Download Managers` Discussion of Competition in the 10

Managers’ Discussion of Competition in the 10-K and Firms’
Financing Policies and Investing Activities
Kyle Peterson†
University of Oregon
Lundquist College of Business
Eugene, OR 97403
541-346-3329
[email protected]
Nam Tran
Melbourne Business School
200 Leicester St.
Carlton VIC 3053, Australia
[email protected]
September 2014
PRELIMINARY. Please do not cite or distribute without permission.
Abstract:
We examine whether managers’ perceived competition, based on firms’ disclosures in
their 10-K filings, influences firms’ financing policies and investments. We document
that firms with high perceived competition have more conservative financing policies
by holding more cash, having lower leverage and lower dividend payout ratio. We
also find that perceived competition is positively related to firms’ investments in
R&D and acquisitions over the subsequent three years. Finally, we examine whether
conservative financing policies and investments in R&D and acquisitions help
mitigate the impact of perceived competition on the rate of diminishing marginal
returns established in prior literature. We find that the impact of perceived
competition on the rate of diminishing marginal return is more severe for firms with
more conservative financing policies and firms with more investments in R&D and
acquisitions, suggesting that perceived competition combined with costly financing
and investing activities provides a more credible signal of the actual level of
competition.
Keywords: competition, financing policies, investment, research and development
JEL classification:
†
Corresponding Author. We thank workshop participants at the University of Oregon and Melbourne
Business School for their helpful comments and suggestions.
1.
INTRODUCTION
Understanding a firm’s competitive environment and its strategies for
competing in that environment is a critical component of standard equity valuation
models (Healy and Palepu 2007, Lundholm and Sloan 2007, Penman 2009). Li,
Lundholm and Minnis (2013) provide evidence that greater managerial perceived
competition, measured based on firms’ disclosures in 10-K filings, is associated with
higher rates of diminishing marginal returns for the firms, suggesting that these
disclosures signal real threats of competition. This raises questions about whether
firms with high perceived competition engage in actions to mitigate the negative
effects of competition on their profitability, and whether those actions are effective.
We extend Li et al. (2013) by examining these questions. Specifically, we examine
how perceived competition affects firms’ financing policies including cash holdings,
leverage, and dividend payout ratios. Second, we examine whether perceived
competition predicts future investments in research and development (R&D) and
acquisitions. Finally, we examine whether the financing policies and investments that
firms with high perceived competition choose to undertake help curtail the negative
impact of strong perceived competition on the rate of diminishing marginal returns
found in Li et al. (2013).
With respect to financing policies, theories suggest that firms faced with strong
market competition should have high cash holdings, low financial leverage and low
dividend payout ratios (Bates et al. 2009; Haushalter et al. 2007; Benoit 1984; Booth
and Zhou 2009). These conservative financing policies help firms respond to negative
cash flow shocks and financial distress, which are more likely to occur in more
competitive markets. More conservative financing policies also provide financial
flexibility, allowing firms to act quickly when a profitable investment opportunity
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arises before their competitors take the opportunity away. High cash reserves and low
leverage can also signal the possibility of aggressive behavior, which is useful in
deterring rivals’ entry and expansion. Prior empirical studies, using various proxies
for market competition, find that firms in more competitive markets hold more cash
(Morellec et al. 2013; Haushalter et al. 2007; Hoberg et al. 2014), have lower leverage
(MacKay and Phillips 2005; Kovenock and Phillips 1995) and pay less dividends
(Booth and Zhou 2009; Hoberg et al. 2014).1 Consistent with these existing theories
and empirical evidence, we find that firms with more frequent references to
competition in their 10-K filings have significantly higher cash holdings, lower
financial leverage and lower dividend payout ratio. Univariate analysis shows that,
compared to firms in the bottom decile of perceived competition, firms in the top
decile have 16.4% higher cash holdings, 13.0% lower leverage, and 11.1% lower
dividend payout ratio. These relations between perceived competition and financing
choices continue to hold after controlling for risk, size, performance, investment
opportunities, industry concentration, and even for lagged dependent variables (i.e.,
prior year cash holdings, leverage and dividend payout ratio).
We predict that firms will also respond to competitive threats by altering their
investment strategies. We test this prediction by examining the relation between
firms’ perceived competition and R&D expenditures and acquisition activities. Prior
theoretical and empirical studies suggest that high market competition generally
1
Proxies for market competition in these studies include price-cost margin, industry concentration,
product market fluidity, the degree of import penetration, and the similarity of a firm’s technology with
its rivals. Two common characteristics of these measures are: (1) they only capture the competitive
pressures from existing competitors (perhaps with errors) but not threats from potential competitors;
(2) they do not necessarily reflect managerial perception of competition. Dedman and Lennox (2009)
survey managers of UK firms and find no relation between industry concentration and managers’
perceived competition. Our measure of competition is capable of capturing managerial perception of
both existing competitive pressures and threats of entry, thus offering incremental explanatory power
beyond what is captured by existing measures of competition.
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motivates firms to invest in R&D (see Gilbert [2006] for an excellent review of both
theoretical and empirical literature on this topic). Thus, we expect a positive
association between managers’ perceived competition and future R&D expenditures.
In addition, we argue that firms facing strong competition are more likely to
undertake acquisitions. Acquisition is an effective growth strategy when firms want to
eliminate existing or potential competitors and quickly gain market share (Gaughan
2005). Acquisition is also preferable to organic growth if the firm wants to expand
into a new market (Coad 2009), which is likely the case as the existing market has
become too competitive to offer any growth opportunity. Finally, acquisition allows
firms to quickly gain access to innovations, which help them “escape competition.”
As a result, we expect a positive association between firms’ perceived competition
and future acquisition activities.2
Our test results reveal a strong positive association between managers’
perceived competition and R&D expenditures over the subsequent three years.
Univariate analysis shows that, compared to firms in the bottom decile, firms in the
top decile of perceived competition have 23.4% higher R&D expenditure (as
percentage of total assets) over the subsequent three years. The positive association
between perceived competition and R&D expenditures remains highly significant
after controlling for other factors known to explain R&D activities such as firm size,
cash flow risk, profitability, growth opportunities, and industry concentration. We
also find that firms with higher perceived competition invest more through
acquisitions in the subsequent three years, both in terms of the likelihood to engage in
acquisitions, the number of acquisitions undertaken and total dollar value of
2
For completeness we also examine how perceived competition relates to capital expenditures
although we have no ex ante prediction about this relation. We find an insignificant relation between
perceived competition and capital expenditure after controlling for past capital expenditure.
3
acquisitions undertaken. The positive associations between perceived competition and
future R&D expenditures and acquisitions persist even after controlling for current
period R&D and acquisition activities, respectively.
Given our findings that firms alter their financing policies and investments in
response to high perceived competition, we revisit the main finding in Li et al. (2013)
that firms with higher perceived competition exhibit higher rates of diminishing
marginal returns. We examine whether more conservative financing policies and more
aggressive investments in R&D and acquisitions help firms mitigate the negative
impact of competition on marginal returns. We posit three possible effects of
financing policies and investments on the relation between perceived competition and
the rate of diminishing marginal returns. First, the financing and investing policies
taken by managers could be effective at counteracting the effects of competition,
leading to lower rates of diminishing marginal returns for companies that follow these
strategies. Second, the real competitive threats for firms with high perceived
competition are strong enough such that firms’ financing and investment strategies
have no significant effect on the rate of diminishing marginal returns. Finally, if
managers are making costly financing choices and investments in response to high
perceived competition, this may be a signal of the strength of the competition they are
facing. In this case, we should observe that a combination of high perceived
competition and conservative financing policies and/or high investments in R&D and
acquisitions is associated with higher rates of diminishing marginal returns. Our tests
reveal that the negative impact of perceived competition on the rate of diminishing
marginal returns is much stronger for firms that adopt conservative financing policies
and firms that invest more in R&D and acquisitions. This result suggests that
managers’ disclosures about competition are more credible and serious if
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accompanied by costly financing and investment strategies that attempt to contest the
perceived competition.3
Our finding that firms faced with high perceived competition tend to increase
future investments in R&D also suggests an alternative explanation for why firms
with high perceived competition experience higher rates of diminishing marginal
returns (Li et al. 2013). U.S. accounting standards require R&D to be expensed
