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Analysts Consensus Fixation and Corporate Investment
First Draft: September 15, 2006
This Draft: May 25, 2007
Sébastien Michenaud*
Abstract
This paper empirically investigates whether executives alter capital budgeting decisions to
meet or exceed analysts’ earnings per share (EPS) consensus forecasts. I find that firms increase
their likelihood to meet or beat analyst EPS consensus forecasts by reducing investment. A
reduction in investment creates positive earnings surprises through a reduction in depreciation costs
and collateral cash expenses. Furthermore, firms reduce investment when analyst pressure is high at
the start of the year (as measured by abnormally high consensus forecasts early in the year). Firms
with better investment opportunities are more inclined to reduce investment to create earnings
surprises and tend to reduce more investment as a response to analyst pressure. In addition, firms
with lower managers’ entrenchment are also more inclined to reduce investment to create earnings
surprises. This pattern is consistent with underinvestment in response to analyst pressure, a
behavior that is harmful to shareholders’ interests. Taken together these pieces of empirical
evidence point to the harmful pressure exerted by the analysts’ forecasting activity on capital
budgeting decisions.
* HEC School of Management, Paris, 1 rue Libération, 78351 Jouy en Josas, Cedex, France, and Swiss
Finance Institute-FINRISK, University of Lugano, Via G. Buffi 13, 6904 Lugano, Switzerland.
Email: michenauds@hec.fr.
I would like to thank François Degeorge, Thierry Foucault, Francesco Franzoni, Ulrich Hege, Frédéric
Palomino and David Thesmar for helpful discussions and suggestions. All errors are my own. Financial
support from FINRISK is gratefully acknowledged.
1
1 Introduction
Analysts have often been accused of exerting excessive harmful short-term pressure on
managers in the United States. Several empirical studies have confirmed the propensity of U.S.
companies to misrepresent financial information to meet or exceed earnings thresholds. For
example, Degeorge, Patel and Zeckhauser (1999) document that the distribution of earnings forecast
errors exhibit patterns strongly suggestive of earnings management to reach earnings targets set by
analysts. Jensen and Fuller (2002) argue that the “earnings game” in which managers are stuck
may represent important real costs for the firm that go beyond the misrepresentation costs induced
by pure accounting earnings management. Managers may take business decisions for the sake of
meeting or exceeding aggressive, sometimes unrealistic, analyst expectations. This conjecture is
somewhat difficult to observe empirically, as we cannot observe the valuable investment projects
that have not been pursued by companies as a result of this supposed adverse pressure. But Graham,
Harvey and Rajgopal (2005) provide evidence, in a survey of U.S. CFOs, that managers are actually
acting as suggested by Jensen and Fuller. Among other things, they find that 55% of the U.S. CFOs
declare themselves quite ready to destroy firm value, by reducing investment in positive NPV
projects, in the hope of reaching their desired earnings targets.
I build on this previous work and examine the consequences of earnings management for
corporate investment. I propose to empirically investigate whether managers alter corporate
budgeting decisions to meet or beat analysts’ consensus earnings per share (EPS) forecasts.
This work rests on two main assumption building blocks. I first posit that corporate investments
may have a direct impact on a firm’s earnings. Postponing or canceling certain corporate
investments may mechanically reduce expenses in the income statement, and result in higher
earnings relative to what they would have been, had the investment gone through. Consider a firm
that plans to start new operations. Such an investment is usually associated with a ramp up phase
during which associated costs - e.g. personnel, advertising, rental and depreciation costs - that
appear in the income statements need to be incurred, while revenues generated from the project will
materialize in later periods. Investing in this project may actually jeopardize the meeting of
analysts’ EPS forecasts for the period considered, and probably also for later periods, as earnings
will be hurt. If meeting financial analysts’ consensus forecasts is important to the CEO, and analysts
cannot be tamed to revise their consensus downwards, he may well be tempted to either postpone
the investment to a later period, or to cancel it altogether. Investment will mechanically reduce net
income through depreciation costs. Indeed, depreciation is an accounting item that is directly
impacted by an increase in investment. I actually find that the economic impact of reducing
corporate investment on net income through the depreciation channel is quite large: a reduction in
investment by one (within-firm) standard deviation results in an estimated increase of 50% in mean
2
net income. In addition, using quarterly data on my sample of firms, I find that investing in one
quarter increases Selling General and Administrative Expenses (SG&A) beyond the increase in
depreciation the following quarter, suggesting that corporate investment generates additional
expenses in the income statement that could potentially act as an impediment to reaching earnings
targets.
Second, I assume that earnings are an important metric to managers, and that they care about
them. Graham, Harvey and Rajgopal (2005) report that CFOs overwhelmingly consider EPS as the
most important metric for firm performance, despite the strong emphasis of cash flow in the
academic finance literature. Meeting or exceeding certain EPS thresholds is considered very
important by managers, because not doing so is perceived as being strongly penalized in the stock
market. Skinner and Sloan (2002) and Kinney, Burgstahler and Martin (2002) show small negative
earnings surprises are associated with significant negative stock price reactions.
Based on these assumptions, I test the hypotheses that (i) firms reduce investment to increase
their probability of meeting or beating the analysts’ earnings consensus forecast and (ii) firms that
face excessive pressure to attain analysts EPS forecasts reduce their investment. While hypothesis
(i) is straightforward to test, notwithstanding potential endogeneity issues, I need some measure of
the excessive pressure exerted by analysts on management early in the year to test hypothesis (ii). I
build a proxy that is a measure of the abnormal level of EPS expected growth for the year relative to
last year’s realized EPS. It is constructed in two steps. I first measure the consensus analysts
forecast growth in EPS relative to last year’s realized EPS. I then regress this measure against past
firm’s performance and characteristics, and consider the residual as the abnormal level of analyst
forecast EPS expected growth.
I use an unbalanced panel of U.S. firms covered by analysts over the period 1981-2005 built
from the merged CRSP-Compustat Industrial database and I/B/E/S and test two different
econometric specifications corresponding to the two hypotheses presented above. The first model is
a fixed effects logit model where a dummy for non-negative earnings surprises is regressed against
corporate investment and a number of standard controls in the earnings surprises literature. The
second specification is a capital investment model where investment is regressed against the proxy
for analyst pressure and a number of standard controls in the investment literature.
The results of this analysis support the above hypotheses, and are both statistically significant,
and economically important. Firms that reduce capital expenditures by one standard deviation
increase the probability of meeting or beating analysts’ consensus forecasts by twice as much as
when they increase accruals by one standard deviation. In the finance and accounting literature,
accruals management has been found to be the main variable through which managers are creating
earnings surprises (Brown (2001) and Burgstahler and Eames (2006)). In addition, firms that face
3
high analyst pressure at the start of the year decrease their investment by 0.1% to 0.2% of total
assets, a 2% to 3% decrease relative to median cross-sectional capital expenditures.
I also find that firms invest more in the year following earnings surprises, suggesting that the
reduction in investment enabling firms to exceed the consensus forecast is partially caused by
project postponement from one fiscal year to the next. Because of the time value of money, project
postponement will cause loss of value. Say cost of capital is 15%: the net present value of the
postponed growth opportunities is reduced by 15%, not a trivial amount.
The causality link between investment and earnings surprises in the logit specification is not
necessarily obvious because these two variables are determined during the same period. Our
specification could thus suffer from simultaneity bias. Two stories could explain the simultaneous
negative correlation between investment and earnings surprises. The first story argues that
investment decisions could be jointly determined by a shock that also affects analysts’ prediction
abilities in the same fiscal year. Analysts would be too pessimistic about firms experiencing a
negative shock that also negatively impacts investment decisions. This story is inconsistent with the
evidence in Elliott, Philbrick and Weidman (1995). These authors find that analysts actually
underreact to bad news occurring during the contemporaneous year. Positive earnings surprises
occur less often when firms experience a negative economic shock. Alternatively, another story
argues that CEOs deliberately engage in cost-cutting or restructuring plans. Such plans would
simultaneously result in investment reduction at the firm level. Analysts would be too pessimistic
about the positive effects on earnings of such plans, and positive earnings surprises would be easier
to achieve. Such story is inconsistent with the finding that analysts revise forecasts downwards after
restructuring news but remain too optimistic about future earnings (Chaney, Hogan and Jeter
(1999)). It actually becomes harder to create positive earnings surprises. As a result, simultaneity
biases should not be too much of an issue. However, to further rule out any concerns about the
direction of the causality between investment and earnings surprises, I use quarterly data from
I/B/E/S and Compustat, and find that meeting or beating consensus forecasts in the last quarter of a
fiscal year is negatively correlated with investments in previous periods. Earnings surprises for the
year are also negatively correlated with investment in the previous fiscal year. Thus, it appears that
investment decisions taken early in the fiscal year influence the outcome of meeting or beating
analysts’ consensus forecasts in later periods.
An interesting question is whether the reduction in investment induced by managers’ fixation
on consensus forecasts corresponds to a reduction of overinvestment (e.g. empire building, pet
projects), or the passing up of valuable investment opportunities. To address this question, I use two
different approaches.
4
First, I run regressions on portfolios of firms ranked by their governance index. Firms with
good governance are expected to suffer less from overinvestment than firms with bad governance
(Jensen, 1986) because managers’ monitoring by shareholders and the board should be tighter. The
managers of these firms are also typically less entrenched, so they should be more prone to be fired
after bad firm performance (Fisman, Khurana and Rhodes-Kropf (2004)). The results suggest that
the decreased investment related to EPS consensus beating is not more pronounced for firms that
have lower quality governance charters. On the contrary, firms ranked by a manager’s entrenchment
index (Bebchuk, Cohen and Ferrell (2004)) or the Gompers, Ishii and Metrick (2003) governance
index only display a significant negative effect of investment for the good governance firms. This
suggests that the short-term pressure created by the analysts’ forecasting activity induces managers
to invest less to avoid negative earnings surprises. By doing so, they also reduce the risk of being
ousted by shareholders unhappy about the firm’s stock price performance.
Second, I sort firms by the size of their investment opportunity set, as proxied by their Tobin’s
Q. If the reduction in investment related to earnings surprise or related to analyst pressure occurs
because it purges excess investment, it should be stronger for firms with low investment
opportunities (those are firms for which, for a given level of investment, the percentage of
investment that is excessive is higher). It turns out that firms with average to good investment
opportunities tend to decrease more their investment to beat consensus forecasts than firms with bad
investment opportunities. This is also consistent with findings by Degeorge, Patel and Zeckauser
(2007) who show that glamour firms worry more about missing the consensus as they have more to
lose. These patterns are consistent with underinvestment in response to analyst pressure: firms
cancel or postpone good projects to avoid negative earnings surprises.
This work contributes to the literature in several respects. First, it adds to the large existing
earnings surprises literature by pointing to a new variable through which managers create non-
negative earnings surprises. The literature had previously identified accruals management (Brown,
2001 and Burgstahler and Eames, 2006) and forecasts management (Matsumoto, 2002 and
Burgstahler and Eames, 2006) as the main levers through which CEOs create non-negative earnings
surprises. Investment in fixed assets had never been considered previously. This is an important
finding, as changes in investment affect the firm’s real activities, as opposed to accounting
manipulations. It suggests that the analysts’ forecasting activity represents an important factor in
corporate financial decisions. Second, this work adds to the fairly recent corporate finance literature
linking financial analysts’ coverage with firms’ corporate finance decisions. Chang, Dasgupta and
Hilary (2006) and Doukas, Kim and Pantzalis (2006) studied the analysts’ coverage influence on
financing and investment decisions, while Li and Zhao (2006) worked on the analysts’ impact on
dividend policy. These studies concluded that analysts reduce information asymmetry between
5
managers and the stock market. These findings suggest that analysts have a beneficial influence on
corporate financial policies in the sense that they impose less financial constraints on firms, be it
costs of equity issuance or costly dividend signaling. On the other hand, Doukas et al. (2006) also
argue that reduced financing constraints owed to abnormal excess analyst coverage result in
overinvestment and subsequent low excess returns. In contrast with these findings, I find that
analysts exert adverse short-term pressure on the firms’ capital budgeting decisions, and that they
induce underinvestment rather than overinvestment in the short term. Finally, this work supports the
findings by Graham, Harvey and Rajgopal (2005) in a survey of 400 U.S. CFOs, where managers
recognized they might reduce investment to meet their desired earnings benchmarks. It suggests
that the “beliefs” described by CFOs in this survey are actually in the works in the U.S. stock
market. Managers do succeed in creating earnings surprises when they reduce investment.
The paper is organized as follows: section 2 develops the main working hypotheses; section 3
presents the empirical strategy while section 4 briefly describes the data. Section 5 presents the
main empirical results while section 6 presents robustness checks and section 7 concludes.
2 Hypothesis development
I posit that corporate investments may have a direct impact on a firm’s earnings, and that
managers, who care about earnings and the target set by the analyst EPS consensus forecast, adjust
their investment policy to meet or exceed this target.
The main hypotheses of this work are based on two main arguments.
I first assume that corporate investment has a direct impact on a firm’s earnings. There are two
reasons why this assumption holds true. The first and most straightforward reason is that corporate
investment directly impacts depreciation expenses. Depreciation is a non-cash cost that depends
directly on the level of past and contemporaneous investment in fixed assets. Capitalized fixed
assets that have not been fully depreciated still generate depreciation expenses up until the end of
their depreciation period, their assumed useful economic life. This is based on usual accounting
conventions for each class of asset. New additions in fixed assets during the year, capital
expenditures for the year, generate depreciation expenses in the same fiscal year. The influence of
corporate investment on depreciation expenses depends on several factors, like the depreciation
method that is used (straight-line, or accelerated depreciation), the class of fixed assets that have
been acquired, and various other exemptions and options left at the discretion of managers. Section
5.2 empirically investigates the economic impact that such a direct effect has on net income. The
depreciation effect on earnings appears to be quite large and significant.
In addition, I argue that the influence of corporate investment on net income goes beyond this
direct channel. Indeed, postponing or canceling certain corporate investments may reduce, although
6
with some lag, other expenses in the income statement and result in higher earnings. Consider a
firm that plans to start new operations, e.g. a plant or a retail store. Such an investment is usually
associated with a ramp up phase during which costs that appear in the income statements need to be
incurred. Workers of the plant or store will be hired and trained, some of the equipment and
property required for the project may be leased, not purchased, so additional expenses that are
directly associated with the project will appear in the income statement. In addition, advertising
expenses may be incurred to promote the new products manufactured or the retail store. The
following excerpt from the 2006 annual Apple 10K report illustrates the above discussion. In this
example, an increase in capital expenditures related to an aggressive strategy of retail stores
development leads to a series of additional costs that the company needs to incur
contemporaneously.
“Through September 30, 2006, the Company had opened 165 retail stores. The Company’s
retail initiative has required substantial investment in equipment and leasehold
improvements, information systems, inventory, and personnel.”
“[…] a relatively high proportion of the Retail segment’s costs are fixed because of
personnel costs, depreciation of store construction costs, and lease expense.”
Apple 10K report, September 2006
Based on the above discussion, it appears that investment in fixed assets will generate several
collateral cash expenses: labor, advertising, leases expenses.
On the other hand, revenues generated from the plant or store will materialize in later periods.
As a result, investing in such a project could jeopardize the meeting of financial analysts’ EPS
forecasts for the period considered, and probably also for later periods. Indeed, launching such a
project will lead to immediate additional costs that appear in the income statement, and to revenues
that will not materialize immediately. If meeting financial analysts’ consensus forecasts is important
to the CEO, and the CEO cannot manage analysts’ expectations downwards, he may well be
tempted to either postpone the investment to a later period, or to cancel it altogether. This
postponement decision will have a negative impact on the firm’s value because investment
opportunities have been abandoned to competitors for some time. More importantly, because of the
time value of money, such a decision destroys value to shareholders. The assumption described in
this example is supported by findings from Graham, Harvey and Rajgopal (2005) in a survey of 400
U.S. firms’ financial executives. The authors find that 55% of the interviewed CFOs state they are
ready to “delay starting a new project even if it entails a small sacrifice in value” to meet the desired
short-term earnings target.
The second assumption is that earnings are an important metric to managers, and that they care
about them. Graham, Harvey and Rajgopal (2005) report that CFOs overwhelmingly consider EPS
7
as the most important metric for firm performance, despite the strong emphasis of cash flow in the
academic literature. Meeting or exceeding certain EPS thresholds is considered very important by
managers, because not doing so would be strongly penalized in the stock market. Skinner and Sloan
(2002) show that growth firms that fail to meet earnings benchmarks such as analysts’ forecasts
suffer large negative price reactions on the reporting date. Graham, Harvey and Rajgopal (2005)
also report that CFOs believe that meeting such earnings benchmarks helps build the credibility
with the stock market and the external reputation of the management.
