
With respect to the longitudinal changes in the value relevance of revenue, Chandra
and Ro (2008) report that while the combined ability of revenue and earnings to
summarise contemporaneous value relevant information has remained stable over
time, the new information conveyed by earnings has declined whereas the ability of
revenue to incrementally convey new information has not diminished. To estimate the
longitudinal changes in value relevance of earnings and revenue variables, regression
equations (2)-(4) are estimated cross-sectionally each year. Then a series of regression
models are estimated (equation 5) using the regression estimates from these equations:
EST
jy
¼ z
0j
þz
1j
YEAR
jy
þe
jy;
j ¼ 1; 2; . . .; 9 ð5Þ
where j denotes a particular parameter from equations (2) to (4). For example, the ?rst
parameter is REVLEV which is 14 yearly coef?cients from 1992 to 2005 from
equation (1).
Table Vpresents longitudinal changes in the value relevance of earnings and revenue
information in Australia from 1992 to 2005. When the combined R
2
from regression
equation (2) is regressed on the TIME variable, the coef?cient estimate on TIME
is 20.0031 implying that the combined ability of earnings and revenue to explain
market adjusted return has declined over the sample period but the decline is not
Figure 2.
Trend in the explanatory
power of the combined,
incremental revenue, and
incremental earnings with
respect to market-adjusted
returns over 1992-2005
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
–0.02
1 2 3 4 5 6 7 8 9 10 11 12 14 13
Year
A
d
j
u
s
t
e
d
R
2
R
2
_COMBINED Incr_REV Incr_EAR
ARJ
23,2
202
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
statistically signi?cant (t-statistic ¼ 21.51). Interestingly, the incremental explanatory
power of revenue (INCREMENTAL REV) has declined signi?cantly over the sample
period (coef?cient 20.0027, t-statistic ¼ 23.09, p-value , 0.001), and so has the
annual revenue coef?cients [(REVLEV þ REVGR) [coef?cient value 20.0067,
t-statistic ¼ 21.90, signi?cant at better than the 10 percent level]]. One possible
reason for such a ?nding may relate to the persistence of revenue reported in the income
statement. The sample for the current study contains both mature, established ?rms as
well as relatively young ?rms. Persistence of revenue reported by more mature ?rms is
likely to be higher compared to their younger ?rm counterparts because of the
signi?cant environmental uncertainties faced by the latter group of companies.
All regressions reported in Table Vare rerun for companies surviving for at least nine of
the fourteen sample years. This choice, though arbitrary, provides a sample of relatively
established ?rms. Untabulated results show that when INCREMENTAL REV is
regressed on TIME for this sub-sample, the coef?cient on TIME still remains negative
(20.0032). However, unlike that of the full sample, it becomes insigni?cant. Similarly,
when annual revenue coef?cients [REVLEV þ REVGR] are regressed on TIME, the
coef?cient on TIME is again insigni?cant though negative in sign. This additional
analysis con?rms the argument that the decline in revenue reported for Australian
companies is primarily due to the inclusion of many young and small companies in the
total sample.
Measures Intercept Coef?cient Adjusted R
2
Adjusted R
2
_Eq(2) (g
7
) 0.08
*
[4.00] 20.0031 [21.51] 0.003
INCR_ REV R
2
(g
8
) 0.04
*
[3.99] 20.0027
*
[23.09] 0.21
INCR_ EAR R
2
(g
9
) 0.04
* * *
[1.93] 20.0010 [20.56] 20.07
REVLEV (g
1
) 0.02 [1.62] 20.0014 [21.35] 20.0053
REVGR (g
2
) 0.13
*
[2.91] 20.0054 [21.35] 0.02
EARLEV (g
3
) 20.26 [22.02] 0.033
*
[3.12] 0.10
EARGR(g
4
) 0.67
* *
[2.19] 20.04 [21.44] 0.08
REVLEV þ REVGR (g
5
) 0.15
*
[3.65] 20.0067
* * *
[21.90] 0.08
EARLEV þ EARGR (g
6
) 0.41 [1.34] 20.0054 [20.20] 20.08
Notes: Signi?cance at:
*
1,
* *
5, and
* * *
10 per cent levels, respectively; coef?cients g
1
to g
6
are
derived from equation (2) and regressed on TIME which takes a value of 1 for 1992, 2 for 1993 and so
on to 2005. Coef?cient g
7
is the adjusted R
2
from equation (2) regressed on TIME. Coef?cients are
derived from a decomposition technique used in Easton (1985). Total explanatory power (R
2
T
) is
decomposed into three parts: 1 – the incremental explanatory power of revenues (incremental REV); 2
– the incremental explanatory power of earnings (incremental EAR); and 3 – the explanatory power
common to both revenues and earnings (incremental COMMON). The coef?cients of determination
from equations (2) to (4) are denoted as R
2
T,
R
2
REV
, and R
2
EAR
, respectively. Then R
2
T
2R
2
EAR
¼
INCREMENTAL REV and R
2
T
2R
2
REV
¼ INCREMENTAL EAR. To estimate the longitudinal
changes in value relevance of earnings and revenue variables, regression equations (2) –(4) are
estimated cross-sectionally each year. Then a series of regression models are estimated using the
regression estimates from these equations:
EST
jy
¼ z
oj
þz
1j
YEAR
jy
þe
jy;
j ¼ 1; 2; . . .; 9 ð5Þ
where j denotes a particular parameter from equations (2) to (4). For example, the ?rst parameter is
REVLEV which is 14 yearly coef?cients from 1992 to 2005 from equation (1)
Table V.