immediately, which could result in mechanically lower future operating returns for
firms with more R&D expenditures. However, additional tests where we adjust for the
effects of R&D expenditure on operating returns reveal that although increased
expenses from R&D do contribute to lower future returns, this does not fully explain
the higher rates of diminishing marginal returns for firms with high perceived
competition. We conclude that economic competitive pressure is still a fundamental
reason for the association between perceived competition and diminishing returns
documented in Li et al. (2013). In additional analysis we also find that our main
results hold for market followers but not market leaders, which suggests that market
leaders may be using competition disclosures strategically.
Our study contributes to the literature by providing large sample evidence on
the actions managers take in response to perceived competition as disclosed in 10-K
filings. Li et al. (2013) provide important evidence that firms’ disclosures about
competition in the 10-K filings can help predict changes in firms’ operating returns.
We document that on average managers respond to perceived competition by altering
their financing policies and future investing activities. Consistent with Li et al. (2013),
3
We cannot rule out the possibility that the diminishing marginal returns for firms that engage in costly
financing and investing strategies could have been even worse had these firms not implemented such
strategies. Therefore, the relatively higher rates of diminishing marginal returns for these firms may
represent the best alternative given their competitive environment.
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our results suggest that firms’ discussion of competition in 10-K filings is not merely
boilerplate as some (including the SEC) have suggested, but are useful indicators of
the firm’s competitive threats. Our findings suggest this is especially true when
competition disclosures are coupled with firms engaging in costly financing policies
and investments. Understanding the relation between firms’ disclosure of competition
in the 10-K and financing and investment strategies could enhance shareholders’ and
information intermediaries’ ability to evaluate firms’ financial statements (i.e.,
understanding financing choices) and forecast their future cash flows through
forecasting future investments. In the wake of recent concerns expressed by the SEC
and FASB about too much disclosure in financial reporting (Gallagher 2014, Seidman
2012), our research provides additional evidence that disclosures about competition
are potentially useful to financial statement users.4
Our second contribution relates to the measurement of competition. Prior
studies in economics and accounting rely primarily on industry-based measures of
competition such as the Herfindahl Index or Lerner Index. While these measures have
their merits, they generally only capture the competitive threats from existing rivals
(perhaps with noises). However, as Schumpeter (1942, p. 85) states: “[Competition]
disciplines before it attacks. The businessman feels himself to be in a competitive
situation even if he is alone in his field.” Measuring competition through managers’
eyes captures a broader set of competitive threats like the threat of new entrants. One
way to infer managers’ perceived competition is to rely their own disclosures about
competition 10-K filings. We find that this measure of perceived competition explains
4
In 2012 remarks by FASB Chairman Leslie Seidman, he said “Many of those stakeholders tell me
that financial reports are just too long…[y]et investors continue to say they want more information,
particularly when there is a business downturn or failure. Often the information that these investors
want is available in the financial statements — but it is hidden in plain sight.” Our evidence indicates
this is the case, suggesting regulators should use caution as they address this disclosure issue.
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managers’ important financing and investing choices beyond that explained by the
Herfindahl Index, the most traditional measure of competition. Our results provide
additional support for the use of the disclosure-based measure of competition as a
proxy for the competitive environment of the firm.
The remainder of this paper is organized as follows: Section 2 reviews the
related literature and develops hypotheses. Section 3 describes the data, empirical
tests and results. Additional analyses are provided in Section 4. Finally, Section 5
concludes.
2.
LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT
Throughout this paper we assume that on average the intensity of firms’
discussion of competition in the 10-K generally reflects managers’ perception of the
level of competitive threats facing the firm. One could argue that managers make
extensive reference to competition in the 10-K filing to overstate the level of
competition facing the firm to achieve certain goals. While certainly there are firms
that provide misleading disclosures in their 10-K filings, we expect that litigation and
reputational risks mitigate this behavior, making competition disclosure a credible
signal of managers’ true belief at least on average. Our assumption is consistent with
the battery of empirical tests in Li et al. (2013) that provide robust evidence that firms
that discuss competition more frequently experience higher rates of diminishing
marginal returns.
As competition will drive down returns and potentially threaten the existence of
the firm, we expect that managers that perceive high levels of competition will take
actions that can help them remove, or at least mitigate, the impacts of competition on
the company’s profitability and existence. In the sections that follow, we develop
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hypotheses about how managers’ perceived competition affects some of the firm’s
financing and investing policies. We also discuss how the financing and investing
policies that firms engage in might affect the relation between perceived competition
and the rate of diminishing marginal returns.
2.1. Perceived Competition and Cash Holdings, Financial Leverage and
Dividend Payout Ratio
There are multiple motives for firms to hold cash (see Bates et al. [2009] for an
excellent summary of those motives). We posit that higher perceived competition
motivates firms to hold more cash for precautionary reasons.5 Specifically, high
competition increases the likelihood of adverse cash flow shocks. Therefore, firms
with strong perceived competition should have an incentive to hold more cash to
cover negative cash flow shocks and avoid financial distress. Moreover, in a
competitive market, profitable investment opportunities are rare and do not last long.
In such a market, firms must act quickly whenever an investment opportunity arises.
Large cash reserves allow firms the financial flexibility to invest and avoid losing
profitable opportunities to their competitors (Haushalter et al. 2007). Finally, large
cash reserves can serve as a credible signal that the firm is willing to engage in
aggressive behavior, which can help deter market entry or rivals’ expansion (Benoit
1984). Taken together, our first hypothesis is:
H1: Managerial perceived competition is positively related to the firm’s cash
holdings.
The trade-off theory of capital structure posits that firms set the optimal capital
structure by trading off the benefits, such as tax shield, versus the costs of debt
5
Other motives for holding cash include transaction costs to convert non-cash assets into cash, the tax
motive for multinational firms, and agency conflicts.
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financing such as financial distress or bankruptcy costs (Barclay et al. 1997; Ramalho
and Silva 2009). We expect that firms with high perceived competition will choose
lower levels of debt for two reasons. First, competition increases the likelihood of
negative cash flow shocks, which increases the expected bankruptcy costs of debt.
Second, as discussed above, in a competitive industry, profitable investment
opportunities are rare and do not last long. In such an environment, high level of debt
imposes financial constraints, limiting firms’ ability to take advantage of those
opportunities. Therefore, our second hypothesis is:
H2: Managerial perceived competition is negatively related to the firm’s
financial leverage.
Prior studies find that the market reaction to dividend changes is asymmetric in
that the reward for dividend increases is smaller than the penalty for dividend cuts
(Michaely et al. 1995), implying that managers prefer a stable stream of dividends to
a volatile one. Consistent with this notion, survey results suggest that managers
consider the stability of future earnings and cash flows among the most important
determinants of the firm’s dividend policies (Brav et al. 2008, 2005; Lintner 1956).
As competition increases the likelihood of negative cash flow shocks, we expect that
firms with high perceived competition have an incentive to maintain a low dividend
payout ratio to reduce the likelihood of future dividend cuts. Consistent with this
intuition, Booth and Zhou (2009) find evidence that firms in more competitive
industries, using proxies such as Herfindahl Index, Lerner Index and Import
Penetration, tend to pay lower dividends. Therefore, our third hypothesis is:
H3: Managerial perceived competition is negatively related to the firm’s
dividend payout ratio.
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2.2. Perceived Competition, R&D Expenditure and Acquisition Activities
Intuition suggests that firms operating in more competitive markets have greater
incentives to invest in R&D, as innovations can help them “escape competition” by
utilizing more efficient technologies to reduce costs or introducing superior products
to gain market share. Anecdotal evidence is generally consistent with this intuition.
For example, Andrew Grove, former CEO of Intel Corporation, is well known for his
motto “Only the paranoid survive” (Gilbert 2006, p.179). Similarly, when Steve Jobs
took the role of CEO at Apple Inc., he said “The cure for Apple is not cost-cutting.
The cure for Apple is to innovate its way out of its current predicament” (Gallo 2014).
While the idea that competition promotes R&D investments is intuitive, prior
research on this relationship is not necessarily conclusive. On the one hand, Arrow
(1962) demonstrates analytically that, when intellectual property rights are exclusive,
competition motivates firms to invest in R&D as the incremental benefits from the
same innovation are higher for competitive firms than for a monopolist. Schmidt
(1997) argues that competition increases the probability of liquidation, which creates