As a result of these two assumptions, I hypothesize that managers will take investment
decisions that are influenced by their willingness to alter earnings outcomes. I am particularly
interested in analysts’ earnings forecasts outcomes for two reasons. First, analysts’ consensus
forecasts are a pure collective construct from the financial analysts community. They are therefore
an ideal variable against which one can measure the influence of analysts on the managers’
decisions. Second, these forecasts have been shown in the literature to be important benchmarks
that are targeted by managers (Degeorge, Patel Zeckhauser (1999)) and the earnings surprises are
influenced by discretionary expenses and accruals management (Brown (2001) and Burgstahler and
Eames (2006)) and analysts’ expectations management (Matsumoto (2002)). Results from a survey
of CFOs by Graham, Harvey and Rajgopal (2005) confirm these earnings management practices are
widespread. They also suggest that managers postpone investment to create positive earnings
surprises. If, as suggested by these authors, managers postpone investment to create positive
earnings surprises, we would expect lower investment to increase the probability of positive
earnings surprises in the same fiscal year, and higher investment in the year following past positive
earnings surprises.
Based on these assumptions and findings, I test the following two hypotheses:
Hypothesis H1: Firms reduce investment to meet or beat analysts’ EPS consensus forecasts.
Hypothesis H2: Firms’ reduction in investment to beat EPS forecasts is temporary: the investment
will be higher following past positive earnings surprises.
Notice that there are several reasons why hypothesis H1 could be difficult to identify in the
data.
First, there is a lot of information asymmetry between the manager and the firm’s outsiders
concerning capital budgeting decisions: as a firm outsider, the researcher cannot observe the firm’s
investment opportunity set and the planned timing of such projects. This is an important benefit to
the manager, because, contrary to earnings manipulation that can be identified through careful
analysis of financial statements, an investment project cancellation or postponement could easily be
unnoticed by the firm’s outsiders, unless it is large enough.
8
Second, managers have a wide array of less costly instruments at their disposal to create
positive earnings surprises. We know from Matsumoto (2002) and Burgstahler and Eames (2006)
that managers use both earnings management and analysts’ expectations management to create
positive earnings surprises. Earnings management is carried out through accruals management.
Managers increase the non-cash component of earnings to increase EPS and meet or beat EPS
forecasts and other earnings thresholds. Such a manipulation is costly because managers will have
to decrease the non-cash components of earnings in the future to make up for past increases, and
because such an action is potentially visible to the careful investor. Managers will also significantly
increase the probability to create positive earnings surprises by guiding analysts’ forecasts
downwards. Doing so will make analysts’ consensus forecasts easier to attain. Forecast
management will be costly because managers will exert effort in convincing analysts that their
forecast are too high, and because the induced downwards forecast revisions may have a negative
impact on stock prices in the short term. However, earnings management and earnings guidance
cause significantly lower firm value destruction than project postponement or cancellation, because,
for the most part, they only involve accounting and expectations manipulations, and as such, they
should not influence too much real firm activities1. Firms are therefore expected to resort less to
investment reduction than earnings or expectations management to create positive earnings
surprises.
All the above arguments go against finding a significant effect of investment on positive
earnings surprises in the data. They suggest it is important to select a precise enough econometric
specification to identify the hypothesized effects.
In addition to the added difficulty in identifying hypothesis H1, the above discussion also
suggests that those firms that face high pressure from analysts early in the year will also be more
likely to reduce investment. I implicitly assume here that firms under high analyst pressure will find
it hard to avoid negative earnings surprises through the sole use of earnings management and
expectations management. Furthermore, in order to increase EPS through investment reduction,
managers need to plan such an investment reduction sufficiently ahead of time. I expect managers
to respond to early signals sent by analysts that current fiscal year EPS forecasts would be difficult
to achieve in the fiscal year without reducing investment, a costly decision.
Hypothesis H3: Firms under high analyst pressure will reduce investment.
Firms that face high analyst pressure will typically be firms that start the fiscal year with an
analysts’ EPS consensus forecast for the current year that is excessively high. I will define more
precisely what I mean by “excessively high” later on.
1 Roychowdhury (2006) provides an indirect evidence of earnings management through real activities (e.g. sales at
discount prices to boost EPS). In this case, earnings management destroys value to shareholders and involves more than
misrepresentation costs due to accruals management.
9
3 Empirical Strategy
To test hypothesis H1, I use a panel logit specification, where a dummy variable for meeting or
beating the analysts’ consensus forecast is regressed against corporate investment and various
standard control variables used in the literature. I take advantage of the dataset’s panel structure to
control for the unobserved time invariant firm heterogeneity, running all logit regressions with firm
fixed effects. I also add year fixed effects to control for time variant unobserved heterogeneity.
Wooldridge (2002, p. 491) argues that the logit panel regression with fixed effects is the least
restrictive specification for binary response models with panel data. It does not make any
assumption about the relationship between the unobserved heterogeneity and the independent
variables, contrary to random effects specifications2. Therefore I use the logit fixed effects model.
This choice is further justified by the results of a Hausman (1978) specification test that strongly
rejects the logit with random-effects model against the logit with fixed-effects model with a
significance level of 0.01%. The estimated coefficients are therefore within-firm effects. The
baseline equation for this specification is presented below:
Pr(Above EPS consensus forecastit = 1) = F( t + i+ . Capital expendituresit+ .CONTROLSit 1+ it )
F( X) =e X
1+ e X (1)
where F(.) is the logistic cumulative distribution function, is the column coefficient vector and X
is the sample observations matrix. If hypothesis H1 is verified, the coefficient on will be negative.
One could be concerned about simultaneity bias because Above EPS consensus forecast and
Capital expenditures are determined over the same period. Non-negative earnings surprises may be
jointly determined by an unobserved factor. Two different stories would be consistent with this
potential issue.
First, investment decisions could be jointly determined by a shock that also affects analysts’
prediction abilities in the same fiscal year. In this story, analysts are too pessimistic about firms that
experience a negative shock that negatively impacts investment decisions. This story is in
contradiction with the empirical evidence by Easterwood and Nutt (1999) and Elliott, Philbrick and
Weidman (1995). The former authors find that analysts underreact to bad news and overreact to
good news contained in the prior year’s performance. The latter authors find that analysts
2 I do not use probit panel regressions with fixed effects because they suffer from inconsistent parameters estimation
(Wooldridge (2002), p.484). Probit panel regressions with random effects suffer from the assumption of independence
between the unobserved effects and independent variables and the normality assumption on the unobserved effects, a
“strong assumption” according to Wooldridge (2002, p.485).
10
underreact to bad and good news within the forecast year. This empirical evidence is consistent
with bad news being associated with less positive earnings surprises, a story that is inconsistent
with reduced investment being associated with positive earnings surprises. Nevertheless, to further
rule out any concerns about this issue, I control for past operating performance and analysts’
forecasts revisions within the fiscal year in some of the model specifications.
Alternatively, investment decisions could be jointly determined by a shock that also affects the
firms’ earnings in the same fiscal year. In this story, firms reduce costs and increase the probability
of positive earnings surprises at the same time as they reduce capital expenditures. Note that if this
shock was exogenous, this story would be in contradiction with a neoclassical theory argument: if
costs go down due to an exogenous shock, investment opportunities should increase and so the firm
should invest more, not less. However, it could be that managers decide to reduce expenses and
investment in the same period, e.g. for restructuring purposes. Unfortunately, controlling for such
decision is difficult because our working assumptions confound the effects of investment and cost
reduction. According to hypothesis H1, I expect costs to be reduced, some of these costs with a lag,
as capital expenditures are reduced. One way to disentangle the two effects will be to use lagged
capital expenditures relative to earnings surprises. There should be no reason that a simultaneous
shock on capital expenditures and costs in earlier periods creates positive earnings surprises in later
periods, unless analysts overreact to restructuring news, anticipating too large reductions in costs. In
fact, Chaney, Hogan and Jeter (1999) find that firms announcing restructuring charges are also less
likely to create positive earnings surprises. Although analysts revise downwards their EPS
forecasts, they underreact to the restructuring news. As a result, one should not be too concerned
about the simultaneity of earnings surprises and lower investment due to the same restructuring
decisions.
Based on the above discussion, endogeneity problems should not be too much of a concern.
Nevertheless, to further strengthen results about the direction of causality, I use quarterly data from
I/B/E/S and Compustat to control for the timing of the investment decision relative to its effects on
earnings surprises. I then estimate the following model:
Pr(Above Q4 EPS consensus forecastit = 1) = F( t + i+ . Q(4 -k)Capital expendituresit+ .CONTROLSit 1+ it )
F( X) =e X
1+ e X (2)
where Above Q4 EPS consensus forecastit is a dummy variable for meeting or beating the analysts’
consensus forecast for the last quarter of the fiscal year t, and Q(4 - k) Capital Expenditures is
11
capital expenditures in quarter 4-k, with 1 k 3 (I use capital expenditures from one the first three
quarters). In addition, I also test specifications where the same dummy variable is regressed against
capital expenditures in the first half of the year (the sum of capital expenditures in the first two
quarters of the year). I further test another specification where Above EPS consensus forecastit is
regressed against Capital expenditures in the previous year. We will see that the results from model
(1) remain robust to these new specifications.
To test hypothesis H3, I use standard investment specification equations on unbalanced panels
and regress Capital expenditures against Analyst pressure and standard controls in the investment
literature. I also use firm fixed effects as in previous corporate investment studies (Baker, Stein and
Wurgler (2003) and Chen, Goldstein and Jiang (2006)). Year fixed effects are also added to the
specification. The corresponding baseline equation is as follows:
Capital expendituresit = t + i + . Analyst pressureit + .CONTROLSit 1 + it (3)
This empirical strategy allows me to measure the effects of Analyst pressure early in the year
on the investment policy for the same fiscal year. Notice that, because Analyst pressure is measured
early in year t while investment decisions are taken throughout the same fiscal year, the direction of
causality appears clearly from the model.
Finally, to test hypothesis H2, I look at the effects of having met or beaten analysts’ forecasts in
the previous year on investment for the current year. I use standard investment specification
equations on unbalanced panels and regress Capital expenditures against Above EPS consensus
forecasts in the previous year. The equation is identical to equation (2) except for the variable of
interest, Above EPS consensus forecasts:
Capital expendituresit = t + i + . AboveEPS forecastsit 1 + .CONTROLSit 1 + it (4)
4 Data
I construct the dataset from three main sources. The sample consists of U.S. firms listed in the
merged Center for Research on Security Prices (CRSP) - Compustat Industrial Annual database at
any point in time between 1981 and 2005. I also use the CRSP – Compustat Industrial Quarterly
database that I merge with the main database to obtain quarterly data for all firm-fiscal year
observations, when available. I exclude financial services firms (SIC code 6000-6900), regulated
utilities (SIC code 4900), firms with book values smaller than $10 million and firms with no analyst
coverage (i.e. not present in the I/B/E/S Historical Summary Files). I winsorize all variables except
Firm Age and Analysts at the first and ninety-ninth percentile. This helps mitigate the impact of
outliers and measurement errors in the data.
12
I obtain data on analyst coverage, EPS consensus forecasts, and EPS realizations from the
I/B/E/S Historical Summary Files. I use the I/B/E/S files that are unadjusted for stock splits: these
files are free of the important rounding errors first identified by Diether, Malloy and Scherbina
(2002) in the I/B/E/S adjusted files. These rounding errors resulted in important measurement errors
in the standard errors of analysts’ forecasts. They would also introduce measurement errors for
earnings surprises: firms may be wrongly classified as generating non-negative earnings surprises
when they are actually creating negative earnings surprises. The I/B/E/S files unadjusted for stock
splits require additional processing to properly account for stock splits between the date the
consensus EPS forecast is recorded and the EPS announcement date. I follow the procedure
recommended in WRDS by Robinson and Glushkov (2006) to adjust for stock splits from the
I/B/E/S unadjusted files. I also obtain governance index data from Andrew Metrick3 and
entrenchment index data from Lucian Bebchuk4.
The full sample includes 65,221 firm-year observations for an average of 2,609 observations
per year from 1981 to 2005. Before looking in details at the empirical strategy, I turn to the
construction of the main variables used in the baseline analysis.
Analysts’ Consensus Forecast
I construct the variable Above EPS consensus forecast as a dummy variable that is equal to 1 if
the firm’s EPS is larger than or equal to the analysts’ consensus forecasts and is equal to 0
otherwise. The baseline analysts’ consensus forecast is defined as follows. For each month before
the reporting of the actual EPS, the I/B/E/S Historical Summary Files provide a median of the
analysts’ EPS forecasts for the fiscal year. I use the latest median I/B/E/S EPS consensus forecasts
before the current fiscal year report date. This measure of the analysts’ EPS consensus forecast is
common in the literature. Other authors use the latest individual analysts’ EPS estimate before the
reporting of the EPS. As a robustness check, I use this alternative measure of EPS forecasts to
define my positive earnings surprise variable and find similar results.
Proxy for Analyst Pressure
I construct a proxy for the level of analyst pressure exerted on managers. To do so, I measure
the abnormal level of analysts’ expected increase in EPS relative to last year’s realized EPS. This
proxy measures whether managers face abnormally high or low EPS forecasts at the start of the
year. It is constructed in two steps. First, I compute the level of increase in EPS that is forecast by
analysts relative to last year’s realized EPS. This variable is measured early in the year after the
announcement date of last year’s EPS, so that analysts have updated their forecasts based on this
new piece of information. This measure, Forecast EPS change, is scaled by stock prices 90 to 120
3 http://finance.wharton.upenn.edu/~metrick/data.htm 4 http://www.law.harvard.edu/faculty/bebchuk/data.htm
13
days before last year’s EPS announcement. Stock prices are taken from I/B/E/S to ensure
consistency with the stock split adjustment process relative to EPS forecasts and EPS realizations.
Forecast EPS change = (EPS forecaststart of year t - actual EPSt-1)/Stock price t-1-90days
It turns out that Forecast EPS change is correlated with past firm’s performance and firm’s
characteristics. Analysts tend to be optimistic about firms that have had low past performance. They
predict higher EPS increase for firms that, in the previous year, had low Tobin’s Q, negative EPS,
low cash flow, large financial constraints, low analyst coverage, high forecast EPS increase in the
same industry (at the 3 digit SIC code level), small size, negative earnings surprises, and for which
analysts predict a turnaround (EPS is expected to be positive in the year and was negative last year).
To control for all these effects and introduce a measure of the abnormal level of analyst forecast
EPS increase, I regress Forecast EPS change against all these variables. The results are presented in
Table 1 in the appendix. This regression is a panel regression where the panel unit is the firm, and
year fixed effects are also included. The R squared of this regression is relatively high at 51%, thus
explaining a large portion of the variation in forecast EPS changes. That way, taking the residuals
of this regression, I obtain the within-firm Analyst Pressure variable that measures abnormal
pressure by analysts. By definition, Analyst Pressure is orthogonal to all the variables included in
the panel regression. It will be positive if the analysts’ forecast increase in EPS is too high relative
to past firm performance and characteristics. Managers will thus face abnormally high analyst
consensus EPS forecast at the start of the year in that case. Conversely, Analyst Pressure will be
negative if the analyst forecast increase in EPS is too low relative to past firm performance and
characteristics. Managers will thus face abnormally low analyst consensus EPS forecasts at the start
of the year in that case. I expect this proxy to be negatively correlated with investment, all else
being equal (Hypothesis H3).
[Insert table 1 about here]
Investment
I construct the main measure of corporate investment, Capital Expenditures, as capital
expenditures (Compustat item 128) scaled by beginning-of-the-year total assets (item 6)5.
It is important to stress that, if the hypothesis that CEOs reduce investment to meet earnings
forecasts is verified, they would like to hide this reduction in investment to the financial
community. They could do so by manipulating their accounts, so that they do not show that they are
investing less than what they should. Therefore, I should be particularly concerned about possible
accounting manipulations by CEOs to hide distortions in their capital budgeting decisions. To do
so, I only use a cash measure of investment that is less susceptible to accounting manipulations. I
exclude measures of investment such as capital expenditures plus research and development
5 All the results still hold when investment is measured as ratio of lagged gross property plant and equipment (item 7).
14
expenses, the baseline variable in Chen, Goldstein and Jiang (2006), because R&D is a noisier
variable of investment, for which managers have more accounting creativity leeway. R&D expenses
have been shown in the literature to be susceptible to accounting manipulation (see e.g. Bushee
1998), and much flexibility is left to managers to compute this expense. Likewise, measures such as
year-to-year changes in total assets are also excluded from the analysis because they include all
sorts of assets, including non-cash flow earnings components: accruals. As argued earlier, accruals
have been shown to be the main vehicle for earnings management: an increase in accruals is
positively correlated with firms meeting or beating analysts forecasts.