Longitudinal changes in
the value relevance of
revenue and earnings
Accounting
performance
measures
203
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
4.4 Firm pro?tability and the value relevance of revenue
Hayn (1995) shows that ?rms reporting negative earnings have smaller ERCs. She
argues that this is because shareholders always have the option to liquidate a ?rm, and
as a result, negative earnings are transitory in nature. She also calls for additional
research to examine the role of alternative accounting information in equity valuation
when companies report negative earnings. Subsequent research has demonstrated that
when earnings are negative and ?rms face ?nancial distress, investors place more
weight on book value than on earnings (Barth et al., 1998; Collins et al., 1999; Burgstahler
and Dichev, 1997). However, the role of revenue in the context of ?rm pro?tability has
recently become the subject of academic scrutiny (Ghosh et al., 2005; Chandra and
Ro, 2008) but remains unexplored in Australia. This is particularly important given that
about 48 percent of the sample observations represent loss ?rms which are quite
substantial, and there has been a steady increase in the number of ?rms reporting losses
over the sample year with the ratio monotonically increasing from 32 percent in 1994 to
56 percent in 2002.
Table VI presents the regression results of equations (2)-(4) for pro?t and loss
sub-samples. For both sub-samples, the combined revenue coef?cient is positive and
statistically signi?cant although it is marginally higher for loss sub-sample (0.07 versus
0.05). On the other hand, the combined earnings coef?cient of the pro?t sub-sample is
signi?cantly higher than that of its loss sub-sample counterparts (1.69 versus 0.03,
respectively) consistent with the ?ndings of earlier research. This result, therefore,
provides weak evidence of the hypothesis that investors focus more on revenue
information in valuing equity when ?rms report negative earnings.
4.5 Firm life cycle stages and the value relevance of revenue
Firm life cycle is comprised of distinct phases, like introduction, growth, maturity, and
decline, and ?rms progress through these phases as a result of strategic decision-making
and competitive environments. Anthony and Ramesh (1992) is one of the ?rst studies in
accounting to use the life cycle hypothesis in explaining stock market responses to two
accounting performance measures: sales growth and capital investment. Using dividend
payout, sales growth and ?rm size as the proxies for ?rm life cycle, they ?nd that
response coef?cients of unexpected sales growth and unexpected capital expenditures
decline monotonically fromthe growth to the stagnant stages. Black (1998) examines the
value relevance of earnings and the components of cash ?ows in each of four life cycle
stages: start up, growth, maturity, and decline. Black ?nds that neither net income nor
cash ?ow from operations (CFO) is value relevant in the start-up phase. CFO becomes a
signi?cant explanatory variable in each of the growth, maturity, and decline phase.
Net income is signi?cant only in the maturity stage. Dickinson (2007) develops and
validates a parsimonious proxy for ?rm life cycle based on the patterns of a ?rm’s
operating, investing and ?nancing cash ?ows.
This paper adopts the life cycle methodology developed by Dickinson because the other
life cycle proxies inherentlyassume a uniformdistributionof life cycle stages (Anthonyand
Rameh 1992, for example). The combination of cash ?ow patterns used by Dickinson, on
the other hand, “represents the ?rm’s resource allocation and operational capabilities
interacted with the ?rm’s choice in strategy”. Four life cycle stages based on the ?rm’s
operating, investing, and ?nancing cash ?ows are developed. The introduction stage is
characterised by negative operating, negative investing, and positive ?nancing cash ?ows.