a disutility for managers. This motivates managers to invest in R&D as innovations
can make the firm more efficient, reducing the liquidation risk. Various other models
also predict that competition creates patent races that would increase R&D
investments (Fudenberg et al. 1983, Harris and Vickers 1985). On the other hand, a
few theoretical models suggest a negative relationship between competition and R&D
investment. For example, Schumpeter (1942) argues that monopolists have stronger
incentives to invest in R&D than competitive firms because competition limits the
ability of innovators to exploit economies of scale if R&D investment results in
innovations (Schmidt [1997] also makes a similar argument). In addition, monopolies
generally have more funds available to finance R&D activities.
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Empirical studies generally provide evidence consistent with competition
motivating R&D expenditure, although there is some evidence that the relation is not
necessarily linear in the sense that “too much” competition might discourage firms
from investing in R&D (see Gilbert [2006] for a review). Taken the literature as a
whole, we predict that firms with higher perceived competition will invest more in
R&D.
H4: Managerial perceived competition is positively related to the firm’s future
R&D expenditure.
One well-known motive of firms to undertake acquisitions is to gain market
share or eliminate potential and existing competitors (Gaughan 2005). This implies
that firms that are more concerned about competition have a stronger incentive to
undertake acquisitions. Moreover, in a competitive environment, firms likely prefer
acquisitions to organic growth because acquisitions allow firms to rapidly acquire
new production capacity, particularly for firms that choose to diversify into another
industry as the existing industry has become too competitive (Coad 2009). Finally,
acquisition is an alternative way for firms to quickly acquire innovations, which allow
them to “escape competition” as discussed above (Ahuja and Katila 2001, Hitt,
Hoskisson, Johnson, and Moesel 1996). Taken together, we predict a positive
association between managers’ perceived competition and acquisition activities.6
H5: Managerial perceived competition is positively related to the firm’s future
investment in acquisitions.
6
There is some evidence that merger and acquisition activities do not have an adverse impact on the
level of market competition (Goldberg 1973, 1974; Feinberg 1984). If managers believe that they
cannot reduce competition by engaging in acquisitions, then we might not find that firms with high
managerial perceived competition invest more in acquisitions.
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2.3. Impacts of Financing and Investing Policies on the Relation Between
Perceived Competition and the Rate of Diminishing Marginal Returns.
We have argued that firms with high perceived competition will have more
conservative financing policies and invest more in R&D and acquisitions. Li et al.
(2013) find that firms with high perceived competition experience higher rates of
diminishing marginal returns. This raises a natural question about whether having
conservative financing policies and investing in R&D and acquisitions help firms
mitigate the effect of competition on the rate of diminishing marginal returns. On the
one hand, if these strategies are effective at contesting competition, we should
observe that the negative effect of competition on marginal returns is weaker for firms
with more conservative financing choices and/or more investments in R&D and
acquisitions. On the other hand, it is possible that firms with more extensive
discussion of competition in their 10-K filings and simultaneously implement costly
financing and investing strategies to counteract the perceived competition are those
that are really faced with strong competition. In other words, a combination of
extensive discussion of competition in the 10-K and conservative financing choices
and/or aggressive investments in R&D and acquisitions is a more credible signal of
managerial strong perceived competition. In this case, we would expect the impact of
competition on the rate of diminishing marginal returns to be stronger for firms with
more conservative financing policies and/or more aggressive R&D and acquisition
investments. Given the opposing possible effects of financing and investing policies
on the relation between perceived competition and the rate of diminishing marginal
returns, we examine this issue without making directional predictions.
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3.
EMPRICAL TESTS
3.1. Sample and Descriptive Statistics
We use the Python software to obtain competition disclosures from 10-K filings
using the EDGAR filings database for the period from 30 June 1994 through 31
March 2013. To obtain financial data, we merge our collected data with Compustat
using the SEC’s Central Index Key (CIK). We eliminate firm-years with market value
of equity or total assets less than $1 million, firm-years with negative or missing
values for cash holdings and leverage. Finally, we remove all financial firms (SIC
6000-6999) because those firms’ cash holdings, leverage, and investment
opportunities differ substantially from other firms. Our final sample includes 72,416
firm-years. The samples used in individual tests might be smaller due to additional
data requirements.
Table 1 presents the number of observations and descriptive statistics for our
measure of perceived competition (Comp_Pct) by Fama-French 48 industry groups.
Comp_Pct is the number of occurrences of competition related words in the 10-K
filing, scaled by total number of words in the 10-K and multiplied by 1,000 so that the
measure counts the competition references per 1,000 words. We follow Li et al.
(2013), we use various forms of the word competition including competitor,
competitive, compete, and competing (both singular and plural forms). We remove
any cases where “not,” “less,” “few,” or “limited” precedes any form of the word
“competition” by three or fewer words. We order industries by their mean Comp_Pct
value in Table 1.
One observation from Table 1 is that firms with high Comp_Pct are more likely
to be in technology and research-related industries. Part of this high perceived
competition reflects the ever-changing technology, but it also reflects the vast number
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of firms in these industries in the U.S that are competing with each other. For
example, the top 10 industries by Comp_Pct represent 52 percent of the sample.
Given these strong industry level effects, we control for industry fixed effects in our
tests.
Another observation from Table 1 is that many of the commodity-related and
regulated industries, such as Utilities, Agriculture, and Petroleum and Natural Gas,
are generally low in perceived competition. This may seem counterintuitive since the
commodities in these industries would generally be viewed as virtually perfect
substitutes. However, what this highlights is that for price-taking firms in purely
competitive markets, the competition they face is so pervasive that it is largely
exogenous to their decision making. From a disclosure perspective, Li et al (2013)
argue that because competition in these industries is so obvious and omnipresent,
mentioning competition might be unnecessary unless these firms are facing some new
threats of competition.
Table 2 presents descriptive statistics for our sample. The median Comp_Pct is
0.93 words per one thousand words in the 10-K. To put this in perspective, the
median number of words in the 10-Ks in our sample is 27,723. Thus, the average firm
in our sample mentions competition approximately 26 times in its 10-K. The variation
in the frequency of references to competition is quite large, ranging from virtually no
mention of competition (minimum Comp_Pct is 0.07) to 2.73 times per thousand
words. Cash holdings are roughly 10 percent of total assets for the median firm in the
sample and median leverage is 19 percent. Less than half of our sample firms pay
dividends. Mean (median) R&D expenditure over the subsequent three years is 19
percent (0 percent) of total assets, while mean (median) investment in acquisitions
over the subsequent three years is 6 percent (0 percent) of total asset.
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3.2. Relations between Perceived Competition and Financing Policies
3.2.1. Univariate Test Results
Table 3 presents the mean and median cash holdings (Cash_Holding), leverage
(Leverage) and dividend payout ratio (Div_Payout_Ratio) for the sample firm-years
sorted into deciles based on the level of perceived competition (Comp_Pct). Both
mean and median cash holdings increase monotonically from the 1st to 10th decile of
perceived competition, suggesting that firms with higher perceived competition tend
to hold more cash. The difference in mean (median) cash holdings between firms in
Decile 10 and those in Decile 1 is 16.4 (18.5) percent of total assets. We consider this
difference economically significant given the sample mean (median) cash holding is
19 (10) percent. Also consistent with our expectation, both leverage and dividend
payout ratio decrease with the level of perceived competition. The difference in mean
(median) leverage between Decile 1 and Decile 10 is 13.0 (21.1) percent, and the
difference in mean (median) dividend payout ratio between Decile 1 and Decile 10 is
11.1 (0.00) percent.
3.2.2. Multivariate Test Results
Although the univariate tests suggest a strong relation between perceived
competition and financing policies, in this section we conduct multivariate tests to
control for other firm characteristics that potentially explain the differences in
financing policies between firms with high and low perceived competition.
Specifically, we estimate the following regression:
FinVarit = β0 + β1Comp_Rankit + ∑φControls + ∑δYear + ∑λIndustry + ζ (1)
The dependent variable (FinVarit) is alternatively Cash_Holding, Leverage and
Div_Payout_Ratio, each is defined as follows:
15
 Cash_Holding: cash and cash equivalents (CHE) scaled by total assets (AT).
 Leverage: the ratio of long-term debt (DLTT) plus debt in current liabilities
(DLC) to total assets.
 Div_Payout_Ratio: the ratio of total common dividends (DVC) to net income
(NI). When the dependent variable is Div_Payout_Ratio, we exclude firmyears with negative dividend or negative net income.
The independent variable of interest, Comp_Rankit, is the decile rank of the
number of references to competition scaled by total number of words in the 10-K (i.e.,
Comp_Pct). To facilitate interpretation, we transform Comp_Rankit to be in [0, 1].
Following prior studies, we include the following control variables.