Controls
Based on previous studies on earnings surprises, I include several control variables in equation
(1) to control for accruals management, macroeconomic, industry and firm specific shocks, firm
past performance, firm size, firm risk, news arrival and uncertainty in the forecasting environment.
Accruals management has been shown to be an important variable through which firms create
earnings surprises (Brown, 2001, Burgstahler and Eames, 2006). The accounting literature on
earnings management has provided several methods to measure earnings management (Dechow et
al. 1995). Accruals represent the non-cash component of earnings. Earnings in year t are equal to
the sum of cash flows and accruals in the same year:
Earningst = Cash flowt + Accrualst
If managers have discretion in deciding the level of accruals for a given year, then we want to
measure the discretionary part of accruals. The simplest such proxy for discretionary accruals has
been provided by De Angelo (1986) who uses Changes in Total Accruals as a proxy for the
discretionary accounting adjustments6. Dechow et al. (1995) find that this measure efficiently
detects earnings management.
Changes in Total Accruals = Total accrualst - Total accrualst-1 = (DAt - DAt-1) – (NAt - NAt-1)
where DA is discretionary accruals and NA is normal accruals. If we assume that changes in normal
accruals are equal to zero on average, then changes in Total Accruals should reflect changes in
discretionary accruals. Following De Angelo (1986) and Dechow et al. (1995), I define Total
Accruals as follows: changes in current assets (item 4) minus changes in cash (item 1), minus
changes in current liabilities (item 5) plus current maturities of long term debt (item 44) plus
changes in income taxes payable (item 71) and I compute the difference between total accruals in
year t and total accruals in year t-1. There is one notable difference between my measure of total
accruals and the measure of accruals by the above authors. Because of the specific hypothesis I test,
I exclude depreciation expenses (item 14). Indeed, in my story, capital expenditures directly impact
6 For expositional clarity I rely on the simplest accruals management proxy. Using the more sophisticated modified
Jones (1991) model, following the procedure described in Dechow et al. (1995), provides similar results for all my tests.
15
depreciation expenses, and depreciation expenses will impact earnings to create earnings surprises.
Including depreciation expenses in the computation of Total Accruals could potentially reduce the
estimated effect of capital expenditures because of collinearity between these two variables7.
Prior research has documented that unexpected macroeconomic shocks affect earnings
surprises (O’Brien (1988)). Year-fixed effects provide control for general macroeconomic shocks. I
also include sales and cash flow to control for firm-specific shocks, and earnings surprises at the
industry level (at the 3 digit SIC code level) to control for industry shocks. Sales and Cash Flow are
respectively contemporaneous sales (item 12 in Compustat) scaled by lagged total assets (item 6),
and contemporaneous cash flow, computed as the sum of earnings before extraordinary items (item
18) and depreciation and amortization (item 14) scaled by start-of-year total assets (item 6).
Percentage above EPS forecasts in industry is the percentage of firms in the same industry that
created non-negative earnings surprises in the contemporaneous year, using the previously defined
variable Above EPS consensus forecasts.
We know from Degeorge, Patel and Zeckhauser (1999) that meeting or beating analysts’
expectations is less important for firms that incur losses. Therefore, I include a dummy variable for
firms that have posted positive EPS in the contemporaneous year.
Analysts underreact to bad news and overreact to good news in the prior year’s performance
according to Easterwood and Nutt (1999). Therefore, I include a control for firm past performance,
with Past profitability, defined as last year’s return on assets, (income before extraordinary items
(item 13) scaled by start-of-year Total Assets). Firms that had good past operating performance are
expected to create less positive earnings surprises while firms with bad past performance are
expected to create more positive earnings surprises. I also include a proxy for positive news arrival
by defining a dummy variable, Upwards consensus change, that is equal to 1 if the last analysts’
consensus forecast before EPS announcement is strictly larger than the first consensus forecast after
the previous fiscal year EPS announcement. Elliott, Philbrick and Weidman (1995) find that
analysts underreact to positive and negative news arriving during the forecasting period. As a result,
good news are positively associated with positive earnings surprises and bad news are associated
with negative earnings surprises.
Firms with high forecasting uncertainty are more likely to face analysts’ EPS consensus
forecast that are more easily attainable (Matsumoto, 2002). I include a control using the median
analyst consensus forecast standard deviation from the I/B/E/S Historical Unadjusted Summary
Files to avoid measurement errors (Diether, Malloy and Scherbina, 2002). Standard deviation of
forecasts is computed as the median of monthly EPS consensus forecasts standard deviation over
7 This modification is made purely on logical grounds. In practical terms, I find that the inclusion of depreciation in
Total Accruals computations does not reduce the significance of my results on Hypothesis H1, both for the De Angelo
(1986) model and for the modified Jones (1991) model presented in Dechow et al. (1995).
16
the fiscal year forecasting period. Analysts are also likely to forecast EPS with less accuracy for
young firms than for older firms, so I include a control for firm age, using a log transformation of
the number of years the company has been present in the Compustat Price Dividends and Earnings
database. In addition, in all the baseline regressions, I include analyst coverage to take into account
the total number of analysts issuing EPS forecasts in the year, and firm size to proxy for the public
information environment. Analysts follow more intensively large firms (Bhushan, 1989), and large
firms are generally considered to be the object of higher scrutiny by the investment community.
Earnings surprises may be more difficult to create for firms followed by a large number of analysts.
Indeed, Degeorge, Ding, Jeanjean and Stolowy (2005) and Yu (2006) show that high analyst
coverage reduces accruals management. I use a logarithm transformation of lagged Total Assets to
control for firm size.
Glamour firms are more likely to create earnings surprises than value firms because they may
have greater incentives to do so (Degeorge, Patel and Zeckhauser, 2007). To proxy value firms, I
use the ratio of tangible assets (item 7) to total assets.
Based on previous studies on corporate investment, I include several control variables in
equations (3) and (4) to control for investment opportunities, cash flow, analyst coverage, firm size,
financial constraints, firm risk, and the firm’s undervaluation.
In all of the investment equations, I include Tobin’s Q, a lagged market-to-book ratio, to
control for investment opportunities. Lagged market-to-book ratio is the market value of equity
(price times numbers of outstanding shares in Compustat, i.e. item 199 multiplied by item 25) plus
book value of assets minus the book value of equity (item 6 - item 60 - item 74 in Compustat)
scaled by the value of book assets (item 6), all values being measured at the end of the previous
year. In addition, following Fazzari, Hubbard and Petersen (1988) who argue that corporate
investment is sensitive to the availability of internal funds, I include contemporaneous cash flow
(Cash Flow) as control, where cash flow is the sum of earnings before extraordinary items (item 18)
and depreciation and amortization (item 14) scaled by start-of-year total assets.
Analyst coverage has been shown to positively influence equity issues and investment (Chang
et al., 2006 and Doukas et al., 2006). I define analyst coverage, Analysts, as the number of analysts
issuing a fiscal year t-1 EPS forecast for the firm in the I/B/E/S Historical Summary Files. I lag the
analyst coverage variable one period and use a logarithm transformation of raw analyst coverage in
all regressions.
In some of the investment equations, I include the firm’s future excess returns (Baker, Stein
and Wurgler (2003)’s undervaluation), the difference between the three year firm’s cumulative
stock return from year t+1 to year t+3 from CRSP and the stock market three year cumulative
return, as a control variable for stock price misvaluation. Baker and Wurgler (2002) and Baker,
17
Stein and Wurgler (2003) show that equity dependent firms invest more when their stock is
overvalued, as there is a negative relation between corporate investment and a firm’s future stock
return. I also follow these authors to construct a modified Kaplan and Zingales (1997) index of
financial constraints that excludes Tobin’s Q to avoid any spurious correlation in the specifications.
This variable is defined as follows:
Kaplan and Zingales (1997) index = -1.002 Cash Flow - 39.368 Dividends - 1.315Cash +
3.139 Leverage
All variables are lagged. Cash is defined as item 1 in Compustat scaled by lagged assets,
Leverage as the sum of long term debt (item 9) and the debt part of short term liabilities (item 34)
divided by the sum of debt and the value of book equity (item 9+item 34 + item 216). Dividends is
the sum of common stock dividends (item 21) and preferred stock dividends (item 19) scaled by
lagged assets. This index has been used extensively in the corporate investment literature (Baker,
Stein and Wurgler (2003), Chen, Goldstein and Jiang (2006)) and has been shown to capture the
effects of financial constraints in a world of costly external finance. Lower values of the index
capture low financial constraints firms (high cash flows, dividend payments, available cash and low
leverage) while higher values of the index stand for high financial constraints firms (high leverage,
low cash flows, dividend payments and cash in bank).
I also control for firm’s size using the logarithm of lagged Total Assets (item 6) as my baseline
measure. Additionally, I control for firm age where age is defined as the number of years a firm is
present in the Compustat Price Dividends and Earnings database to proxy for firm risk.
All the main variables are described in Table 2.
[Insert table 2 about here]
5 Corporate Investment and Earnings Surprises
5.1 Summary Statistics
Panel A of Table 3 reports the summary statistics for the whole sample of 65,221 firm-year
observations and for the two subsets of firms that met or exceeded analysts’ consensus forecast
during the year and firms that did not.
Firms that met or beat analyst forecasts are significantly larger, older, more profitable, have a
higher Tobin’s Q and larger contemporaneous cash flow. These firms are also less financially
constrained, as measured by the Kaplan and Zingales (1997) modified equity dependence index and
sell-side analysts more intensively cover them. They also managed their earnings upwards a lot
more, as testified by the positive mean change in total accruals. Firms that created non-negative
earnings surprises also invested more than firms that missed analysts’ consensus EPS forecasts.
While this finding is in contradiction with hypothesis H1, we should not be surprised about this
18
result. These firms have much larger Tobin’s Q and cash flow and are less financially constrained:
they are expected to invest more. Taken together, these results suggest that meeting or beating
analysts consensus forecast is associated with good news about the firm, and that not meeting these
forecasts contains somewhat bad news. Note that there is positive serial correlation for the variable
Above EPS consensus forecast between year t and year t+1 (0.16). Firms that outperform consensus
EPS forecasts in one year tend to repeat this performance in the following year.
Panel B of Table 3 presents correlation coefficients among the key variables of our analysis.
What is striking in this table is the positive and slightly significant correlation between Capital
expenditures and the dummy variable Above analysts’ EPS consensus forecast. Nevertheless, we
also notice a significant positive correlation with cash flows, past profitability and Tobin’s Q; and a
negative correlation with financial constraints. To be able to draw conclusions about the ceteris
paribus correlation between the earnings surprise variable and investment, we will need to
disentangle the effects of cash flows, investment opportunities and financial constraints. On the
other hand, Analyst pressure is significantly negatively correlated with Capital expenditures and
positive earnings surprises and positively correlated with contemporaneous cash flows. Firms that
face high analysts’ consensus forecasts will generate high cash flow but will invest less and be less
likely to create positive earnings surprises.
[Insert table 3 about here]
In Figure 1, we look at the time series of mean Capital expenditures and Tobin’s Q,
conditioning on the attainment of analysts forecasts at time t8. We get an interesting picture of the
firms’ investment pattern over time. Firms that are above analyst forecasts at time t had mean
investment ratios only slightly larger than firms that have not met analyst forecasts at time t.
However, as seen in Panel A of Figure 1, firms in this group have much better investment
opportunities, as proxied by their Tobin’s Q, they also have much larger contemporaneous cash
flow than firms not meeting analysts’ forecasts a time t, and as such, should invest much more.
What is striking in this graph is the slight investment reversal pattern for those firms that are
above expectations at time t. They invested less than the other group of firms between year t-3 and
year t, while they invest much more for the two following years and they invest more or less the
same in the following years. On the other hand, firms that have not met analysts’ forecasts show a
constant downward trend in investment that is only slightly more pronounced between year t and
t+19. This suggests that meeting or not analysts’ consensus forecast contains information about the
firms’ future prospects: firms that meet or beat analysts’ consensus forecasts tend to perform well
afterwards, while firms that did not tend to perform less well. This is confirmed by Panel A of
8 We also condition on the availability of the whole time series to avoid survival bias. 9 The average downwards trend in investment is owed to the decrease in investment from 2001 onwards. The results of
our analysis are unaffected by using different time periods for the multivariate regressions.
19
Figure 1 that exhibits the evolution of lagged Tobin’s Q for the same cross section of firms that are
above analysts’ expectations or not at time t.
This investment pattern could be explained by bad past economic conditions followed by an
unforeseen economic recovery in the year in which the firm beats analyst forecasts. It could also
suggest that earnings surprises occur in industries at certain points in time of the economic cycles.
Firms in industries that recover from previous sluggish market conditions could create non-negative
earnings surprises. Therefore, when we perform the multivariate analysis, we will need to take into
account such plausible explanations for this investment pattern.
Another possible explanation is based on the story that managers reduce investment to meet or
beat analysts’ consensus forecasts. Managers would be planning earnings surprises ahead by
reducing investment, and, afterwards, they would catch up postponed investments. This casual
observation from the graphs will be confirmed in the multivariate analysis when I control for
various factors, among which industry and past economic performance.
[Insert figure 1 about here]
I now turn to the presentation of evidence that corporate investment influences the firm’s cost
structure as postulated in the development of my hypotheses, and analyze its economic significance.
5.2 Corporate Investment and Cost Structure
To get an idea of the economic importance that a reduction in corporate investment would have
on net income through the depreciation channel, it is useful to measure the average impact of a
year-over-year change in capital expenditures on the contemporaneous year-over-year change in
depreciation and amortization. To do so, I run panel regressions with firm fixed effects where the
dependent variable, the first difference in depreciation scaled by lagged total assets, is regressed
against the first difference in capital expenditures scaled by lagged total assets10. The first column
in Panel A of Table 4 presents the results of this regression. Results indicate that a 1% increase in
capital expenditures in year t results in a 0.066% increase in depreciation in the same year (all
variables are expressed as a percentage of total assets). On the other hand, in the second column of
the table, we observe that a 1% increase in depreciation decreases net income by approximately 1%
of total asset. Taken together, these results indicate that a reduction in the first difference in
investment by one (within-firm) standard deviation (6.87%) would result in an estimated increase of
50% in mean net income (6.87%*0.066*(-1.07)/0.98%), or an increase of 11% in median net
income (6.87%*0.066*(-1.07)/4.5%). So, it appears that the direct effects of corporate investment
on net income are economically large.
10 Year fixed effects are also included in the regression to control for time variant firm heterogeneity.
20
However, as argued in section 2, I also argue that the influence of corporate investment goes
beyond this direct channel. Empirical support for the above assumptions is mixed on annual data
and more convincing on quarterly data. Panel B of Table 4 exhibits correlation coefficients between
capital expenditures, Selling General and Administrative (SG&A), depreciation, rental, advertising
and labor expenses on annual data for my sample of firms. Both contemporaneous and lagged
capital expenditures are significantly correlated with depreciation, and rental expenses, but not with
advertising expenses or labor expenses, and they are negatively correlated with SG&A.
I also use quarterly accounting data from the Compustat Quarterly Industrial database. This
database does not provide a detail of SG&A expenses, but it allows me to analyze the impact of
Lagged quarterly capital expenditures on Quarterly SG&A expenses beyond its impact on
Quarterly depreciation and amortization expenses. Panel C of Table 4 presents results of the panel
regression with firm and quarter-year fixed effects where quarterly SG&A expenses is regressed on
quarterly depreciation expenses and lagged quarterly capital expenditures plus a control for the
common scaling factor, the inverse of total assets. This regression suggests that lagged quarterly
capital expenditures are positively correlated with quarterly SG&A beyond their influence on
depreciation expenses. However, these results should be interpreted with caution, as they could be
driven by an unobserved factor that would simultaneously influence investment and SG&A
expenses. For instance, a managerial decision to “cut all costs”, including investment could be
driving these results, e.g. for restructuring purposes.
Note, however, that the depreciation channel is important enough in magnitude to potentially
explain the effect of investment on earnings surprises.
[Insert table 4 about here]
5.3 Investment Reduction Hypothesis
I now move to the estimation and analysis of specification (1) presented in section 3, the main
specification. I test whether meeting or beating analysts’ consensus forecasts occurs more often or
less often the higher or the lower the investment at the firm level. According to hypothesis H1, I
expect to find a negative correlation between corporate investment and the probability to create
positive earnings surprises.