ARJ
23,2
204
D
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Table VI.
Firm pro?tability and
incremental value
relevance of revenue
Accounting
performance
measures
205
D
o
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d
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y
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O
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C
H
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R
R
Y
U
N
I
V
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R
S
I
T
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t
2
1
:
1
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
The growth phase is characterised by positive operating, negative investing, and positive
?nancing cash ?ows. The mature stage re?ects positive operating, negative investing, and
negative ?nancing cash ?ows. Finally, the decline stage is characterised by negative
operating, positive investing, and positive/negative ?nancing cash ?ows patterns.
The regression results reported in Table VII reveal that combined revenue and
earnings coef?cients are statistically highly signi?cant only in the growth stage. Firms
with signi?cant growth opportunities are likely to have experienced positive unexpected
(abnormal) earnings in recent periods which are expected to persist. Additionally,
growth stage ?rms may be associated with positive net present value investment
opportunities, and unexpected revenue as well as earnings may be used as a proxy to
infer changes in market expectations about such opportunities (Charitou et al., 2001).
Firms in the growth stage continue to invest not only in ?nancial and tangible assets, but
also in organisational capital which allow a ?rm to earn temporary monopoly rents.
Growth ?rms, however, are more likely to manipulate reported information for the
purpose of avoiding a negative stock price reaction. For example, Skinner and Sloan
(2002, p. 299) ?nd that growth ?rms missing analysts’ forecasts by 0.5 percent of the
stock price suffer a signi?cantly negative abnormal return of 210 to 215 percent.
However, ?rms in the growth phase need to signal private information to the
marketplace, because it is relatively dif?cult for outsiders to monitor operating
performance of such ?rms owing to the unique nature of the assets these ?rms
possess (intangible-intensive industries). Therefore, managers could judiciously use
Variables Introduction Growth Maturity Decline
INTERCEPT (a
0
) 0.52
*
(2.95) 0.21
*
(2.64) 0.07 (1.34) 0.58
*
(2.79)
REVLEV (a
1
) 20.11
* * *
(21.75) 0.00078 (0.09) 0.16
*
(3.14) 0.0013 (0.09)
REVGR (a
2
) 0.02 (1.03) 0.18
*
(4.79) 0.02 (1.03) 0.04 (1.52)
EARLEV (a
3
) 20.04 (20.83) 0.16 (1.16) 0.49
*
(3.44) 0.06 (0.73)
EARGR (a
4
) 0.16
*
(2.76) 0.34
*
(2.82) 0.27
*
(2.36) 0.03 (0.61)
Adjusted R
2
0.08 0.13 0.15 0.11
(a
2þ
a
3
) 20.09 [20.50] 0.18
*
[4.86] 0.19
* * *
[1.81] 0.043 [1.41]
(a
4þ
a
5
) 0.12
* * *
[1.93] 0.50
*
[3.27] 0.76
*
[4.47] 0.09 [1.06]
Year and industry
dummies Included Included Included Included
Observations 2,461 1,692 2,113 848
Notes: Signi?cance at:
*
1,
* *
5, and
* * *
10 per cent levels, respectively; the table reports coef?cient
estimates and t-statistics from the following regression equation:
RETURN
it
¼ a
0
þa
1
REVLEV
it
þa
2
REVGR
it
þa
3
EARLEV
it
þa
4
EARGR
it
þ1; ð2Þ
Firm life cycle is operationalised following Dickinson (2007) model. She develops four life cycle stages
based on the patterns of a ?rm’s operating, investing and ?nancing cash ?ows. Introduction stage is
characterised by negative operating, negative investing, and positive ?nancing cash ?ows. Growth
phase is characterised by positive operating, negative investing, and positive ?nancing cash ?ows.
Mature stage re?ects positive operating, negative investing, and negative ?nancing cash ?ows.
Finally, decline stage is characterised by negative operating, positive investing and positive/negative
?nancing cash ?ows patterns. REVLEV and EARLEV are revenue and earnings levels, respectively,
and REVGR and EARGR are revenue and earnings growth, respectively, calculated as the difference
between current year revenue (earnings) and last year revenue (earnings) divided by lagged MVE.