Herfindahl_Index: sum of squared market shares of all firms in each
industry. Industries are defined in terms of the two-digit SIC assigned by
Compustat. We include this variable to mitigate the concern that our
measure of competition merely captures the effects of industry
concentration, a traditional measure of market competition.

Size: natural logarithm of market value of equity (PRCC_F x CSHO) at the
end of the current year.

MB_Ratio: market value divided by book value of equity (CEQ) at the end
of the current year. This variable controls for growth opportunities. We
expect firms with more growth opportunities to hold more cash to prepare
for future investments and to have lower leverage to mitigate the
underinvestment problem.

Sale_Growth: current year sales (SALE) minus prior year sales, divided by
prior year sales.
16

CFO_Vol: standard deviation of operating cash flows (OANCF) scaled by
total assets, calculated over 10 years up to the current year, inclusive. We
require that firms have available operating cash flows for at least three
years. This variable is included to control for cash flow risk. We expect
firms with higher cash flow risk to hold more cash, have lower leverage and
lower dividend payout ratio.

ROA: net income (NI) divided by average total assets. This variable
controls for the firm’s profitability. We expect more profitable firms to hold
more cash and have lower leverage.

PPE: net property, plant and equipment (PPENT) divided by total assets.
This variable controls for borrowing capacity (as PP&E can serve as
collateral against debts).

Net_WC: working capital (WCAP) minus cash and cash equivalents (CHE),
scaled by total assets at the end of the current year. We include this variable
because non-cash working capital can be a substitute for cash holdings.
Therefore, we expect this variable to be negatively correlated with cash
holdings.

Cap_Exp: capital expenditure (CAPX) for current year scaled by lagged
total assets. We include this variable because we expect that engaging in
capital expenditures will influence a firms’ financing choices as they may
need more cash on hand, but also may have more debt to finance those
expenditures.

Acquisition: total cash paid for acquisitions (AQC) for current year scaled
by lagged total assets. We include this variable because we expect that
engaging in acquisitions will influence their financing choices.
17

RD_Exp: R&D expenditure (XRD) for current year scaled by lagged total
assets. As above, we include this variable because we expect firm
investments in R&D will be associated with their financing choices.
The regression also includes controls for year and industry fixed effects.
Industries are Fama-French 48 industries.
Table 4 Panel A presents estimated coefficients for regression model (1).
Consistent with our hypotheses, perceived competition is positively associated with
cash holdings and negatively associated with leverage and dividend payout ratio after
controlling for other traditional explanatory variables. The coefficients on
Comp_Rank are statistically significant at the 1-percent level in all three regressions.
The economic magnitude of these relationships is quite large. Firms in the highest
decile of perceived competition hold 6.8 percent more of their total assets as cash
relative to firms in the lowest decile. Firms in the top decile of perceived competition
also have on average 6.3 percent lower leverage than firms in the lowest decile.
Finally, dividend payout ratio is 4 percent lower for firms in the highest decile of
perceived competition compared to firms in the lowest decile. These results provide
evidence consistent with Hypotheses 1-3 and suggest that firms with higher perceived
competition tend to implement more conservative financing policies.
The coefficient on Herfindahl Index is negative and statistically significant
when cash holding is the dependent variable and positive and statistically significant
when leverage and dividend payout ratio are dependent variables. The signs of the
coefficient on Herfindahl Index are consistent with prior studies. The fact that both
Herfindahl Index and our measure of competition predict financing policies suggest
18
that our measure of perceived competition captures a unique aspect of competition
distinct from industry concentration.
Table 4 Panel B presents regression results for model (1) after adding lagged
dependent variables to the model (i.e., prior year cash holdings, leverage and dividend
payout ratio). There is both an advantage and disadvantage of including lagged
dependent variables as a control when the dependent variables are highly persistent,
as would be expected for cash holdings, leverage and dividend payout ratio. On the
one hand, including lagged dependent variables helps mitigate concerns about omitted
variable biases because the coefficients on lagged dependent variables capture
unobservable characteristics associated with the dependent variable. On the other
hand, including highly persistent lagged dependent variables may cause a downward
bias in the coefficients of other independent variables, especially those independent
variables that are also persistent (Achen 2001). This is particularly a concern in the
setting we are investigating because our explanatory variable of interest is perceived
competition, which is expected to be highly persistent. The regression results appear
to confirm this concern. When we include lagged dependent variables in the
regressions, the coefficients on Comp_Rank decrease sharply, but still remain highly
statistically significant, which provides confirming evidence of our hypotheses using
this alternative specification.7 In addition, the R2 significantly increases when the
lagged dependent variables are included. Given the advantage and disadvantage of
7
In terms of economic significance using this specification, the results suggest that firms in the highest
decile of perceived competition increase their holdings of cash by 1 percent of total assets relative to
firms in the lowest decile. Although this may seem small, given the average firm holds 19 percent of
their assets in cash, this represents a 5.3-percent increase in their cash holdings for the average firm.
For leverage, the coefficient in Panel B suggests 0.5 percent lower leverage for firms in the highest
versus those in the lowest decile, equivalent to a 2.1-percent decrease in leverage for the average firm.
For the dividend payout ratio, the coefficient in Panel B suggests a decrease in the payout ratio of 2.3
percent, equivalent to a 19.2-percent decrease for the average firm.
19
including lagged dependent variables in the regression, we present both results and
leave it for readers to decide which results to take away from the paper.
Turning to control variables, large firms tend to hold more cash, have lower
leverage and higher dividend payout ratio. Firms with more cash flow volatility tend
to hold more cash and have lower leverage. Firms with more growth opportunities (as
measured by market-to-book ratio) hold more cash and have lower leverage. More
profitable firms have more cash, lower leverage and higher dividend payout ratio.
Firms with higher PP&E tend to have lower cash, higher leverage and pay more
dividends. As expected, firms with more net working capital tend to hold less cash as
net working capital can be a substitute for cash. Those firms also have lower leverage
and higher dividend payout ratio. Firms with more investments in either R&D,
acquisition or capital expenditure tend to have lower dividend payout ratio. However,
the effects of these investments on cash holdings and leverage are not consistent.
3.3. Relations between Perceived Competition and Future Investments
3.3.1. Univariate Test Results
Table 5 presents the mean and median R&D expenditure (RD_Exp[t+1, t+3]),
number of acquisitions undertaken (MA_Deals[t+1, t+3]) and total value of acquisitions
undertaken (MA_Value[t+1, t+3]) over the subsequent three years for the sample firmyears sorted into deciles based on the level of perceived competition (Comp_Pct).
Mean future R&D expenditure increases monotonically with the level of perceived
competition. Future number of acquisitions (MA_Deals[t+1,t+3]) and value of
acquisitions (MA_Value[t+1, t+3]) undertaken also increase with the level of perceived
competition, although only the latter increases monotonically. In terms of economic
significance, the variation is most significant for R&D expenditure. Specifically, the
20
difference in mean (median) RD_Exp[t+1, t+3] between firms in Decile 10 and those in
Decile 1 is 23.4 (19.0) percent of total assets, which is economically significant given
the sample mean (median) RD_Exp[t+1, t+3] is 19 percent (0 percent). While univariate
tests provide evidence consistent with our hypotheses that firms with high perceived
competition tend to invest more in R&D and acquisitions, these tests do not control
for other differences across firms that can potentially explain their different investing
activities. In the multivariate tests that follow, we attempt to rule out alternative
explanations by controlling for variables that prior studies have found related to
firms’ investments.
3.3.2. Multivariate Test Results
In our multivariate tests, we examine the relation between perceived
competition and future investments by estimating the following regression model.
Fut_Investit = β0 + β1Comp_Rankit + ∑φControls + ∑δYear + ∑λIndustry + ζ
(2)
The dependent variable (Fut_Investit) is alternatively RD_Exp[t+1, t+3],
MA_Active[t+1,t+3], MA_Deals[t+1,t+3] and MA_Value[t+1,t+3], each is defined as follows:

RD_Exp[t+1, t+3]: sum of R&D expenditure (XRD) scaled by lagged total
assets over the three years t+1 through t+3.

MA_Active[t+1,t+3]: a dummy variable equal to 1 if the firm engages in at
least one acquisition over the three years t+1 through t+3, 0 otherwise.

MA_Deals[t+1,t+3]: natural logarithm of (1+NM&A), with NM&A being the
number of acquisitions that the firm undertakes over the three years t+1
through t+3. We add one to the number of acquisitions so that the variable
is defined for firms that do not undertake any acquisition.
21

MA_Value[t+1,t+3]: total value of all acquisitions scaled by lagged total
assets that the firm undertakes over the three years t+1 through t+3.
The independent variable of interest is Comp_Rankit, defined as in regression
model (1). Following prior studies, we include the following control variables.

Herfindahl_Index: sum of squared market shares of all firms in each
industry. Industries are defined in terms of the two-digit SIC assigned by
Compustat. We include this variable to mitigate the concern that our
measure of competition merely captures the effects of industry
concentration, a traditional measure of market competition.

Size: natural logarithm of market value of equity (PRCC_F x CSHO) at the
end of the current year.

MB_Ratio: market value divided by book value of equity (CEQ) at the end
of the current year. This controls for growth opportunities. We expect this
variable to be positively correlated with future investments.

Sale_Growth: current year sales (SALE) minus prior year sales, divided by
prior year sales.

CFO_Vol: standard deviation of operating cash flows (OANCF) scaled by
total assets, calculated over 10 years up to and including the current year.
We require that firms have available operating cash flows for at least three
years. This variable is included to control for cash flow risk.