The results of these tests are presented in Table 5. The number of firm-year observations is
reduced relative to our full sample because the logit regression with fixed effects does not use
observations where the dependent variable for the firm observations, Above analysts’ EPS
consensus forecasts, are either all equal to 0 or all equal to 1. The reason being that these
observations, 5,279 observations for 2,296 firms, do not provide any estimation information11. They
11 For more on this and an illustrative example, see Wooldridge (2002) page 491.
21
are thus automatically rejected from the sample by traditional statistical software packages such as
STATA®. Column 1 presents the results for a simple specification where, in addition to Capital
expenditures, I add contemporaneous Sales, Cash flow, the logarithm transformation of Analysts
and of total Assets as controls. I also control for Past profitability and earnings management,
through variable Changes in total accruals. Column 2 adds controls for the average earnings
surprise level in the industry, Percentage EPS surprises in industry, Column 3 adds a control for the
dispersion of analysts forecasts, Standard deviation of forecasts, a control for firms that post
positive EPS at the end of the year, and for changes in consensus forecasts over the year. Column 4
introduces additional controls for firm risk, as proxied by the log transformation of firm age, and a
control for value firms, proxied by the ratio of tangible assets to total assets (Tangibles).
Columns 1 to 4 show that the coefficients on all the investment variables are negative and
significant at the 1% level. Investing more during the year decreases the likelihood of meeting or
beating analysts’ consensus forecasts.
The coefficients on contemporaneous Sales and Cash flow are positive and significant at the
1% level in all variants of the model. Sales provide a control for firm specific shocks in their ability
to generate revenue. On the other hand, contemporaneous Cash flow proxies for the firm’s ability to
generate high earnings per share, the object of the analysts’ forecasts. It also provides a control for
firm specific cash expenses shocks in the fiscal year as Sales already provide a control for revenues.
The coefficient on Past profitability is negative and highly significant. As suggested earlier in
our discussion of Figure 1, firms that recover from past economic difficulties find it easier to meet
or beat analysts’ consensus EPS forecasts. This result is also consistent with Easterwood and Nutt
(1999) who find that firms with low past performance create positive earnings surprises more often
in the subsequent year because analysts underreact to bad news and overreact to good news.
However, this past low performance is not what is driving the reduction in capital expenditures, as
testified by the negative coefficient on Capital expenditures.
Consistent with the literature on accounting manipulations, the coefficients on Changes in total
accruals are all positive and significant. The higher the increase in total accruals in the
contemporaneous year, i.e. the higher the increase in the non-cash component of net income, the
more likely the firm will meet or beat analysts’ consensus forecasts. Note however that the
significance is not very high in the last two models (columns 3 and 4) as the level drops to the 10%
and 5% level respectively12. Untabulated results show that the significance level is greatly increased
by the use of a random effects model or of a traditional logit model that does not take into account
the panel structure of the data, as previously carried out in the earnings surprise literature. However,
12 The use of modified Jones (1991) model to proxy for discretionary accruals management actually results in even
lower significance levels for the earnings management proxy: it no longer significantly increases the probability to meet
or beat analysts’ forecasts.
22
as discussed earlier, a Hausman (1978) specification test strongly rejects the random effects model
at the 0.0001 level against the fixed effects model.
Analyst coverage and firm size do not seem to strongly influence earnings surprises. Analyst
coverage is only significant in the first two specifications (columns 1 and 2) with the positive sign,
when a negative sign was expected, while firm size appears to have a negative significant
coefficient only in the last two specifications (columns 3 and 4). Consistent with Degeorge, Ding,
Jeanjean and Stolowy (2005) and Yu (2006), analyst coverage reduces earnings management, so the
negative effect of analyst coverage on earnings surprises should be captured by variable Changes in
total accruals.
As discussed previously, one could suspect that the patterns in the earnings surprises are related
to the overall earnings surprises in the same industry. Firms in the same industry could perform
better than expected by analysts because of unexpected changes in the industry economic cycle and
correlated forecasting errors at the industry level. More specifically, the decreased investment
would be correlated with earnings surprises because analysts forecast errors are correlated within
industries that experience a downturn. I control for this possibility by introducing variable
Percentage above EPS forecasts in industry, the proportion of firms in the same industry meeting or
exceeding analysts’ EPS expectations, where industry is defined at the 3 digit SIC code level. As
expected, the coefficient on this variable is positive and significant at the 1% level in all
specifications. Nevertheless, the introduction of this control does not reduce the significance of the
negative coefficient on Capital expenditures. On the contrary, the negative coefficient decreases
with the introduction of this control variable and its significance increases.
The negative correlation between Capital expenditures and earnings surprises is robust to the
introduction of additional controls. Indeed, Columns 3 and 4 show that higher dispersion in
analysts’ consensus forecasts, Standard deviation of forecasts, decreases the probability to create
positive earnings surprises. Firms with positive earnings per share have a higher likelihood of
beating forecasts, consistent with Degeorge, Patel and Zeckhauser (1999). Positive forecasts
changes are positively associated with earnings surprises. Analysts seem to underreact to positive
news about the firm that occur during the forecasting period. This result is consistent with the
results from Elliott, Philbrick and Weidman (1995) who show that analysts underreact to good
news. However, the introduction of this variable does not reduce the significance of the negative
coefficient on Capital expenditures. On the contrary, the significance is increased in column 3
relative to column 4.
Value firms create less positive earnings surprises than other firms, consistent with Degeorge,
Patel and Zeckhauser (2007).
23
The marginal effects of the last specification (presented in column 4) are presented for
reference. Nevertheless, as pointed out by Wooldridge (2002), any interpretation is subject to
caution. Indeed, fixed effects logit regressions do not estimate the fixed effects parameters. To
compute the marginal effect on the probability to meet or beat analysts’ consensus EPS forecasts,
one would need to get an estimate of the fixed effect for each firm. Therefore, I need to assume that
the fixed effects are equal to 0 and compute the marginal effects at the mean value of control
variables. The assumption that the fixed effects are zero on average is arbitrary because fixed
effects logit regression estimation does not impose any restriction on the mean value of fixed
effects. However, these marginal effects provide a rough estimation of the relative contribution of
each variable in the model on the probability to create non-negative earnings surprises. For
example, the marginal effect of a reduction by a one within-firm standard deviation in Capital
expenditures (-6.46%*-0.178=1.15%) is approximately twice as large as the marginal effect of a
one-within-firm standard deviation in Changes in total accruals (13.53%*0.043=0.58%). Although
the marginal effect in itself is difficult to evaluate, it is important to observe that we find such a
strong effect of investment on earnings surprises relative to what has been considered as the main
discretionary lever to create positive earnings surprises in the finance and accounting literature.
[Insert table 5 about here]
In order to verify that a discretionary reduction in investment creates earnings surprises through
the depreciation channel, I perform the same regressions as the one presented above, replacing
variable Capital expenditures with Depreciation. Depreciation is the ratio of depreciation and
amortization expenses (item 14 in Compustat) scaled by lagged total assets (item 6). The results are
presented in Table 6. The results remain identical to those discussed previously with respect to most
of the comparable coefficients. What is interesting is that the coefficient on Depreciation is
negative and significant at the 1% level in all specifications except the last one, where it is still
significant at the 5% level. A reduction in depreciation increases the probability to create positive
earnings surprises. As discussed in section 2, a reduction in investment will directly reduce
depreciation expenses. A reduction in expenses will increase earnings and contribute to increase the
likelihood that firms meet or beat analysts’ consensus EPS forecasts.
[Insert table 6 about here]
As discussed in section 3, simultaneity biases should not be too much of a concern, because the
two stories that are consistent with this bias and the effects that we find are at odds with the existing
empirical evidence. The story that investment decisions could be jointly determined by a shock that
also affects analysts’ prediction abilities in the same fiscal year is inconsistent with the evidence
found by Easterwood and Nutt (1999). These authors find that analysts underreact to bad news and
overreact to good news contained in prior year’s performance. Elliott, Philbrick and Weidman
24
(1995) find that analysts underreact to good and bad news during the fiscal year, contradicting the
story that bad news, associated with reduced investment, could be positively correlated with more
positive earnings surprises. This is further confirmed by results from regressions presented in Table
5 and 6. The coefficient on variable Upwards consensus change is positive and the coefficient on
Past Profitability is negative.
Alternatively, the story that investment decisions could be jointly determined by a shock that
also affects the firms’ earnings in the same fiscal year is also inconsistent with the finding that
analysts underreact to restructuring news (Chaney, Hogan and Jeter, 1999). However, to rule out
any concerns about the direction of the causality between investment and earnings surprises, I
exploit the Compustat Quarterly Industrial database to test models where earnings surprises are
regressed against investment in earlier periods. I include all the control variables from the previous
specification in these regressions. The results are reported in Table 7.
Column 1 reports the results for a regression where Above consensus EPS forecast, based on
the consensus for the current fiscal year, is regressed against Lagged Capital Expenditures, capital
expenditures from the previous fiscal year. Columns 2 to 5 report the results for a regression where
Above consensus EPS forecasts in 4th quarter, the consensus forecast for the 4th quarter EPS, is
regressed against Capital expenditures in the first semester in column 2, and in the first, second and
third quarters of the same fiscal year in column 3 to 5 respectively. Note that these variables are
including total investment for the period considered only. I do not rebase these investment variables
on a yearly rate basis. As a result, firms invest approximately half of what is invested in a year in
the first semester, while in a quarter, firms invest about one fourth of what is invested in a year.
The previously discussed results remain robust to these new specifications. Decreased
investment in earlier periods increases the probability that a firm will create positive earnings
surprises in the future. Therefore, one can confidently conclude that a reduction in investment at the
firm level will help create positive earnings surprises.
[Insert table 7 about here]
What is striking in the results presented in Table 7 is that analysts do not seem to fully grasp
the effects that investment will have on the earnings they forecast. These capital expenditures data
are available to them, so they should be able to infer that a reduction in investment will result in
higher earnings and adjust their forecasts accordingly. Even capital expenditures in the previous
fiscal year can positively predict earnings surprises in the next fiscal year.
However, note that this relationship was not so easy to disentangle at first sight, because the
simple univariate correlation between investment and earnings surprises was significantly positive.
25
5.4 Analyst Pressure Hypothesis
I now move to the analysis of hypothesis H3. It states that firms facing abnormally high
pressure from analysts, in the form of abnormally high EPS consensus forecasts at the start of the
year, will invest less all else being equal. Table 8 reports the results from estimating equation (3) on
my sample of firms, using Capital expenditures as the dependent variable and variable Analyst
Pressure in all specifications. Column 1 estimates a simple model with lagged Tobin’s Q,
contemporaneous cash flow, a logarithm transformation of lagged analyst coverage, past return on
assets and the logarithm transformation of lagged total assets as controls. In the following columns,
I add various other control variables. Column 2 adds a control for financials constraints in the form
of the Kaplan and Zingales (1997) index and an interaction term between this control and lagged
Tobin’s Q. These variables control for the fact that financially constrained firms invest less than
unconstrained firms (Kaplan and Zingales, 1997). Furthermore, Baker, Stein and Wurgler, 2003
find that financially constrained firms have corporate investment policies that are sensitive to stock
price variations. They invest more when stock prices are high and less when stock prices are low.
Hence I introduce an interaction term between Tobin’s Q and the Kaplan and Zingales (1997)
index. Column 3 adds a proxy for firm age, and a proxy for value firms. Firms at early stages of
their development are expected to invest more than mature firms, while value firms may invest less
than growth firms. Finally, I add a control for stock price misvaluation in column 4. Indeed, as
found in Baker, Stein and Wurgler (2003), firms invest more when overvalued, i.e. when they have
low future excess stock returns.
Overall, the coefficient on Analyst pressure is negative and significant at the 1% level in all
specifications. It is estimated at -2.447 in column 1 and at -1.466 in column 3. Economically this
suggests that a firm facing abnormally high analyst forecasts increasing by a one (within-firm)
standard deviation decreases investment by 0.10% to 0.16% of total assets. This represents a
decrease of 2% to 3% of median corporate investment or a decrease of 1% to 2% of mean corporate
investment.
Coefficients on the control variables are consistent with previous results from the investment
literature. Firms invest more the better the investment opportunities, as proxied by Tobin’s Q.
Firms also invest more the larger the contemporaneous cash flow. Fazzari Hubbard and Petersen
(1988) claim that firms that face financial constraints will be sensitive to cash flow shocks and will
adjust the investment level to the level of available cash flow. Alti (2003) argues that cash flow
shocks contain information that is not properly captured by Tobin’s Q. Firms with positive cash
flow shocks should invest more, even in the absence of financial constraints. Consistent with
Doukas et al. (2006), firms invest more the more intense the analyst coverage, as analysts reduce
asymmetric information with the stock market. Firms also invest more the higher the past
26
profitability and less the larger their size, as measured by the logarithm of Assets. In addition firms
with large financial constraints invest less (Kaplan and Zingales, 1997), and are more sensitive to
stock price variations (Baker, Stein and Wurgler, 2003). Firms also invest less when they grow
older and the larger their total assets base. All firms, irrespective of their level of financial
constraints, invest more when they are overvalued (Baker, Stein and Wurgler, 2003).
[Insert table 8 about here]
5.5 Investment Postponement Hypothesis
So far, we have shown that firms investing less than usual in a year create more non-negative
earnings surprises, and that firms facing high analyst pressure reduce investment. I also posited, in
hypothesis H2, that the reduction in investment that is intended to cause the earnings surprise will
be temporary and lead to a subsequent increase in investment. If the firms’ reduction in investment
caused by beating EPS consensus forecasts is temporary, i.e. the firms compensate the reduction in
investment at time t-1 by investing more at time t, we should observe a positive correlation between
the lagged value of Above consensus EPS forecastit-1 and Capital expenditures. This is the result
that we get from estimating equation (4) as exhibited in Table 9. The coefficients of Above
consensus EPS forecastit-1 are positive and significant at the 1% level.
[Insert table 9 about here]
5.6 Robustness Checks
I perform several robustness checks relative to the previous choices of variables and
specifications.
First, I construct an alternative measure of analysts consensus forecast using the last forecast
reported by a sell-side analyst in the I/B/E/S Individual Detail files13 before the EPS reporting date.
This measure has been used in the earnings surprise literature as an alternative to the last median
EPS forecast in the I/B/E/S Individual Summary files on the grounds that it reflects more up to date
information by analysts issuing forecasts in the vicinity of the reporting date. The use of such
variable makes sense in those analyses, because researchers are primarily interested in earnings
management that is decided right before the reporting date, through e.g. accruals management. In
our case, however, using the baseline consensus forecasts makes more sense as the investment
decisions by managers should be influenced less by the desire to meet or beat the very last analyst
EPS forecast, that is unknown yet. Indeed the investment decisions have been made already, and for
the most part have been reported in previous quarterly reports. Managers have no way to influence
the meeting or the beating of such late forecast through a reduction in investment. However, I use
13 I again use the file with unadjusted data for stock splits, and adjust them using the procedure recommended by
WRDS in Robinson and Glushkov (2006).
27
this forecast variable to further strengthen the results that were found in previous regressions, and to
further argue that analysts do not seem to take into account the effect of investment reduction on
earnings surprises. The results of model (1) with this new variable as the dependent variable are
presented in Table 10. The coefficient on Capital expenditures is always negative in all
specification of the model with a significance level of 1% and 5% in column 3 and 4 respectively.
The significance is slightly reduced relative to the main specification, especially in specifications
where fewer controls are included (column 1 and 2). Nevertheless, this reduced significance was
expected, as discussed above. Analysts issuing forecasts before the reporting date should
incorporate more information about the effects corporate investment has on earnings. Still, they do
not incorporate enough information to cancel out the negative impact of investment on earnings
surprises.
[Insert table 10 about here]
Second, I build a measure of sales surprises to check that sales surprises are not correlated with
investment, because in my story, they have no reasons to be. A reduction in investment creates
surprises in costs (depreciation and collateral expenses) that are not fully accounted for by analysts.
Therefore, investment should not be significantly negatively correlated with sales surprises. I
construct variable Above analysts’ sales consensus forecast as a dummy variable equal to one if
actual sales are larger than the analysts’ sales consensus forecast and equal to zero otherwise. The
analysts’ sales consensus forecast is constructed in the exact same way as the EPS consensus
forecast used in the baseline analysis and described in section 4. I take the last median consensus
forecast from the I/B/E/S Summary files. Because sales forecast are scarcer in the I/B/E/S
database14, the number of observations available for our analysis drops from 65,221 for the period
1981 to 2005 to 17,895 firm-year observations for the period 1993 to 2005. In addition, because of
the specific procedures used in the fixed effects logit estimation and the various variable
requirements, the number of firm year-observations drops to numbers ranging from 10,562 to
11,793 firm-year observations, depending on the specification. The number of observations should
be large enough to obtain good estimation from the logit fixed-effects regressions.