EAR is de?ned as operating earnings (EBIT)
Table VII.
Firm life cycle stages and
incremental value
relevance of revenue
ARJ
23,2
206
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o
a
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d
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C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
the reporting ?exibilities offered by the generally accepted accounting principles
(GAAP) to increase the value-relevance of accounting information primarily via the use
of discretionary accruals (DACCR). Although these competing hypotheses have not been
tested in this paper, the sign of the coef?cients suggests an ef?cient use of DACCR by
managers of growth ?rms.
In the introduction stage of the life cycle the combined earnings coef?cients are
marginally signi?cant while that of revenue is insigni?cant implying that ?rms make
investments in the introduction phase with the expectation of generating more revenues
and earnings in the growth phase. The combined earnings coef?cient is highest in the
maturity stage (0.76) and this life cycle stage also results in the highest explanatory
power for market adjusted returns. Combined revenue coef?cient is positive and
statistically signi?cant at better than the 1 percent level in both the growth and maturity
stages of the ?rm life cycle (0.18 and 0.19, respectively). Consistent with predictions,
none of the earnings and revenue variables is statistically signi?cant inthe decline stage.
Anthony and Ramesh (1992) report the largest coef?cient on the sales growth variable
during the growth phase (a coef?cient of 0.21), but this monotonically decreases
to 20.0022 in the stagnant phase. Overall, evidence clearly suggests differential pricing
of earnings and revenue information in different life cycle stages.
4.6 Robustness checks
4.6.1. Test of non-linearity. The main results reported in the study assume a linear
association between market returns and the unexpected accounting performance
measures. However, prior capital market research has shown that the relationship
betweenunexpectedearnings andmarket returns assumes a non-linear S-shapedpattern
(Freeman and Tse, 1992). This is because tails of the unexpected earnings distributions
are dominated by transitory earnings which are dif?cult to forecast and therefore
generate lower marginal price response to unexpected earnings shock. To test for the
non-linear effect in the association between market return and unexpected performance
measures The following regression adapted from Chandra and Ro (2008) is estimated:
RETURN
it
¼ g
0
þg
1
ðPerformance Measure jÞ
it
þg
2
ðDPerformance Measure jÞ
it
þg
3
ðNLDPerformance Measure jÞ
it
þg
4-14
YRDUM
þg
15-32
INDUM þ1
it;
ð1aÞ
where NLDPerformance Measure j is the product of DPerformance Measure j and the
absolute value of DPerformance Measure j. If the association between market return and
performance measures is S-shaped then the coef?cient g
3
will be negative. Unreported
results indeed ?nd that the coef?cients are all negative and statistically signi?cant, with
the exception of the OCF variable, con?rming the non-linear relationship. To further
explore the non-linear association between market returns and unexpected revenue in
the presence of unexpected earnings, the following modi?ed non-linear regression is
estimated:
RETURN
it
¼ a
0
þa
1
REVLEV
it
þa
2
REVGR
it
þa
3
NLREVGR
it
þa
4
EARLEV
it
þa
5
EARGR
it
þa
6
NLEARGR
it
þa
7-15
YRDUM þa
16-33
INDUM þ1
it
ð2aÞ
Accounting
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Again, a non-linear relationshipwouldresult inthe a
3
anda
6
coef?cients beingnegative.
Unreported results ?nd evidence consistent with a non-linear relationship. In particular,
the coef?cients on a
3
and a
6
are 20.03 and 20.25, respectively, which are statistically
signi?cant at better than the 1 percent level.
4.6.2 Alternative return measure. As an alternative market measure to
MKTADJRET, buy-and-hold annual returns (BHRET) are used and the results are
almost identical to those previously reported. This is not surprising given that the
pair-wise correlation between the two market return measures is 0.98.
5. Concluding remarks
Financial statements provided by corporate managers contain an array of ?rm
performance measures for current and prospective investors to evaluate in investment
decision making. Market reaction to different performance line items could be different
because of differences in persistence among the measures, as well as differences in ?rm
characteristics that could make one performance measure superior to another. This
paper attempts to shed light on the relative superiority of alternative accounting
performance measures in Australia. Results reveal that EBITDA has the highest
explanatory power, followed by TOTREV number. With respect to the impact of ?rm
size, this study ?nds that for small and medium sized ?rms TOTREV has the highest
explanatory power, while the corresponding variable for large ?rms is OPINC.