ROA: net income (NI) divided by average total assets. This controls for the
firm’s profitability.

Cash_Holding: cash and cash equivalents (CHE) scaled by total assets. We
expect that firms with more cash have more resources to invest and,
22
therefore, this variable should be positively associated with future
investments.

Leverage: the ratio of long-term debt (DLTT) plus debt in current liabilities
(DLC), divided by total assets. Leverage might constrain firms’
investments. Therefore, we expect a negative association between leverage
and future investments.
The regression also includes controls for year and industry fixed effects. Industries are
Fama-French 48 industries.
Table 6 Panel A presents the estimated coefficients for regression model (2).
Consistent with the results in Table 5 and with our hypotheses, perceived competition
is positively associated with both future R&D expenditure and acquisition activities
after controlling for other traditional explanatory variables. The coefficients on
Comp_Rank are statistically significant at the 1-percent level in all regressions. The
economic magnitudes of these results are also quite significant. Firms in the highest
decile of perceived competition spend 5.9 percent more of their total assets on R&D
over the subsequent three years relative to firms in the lowest decile. With regard to
future spending on acquisitions, firms in the highest competition decile spend 2.9
percent more of total assets on acquisitions in the subsequent three years relative to
firms in the lowest competition decile. This represents a 45 percent increase in
spending on acquisitions for the average firm over the subsequent three years. In
summary, these results provide evidence consistent with Hypotheses 4 and 5 that
firms with higher managerial perceived competition tend to invest more in R&D and
acquisitions.
The coefficient on the Herfindahl Index is negative but statistically
insignificant in all but one regression. A negative coefficient is expected if the
23
Herfindahl Index captures to some extent the degree of market competition, and
competitive threats motivate firms to invest in R&D and acquisitions. However, the
lack of statistical significance for the coefficient suggests that the Herfindahl Index
may not be a very reliable measure of competition at least with respect to its ability to
predict future investments.
Similar to the tests of financing policies, in Table 6 Panel B we present the
regression results for model (2) after adding lagged dependent variables to the model
(i.e., current year R&D expenditure and acquisition activities). When predicting
future R&D expenditure, including current R&D as a control substantially decreases
the coefficient on Comp_Rank, although the coefficient is still statistically significant
at the 1-percent level.8 This is expected as both R&D expenditure and competition are
all highly persistent (Achen, 2001). When predicting future acquisition activities,
including current year acquisition activities as a control does not significantly affect
the coefficient on Comp_Rank.
Turning to other control variables, large firms are more likely to undertake
acquisitions. However, firms size has no consistent effect on R&D expenditure, the
coefficient is positive when the regression does not include lagged R&D as a control
variable, but turns negative when lagged R&D is included. There is some evidence
that growth firms (those with high market-to-book ratio and sales growth) are more
likely to invest in acquisitions. In contrast, the effects of market-to-book ratio and
sales growth on R&D expenditure is not robust, depending on whether lagged R&D is
included in the model. Interestingly, more profitable firms are less likely to invest in
R&D as well as acquisitions. Firms with more cash holdings are more likely to invest
8
In this specification, firms in the highest decile of perceived competition increase their spending on
R&D by 1.6 percent of total assets relative to firms in the lowest decile, which represents an 8.4percent increase in R&D for the average firm.
24
in future R&D, but less likely to engage in acquisitions, although the dollar amount
that those firms spend on acquisitions is higher.
3.4. Effects of Perceived Competition on the Rate of Diminishing Marginal
Returns Conditional on Financing and Investing Activities.
In this section, we test whether firms’ conservative financing policies and
aggressive investments in R&D and acquisitions mitigate the effect of competition on
the rate of diminishing marginal returns. Li et al (2013) use the following model to
examine how perceived competition affects the rate of diminishing marginal returns
on both existing and new investments.
∆RNOAit+1 = β0 + β1RNOAit + β2∆NOAit + β3Comp_Rankit*RNOAit
+ β4Comp_Rankit*∆NOAit + ∑δYear + ∑λIndustry + ζ
(3)
RNOAit is the return on net operating assets for firm i in year t, calculated as
operating income after depreciation (OIADP) for year t divided by average NOA for
year t, where NOA is defined as net accounts receivable (RECT) + inventories
(INVT) + other current assets (ACO) + net property, plant and equipment (PPENT) +
intangibles (INTAN) + other non-current assets (AO) – accounts payable (AP) – other
current liabilities (LCO) – other non-current liabilities (LO). ∆RNOAit+1 is the change
in RNOA from year t to year t+1. ∆NOAit is the change in NOA over year t. In model
(3), the coefficients β1 and β2 capture the rate of diminishing marginal returns on
existing and new investments made in year t, respectively, while the coefficients β3
and β4 capture the effects of perceived competition on the rate of diminishing marginal
25
returns.9 Using a battery of tests, Li et al (2013) find that the coefficients β3 and β4 are
negative and statistically significant, consistent with firms with high perceived
competition experiencing higher rates of diminishing marginal returns.
We first replicate the results in Li et al (2013). We then partition our sample
into several subsamples, based on their financing policies and levels of investment in
R&D and acquisitions. We define firms with conservative financing policies as those
with cash holdings above the median, leverage below the median and dividend payout
ratio below the median (measured for year t). High R&D firms are those with the
average R&D expenditure over the three years t-2 through t above the 75th percentile.
M&A active firms are those that undertake at least one acquisition over the three
years t-2 through t. We then estimate model (3) separately for each subsample. We
examine whether the coefficients β3 and β4 differ for firms with more conservative
financing policies versus firms with less conservative financing policies, firms with
high versus low R&D, and firms that are M&A active versus those that are not. The
results are shown in Table 7.
The first column of Table 7 confirms the results in Li et al (2013). The
coefficients on the interaction terms Comp_Rank*RNOA and Comp_Rank*∆NOA are
both negative and statistically significant. The next two columns present coefficients
for subsamples of firms with more conservative financing policies versus other firms.
While the coefficient on the interaction Comp_Rank*RNOA is similar across the two
subsamples, the coefficient on the interaction Comp_Rank*∆NOA differs
significantly. In fact, the coefficient is only statistically significant for firms with
conservative financing policies. The last four columns present coefficients for firms
9
Following Li et al (2013), we exclude the following observations from the regression: (1) firm-years
with negative sales, NOA or total assets; (2) firm-years with market value of equity lower than $1
million; (3) firm-years with the absolute value of RNOA greater than 1; (4) firm-years with sales
growth greater than 1,000% or less than -100%.
26
with high R&D versus firms with low R&D, and firms that actively engaged in
acquisitions versus firms that did not. The coefficients on both interaction terms
(Comp_Rank*RNOA and Comp_Rank*∆NOA) are more negative for firms with
higher R&D and firms that actively engaged in acquisitions. These results suggest that
firms that invest more in R&D or actively engage in acquisitions experience higher
rates of diminishing marginal returns. Thus, we do not find evidence that conservative
financing policies or aggressive investments in R&D and acquisitions help firms
mitigate the negative impact of competition on the rate of diminishing marginal