As expected, results reported in Table 11 show that there is no significant negative correlation
between sales surprises and investment. The coefficient on Capital expenditures is negative in all
specifications, but is not significant at any usual level. The z statistics are low, ranging from 0.51 to
1.08. Sales surprises are not negatively correlated with investment. This result is consistent with
the analysts’ general prediction ability not being correlated with investment, whereas the analysts’
prediction ability concerning the firms’ costs is correlated with investment.
[Insert table 11 about here]
14 Data on sales forecasts and sales realizations have been collected from year 1993 onwards in I/B/E/S.
28
Taken together, the results of section 5 suggest that firms that invest less in a given year will
increase their probability to meet or outperform analysts’ EPS consensus forecasts in the same year.
This result is obtained through the creation of surprises in costs. We identified more specifically
surprises in depreciation expenses, but surprises in other collateral expenses could also explain this
result.
6 Increased Underinvestment or Reduced Overinvestment?
An interesting question is whether the reduction in investment induced by consensus fixation
corresponds to a reduction of overinvestment, or the passing up of valuable investment
opportunities.
Testing such question is difficult because the profitability of project investment is
unobservable. Moreover, it is not clear whether the average firm in the sample is (i) investing the
right amount of money, is (ii) underinvesting or (iii) overinvesting. Therefore, observing a
reduction in investment can be interpreted as underinvestment under hypothesis (i) or an
aggravation of the underinvestment problem under hypothesis (ii). Under these two assumptions,
the results presented thus far support the view that analysts create an adverse effect on investment
through increased underinvestment. Conversely, under hypothesis (iii), the observation of a
reduction in investment to create earnings surprises can be interpreted as an improvement in the
firms’ capital budgeting policy through reduced overinvestment.
Recent empirical evidence provided by Bertrand and Mullainathan (2003) and Bøhren, Cooper
and Richard (2007) suggests that overinvestment is not the norm among U.S. firms. Managers enjoy
the “quiet life”. They tend to underinvest rather than overinvest. Bøhren, Cooper and Richard
(2007) show that firms where managers are more entrenched tend to invest less than firms in which
governance mechanisms protect shareholders more against managerial discretion. Based on these
results, the reduction in investment found previously is difficult to reconcile with a beneficial effect
for firms and their shareholders. Nevertheless, to further strengthen this argument, I address the
issue using two different perspectives.
In the first approach, I rank firms based on their corporate governance quality. Consistent with
the arguments in Jensen (1986), I posit that firms in which agency costs are high are more
susceptible to overinvestment than firms in which agency costs are low. Self-serving managers may
want to build empires at the expense of shareholders. Managers may be given less leeway by the
shareholders and the board to pursue pet projects and unprofitable investment opportunities in firms
in which good corporate governance mechanisms are in place (Gompers, Ishii and Metrick, 2003).
Firms in which shareholders are less protected against managerial discretion are more susceptible to
overinvestment than firms with stringent shareholders’ rights protection. Therefore these firms are
29
expected to decrease more their investment to meet or beat consensus forecasts if the decrease in
investment associated with beating EPS consensus forecasts is beneficial (overinvestment is
reduced). On the other hand, observing a larger reduction to create earnings surprises among the
firms with better corporate governance could be interpreted as a proof of the increased short-term
pressure brought about by analysts’ consensus EPS forecasts. Firms in which managers are less
protected are more likely to be ousted by unhappy shareholders after bad firm performance
(Fisman, Khurana and Rhodes-Kropf (2004)). Therefore, it is in those firms that managers have the
highest incentives to keep the shareholders’ happy in the short term. Beating consensus forecasts is
one such way of keeping shareholders happy.
I test these two hypotheses by using two different measures of corporate governance. The first
measure is the Gompers, Ishii and Metrick (2003) index of corporate governance, and the second
measure is the Bebchuk, Cohen, and Ferrell (2004) managers’ entrenchment index. Gompers, Ishii
and Metrick (2003) construct their governance index by considering all provisions from a company
corporate charter reported by IRRC (Investors Responsibility Research Center) that go against the
protection of the firms’ shareholders. A total of 24 unique provisions are considered, and, for every
firm, the authors add one point for each provision that restricts shareholders’ rights. Thus Git has a
possible range from 1 to 24, where the lowest values of Git stand for the firms with the best
shareholders’ rights protections and the highest Git values stand for the firms with the worst such
protection provisions. Bebchuk, Cohen and Ferrell (2004) argue that only six such provisions need
be considered in the construction of a corporate governance index: staggered boards, limits to
shareholder bylaw amendments, supermajority requirements for mergers, supermajority
requirements for charter amendments, poison pills and golden parachutes. The first four provisions
prevent a majority of shareholders from having their way, while the last two provisions make a
hostile takeover more costly, and thus less likely. This index actually measures managers’
entrenchment.
I construct the portfolios of corporate governance as follows. I classify firms as belonging to a
governance tercile based on their previous year corporate governance index value. I then run
regressions where Capital expenditures is the control variable in the logit specification. I use
terciles because of the lower number of observations available for these indices. I only get the
Gompers, Ishii and Metrick (2003) governance index for 15,421 firm-year observations and the
Bebchuk, Cohen, and Ferrell (2004) managers’ entrenchment index for 13,856 firm-year
observations. The firms in that sample are biased towards large firms.
Results for the logit firm fixed effects specification are presented in Table 12 Panel A and
Panel B. Panel A shows the results for the portfolios constructed based on the Gompers, Ishii and
Metrick (2003) governance index. Panel B does the same for portfolios based on the Bebchuk,
30
Cohen, and Ferrell (2004) managers’ entrenchment index. Higher scores of these indices mean
worse corporate governance, so that the bad governance firms are ranked in the last tercile and good
governance firms are ranked in the first tercile.
The results in Table 12 Panel A and B seem to confirm the hypothesis that the previously
observed reduction in investment to create earnings surprise is not primarily present among weakest
governance firms. The regressions in the lowest tercile portfolios, firms with the best governance
mechanisms according to both indices, are the only portfolio in which the negative coefficients are
significant. The significance level is at the 1% level despite the low number of observations. This
result suggests that firms with less entrenched managers are more subject to a reduction in
investment to create positive earnings surprises than firms with more entrenched managers. One
could be tempted to interpret this finding as a proof of the analysts’ induced managerial short-term
bias. However the low number of observations in tercile 2 and 3 advocate for caution in the
interpretation of these findings15. Nevertheless, one can confidently discard the hypothesis that the
reduction in investment related to earnings surprise that we found previously is driven by weak
governance firms. In those conditions, it appears difficult to argue that such reduction in investment
is beneficial to firms in our sample.
Panel C and Panel D report the results for the estimation of model (3) where firms are ranked
by governance index. The reported results show that variable Analyst pressure is no longer
negatively correlated with Capital expenditures in any of the governance portfolio at a significant
level. Firms facing high pressure from analysts early in the year do not significantly reduce
investment more when firms have weak or strong corporate governance mechanisms. Again, the
results previously found in section 5.4 do not appear to be driven by weak corporate governance
firms.
[Insert table 12 about here]
In the second approach, I rank firms based on their investment opportunity set. If the reduction
in investment induced by consensus beating occurs because it purges excess investment, it should
be stronger for firms with low investment opportunities. Indeed these firms are those for which, for
a given level of investment, the percentage of investment that is excessive is higher. It turns out that
firms with good investment opportunities decrease more their investment to beat consensus than
firms with bad investment opportunities, as shown in Table 13 Panel A. Firms in the lowest Tobin’s
Q tercile, the firms that have the lowest investment opportunities, do not decrease their investment
to create earnings surprises. Although the coefficient on Capital expenditures is negative, it is not
significant at conventional levels. On the other hand, firms with the largest investment
15 The governance index variables are discrete with a limited range (from 1 to 24 for the Gompers, Ishii and Metrick
(2003) index and from 1 to 6 for the Bebchuk Cohen and Ferrel (2004) index) and are biased towards low values of the
index. This can explain the imbalance in the number of firm-year observations in the three terciles.
31
opportunities, firms in the highest Tobin’s Q tercile have a large negative coefficient on Capital
expenditures. It is significantly negative at the 1% level and more negative than the coefficient for
firms in the second tercile. The significance level is also larger. This, again, suggests that the
reduction in investment related to consensus beating is not beneficial to the firms in our sample.
Panel B also reports that firms with high Tobin’s Q, firms in the highest tercile, respond even
more than other firms by reducing investment as a response to analyst pressure. The interaction
term between variable High Tobin’s Q and Analyst pressure is negative and significant at the 10%
level.
[Insert table 13 about here]
These results suggests that we cannot attribute the previously found reduction in investment to
create positive earnings surprises to firms that are not aligned with their shareholders interests, or to
firms that do not have large investment opportunities. These results are consistent with firms giving
up or postponing profitable investment opportunities to create positive earnings surprises.
7 Conclusion
Analysts seem to exert adverse short-term pressure on the firms they follow when capital
budgeting decisions are concerned. This pressure is exerted through their earnings per share
forecasting activity. Firms invest less to outperform analysts’ consensus EPS forecasts and firms
reduce investment when analysts’ consensus EPS forecasts are abnormally high at the start of the
year. The reduction in investment related to earnings surprises and abnormal analyst pressure is not
beneficial to the weaker corporate governance firms or to the firms with lowest investment
opportunities. These findings suggest that analysts may play a role in the managers’ myopia as
suggested in Stein (1989). It is difficult to conclude whether the negative impact analysts have on
corporate investment outweigh the benefits of reduced information asymmetry previously identified
in the literature (Chen et al. (2006) and Doukas et al. (2006)). Financial analysts may indeed relieve
firms from these constraints on investment. However they also seem to encourage underinvestment
at the firm level in order to meet consensus EPS forecasts. This result suggests that, as found by
Graham, Harvy and Rajgopal (2005) in a survey of American CFOs, managers are ready to sacrifice
investment projects to meet their desired earnings targets.
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35
Table 1 - Construction of the Analyst Pressure Proxy
Variable Analyst Pressure is the residual of the following panel regression with firm-fixed effects. The dependent
variable is Forecast EPS change, the forecast change in EPS from last year’s realized EPS, normalized by lagged stock
price, (EPS forecaststart of year t - actual EPSt-1)/Stock price t-1-90days. Coefficient estimates appear in bold while t-statistics are
displayed within brackets under each coefficient. Standard errors adjust for heteroskedasticity and within correlation
clustered by firm. Tobin’s Q is the beginning of the year market-to-book ratio computed as the market value of equity
plus book value of assets minus the book value of equity minus balance sheet deferred taxes scaled by the value of book
assets. Past Profitability is last year’s return on assets, defined as operating income before depreciation divided by total assets. Cash Flow (t-1) is the sum of earnings before extraordinary items, depreciation and amortization scaled by start-
of-year Total Assets (multiplied by 100) in the previous year. Positive EPS (t-1) is a dummy variable equal to 1 if last
fiscal year’s EPS is equal or superior to zero. Kaplan and Zingales (1997) index is a modified Kaplan and Zingales
(1997) index of Financial Constraints, excluding Tobin’s Q. Log (Analysts) is the logarithm of the number of analysts that
made annual earnings forecasts any month over the 12 months previous fiscal-year period. Forecast EPS change in
industry, is the average Forecast EPS change in the same industry (defined at the 3 digit SIC code level). Log (Assets) is
the logarithm of the beginning-of-the-year Total Assets. Expected positive turnaround is a dummy variable equal to 1 if
the forecast EPS for fiscal year t is positive and the actual EPS in year t-1 was negative. Expected negative turnaround is
a dummy variable equal to 1 if the forecast EPS for fiscal year t is negative and the actual EPS in year t-1 was positive.
Above consensus EPS forecast (t-1) is a dummy variable equal to 1 if the firm posted earnings per share (EPS) superior to
the analysts’ the last outstanding consensus EPS forecast before EPS announcement in the last fiscal year and equal to 0 otherwise. Year-fixed effects are also included.
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
Forecast EPS change
Tobin's Q -0.002***
(-3.64)
Past Profitability 0.029***
(2.59)
Positive EPS (t-1) -0.068***
(-13.59)
Cash Flow (t-1) -0.001***
(-8.04)
Kaplan and Zingales (1997) index 0.000***
(12.58)
Log (Analysts) -0.005***
(-3.88)
Forecast EPS change in industry 0.106***
(6.47)
Log (Assets) -0.009***
(-6.49)
Expected positive turnaround 0.039***
(7.91)
Expected negative turnaround -0.064***
(-14.27)
Above consensus EPS forecasts (t-1) -0.010***
(-11.71)
Year-fixed effects Yes
N obs 37,805
Adj. R2 0.51
36
Table 2 – Definition of Main Variables
Capital expenditures Capital expenditure (Compustat item 128) scaled by start-of-year total assets (item 6)
Total assets Start-of-year total assets (item 6) (in million USD)
Tobin’s Q Market value of equity (item 199 multiplied by item 25) plus book value of assets minus book value of equity minus deferred taxes (item 6 -
item 60 - item 74), scaled by book value of total assets (item 6). Variable is lagged one year
Past profitability Ratio of operating income before depreciation and amortization (item 13) to start-of-year total assets. Variable is lagged one year
Cash flow Net income before extraordinary items (item 18) + depreciation and amortization expenses (item 14) scaled by start-of-year total assets
Kaplan and Zingales (1997)
index
Start-of-year Kaplan-Zingales (1997) index of equity dependence (excluding Tobin’s Q): Kaplan and Zingales index (1997) index = -1.002*Cash Flow -39.368*Dividends -1.315*Cash +3.139*Leverage
Dividends is Common stock dividends (item 21) + Preferred Stock dividends (item 19) scaled by start-of-year total assets. Cash is item 1
scaled by start-of-year assets Leverage is long-term debt (item 9) plus debt in current liabilities (item 34) divided by total debt (item 9 + item
34) plus book value of common equity (item 216)
Firm age Number of years the company has been present in the Compustat Price Dividend and Earnings database
Positive EPS Dummy variable equal to 1 if the current fiscal year EPS (earnings per share) is equal or superior to zero
Analysts Maximum number of analysts that posted EPS forecasts any month during the fiscal year for the fiscal year-end. Variable is lagged one year
Above EPS consensus
forecasts
Dummy variable equal to 1 when EPS is above the last analysts’ consensus forecast published before EPS reporting date
Percentage above EPS
forecasts in industry
Percentage of firms in the same industry (at 3 digit SIC code level) that posted EPS above last EPS analysts consensus forecasts
Analyst pressure Proxy for the level of analyst pressure at the beginning of the fiscal year. The variable is constructed as the residual of a firm- and year- fixed
effects panel regression where the forecast increase in EPS from last year’s realized EPS, normalized by lagged stock price, (EPS forecaststart
of year t - actual EPSt-1)/Stock price t-1-90days, is regressed against Past profitability, lagged Above EPS consensus forecasts, Tobin’s Q, lagged
Cash flow, Kaplan and Zingales (1997) index, lagged Positive EPS, lagged log(Analysts), lagged log(Total Assets), and other specific
variables (see section 4 page 12 and 13 of main text and table 1 for details)
Upwards forecast revisions Dummy variable equal to 1 if the latest EPS consensus forecast before EPS announcement is larger than the first EPS consensus forecast
Changes in total accruals Changes in total accruals from year t-1 to year t. Total accruals are defined as changes in current assets (item 4) minus changes in cash (item
1) minus changes in current liabilities (item 5) plus changes in current maturities of long term debt (item 44) plus changes in income taxes
payable (item 71), all of these variables being scaled by beginning of the year total assets
Baker Stein and Wurgler’s
(2003) undervaluation
Compounded cumulative excess return (stock market return for the firm minus the value weighted stock market return) computed from CRSP
over fiscal year t+1 to year t+3, as in Baker, Stein and Wurgler (2003)
37
Table 3 – Summary Statistics Data are collected from the merged CRSP/Compustat Industrial database and I/B/E/S for the years 1981 to 2005 and exclude firms not covered by analysts, financial services firms (SIC
code 6000-6999), regulated utilities (SIC code 4900), and firms with book value of equity smaller than $10 million. Capital Expenditures is Compustat item 128 scaled by start-of-year
Total Assets. Total Assets is the beginning-of-the-year total assets. Tobin’s Q is the lagged market-to-book ratio. Past profitability is the lagged return on assets computed as income before
extraordinary items scaled by Total Assets. Kaplan and Zingales (1997) index is a modified Kaplan and Zingales (1997) index of Financial Constraints, excluding Tobin’s Q, Cash Flow is
the sum of earnings before extraordinary items, depreciation and amortization scaled by start-of-year Total Assets. Firm Age is the number of years a company has been present in the
Compustat Price Dividend and Earnings database. Analysts is the number of analysts that make annual earnings forecasts over the 12 months previous fiscal-year period. Above EPS
consensus forecasts is a dummy variable equal to 1 when the firm posts earnings per share (EPS) superior to the last analysts’ consensus EPS forecast and equal to 0 otherwise.