Regarding the incremental value relevance of revenue in explaining contemporaneous
stock returns, this paper shows that the combined ability of earnings and revenue to
explain stock returns has not diminished, but the explanatory power of incremental
revenue has signi?cantly declined over the sample period. However, the latter result is
primarily driven by young ?rms. Further analysis reveals that the combined revenue
regression coef?cients are positive and statistically signi?cant for ?rms reporting
negative earnings, but the combined earnings coef?cients are not. For pro?t making
?rms, the combined earnings coef?cients are much higher than their revenue
counterparts. Finally, life cycle analysis shows that both the combined revenue and
earnings coef?cients are statistically signi?cant only in the growth stage, while both
earnings variables are signi?cant in the maturity stage.
The results of this study will be of practical use to prospective investors and
accounting regulators. When making equity valuation decisions investors consider
?rms’ fundamentals as re?ected in ?nancial statements. However, which line item is
more important for equity valuation is an important consideration. Because of
differences in persistence among performance measures, investors need to know which
of the measures is more strongly associated with investors’ beliefs as re?ected in stock
market returns. A recent comprehensive survey of chief ?nancial of?cers (CFO) by
Graham et al. (2005) shows the GAAP earnings number, especially the earnings per
share, is the key metric upon which the market focuses. This is mainly because
investors need a simple benchmark to evaluate a ?rm’s performance that reduces the
costs of information processing due to information overload (Graham et al., 2005, p. 21).
However, evidence from this study ?nds that investors do factor in ?rm-speci?c
characteristics in weighing alternative performance measures. The ?nding that there
has been no systematic decline in the value relevance of accounting earnings in
Australia will enhance the con?dence of accounting regulators and corporate
stakeholders regarding accounting information quality.
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Notes
1. Interestingly, increasing value-relevance of revenue information is reported at a time when
the allegations about revenue manipulation are also mounting. Anecdotal evidence shows
that a signi?cant proportion of earnings management cases take the form of revenue
manipulation. For example, in a recent report by the General Accounting Of?ce (GAO) on
?nancial restatements, revenue recognition-related restatements account for about
37 percent of the 919 restatements announced during 1997-2002 (GAO, 2003). Similarly,
Dechow and Schrand (2004) indicate that over 70 percent of the 294 Securities and Exchange
Commission Accounting and Auditing Enforcement Releases they examine involve
overstated revenue.
2. Research on the value relevance of revenue gained momentum during the dot-com bubble in
the late 1990s, when many ?rms – mainly in the technology sector – earning minimal pro?ts,
or incurring losses, had large market valuations based on sales growth. Trueman et al.
(2000, 2001) ?nd that earnings are unrelated to stock price on average for internet ?rms, but
that both ?nancial and non?nancial measures of revenue are value relevant. Davis (2002)
reports that revenue surprises are positively related to announcement-period abnormal
returns for a sample of internet ?rms.
3. It is important to note that returns and levels speci?cations do not address the same research
question. Returns speci?cation can be used to determine whether independent variables
re?ect information that over a speci?c period of time: the returns window; causes investors
to change their beliefs and, consequently, prices (i.e. used to capture the timeliness notion of
?nancial reporting). On the other hand, levels speci?cation can be used to determine whether
accounting variables re?ect information associated with information used to price shares
over all periods up to a speci?c point in time. The research design of this study re?ects the
timeliness notion of performance measures, and hence uses returns speci?cation. Beaver
(2002, pp. 461-2) cautions that changing the form of the variable may fundamentally change
the question addressed. He notes:
One chooses the levels design when the problem is to determine what accounting
numbers are re?ected in ?rm value, whereas one chooses the ?rst difference research
design when the problem is to explain changes in value over a speci?c period of time.
Hence, in the ?rst differences formulation, the issue of the timing of information is
important.
Gonedes and Dopuch (1974) argue that returns models are theoretically superior to price
models in the absence of well-developed theories of valuation. Lev and Ohlson (1982)
describe the two approaches as complementary. Landsman and Magliolo (1988) show that
levels models can dominate returns models, for example, when model parameters and
omitted variables are not inter-temporally constant.
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Further reading
Francis, J. and Schipper, K. (1999), “Have ?nancial statements lost their relevance?”, Journal of
Accounting Research, Vol. 37, pp. 319-52.
Corresponding author
Ahsan Habib can be contacted at: [email protected]
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints
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