returns. Rather, the results appear to suggest that firms that extensively discuss
competition in their 10-K filings and simultaneously take costly actions (i.e.,
conservative financing policies and aggressive investments in R&D and acquisitions)
are those that are genuinely faced with strong competition. In other words, the
extensive competition disclosures of those firms are more serious and credible.
4. ADDITIONAL ANALYSIS
Our main empirical findings suggest a positive association between
managerial perceived competition and the firm’s future investments in R&D and
acquisitions, presumably to combat competitive threats. In additional analysis, we
examine whether the investment vehicle firms use (R&D or acquisitions) reflects the
investment strategies that are common to their industries. The availability of
particular investment options may be limited by the particular type of firm. For
example, we generally would not expect retail firms to engage in additional R&D
expenditures as a strategy to combat competition. We perform this analysis by
partitioning firms into intensive and non-intensive industries for R&D and acquisition
activities. In each year, industries that have average R&D expenditures over the prior
27
five years that are above the 75th percentile of all industries are classified as R&D
intensive. We partition sample firm-years similarly based on the average number of
acquisitions undertaken by firms in each industry over the past five years. We then reestimate regression (2) separately on subsamples of firms in R&D intensive
industries, firms in acquisition intensive industries versus the remaining firms. Table
8 presents the results of these tests. Control variables are included in the model but
not presented. We note the positive coefficients on Comp_Rank are much stronger for
the firms in R&D intensive industries when predicting future R&D expenditures and
acquisition intensive industries when predicting future acquisition activities. The
results of these tests suggest that firms generally use the investment strategies that are
common in the industry when responding to perceived competitive threats. This test
also rules out an alternative explanation that our main results are driven by differences
across industries (i.e., firms in certain industries are less likely to invest in R&D
regardless of the level of competition, and those same firms also have less extensive
discussion of competition in their 10-K filings).
We also examine whether our observed relations between perceived
competition and financing policies and investments in R&D and acquisitions apply to
both market leaders and market followers. Market leaders that extensively discuss
competition in their 10-K filings may be more likely to take costly actions to protect
market share if their disclosure represents real competitive pressure. However, firms
with a large market share may strategically use competition disclosure as a deterrent
to shareholder litigation and regulatory intervention, but do not change their financing
policies and strategic investments because they view their dominant positions as
mostly safe. We define market leaders as firms that have at least 5 percent market
share in their industry and followers as firms with less than 5 percent market share.
28
Industries are defined based on the Fama-French 48 industry classifications. Table 9
Panel A presents the results of these tests when the dependent variables are financing
choices, and Panel B presents the regression results when the dependent variables are
R&D expenditure or acquisition activities. Control variables are included in the model
but not presented. In the financing policies regressions, the positive association
between perceived competition and cash holdings, and the negative coefficients
between perceived competition and both leverage and dividend payout ratio only hold
for market followers but not market leaders. Similarly, in the regressions that predict
future R&D and acquisition activities, the positive and significant coefficient on
Comp_Rank is only observed for market followers but not market leaders. These
results are expected if market followers feel and react to competitive pressure more
strongly than market leaders, since their survival requires immediate actions. It also
suggests that market leaders may be using their competition disclosures for strategic
purposes to perhaps ward off potential entrants.
Finally, we revisit the findings in Li et al. (2013) that high perceived
competition leads to higher rate of diminishing marginal returns. Given our findings
that firms with high perceived competition have higher future R&D investments, a
potential explanation for their findings could be that increased R&D spending in the
future, which must be expensed under U.S. accounting standards, mechanically leads
to lower future operating returns. To test if this is the case, we re-estimate model (3)
in the previous section after adjusting both RNOA and ∆NOA by adding back R&D
expenditure to net operating assets and operating income. If the results in Li et al
(2013) were driven by the expensing of R&D expenditure, the coefficients on the
interactions between Comp_Rankt and RNOAt and ∆NOAt would be diminished or
even become zero after this adjustment. Table 10 presents the results for this test, for
29
comparison the first column shows the results without adjustment for R&D
expenditure.10 We find that, after adjusting for the effects of R&D expenditure, the
coefficient on the interaction between Comp_Rank and RNOAt decreases in
magnitude but is still highly significant, and the coefficient on the interaction between
Comp_Rank and ∆NOAt is still negative and even increases in magnitude. These
results suggest that additional future R&D expenses do not fully explain the results in
Li et al (2013), and competitive pressures remain as the fundamental reason for the
higher rates of diminishing marginal returns for firms with more extensive discussion
of competition in their 10-K filings.
5. CONCLUSION
We examine how firms’ disclosure of competition in the 10-K can be useful
for financial statement users in interpreting financial statements and predicting future
investments. We show that the frequency of competition references in the 10-K is
associated predictably with important financing choices such as cash holdings,
financial leverage and dividend payout ratio. Thus, firms’ disclosure of competition in
the 10-K can be useful for the interpretation of financial statements. We also find that
firms with greater frequency of competition references have significantly higher
future investments in R&D and acquisitions. This result suggests that incorporating
firms’ discussion of competition can enhance investors’ ability to forecast the firm’s
future investments and cash flows. Consistent with Li et al (2013), our results suggest
that firms’ disclosure of competition in the 10-K is not merely boilerplate, but
contains important information about the firm’s competitive environment as well as
10
The sample used in this test is slightly smaller than the sample presented in the first column in Table
7 due to an additional data requirement. Specifically, we exclude observations with adjusted RNOA
lower than -1 or higher than 1
30
firms’ strategies in response to competitive threats. Our findings provide additional
support for using firms’ own disclosure as a measure of perceived competition to
understand how firms respond to competitive threats.
31
APPENDIX
VARIABLE DESCRIPTIONS
Variable Name
Description
Calculation
RD_Exp[t+1, t+3]
R&D expenditure
three years ahead
Sum of R&D expenditure (XRD) scaled by
lagged total assets over three years t+1 through
t+3
MA_Active[t+1,t+3]
Indicator if a firm is
active in M&A over
three years ahead
An indicator equal to 1 if the firm undertakes at
least one acquisition over three years t+1
through t+3, 0 otherwise
MA_Deals[t+1,t+3]
Number of
acquisitions
undertaken three
years ahead
Natural logarithm of (1+NM&A), with NM&A
being the number of acquisitions that the firm
undertakes over the three years t+1 through t+3
MA_Value[t+1,t+3]
Dollar value of
acquisitions
undertaken three
years ahead
Total dollar value of acquisitions undertaken by
the firm scaled by lagged total assets over three
years t+1 through t+3
Cash_Holding
Cash holdings at the
end of current year
Cash and cash equivalents (CHE) scaled by
total assets (AT)