Percentage above EPS forecasts in industry is the percentage of firms in the same industry (defined at the 3 digit level SIC code) that met or beat analysts consensus forecasts in the same
fiscal year, as previously defined, excluding the firm-year observation. Analyst pressure is a proxy for the level of analyst pressure at the beginning of the fiscal year. The variable is constructed as the residual of a firm-fixed effects panel regression where the consensus forecast change in EPS from last year’s realized EPS, normalized by lagged stock price, is
regressed against a set of lagged variables controlling for past firm performance, firms characteristics and analyst coverage. Upwards forecast revisions is a dummy variable equal to 1 if
the latest EPS consensus forecast before EPS announcement is strictly larger than the first EPS consensus forecast. Changes in total accruals is measured as a year-to-year change in total
accruals. Baker, Stein and Wurgler (2003)’s undervaluation is the compounded cumulative excess return (stock market return for the firm minus the value weighted stock market return)
computed from CRSP over fiscal year t+1 to year t+3, as in Baker, Stein and Wurgler (2003). All data, except Firm Age, Analysts and the dummy variables, are winsorized at the 1st and
99th percentile. Panel A reports summary statistics, the number of observations (N), the mean, median and standard deviation of the key variables in our analysis for the overall sample of
firms covered by analysts, and for sub-samples of firms that met or beat analysts forecasts (Above EPS forecasts=1) and firms that did not (Above EPS forecasts=0). All variables scaled
by Total Assets are multiplied by 100. Panel B reports correlation among selected variables.
Panel A: Summary Statistics
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
Group mean
comparison test
N Mean Median Std. Dev. N Mean Median Std. Dev. N Mean Median Std. Dev.
H0: Difference is not
equal to 0
Capital expenditures 64,362 8.62 5.67 9.51 35,776 8.69 5.89 9.24 27,191 8.55 5.44 9.76 **
Total assets 65,221 1,415 181 4,963 36,192 1,663 233 5,421 27,599 1,144 137 4,39 ***
Tobin’s Q 62,597 2.09 1.48 1.91 34,533 2.17 1.55 1.95 26,67 1.99 1.39 1.85 ***
Past Profitability 54,931 12.78 15.00 20.64 30,738 14.83 16.47 19.44 23,36 10.55 13.32 21.46 ***
Kaplan and Zingales (1997) index 54,673 20.97 20.17 131.26 30,584 10.74 9.09 127.16 23,36 32.18 32.18 134.52 ***
Cash flow 64,998 5.41 9.46 21.25 36,06 9.11 11.47 18.31 27,52 1.25 6.76 22.95 ***
Firm age 65,221 14.53 11 10.60 36,192 15.11 12 10.91 27,599 13.92 11 10.23 ***
Analysts 62,750 7.59 5 7.50 35,917 8.46 6 7.73 26,599 6.46 4 7.02 ***
Above EPS consensus forecasts 63,791 0.57 1 0.50 36,192 1 1 0 27,599 0 0 0
Percentage above EPS forecasts in industry 63,122 0.57 0.58 0.18 35,797 0.58 0.60 0.18 27,325 0.54 0.55 0.18 ***
Analyst Pressure 37,805 0 0 0.07 21,939 -5E-4 -3E-4 0.06 15,729 6E-4 -7E-4 0.07
Upwards forecast revisions 62,750 0.44 0 0.50 35,917 0.44 0 0.50 26,599 0.44 0 0.50
Changes in total accruals 54,865 -0.86 -0.34 14.18 30,179 0.00 0.10 12.92 23,543 -1.96 -0.99 15.30 ***
Baker, Stein and Wurgler (2003)’s undervaluation 38,823 0.13 -0.17 1.26 23,187 0.13 -0.15 1.20 15,313 0.13 -0.20 1.32
Overall Sample
Sample of firms meeting or beating
analysts consensus forecast
Sample of firms NOT meeting or
beating analysts consensus forecast
38
Panel B: Correlation Among Key Variables
Capital
expenditures Total
assets Tobin’s
Q Past profi-
-tability
Kaplan
and
Zingales
(1997)
index Cash flow Firm
Age Analysts
Above
EPS
consensus
forecasts
Percentage
above EPS
forecasts
in industry Analyst
pressure
Upwards
forecast
revisions
Changes
in total
accruals
Capital expenditures 1
Total assets -0.03*** 1
Tobin’s Q 0.12*** -0.06*** 1
Past profitability 0.22*** 0.05*** -0.08*** 1
Kaplan and Zingales
(1997) index -0.02*** 0.03*** -0.30*** -0.14*** 1
Cash flow 0.17*** 0.06*** -0.19*** 0.71*** -0.13*** 1
Firm Age -0.13*** 0.29*** -0.20*** 0.13*** -0.02*** 0.17*** 1
Analysts 0.08*** 0.51*** 0.06*** 0.21*** -0.12*** 0.18*** 0.29*** 1
Above EPS consensus
forecasts 0.01* 0.05*** 0.05*** 0.10*** -0.08*** 0.19*** 0.05*** 0.13*** 1
Percentage above EPS
forecasts in industry -0.05*** 0.00 0.03*** -0.03*** -0.03*** -0.01 0.03*** -0.00 0.11*** 1
Analyst pressure -0.02*** 0.01 0.00 0.00 0.00 0.03*** 0.00 -0.01 -0.01* 0.01 1
Upwards forecast
revisions -0.01*** -0.00 0.06*** -0.11*** 0.07*** -0.10*** -0.06*** -0.01** -0.00 0.03*** -0.02*** 1
Changes in total
accruals 0.03** 0.01** -0.01* -0.09*** 0.00 0.12*** 0.04*** 0.01** 0.07*** 0.02*** 0.05*** 0.00 1
Baker, Stein and
Wurgler (2003)’s
Undervaluation -0.06*** -0.02*** -0.03*** -0.04*** 0.05*** -0.05*** -0.04** -0.04*** 0.00 0.00 -0.01 0.02*** -0.02***
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
39
Figure 1
Meeting or Not Analysts’ EPS Consensus Forecasts:
Ex-Ante Investment Opportunities and Investment Policy Evolution
Panel A displays the median firms’ ex-ante investment opportunities (Tobin’s Q) evolution over time for the sub-sample of firms that met or beat analysts consensus forecast at time t and
the sub-sample of firms that did not, while Panel B displays the median firms’ Capital Expenditures for the same two sub-samples. Tobin’s Q is the lagged market-to-book ratio computed
as the market value of equity plus book value of assets minus the book value of equity minus balance sheet deferred taxes scaled by the value of book assets, all values being measured at
the beginning of the fiscal year. Capital Expenditures is (Compustat) item 128 scaled by start-of-year Total Assets multiplied by 100.
Panel A – Median Lagged Tobin’s Q
Panel B – Median Capital Expenditures
40
Table 4 - Corporate Investment and Cost Structure
All data are collected from the Compustat Quarterly Industrial database for quarterly data and the Compustat Annual
Industrial database for yearly data. Our sample of firms excludes firms not covered by analysts, financial services firms
(SIC code 6000-6999), regulated utilities (SIC code 4900), and firms with book value of equity smaller than $10
million. Panel A presents panel regressions with firm-fixed effects. Year-fixed effects are also included in all
regressions. All variables are scaled by lagged Total Assets (item 6 in Compustat). The dependent variables,
Depreciation expenses is item 14, and Net Income is net income before extraordinary items (item 18). Capital
Expenditures is item 128. Sales is net sales (item 12), SG&A is Selling, General and Administrative Expenses (item
189), Cost of Goods Sold is item 41, Tangibles is Gross Property, Plant Equipment (item 7) and Inverse of Total Assets
is one divided by lagged total assets (item 6). means that I use the first difference for the variable under consideration
in the regression. Coefficient estimates appear in bold while t-statistics are displayed within brackets under each
coefficient. Standard errors adjust for heteroskedasticity and within correlation clustered by firm. Panel B presents
correlation coefficients between selected variables that are all scaled by lagged total assets (item 6). Rental expenses is
item 47 in the Compustat Industrial Annual database, Advertising expenses is item 45, Labor expenses is item 42. Panel
C presents panel regressions with quarter-year and firm-fixed effects where the dependent variable, Quarterly SG&A, is
quarterly selling, general and administrative expenses (item 1 in the Compustat Industrial Quarterly database) scaled by
beginning of quarter total assets (item 44). Coefficient estimates appear in bold while t-statistics are displayed within
brackets under each coefficient. Standard errors adjust for heteroskedasticity and within correlation clustered by firm. All variables are scaled by beginning of quarter Total Assets (item 44). Quarterly Depreciation is depreciation and
amortization (item 5), Lagged Quarterly Capital Expenditures is lagged capital expenditures for the quarter only,
recalculated from Compustat item 90 (cumulated capital expenditures from the beginning of the year to the end of the
quarter), and Inverse of Total Assets is one divided by quarterly beginning of quarter total assets (item 44).
Panel A – Yearly Corporate Investment, Depreciation Expenses, and Net Income
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
Depreciation Net income
Capital expenditures 0.066***
(25.42)
Sales 0.332***
(25.94)
Depreciation -1.070***
(-19.67)
SG&A -0.296***
(-17.79)
Cost of Goods Sold -0.302
(-19.96)
Tangibles 1.235*** 5.474***
(12.40) (14.39)
Inverse of Total Assets 9.880*** 20.473***
(7.33) (3.81)
Year-fixed effects Yes YesN obs 53,691 49,202
Adj. R2 0.21 0,38
41
Panel B – Correlation Between Yearly Investment, Depreciation,
Rental, Advertising and Labor expenses.
Capital expenditures
Lagged Capital
expenditures SG&A
expenses Depreciation
expenses Rental
expenses Advertising
expenses Labor
expenses
Capital
expenditures 1.00
Lagged Capital
expenditures 0.68*** 1.00
SG&A expenses -0.02*** -0.10*** 1.00
Depreciation
expenses 0.47*** 0.43*** 0.06*** 1.00
Rental expenses 0.17*** 0.13*** 0.38*** 0.19*** 1.00
Advertising
expenses 0.06*** 0.00 0.45*** 0.01** 0.15*** 1.00
Labor expenses 0.06*** 0.02 0.21*** 0.08*** 0.21** 0.04*
Number of total
observations 64,362 54,373 58,933 64,838 57,678 22,844 5,753
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
Panel C – Quarterly Corporate Investment and SG&A expenses
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
Quarterly SG&A
Quarterly depreciation 1.33***
(24.24)
Lagged quarterly capital expenditures 0.07***
(8.77)
Inverse of total assets 0.30***
(6.83)
Quarter-year-fixed effects YesN obs 165,746
Adj. R2 0.86
42
Table 5 - Earnings Surprises and Corporate Investment
This table presents the results of panel logit regressions with firm-fixed effects where the dependent variable is Above
consensus EPS forecast, a dummy variable equal to 1 when the firm posts earnings per share (EPS) superior to the
analysts’ last outstanding consensus EPS forecast before the fiscal year EPS announcement and equal to 0 otherwise.
Coefficient estimates appear in bold while z-statistics are displayed within brackets under each coefficient. Standard
errors adjust for heteroskedasticity and within correlation clustered by firm. Marginal Effects are presented for the last
model (model 4): they are computed at the mean values of variables, assuming fixed effects are equal to zero. All accounting variables are scaled by lagged Total Assets (item 6 in Compustat). Capital Expenditures is a fraction of
beginning of year Total Assets. Sales is net sales (item 12), Cash Flow is the sum of earnings before extraordinary
items, depreciation and amortization. Log (Analysts) is the logarithm of the number of analysts that made annual
earnings forecasts any month over the 12 months previous fiscal-year period. Log (Assets) is the logarithm of the
beginning-of-the-year Total Assets. Past Profitability is last year’s return on assets, defined as operating income before
depreciation divided by total assets. Changes in total accruals is measured as a year-to-year change in total accruals,
excluding depreciation. Percentage above EPS forecasts in industry is the percentage of firms in the same industry
(defined at the 3 digit level SIC code) that met or beat analysts’ consensus forecasts, as previously defined, in the same
fiscal year, excluding the firm-year observation. Standard deviation of forecasts is the median standard deviation of
monthly consensus forecasts over the fiscal year. Positive EPS is a dummy variable equal to 1 if the current fiscal year
EPS is equal or superior to zero. Upwards consensus change is a dummy variable equal to 1 if the last consensus EPS forecast before EPS results announcement is strictly larger than the first consensus forecast after the previous fiscal year
EPS announcement. Log(Age) is the logarithm of the firm’s age, where age is computed as the number of years a firm
has been present in the Compustat Price Dividends and Earnings database. Tangibles is Gross Property Plant and
Equipment. Year-fixed effects are included in all regressions.
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
1 2 3 4 Marginal Effect
Capital expenditures -0.575*** -0.617*** -0.740*** -0.775*** -0.178
(-2.73) (-2.92) (-3.19) (-3.33)
Sales 0.262*** 0.260*** 0.189*** 0.202*** 0.046
(6.88) (6.81) (4.49) (4.71)
Cash Flow 3.699*** 3.644*** 2.881*** 2.893*** 0.666
(19.37) (19.20) (14.16) (14.10)
Log (Analysts) 0.057** 0.057** -0.043 -0.040 -0.009
(2.08) (2.08) (-1.31) (-1.21)
Log (Assets) -0.040 -0.043 -0.067* -0.082** -0.015
(-1.16) (-1.26) (-1.76) (-2.08)
Past Profitability -0.727*** -0.706*** -0.848*** -0.895*** -0.206
(-6.14) (-5.96) (-6.58) (-6.85)
Changes in total accruals 0.305*** 0.315*** 0.183* 0.186** 0.043
(3.66) (3.76) (1.96) (1.99)
Percentage above EPS forecasts in industry 0.342*** 0.337*** 0.335*** 0.077
(5.12) (4.60) (4.56)
Standard deviation of forecasts -0.068** -0.065** -0.017
(-2.05) (-2.00)
Positive EPS 0.559*** 0.553*** 0.130
(12.00) (11.80)
Upwards Consensus Change 0.173*** 0.173*** 0.040
(6.18) (6.18)
Log(Age) -0.014 -0.003
(-0.16)
Tangibles -0.309*** -0.071
(-2.78)
Year-fixed effects Yes Yes Yes Yes Yes
N obs 43,539 43,030 36,405 36,356
Pseudo R2 0.05 0.05 0.06 0.06
Above consensus EPS forecasts
43
Table 6 - Earnings Surprises and Depreciation
This table presents the results of panel logit regressions with firm-fixed effects where the dependent variable is Above
consensus EPS forecast, a dummy variable equal to 1 when the firms posts earnings per share (EPS) superior to the
analysts’ last outstanding consensus EPS forecast before the fiscal year EPS announcement and equal to 0 otherwise.
Coefficient estimates appear in bold while z-statistics are displayed within brackets under each coefficient. Standard
errors adjust for heteroskedasticity and within correlation clustered by firm. Marginal Effects are presented for the last
model (model 4): they are computed at the mean values of variables, assuming fixed effects are equal to zero. All accounting variables are scaled by lagged Total Assets (item 6 in Compustat). Depreciation is depreciation and
amortization (item 14 in Compustat) scaled by beginning of year Total Assets. Sales is net sales (item 12), Cash Flow
is the sum of earnings before extraordinary items, depreciation and amortization. Log (Analysts) is the logarithm of
the number of analysts that made annual earnings forecasts any month over the 12 months previous fiscal-year period.
Log (Assets) is the logarithm of the beginning-of-the-year Total Assets. Past Profitability is last year’s return on
assets, defined as operating income before depreciation divided by total assets. Changes in total accruals is measured
as a year-to-year change in total accruals, excluding depreciation. Percentage above EPS forecasts in industry is the
percentage of firms in the same industry (defined at the 3 digit level SIC code) that met or beat analysts’ consensus
forecasts, as previously defined, in the same fiscal year, excluding the firm-year observation. Standard deviation of
forecasts is the median standard deviation of monthly consensus forecasts over the fiscal year. Positive EPS is a
dummy variable equal to 1 if the current fiscal year EPS is equal or superior to zero. Upwards consensus change is a dummy variable equal to 1 if the last consensus EPS forecast before EPS results announcement is strictly larger than
the first consensus forecast after the previous fiscal year EPS announcement. Log(Age) is the logarithm of the firm’s
age, where age is computed as the number of years a firm has been present in the Compustat Price Dividends and
Earnings database. Tangibles is Gross Property Plant and Equipment. Year-fixed effects are included in all
regressions.
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
1 2 3 4 Marginal Effect
Depreciation -3.480*** -3.392*** -2.693*** -2.236** -0.670
(-4.71) (-4.60) (-3.26) (-2.52)
Sales 0.287*** 0.282*** 0.196*** 0.198*** 0.046
(7.46) (7.33) (4.57) (4.57)
Cash Flow 3.651*** 3.597*** 2.843*** 2.852*** 0.723
(19.29) (19.14) (14.18) (14.20)
Log (Analysts) 0.056** 0.056** -0.044 -0.044 -0.011
(2.10) (2.08) (-1.38) (-1.36)
Log (Assets) -0.046 -0.048 -0.067* -0.072* -0.013
(-1.35) (-1.41) (-1.79) (-1.85)
Past Profitability -0.794*** -0.776*** -0.910*** -0.940*** -0.260
(-6.67) (-6.52) (-7.06) (-7.20)
Changes in total accruals 0.320*** 0.330*** 0.204** 0.203** 0.050
(3.86) (3.95) (2.19) (2.17)
Percentage above EPS forecasts in industry 0.331*** 0.335*** 0.332*** 0.075
(5.01) (4.61) (4.57)
Standard deviation of forecasts -0.065** -0.064** -0.016
(-2.02) (-2.00)
Positive EPS 0.545*** 0.543*** 0.121
(11.72) (11.65)
Upwards Consensus Change 0.176*** 0.176*** 0.039
(6.32) (6.34)
Log(Age) -0.017 -0.005
(-0.18)
Tangibles -0.169 -0.050
(-1.44)
Year-fixed effects Yes Yes Yes Yes Yes
N obs 44,053 43,543 36,862 36,809
Pseudo R2 0.05 0.05 0.06 0.06
Above consensus EPS forecasts
44
Table 7 - Controlling for Reversed Causality
This table presents the results of panel logit regressions with firm-fixed effects where the dependent variable are Above
consensus EPS forecast and Above consensus EPS forecast in the 4th
quarter, a dummy variable equal to 1 when the
firms posts earnings per share (EPS) superior to the analysts’ last outstanding consensus EPS forecast before the fiscal
year or fourth quarter EPS announcement and equal to 0 otherwise. Coefficient estimates appear in bold while z-
statistics are displayed within brackets under each coefficient. Standard errors adjust for heteroskedasticity and within
correlation clustered by firm. All accounting variables are scaled by lagged Total Assets (item 6 in Compustat). Capital
Expenditures is a fraction of beginning of year Total Assets. It is computed for the previous year, first semester, the 1st ,
2nd and 3rd quarters of the contemporaneous year from the Compustat quarterly Industrial database. All other financial
variables are yearly data. Sales is net sales (item 12), Cash Flow is the sum of earnings before extraordinary items,
depreciation and amortization. Log (Analysts) is the logarithm of the number of analysts that made annual earnings
forecasts any month over the 12 months previous fiscal-year period. Log (Assets) is the logarithm of the beginning-of-
the-year Total Assets. Past Profitability is last year’s return on assets, defined as operating income before depreciation
divided by total assets. Changes in total accruals is measured as a year-to-year change in total accruals, excluding
depreciation. Percentage above EPS forecasts in industry is the percentage of firms in the same industry (defined at the
3 digit level SIC code) that met or beat analysts’ consensus forecasts, as previously defined, in the same quarter,
excluding the firm-year observation. Standard deviation of forecasts is the median standard deviation of monthly
consensus forecasts over the fiscal year. Positive EPS is a dummy variable equal to 1 if the current fiscal year EPS is equal or superior to zero. Upwards consensus change is a dummy variable equal to 1 if the last consensus EPS forecast
before EPS results announcement is strictly larger than the first consensus forecast after the previous fiscal year EPS
announcement. Log(Age) is the logarithm of the firm’s age, where age is computed as the number of years a firm has
been present in the Compustat Price Dividends and Earnings database. Tangibles is Gross Property Plant and
Equipment. Year-fixed effects are included in all regressions.
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
Above consensus EPS forecasts
1 2 3 4 5
Lagged Capital Expenditures -0.464**
(-2.40)
Capital Expenditures 1st semester -2.126***
(-4.10)
Capital Expenditures 1st quarter -2.394**
(-2.38)
Capital Expenditures 2nd quarter -4.004***
(-4.55)
Capital Expenditures 3rd quarter -2.859***
(-3.64)
Sales 0.177*** 0.169*** 0.161*** 0.172*** 0.173***
(4.18) (3.74) (3.61) (3.79) (3.87)
Cash Flow 2.829*** 2.615*** 2.554*** 2.620*** 2.634***
(14.18) (13.37) (13.15) (13.36) (13.35)
Log (Analysts) -0.044 -0.012 -0.020 -0.013 -0.012
(-1.36) (-0.34) (-0.58) (-0.37) (-0.35)
Standard deviation of forecasts -0.063** -0.042 -0.043 -0.042 -0.042
(-1.98) (-1.48) (-1.51) (-1.50) (-1.49)
Percentage above EPS forecasts in industry 0.326*** 1.604*** 1.605*** 1.605*** 1.604***
(4.44) (21.02) (21.09) (21.03) (20.97)
Past Profitability -0.851*** -1.101*** -1.121*** -1.109*** -1.137***
(-6.42) (-7.82) (-7.96) (-7.90) (-8.11)
Changes in total accruals 0.196** 0.176* 0.163 0.172 0.164
(2.09) (1.67) (1.56) (1.64) (1.57)
Positive EPS 0.547*** 0.662*** 0.666*** 0.664*** 0.663***
(11.71) (13.22) (13.37) (13.25) (13.21)
Upwards Consensus Change 0.174*** 0.316*** 0.317*** 0.317*** 0.314***
(6.21) (10.39) (10.43) (10.42) (10.35)
Log(Age) -0.032 -0.033 -0.036 -0.031 -0.020
(-0.35) (-0.34) (-0.38) (-0.32) (-0.21)
Tangibles -0.291*** -0.205* -0.197 -0.207* -0.213*
(-2.61) (-1.65) (-1.60) (-1.67) (-1.72)
Log (Assets) -0.066* -0.082* -0.071* -0.082* -0.075*
(-1.69) (-1.91) (-1.66) (-1.91) (-1.76)
Year-fixed effects Yes Yes Yes Yes YesN obs 36,329 31,870 31,983 31,870 31,904
Pseudo R2 0.06 0.08 0.08 0.08 0.08
Above consensus EPS forecasts in 4th quarter
45
Table 8 - Corporate Investment and Measures of
Analyst Pressure on Management
This table presents the results of panel regressions with firm-fixed effects where the dependent variable is Capital
Expenditures (Compustat item 128), a fraction of beginning of year Total Assets (item 6) multiplied by 100. Coefficient
estimates appear in bold while t-statistics are displayed within brackets under each coefficient. Standard errors adjust
for heteroskedasticity and within correlation clustered by firm. Analyst Pressure is a proxy for the level of pressure
exerted by analyst at the start of the year on the firm’s management to increase earnings per share (EPS) relative to last
year’s EPS. Tobin’s Q is the beginning of the year market-to-book ratio computed as the market value of equity plus
book value of assets minus the book value of equity minus balance sheet deferred taxes scaled by the value of book
assets. Cash Flow is the sum of earnings before extraordinary items, depreciation and amortization scaled by start-of-
year Total Assets (multiplied by 100). Log (Analysts) is the logarithm of the maximum number of analysts that made
annual earnings forecasts any month over the 12 months previous fiscal-year period. Kaplan and Zingales (1997) index
is a modified Kaplan and Zingales (1997) index of Financial Constraints, excluding Tobin’s Q. Firm Age is the number
of years a company has been present in the Compustat Price Dividends and Earnings database. Tangibles is Gross
Property Plant and Equipment. Baker, Stein and Wurgler (2003)’s undervaluation is the cumulative excess return computed from CRSP over year t+1 to year t+3 as in Baker, Stein and Wurgler (2003). Year-fixed effects are included
in all regressions.
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
1 2 3 4
Analyst pressure -2.447*** -1.723*** -1.466*** -1.776***
(-5.44) (-3.88) (-3.31) (-2.62)
Tobin's Q 0.807*** 0.950*** 0.932*** 0.991***
(17.66) (18.07) (17.88) (13.33)
Cash Flow 0.035*** 0.035*** 0.036*** 0.043***
(7.15) (7.16) (7.36) (5.29)
Log (Analysts) 0.769*** 0.562*** 0.612*** 0.564***
(7.24) (5.32) (5.75) (3.69)
Past Profitability 0.059*** 0.047*** 0.045*** 0.055***
(12.83) (10.50) (10.04) (8.90)
Log (Assets) -2.557*** -2.364*** -2.425*** -2.768***
(-18.42) (-17.34) (-15.74) (-13.35)
Kaplan and Zingales (1997) index -0.015*** -0.015*** -0.017***
(-15.97) (-15.98) (-13.36)
Kaplan and Zingales (1997) index * Tobin's Q 0.003*** 0.003*** 0.003***
(10.19) (10.38) (9.58)
Log (Firm age) -1.457*** -1.289***
(-4.26) (-2.71)
Tangible Assets -1.678*** -1.039
(-3.13) (-1.49)
Baker, Stein & Wurgler (2003)'s undervaluation -0.315***
(-7.03)
Year-fixed effects Yes Yes Yes YesN obs 37,302 37,302 37,165 22,578
Adj. R2 0.65 0.66 0.66 0.68
Capital Expenditures
46
Table 9 - Corporate Investment and Past Earnings Surprises
This table presents the results of panel regressions with firm-fixed effects where the dependent variable is Capital
Expenditures (Compustat item 128), a fraction of beginning of year Total Assets (item 6) multiplied by 100. Coefficient
estimates appear in bold while t-statistics are displayed within brackets under each coefficient. Standard errors adjust
for heteroskedasticity and within correlation clustered by firm. Above consensus EPS forecast (last year) is a dummy
variable equal to 1 if last year, the firm posted earnings per share (EPS) superior to the analysts’ the last outstanding
consensus EPS forecast before the fiscal year EPS announcement and equal to 0 otherwise. Tobin’s Q is the beginning of the year market-to-book ratio computed as the market value of equity plus book value of assets minus the book value
of equity minus balance sheet deferred taxes scaled by the value of book assets. Cash Flow is the sum of earnings
before extraordinary items, depreciation and amortization scaled by start-of-year Total Assets (multiplied by 100). Log
(Analysts) is the logarithm of the maximum number of analysts that made annual earnings forecasts any month over the
12 months previous fiscal-year period. Kaplan and Zingales (1997) index is a modified Kaplan and Zingales (1997)
index of Financial Constraints, excluding Tobin’s Q. Firm Age is the number of years a company has been present in
the Compustat Price Dividends and Earnings database. Tangibles is Gross Property Plant and Equipment. Baker, Stein
and Wurgler (2003)’s Undervaluation is the cumulative excess return computed from CRSP over year t+1 to year t+3
as in Baker, Stein and Wurgler (2003). Year-fixed effects are included in all regressions.
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
1 2 3 4
Above consensus EPS forecast (last year) 0.532*** 0.428*** 0.420*** 0.348***
(8.27) (6.73) (6.60) (4.47)
Tobin's Q 0.849*** 1.004*** 0.977*** 1.035***
(18.96) (20.30) (20.04) (15.38)
Cash Flow 0.036*** 0.035*** 0.036*** 0.045***
(8.63) (8.80) (8.98) (6.61)
Log (Analysts) 0.767*** 0.562*** 0.597*** 0.567***
(8.55) (6.26) (6.63) (4.37)
Return on Assets 0.057*** 0.045*** 0.043*** 0.051***
(14.45) (11.68) (11.10) (9.63)
Log (Assets) -2.475*** -2.300*** -2.250*** -2.532***
(-21.22) (-19.94) (-17.71) (-14.68)
Kaplan and Zingales (1997) index -0.015*** -0.016*** -0.017***
(-19.11) (-19.47) (-15.98)
Kaplan and Zingales (1997) index * Tobin's Q 0.003*** 0.003*** 0.003***
(12.14) (12.42) (11.32)
Log (Firm age) -2.077*** -1.959***
(-6.62) (-4.48)
Tangible Assets -1.027** -0.025
(-2.26) (-0.04)
Baker, Stein & Wurgler (2003)'s undervaluation -0.337***
(-8.39)
Year-fixed effects Yes Yes Yes Yes
N obs 50,960 50,738 50,557 30,056
Adj. R2 0.63 0.64 0.64 0.67
Capital Expenditures
47
Table 10 - Robustness Checks:
Alternative Measure of Analyst Consensus Forecasts
This table presents the results of panel logit regressions with firm-fixed effects where the dependent variable is Above last EPS forecast, a dummy variable equal to 1 when the firms posts earnings per share (EPS) superior to
the last analyst EPS forecast before the fiscal year EPS announcement and equal to 0 otherwise. Coefficient
estimates appear in bold while z-statistics are displayed within brackets under each coefficient. Standard errors
adjust for heteroskedasticity and within correlation clustered by firm. All financial variables are scaled by lagged
Total Assets (item 6 in Compustat). Capital Expenditures is a fraction of beginning of year Total Assets. Sales is
net sales (item 12), Cash Flow is the sum of earnings before extraordinary items, depreciation and amortization.
Log (Analysts) is the logarithm of the number of analysts that made annual earnings forecasts any month over the
12 months previous fiscal-year period. Log (Assets) is the logarithm of the beginning-of-the-year Total Assets.
Past Profitability is last year’s return on assets, defined as operating income before depreciation divided by total
assets. Changes in total accruals is measured as a year-to-year change in total accruals, excluding depreciation.
Percentage above EPS forecasts in industry is the percentage of firms in the same industry (defined at the 3 digit
level SIC code) that met or beat analysts’ consensus forecasts, as previously defined, in the same fiscal year, excluding the firm-year observation. Standard deviation of forecasts is the median standard deviation of monthly
consensus forecasts over the fiscal year. Positive EPS is a dummy variable equal to 1 if the current fiscal year EPS
is equal or superior to zero. Upwards consensus change is a dummy variable equal to 1 if the last consensus EPS
forecast before EPS results announcement is strictly larger than the first consensus forecast after the previous fiscal
year EPS announcement. Log(Age) is the logarithm of the firm’s age, where age is computed as the number of
years a firm has been present in the Compustat Price Dividends and Earnings database. Tangibles is Gross
Property Plant and Equipment. Year-fixed effects are included in all regressions.
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
1 2 3 4 Marginal Effect
Capital Expenditures -0.519* -0.500* -0.772*** -0.721** -0.107
(-1.93) (-1.86) (-2.65) (-2.48)
Sales 0.250*** 0.240*** 0.039 0.030 0.004
(5.02) (4.83) (0.74) (0.56)
Cash Flow 2.740*** 2.711*** 0.830*** 0.855*** 0.126
(13.73) (13.61) (4.17) (4.28)
Log (Analysts) -0.002 -0.006 -0.055 -0.060 -0.009
(-0.06) (-0.15) (-1.30) (-1.41)
Log (Assets) -0.220*** -0.224*** -0.276*** -0.371*** -0.055
(-4.85) (-4.88) (-5.54) (-7.02)
Past Profitability -0.104 -0.091 -0.527*** -0.498*** -0.074
(-0.69) (-0.61) (-3.28) (-3.06)
Changes in total accruals 0.390*** 0.385*** 0.180 0.169 0.025
(3.86) (3.78) (1.60) (1.50)
Percentage above EPS forecasts in industry 0.246*** 0.292*** 0.279*** 0.041
(2.71) (2.95) (2.81)
Standard deviation of forecasts 0.006 0.010 0.015
(0.21) (0.33)
Positive EPS 1.608*** 1.604*** 0.303
(22.56) (22.60)
Upwards Consensus Change 0.380*** 0.384*** 0.056
(10.40) (10.52)
Log(Age) 0.705*** 0.104
(5.82)
Tangibles -0.509*** -0.075
(-3.39)
Year-fixed effects Yes Yes Yes Yes Yes
N obs 32,297 31,731 27,524 27,476
Pseudo R2 0.04 0.04 0.08 0.08
Above last EPS forecasts
48
Table 11 - Robustness Checks:
Sales Surprises and Corporate Investment
This table presents the results of panel logit regressions with firm-fixed effects where the dependent variable is Above consensus sales forecast, a dummy variable equal to 1 when the firms posts sales superior to the last
analysts consensus sales forecast before the fiscal year sales announcement and equal to 0 otherwise. Coefficient
estimates appear in bold while z-statistics are displayed within brackets under each coefficient. Standard errors
adjust for heteroskedasticity and within correlation clustered by firm. All accounting variables are scaled by lagged
Total Assets (item 6 in Compustat). Capital Expenditures is a fraction of beginning of year Total Assets. Sales is
net sales (item 12), Cash Flow is the sum of earnings before extraordinary items, depreciation and amortization.