Leverage
Financial leverage at
the end of current
year
Long-term debts (DLTT) plus debt in current
liabilities (DLC), divided by total assets (AT)
Div_Payout_Ratio
Dividend payout
ratio
Common dividend (DVC) divided by net
income (NI). We only include firm-years with
non-negative DVC and positive NI.
RD_Exp
R&D expenditure
year t
XRD for year t scaled by lagged total assets.
Acquisition
Cash paid for
acquisitions in year t
AQC for year t scaled by lagged total assets.
Note that this variable does not include the
value of stock-for-stock acquisitions.
Cap_Exp
Capital expenditure
year t
CAPX for year t scaled by lagged total assets.
MA_Active
Indicator if a firm is
active in M&A in
year t
An indicator equal to 1 if the firm undertakes at
least one acquisition in year t, 0 otherwise
MA_Deals
Number of
acquisitions
undertaken in year t
Natural logarithm of (1+NM&A), with NM&A
being the number of acquisitions that the firm
undertakes in year t
MA_Value
Dollar value of
acquisitions
undertaken in year t
Total dollar value of acquisitions undertaken by
the firm in year t scaled by lagged total assets
Comp_Rank
Perceived
competition rank
Decile rank of the number of references to
competition scaled by total number of words in
the firm’s 10-K, scaled to be in [0, 1]
Herfindahl_Index
Herfindahl index
Sum of squared market shares of all firms in
each industry. Industries are Fama-French 48
industries.
Size
Firm size
Natural logarithm of market value of equity at
the end of year t
32
MB_Ratio
Market-to-book
ratio
Market value divided by book value of equity at
the end of year t
Sale_Growth
Sales growth
Sales (SALE) for year t minus sales for year t-1,
divided by sales for year t-1.
Net_WC
Non-cash working
capital
Working capital (WCAP) minus cash and cash
equivalents (CHE), scaled by total assets.
CFO_Vol
Operating cash flow
volatility
Standard deviation of operating cash flows
scaled by total assets over 10 most recent years
(firms must have operating cash flows data for
at least 3 years).
ROA
Return on assets
Net income (NI) divided by the average total
assets.
PPE
Net property, plant
and equipment
Net property, plant and equipment (PPENT)
scaled by total assets at the end of year t
RNOA
Return on net
operating assets
Operating income after depreciation (OIADP)
divided by average NOA, where NOA is
defined as net accounts receivable (RECT) +
inventories (INVT) + other current assets
(ACO) + net property, plant and equipment
(PPENT) + intangibles (INTAN) + other noncurrent assets (AO) – accounts payable (AP) –
other current liabilities (LCO) – other noncurrent liabilities (LO).
∆RNOA[t+1]
Change in return on
net operating assets
from year t to year
t+1
RNOA for year t+1 minus RNOA for year t
∆NOA
Change in net
operating assets in
year t
NOA at the end of year t minus NOA at the end
of year t-1
33
References
Achen, C.H., 2001. Why Lagged Dependent Variables Can Suppress the Explanatory
Power of Other Independent Variables. Working Paper.
Ahuja, G. and Katila, R. 2001. Technological acquisitions and the innovation
performance of acquiring firms: a longitudinal study. Strategic Management
Journal 22 (3), 197-220.
Arrow, K. 1962. Economic welfare and the allocation of resources for invention. The
Rate and Direction of Inventive Activity: Economic and Social Factors.
Barclay, M.J., Smith, C.W., Watts, R.L., 1997. The determinants of corporate
leverage and dividend policies. Journal of Financial Education 23: 1-15.
Bates, T.W., Kahle, K.M., Stulz, R.M., 2009. Why do US firms hold so much more
cash than they used to? The Journal of Finance 64 (5): 1985-2021
Benoit, J. P., 1984. Financially Constrained Entry in a Game with Incomplete
Information. The RAND Journal of Economics 15: 490–499.
Booth, L., Zhou, J., 2009. Market Power and Dividend Policy: a Risk-Based
Perspective. Working Paper.
Brav, A., Graham, J.R., Harvey, C.R., Michaely, R., 2005. Payout policy in the 21st
century. Journal of Financial Economics 77: 483–527.
Brav, A., Graham, J.R., Harvey, C.R., Michaely, R., 2008. Managerial response to the
May 2003 dividend tax cut. Financial Management 37: 611–624.
Coad, A., 2009. The Growth of Firms: A Survey of Theories and Empirical Evidence.
Edward Elgar Publishing Ltd, Cheltenham, UK.
Dedman, E., and C. Lennox, 2009. Perceived Competition, Profitability and the
Withholding of Information about Sales and the Cost of Sales. Journal of
Accounting and Economics 48: 210–30.
Feinberg, R. 1984. Conglomerate Mergers and Subsequent Industry Effects. Review
of Industrial Organization 1: 128-137.
Fudenberg, D., Gilbert, R., Stiglitz, J., and Tirole, J. 1983. Preemption, leapfrogging
and competition in patent races. European Economic Review 22 (1): 3-31.
Gallagher, D. 2014. Remarks to the Forum for Corporate Directors, Orange County,
California. January 24. Available at:
http://www.sec.gov/News/Speech/Detail/Speech/1370540680363#.VBLvyxCa8Q
A
Gallo, C. 2014. The 7 Innovation Secrets of Steve Jobs. Forbes.
http://www.forbes.com/sites/carminegallo/2014/05/02/the-7-innovation-secretsof-steve-jobs/.
Gaughan, P.A., 2005. Mergers: What Can Go Wrong and How to Prevent It. John
Wiley & Son, Inc.
Gilbert, R., 2006. Looking for Mr. Schumpeter: Where Are We in the CompetitionInnovation Debate? Innovation Policy and the Economy 6: 159–215.
Goldberg, L. 1973. The Effects of Conglomerate Mergers on Competition. Journal of
Law and Economics 16: 137-158.
34
Goldberg, L. 1974. Conglomerate Mergers and Concentration Ratios. Review of
Economics and Statistics 56: 303-309.
Harris, C. and Vickers, J. 1985. Patent Races and the Persistence of Monopoly. The
Journal of Industrial Economics 33: 461-481.
Haushalter, D., Klasa, S., Maxwell, W., 2007. The influence of product market
dynamics on a firm's cash holdings and hedging behavior. Journal of Financial
Economics 84: 797–825.
Healy, P., and K. Palepu. 2007. Business Analysis and Valuation: Using Financial
Statements, Text and Cases, Fourth edition. Boston, MA: South-Western College
Publishers.
Hitt, M., Hoskisson, R., Johnson, R., and Moesel, D. 1996. The Market for Corporate
Control and Firm Innovation. Academy of Management Journal 39 (5): 10841119.
Hoberg, G., Phillips, G., Prabhala, N., 2014. Product Market Threats, Payouts, and
Financial Flexibility. The Journal of Finance 69: 293–324.
J S Ramalho, J., da Silva, J.V., 2009. A two-part fractional regression model for the
financial leverage decisions of micro, small, medium and large firms.
Quantitative Finance 9: 621–636.
Kovenock, D., Phillips, G., 1995. Capital Structure and Product-Market Rivalry: How
Do We Reconcile Theory and Evidence? The American Economic Review 85:
403–408.
Li, F., Lundholm, R., Minnis, M., 2013. A Measure of Competition Based on 10-K
Filings. Journal of Accounting Research 51: 399–436.
Lintner, J., 1956. Distribution of incomes of corporations among dividends, retained
earnings, and taxes. The American Economic Review 46 (2): 97-113.
Lundholm, R., and R. Sloan. 2007. Equity Valuation and Analysis. New York:
McGraw-Hill
MacKay, P., Phillips, G., 2005. How Does Industry Affect Firm Financial Structure?
Review of Financial Studies 18: 1433–1466.
Michaely, R., Thaler, R.H., Womack, K.L., 1995. Price Reactions to Dividend
Initiations and Omissions: Overreaction or Drift? The Journal of Finance 50 (2):
573–608.
Morellec, E., Nikolov, B., Zucchi, F., 2013. Competition, Cash Holdings, and
Financing Decisions. Working Paper.
Penman, S., 2009. Financial Statement Analysis and Security Valuation. Fourth
edition. New York: McGraw-Hill.
Ramalho, J. J. S., & Silva, J. V. D., 2009. A two-part fractional regression model for
the financial leverage decisions of micro, small, medium and large firms.
Quantitative Finance 9 (5): 621–636.
Schmidt, K.M., 1997. Managerial Incentives and Product Market Competition. The
Review of Economic Studies 64 (2): 191-213.
Schumpeter, J.A., 1942. Capitalism, Socialism, and Democracy. Harper and Brothers,
N.Y.
35
Seidman, L.F. 2012. Remarks at Compliance Week Annual Conference. June 4.
Available at:
http://www.fasb.org/resources/ccurl/263/963/Seidman%20Remarks%20%20Compliance%20Week%20Conference%20-%2006-04-12%20%20FINAL.pdf
36
Table 1
Sample Distribution by Industry
Fama-French Industry
Obs
Communication
Computers
Electronic Equipment
Business Services
Measuring and Control Equipment
Medical Equipment
Recreation
Pharmaceutical Products
Wholesale
Machinery
Candy & Soda
Electrical Equipment
Retail
Fabricated Products