Log (Analysts) is the logarithm of the number of analysts that made annual earnings forecasts any month over the
12 months previous fiscal-year period. Log (Assets) is the logarithm of the beginning-of-the-year Total Assets.
Past Profitability is last year’s return on assets, defined as operating income before depreciation divided by total
assets. Changes in total accruals is measured as a year-to-year change in total accruals, excluding depreciation.
Percentage above sales forecasts in industry is the percentage of firms in the same industry (defined at the 3 digit
level SIC code) that met or beat analysts’ consensus forecasts, as previously defined, in the same fiscal year, excluding the firm-year observation. Standard deviation of forecasts is the median standard deviation of monthly
consensus EPS forecasts over the fiscal year. Positive EPS is a dummy variable equal to 1 if the current fiscal year
EPS is equal or superior to zero. Upwards consensus change is a dummy variable equal to 1 if the last consensus
EPS forecast before EPS results announcement is strictly larger than the first consensus forecast after the previous
fiscal year EPS announcement. Log(Age) is the logarithm of the firm’s age, where age is computed as the number
of years a firm has been present in the Compustat Price Dividends and Earnings database. Tangibles is Gross
Property Plant and Equipment. Year-fixed effects are included in all regressions.
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
1 2 3 4
Capital Expenditures -0.003 -0.003 -0.003 -0.006
(-0.64) (-0.51) (-0.60) (-1.08)
Sales 0.606*** 0.595*** 0.480*** 0.508***
(7.19) (7.03) (5.44) (5.65)
Cash Flow 0.690*** 0.634*** 0.358 0.392*
(3.18) (2.93) (1.55) (1.69)
Log (Analysts) -0.015 -0.025 -0.067 -0.066
(-0.28) (-0.45) (-1.11) (-1.09)
Log (Assets) -0.120 -0.114 -0.147* -0.225***
(-1.62) (-1.51) (-1.85) (-2.59)
Past Profitability -0.966*** -0.906*** -0.921*** -0.939***
(-4.88) (-4.56) (-4.29) (-4.36)
Changes in total accruals -0.106 -0.134 -0.236 -0.231
(-0.66) (-0.82) (-1.36) (-1.33)
Percentage above sales forecasts in industry 0.863*** 0.835*** 0.829***
(7.04) (6.55) (6.47)
Standard deviation of forecasts 0.008 0.011
(0.17) (0.23)
Positive EPS 0.298*** 0.287***
(3.66) (3.53)
Upwards Consensus Change 0.307*** 0.302***
(6.30) (6.19)
Log(Age) -0.356
(-1.42)
Tangibles -0.748***
(-2.82)
Year-fixed effects Yes Yes Yes Yes
N obs 11,793 11,507 10,588 10,562
Pseudo R2 0.04 0.05 0.05 0.05
Above consensus sales forecasts
49
Table 12 - Controlling for Corporate Governance Quality
These tables present the results of panel regressions where all observations are sorted into three sub-samples depending on the tercile in which a firm’s start-of-the-year corporate governance index falls for each year. The regressions are run with firm-fixed effects. In Panel A and B, the dependent variable is Above consensus EPS forecast, a dummy variable equal
to 1 when the firms posts earnings per share (EPS) superior to the analysts’ last outstanding consensus EPS forecast before the fiscal year EPS announcement and equal to 0 otherwise.
All accounting variables are scaled by lagged Total Assets (item 6 in Compustat). Coefficient estimates appear in bold while z-statistics are displayed within brackets under each
coefficient. Standard errors adjust for heteroskedasticity and within correlation clustered by firm. Capital Expenditures is a fraction of beginning of year Total Assets. Sales is net sales
(item 12), Cash Flow is the sum of earnings before extraordinary items, depreciation and amortization. Log (Analysts) is the logarithm of the number of analysts that made annual
earnings forecasts any month over the 12 months previous fiscal-year period. Log (Assets) is the logarithm of the beginning-of-the-year Total Assets. Past Profitability is last year’s
return on assets, defined as operating income before depreciation divided by total assets. Changes in total accruals is measured as a year-to-year change in total accruals, excluding
depreciation. Percentage above EPS forecasts in industry is the percentage of firms in the same industry (defined at the 3 digit level SIC code) that met or beat analysts’ consensus
forecasts, as previously defined, in the same fiscal year, excluding the firm-year observation. Standard deviation of forecasts is the median standard deviation of monthly consensus
forecasts over the fiscal year. Positive EPS is a dummy variable equal to 1 if the current fiscal year EPS is equal or superior to zero. Upwards consensus change is a dummy variable
equal to 1 if the last consensus EPS forecast before EPS results announcement is strictly larger than the first consensus forecast after the previous fiscal year EPS announcement.
Log(Age) is the logarithm of the firm’s age, where age is computed as the number of years a firm has been present in the Compustat Price Dividends and Earnings database. Tangibles is Gross Property Plant and Equipment. Year-fixed effects are included in all regressions.
Panel A: Gompers Ishii and Metrick (2003) governance index Panel B: Bebchuk, Cohen and Ferrel (2004) entrenchment index
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
Best Governance Worst Governance
T1 T2 T3
Capital Expenditures -2.533*** -1.808 -1.648
(-2.82) (-1.52) (-0.85)
Sales 0.133 -0.194 0.179
(0.85) (-1.14) (0.81)
Cash Flow 0.846 2.037** 3.469***
(1.61) (2.55) (3.02)
Log (Analysts) 0.072 0.034 0.024
(0.65) (0.28) (0.14)
Percentage above EPS forecasts in industry 2.905*** 3.532*** 3.110***
(12.50) (12.00) (10.55)
Return on Assets -0.967* -1.419** -1.714
(-1.88) (-1.99) (-1.64)
Changes in total accruals 0.003 0.002 0.001
(0.84) (0.37) (0.12)
Upwards Consensus change 0.607*** 0.282*** 0.794***
(6.81) (2.63) (7.06)
Positive realized EPS 0.582*** 1.110*** 0.823***
(3.62) (5.93) (2.91)
Log (Assets) 0.020 -0.029 -0.141
(0.13) (-0.17) (-0.71)
Year-fixed effects Yes Yes Yes
N obs 4,152 3,258 2,707
Adj. R2 0.10 0.12 0.14
Above EPS consensus forecasts
Best Governance Worst Governance
T1 T2 T3
Capital Expenditures -2.011** -0.393 -1.235
(-2.32) (-0.22) (-0.66)
Sales 0.179 0.025 0.029
(1.22) (0.12) (0.12)
Cash Flow 1.408*** 1.216 3.447**
(2.70) (1.48) (2.12)
Log (Analysts) 0.031 0.053 0.091
(0.31) (0.33) (0.51)
Percentage above EPS forecasts in industry 2.924*** 3.147*** 3.197***
(12.24) (9.75) (10.41)
Return on Assets -0.854* -1.192 -2.240*
(-1.72) (-1.24) (-1.86)
Changes in total accruals 0.001 0.010* -0.009
(0.31) (1.86) (-1.50)
Upwards Consensus change 0.462*** 0.596*** 0.650***
(5.66) (5.04) (4.63)
Positive realized EPS 0.652*** 0.709*** 1.687***
(4.42) (3.41) (4.76)
Log (Assets) -0.020 0.086 0.050
(-0.15) (0.41) (0.19)
Year-fixed effects Yes Yes Yes
N obs 5,029 2,372 1,990
Adj. R2 0.10 0.12 0.15
Above EPS consensus forecasts
50
In Panel C and D, the dependent variable is Capital Expenditures (Compustat item 128), a fraction of beginning of year Total Assets (item 6) multiplied by 100. Coefficient estimates
appear in bold while t-statistics are displayed within brackets under each coefficient. Standard errors adjust for heteroskedasticity and within correlation clustered by firm. Capital is a
fraction of beginning of year Total Assets (item 6) Analyst Pressure is a proxy for the level of pressure exerted by analyst at the start of the year on the firm’s management to increase earnings per share (EPS) relative to last year’s EPS. Tobin’s Q is the beginning of the year market-to-book ratio computed as the market value of equity plus book value of assets minus
the book value of equity minus balance sheet deferred taxes scaled by the value of book assets. Cash Flow is the sum of earnings before extraordinary items, depreciation and
amortization scaled by start-of-year Total Assets (multiplied by 100). Log (Analysts) is the logarithm of the maximum number of analysts that made annual earnings forecasts any month
over the 12 months previous fiscal-year period. Kaplan and Zingales (1997) index is a modified Kaplan and Zingales (1997) index of Financial Constraints, excluding Tobin’s Q. Firm
Age is the number of years a company has been present in the Compustat Price Dividends and Earnings database. Tangibles is Gross Property Plant and Equipment. Year-fixed effects
are included in all regressions.
Panel C: Gompers Ishii and Metrick (2003) governance index Panel D: Bebchuk, Cohen and Ferrel (2004) entrenchment index
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
Best Governance Worst Governance
T1 T2 T3
Analyst pressure 0.836 2.176 -0.031
(0.80) (1.36) (-0.01)
Tobin's Q 0.687*** 0.866*** 0.720**
(4.62) (5.45) (2.32)
Cash Flow 0.049*** 0.050*** 0.074***
(3.44) (2.95) (4.46)
Log (Analysts) 0.939*** 0.100 0.281
(3.95) (0.43) (0.98)
Return on Assets 8.803*** 7.498*** 11.329***
(6.49) (5.12) (5.05)
Kaplan and Zingales (1997) index -0.013*** -0.015*** -0.006**
(-4.82) (-5.30) (-2.52)
Kaplan and Zingales (1997) index * Tobin's Q 0.003*** 0.004*** 0.003**
(3.06) (4.67) (2.19)
Log (Assets) -1.792*** -0.766* -1.083**
(-5.92) (-1.91) (-2.14)
Log (age) -0.702 -0.481 0.359
(-0.60) (-0.44) (0.23)
Tangible Assets 1.271 0.690 0.776
(1.14) (0.68) (0.71)
Year-fixed effects Yes Yes Yes
N obs 5,113 3,878 3,175
Adj. R2 0.73 0.76 0.73
Capital Expenditures
Best Governance Worst Governance
T1 T2 T3
Analyst pressure 0.895 2.211* -0.918
(0.77) (1.79) (-0.24)
Tobin's Q 0.696*** 0.943*** 1.151***
(4.83) (5.32) (4.04)
Cash Flow 0.059*** 0.035* 0.046***
(4.29) (1.83) (3.37)
Log (Analysts) 0.830*** 0.053 0.289
(3.46) (0.21) (1.03)
Return on Assets 9.890*** 5.950*** 8.524***
(7.50) (3.84) (3.19)
Kaplan and Zingales (1997) index -0.013*** -0.014*** -0.013***
(-5.35) (-5.74) (-3.54)
Kaplan and Zingales (1997) index * Tobin's Q 0.003*** 0.004*** 0.007***
(3.49) (5.48) (4.38)
Log (Assets) -1.650*** -0.896* -0.925*
(-6.03) (-1.74) (-1.82)
Log (age) -1.756 2.308 -0.901
(-1.61) (1.42) (-0.75)
Tangible Assets 1.058 1.166 1.869
(1.18) (0.92) (1.59)
Year-fixed effects Yes Yes Yes
N obs 5,723 2,909 2,346
Adj. R2 0.72 0.77 0.75
Capital Expenditures
51
Table 13 - Controlling for Firms’ Investment Opportunities
Panel A presents the results of panel logit regressions where all observations are sorted into three sub-samples depending
on the tercile in which a firm’s start-of-the-year corporate Tobin’s Q falls for each year. The regression is run with firm-
fixed effects. The dependent variable is Above consensus EPS forecast, a dummy variable equal to 1 when the firms posts
earnings per share (EPS) superior to the analysts’ last outstanding consensus EPS forecast before the fiscal year EPS
announcement and equal to 0 otherwise. Coefficient estimates appear in bold while z-statistics are displayed within brackets under each coefficient. Standard errors adjust for heteroskedasticity and within correlation clustered by firm. All
financial variables are scaled by lagged Total Assets (item 6 in Compustat). Capital Expenditures is a fraction of beginning
of year Total Assets. Sales is net sales (item 12), Cash Flow is the sum of earnings before extraordinary items, depreciation
and amortization. Log (Analysts) is the logarithm of the number of analysts that made annual earnings forecasts any month
over the 12 months previous fiscal-year period. Percentage above EPS forecasts in industry is the percentage of firms in the
same industry (defined at the 3 digit level SIC code) that met or beat analysts’ consensus forecasts, as previously defined, in
the same fiscal year, excluding the firm-year observation. Standard deviation of forecasts is the median standard deviation
of monthly consensus forecasts over the fiscal year. Changes in total accruals is measured as a year-to-year change in total
accruals, excluding depreciation. Log (Assets) is the logarithm of the beginning-of-the-year Total Assets. Upwards
consensus change is a dummy variable equal to 1 if the last consensus EPS forecast before EPS results announcement is
strictly larger than the first consensus forecast after the previous fiscal year EPS announcement. Positive EPS is a dummy
variable equal to 1 if the current fiscal year EPS is equal or superior to zero. Past Profitability is last year’s return on assets, defined as operating income before depreciation divided by total assets. Year-fixed effects are included in all regressions.
Panel A
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
Low Tobin's Q High Tobin's Q
T1 T2 T3
Capital Expenditures -0.687 -1.302** -1.762***
(-1.27) (-2.45) (-3.92)
Sales -0.021 0.088 0.160*
(-0.20) (1.01) (1.85)
Cash Flow 2.813*** 2.743*** 1.328***
(4.27) (5.21) (5.56)
Log (Analysts) -0.084 0.036 -0.083
(-1.15) (0.52) (-1.11)
Percentage above EPS forecasts in industry 0.445*** 0.213 0.341*
(3.08) (1.43) (1.83)
Changes in total accruals 0.272 0.124 0.055
(1.22) (0.57) (0.32)
Upwards Consensus change 0.371*** 0.454*** 0.474***
(5.65) (7.83) (7.86)
Positive EPS 1.535*** 1.216*** 1.043***
(14.00) (9.06) (7.90)
Past Profitability -1.933*** -0.761** -0.635***
(-3.99) (-2.12) (-3.26)
Log (Assets) 0.016 -0.218*** -0.097
(0.16) (-2.63) (-1.20)
Year-fixed effects Yes Yes Yes
N obs 7,617 8,654 8,303
Adj. R2 0.10 0.07 0.06
Above EPS consensus forecasts
52
Panel B presents the results of panel regression with firm-fixed effects. The dependent variable is Capital Expenditures, a
fraction of beginning of year Total Assets multiplied by 100. Coefficient estimates appear in bold while t-statistics are
displayed within brackets under each coefficient. Standard errors adjust for heteroskedasticity and within correlation
clustered by firm. Analyst Pressure is a proxy for the level of pressure exerted by analyst at the start of the year on the
firm’s management to increase earnings per share (EPS) relative to last year’s EPS. This variable is interacted with a
dummy variable equal to 1 when the firm’s Tobin’s Q falls in the highest tercile (High Tobin’s Q) at the beginning of the
year. Tobin’s Q is the market-to-book ratio computed as the market value of equity plus book value of assets minus the book value of equity minus balance sheet deferred taxes scaled by the value of book assets. Cash Flow is the sum of
earnings before extraordinary items, depreciation and amortization scaled by start-of-year Total Assets (multiplied by 100).
Log (Analysts) is the logarithm of the maximum number of analysts that made annual earnings forecasts any month over the
12 months previous fiscal-year period. Kaplan and Zingales (1997) index is a modified Kaplan and Zingales (1997) index
of Financial Constraints, excluding Tobin’s Q. Firm Age is the number of years a company has been present in the
Compustat Price Dividends and Earnings database. Tangibles is Gross Property Plant and Equipment. Year-fixed effects
are included.
Panel B
*, **, *** indicate a significance level of less than 10%, 5%, and 1% respectively
Capital Expenditures
1
Analyst pressure -1.875***
(-3.81)
High Tobin's Q * Analyst Pressure -2.256*
(-1.82)
HighlQ 1.347***
(10.28)
Tobin's Q 0.650***
(13.97)
Cash Flow 0.033***
(6.78)
Log (Analysts) 0.703***
(6.64)
Log (Assets) -2.470***
(-17.98)
Past Profitability 5.392***
(11.87)
Year-fixed effects Yes
N obs 37,302
Adj. R2 0.66
Recommended