Printing and Publishing
Consumer Goods
Steel Works Etc
Beer & Liquor
Textiles
Rubber and Plastic Products
Automobiles and Trucks
Entertainment
Business Supplies
Shipbuilding, Railroad Equipment
Healthcare
Apparel
Construction Materials
Defense
Food Products
Shipping Containers
Transportation
Aircraft
Miscellaneous
Chemicals
Restaurants, Hotels, Motels
Construction
Personal Services
Tobacco Products
Utilities
Agriculture
Petroleum and Natural Gas
Coal
Non-Metallic and Industrial Metal Mining
Precious Metals
Total
2,336
3,684
5,177
10,221
1,998
2,951
725
4,809
3,147
2,730
173
1,314
4,135
293
621
1,243
1,074
262
377
752
1,150
1,385
911
167
1,579
1,153
1,504
151
1,346
222
1,976
338
982
1,562
1,578
989
1,030
90
2,110
273
3,275
158
252
213
72,416
37
Mean
Comp
1.411
1.318
1.312
1.173
1.171
1.156
1.104
1.062
0.997
0.983
0.951
0.951
0.923
0.917
0.897
0.897
0.893
0.892
0.888
0.878
0.877
0.876
0.875
0.872
0.868
0.868
0.866
0.852
0.842
0.833
0.824
0.815
0.815
0.813
0.810
0.808
0.799
0.716
0.680
0.651
0.557
0.548
0.479
0.214
1.004
Median
Comp
1.311
1.256
1.259
1.123
1.111
1.096
1.006
1.031
0.935
0.897
0.930
0.926
0.845
0.889
0.862
0.830
0.854
0.765
0.851
0.811
0.821
0.813
0.798
0.868
0.796
0.815
0.826
0.835
0.793
0.756
0.759
0.741
0.747
0.759
0.776
0.757
0.748
0.719
0.583
0.586
0.499
0.539
0.391
0.187
0.927
Sd
0.676
0.583
0.573
0.550
0.533
0.492
0.525
0.402
0.482
0.503
0.412
0.447
0.485
0.432
0.455
0.455
0.456
0.537
0.402
0.443
0.413
0.464
0.482
0.347
0.408
0.382
0.464
0.348
0.448
0.425
0.404
0.396
0.412
0.414
0.364
0.413
0.393
0.260
0.477
0.407
0.324
0.204
0.348
0.127
0.532
Table 2
Descriptive Statistics
Comp_Pct is the number of references to competition per thousand words in the 10-K. MVE
is market value of equity at the end of the current fiscal year. Total_Asset is total assets at the
end of the current fiscal year. See the Appendix for a description of other variables. All
continuous variables are winsorized at the 1st and 99th percentiles.
Variable
N
Mean
Median
Sd
Min
Max
Comp_Pct (%)
72,416
1.00
0.93
0.53
0.07
2.73
Cash_Holding
72,416
0.19
0.10
0.22
0.00
0.90
Leverage
72,416
0.24
0.19
0.26
0.00
1.44
Div_Payout_Ratio
70,171
0.12
0.00
0.31
0.00
2.01
RD_Exp[t+1, t+3]
49,894
0.19
0.00
0.38
0.00
2.24
MA_Deals[t+1,t+3]
50,092
0.26
0.00
0.62
0.00
3.00
MA_Value[t+1,t+3]
47,141
0.06
0.00
0.25
0.00
1.78
RD_Exp
72,413
0.07
0.00
0.13
0.00
0.81
MA_Deals
72,416
0.10
0.00
0.34
0.00
2.00
MA_Value
70,943
0.02
0.00
0.11
0.00
0.89
Herfindahl_Index
72,416
0.06
0.04
0.05
0.01
0.31
MVE
72,179 1,826.57
171.03
5,783.49
1.61 43,453.13
Total_Asset
72,416 1,774.78
176.67
5,209.25
1.71 35,841.13
MB_ratio
72,166
2.79
1.87
5.70
-19.99
35.19
ROA
72,413
-0.09
0.02
0.35
-2.02
0.35
Sale_Growth
71,731
0.24
0.08
0.85
-0.76
6.41
38
Table 3
Perceived Competition and Financing Policies
(Univariate Analysis)
This table presents mean and median cash holdings (Cash_Holding), leverage (Leverage) and dividend payout ratio (Div_Payout_Ratio) for
sample firm-years sorted into deciles based on perceived competition (Comp_Pct). See the Appendix for a description of variables. All
continuous variables are winsorized at the 1st and 99th percentiles. To calculate dividend payout ratio, we only include firm-years with nonnegative common dividend (DVC) and positive net income (NI).
Cash_Holding
Mean
Median
0.122
0.052
Obs
7,233
Leverage
Mean
0.293
0.054
7,246
0.304
0.269
6,903
0.171
0.000
0.137
0.065
7,238
0.296
0.254
6,941
0.153
0.000
7,243
0.159
0.075
7,243
0.279
0.227
6,977
0.123
0.000
5
7,242
0.174
0.083
7,242
0.262
0.211
7,040
0.119
0.000
6
7,243
0.194
0.099
7,243
0.244
0.189
7,039
0.097
0.000
7
7,237
0.217
0.123
7,237
0.221
0.155
7,047
0.082
0.000
8
7,244
0.241
0.157
7,244
0.198
0.118
7,094
0.075
0.000
9
7,240
0.258
0.187
7,240
0.179
0.085
7,113
0.061
0.000
10
7,250
0.285
0.237
7,250
0.163
0.050
7,101
0.083
0.000
0.164
0.185
-0.130
-0.211
-0.111
0.000
Comp Decile
1
Obs
7,233
2
7,246
0.118
3
7,238
4
(10) - (1)
39
Median
0.261
Div_Payout_Ratio
Obs
Mean
Median
6,916
0.194
0.000
Table 4
Perceived Competition and Financing Policies
(Multivariate Analysis)
This table presents estimated coefficients for regression model (1). The dependent variable is
alternatively Cash_Holding, Leverage or Div_Payout_Ratio. See the Appendix for a description of
variables. All continuous variables are winsorized at the 1st and 99th percentiles.
Panel A: Regression Results Without Controlling for Lagged Dependent Variables
Comp_Rank
Herfindahl_Index
Size
MB_ratio
Sale_Growth
CFO_Vol
ROA
PPE
Net_WC
Cap_Exp
Acquisition
RD_Exp
Constant
Cash_Holding
Leverage
Div_Payout_Ratio
0.068***
(30.20)
-0.104***
(8.25)
0.001***
(4.41)
0.002***
(15.34)
0.003***
(2.73)
0.023***
(6.39)
0.063***
(17.38)
-0.344***
(81.34)
-0.107***
(26.40)
0.097***
(11.42)
-0.268***
(50.05)
0.462***
(46.81)
0.198***
(18.62)
Yes
Yes
69136
0.44
-0.063***
(21.69)
0.088***
(4.11)
-0.005***
(11.20)
-0.004***
(16.52)
-0.002
(1.34)
-0.028***
(5.55)
-0.116***
(20.30)
0.247***
(36.39)
-0.332***
(47.74)
-0.165***
(11.92)
0.261***
(35.85)
-0.243***
(20.28)
0.264***
(17.09)
Yes
Yes
69136
0.3
-0.040***
(10.55)
0.155***
(4.32)
0.026***
(48.35)
0
(0.10)
-0.005***
(5.81)
0
(0.23)
0.021***
(9.07)
0.173***
(20.79)
0.017***
(4.48)
-0.328***
(22.19)
-0.027***
(2.66)
-0.047***
(8.61)
0.089***
(2.76)
Yes
Yes
67011
0.2
Year fixed effects
Industry fixed effects
Observations
R-squared
Robust t statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
40
Table 4 (continued)
Panel B: Regression Results Controlling for Lagged Dependent Variables
Cash_Holding
Leverage
Div_Payout_Ratio
Comp_Rank
0.010***
(6.81)
-0.005***
(2.83)
-0.023***
(7.41)
Cash_Holding[t-1]
0.733***
(223.54)
Leverage[t-1]
0.760***
(135.12)
Div_Payout_Ratio[t-1]
Herfindahl_Index
Size
MB_ratio
Sale_Growth
CFO_Vol
ROA
PPE
Net_WC
Cap_Exp
Acquisition
RD_Exp
Constant
-0.021***
(2.58)
0.001***
(5.59)
0.001***
(7.31)
-0.009***
(9.52)
0.004
(1.28)
0.043***
(14.24)
-0.130***
(42.82)
-0.052***
(19.11)
-0.016**
(2.03)
-0.212***
(40.02)
0.144***
(18.71)
0.063***
(11.49)
Yes
Yes
69133
0.78
0.035***
(2.73)
-0.002***
(5.93)
-0.002***
(10.82)
-0.004***
(3.40)
-0.048***
(11.08)
-0.092***
(18.62)
0.072***
(15.28)
-0.142***
(26.11)
0.004
(0.31)
0.232***
(31.70)
-0.143***
(13.53)
0.065***
(8.50)
Yes
Yes
69051
0.73
Year fixed effects
Industry fixed effects
Observations
R-squared
Robust t statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
41
0.524***
(52.78)
0.073***
(2.58)
0.012***
(24.96)
0
(0.08)
-0.004***
(5.89)
-0.001
(0.30)
0.005***
(2.97)
0.081***
(13.15)
0.011***
(3.59)
-0.156***
(14.07)
-0.024***
(2.95)
-0.021***
(4.75)
-0.003
(0.11)
Yes
Yes
65487
0.45
Table 5
Perceived Competition and Future Investments in R&D and Acquisitions
(Univariate Analysis)
This table presents mean and median research and development expenditure (RD_Exp[t+1, t+3]), number of acquisitions undertaken
(MA_Deals[t+1,t+3]) and total value of acquisitions undertaken (MA_Value[t+1,t+3]) over the subsequent three years for sample firm-years sorted
into deciles based on perceived competition (Comp_Pct). See the Appendix for a description of variables. All continuous variables are
winsorized at the 1st and 99th percentiles.
RD_Exp[t+1, t+3]
Obs
Mean
Median
5120
0.062
0.000
MA_Deals[t+1, t+3]
Obs
Mean
Median
5151
0.237
0.000
MA_Value [t+1, t+3]
Obs
Mean
Median
4763
0.038
0.000
2
5058
0.088
0.000
5079
0.257
0.000
4730
0.044
0.000
3
4989
0.129
0.000
5002
0.258
0.000
4684
0.047
0.000
4
5014
0.145
0.000
5031
0.260
0.000
4710
0.051
0.000
5
4963
0.177
0.000
4976
0.251
0.000
4683
0.058
0.000
6
5048
0.200
0.014
5068
0.256
0.000
4776
0.062
0.000
7
4995
0.236
0.036
5016
0.254
0.000
4764
0.067
0.000
8
4948
0.268
0.068
4969
0.278
0.000
4718
0.083
0.000
9
4927
0.281
0.128
4944
0.286
0.000
4713
0.083
0.000
10
4832
0.296
0.190
4856
0.316
0.000
4600
0.091
0.000
0.234
0.190
0.078
0.000
0.053
0.000
Comp Decile
1
(10) - (1)
42
Table 6
Perceived Competition and Future Investments in R&D and Acquisitions
(Multivariate Analysis)
This table present estimated coefficients for regression model (2). The dependent variable is
alternatively RD_Exp[t+1, t+3], MA_Active[t+1, t+3], MA_Deals[t+1, t+3] or MA_Value[t+1, t+3]. See the
Appendix for a description of variables. All continuous variables are winsorized at the 1st and 99th
percentiles.
Panel A: Regression Results Without Controlling for Lagged Dependent Variables
Comp_Rank
Herfindahl_Index
Size
MB_ratio
Sale_Growth
CFO_Vol
ROA
Cash_Holding
Leverage
Constant
RD_Exp[t+1,t+3]
0.059***
(14.48)
-0.006
(0.41)
0.006***
(9.53)
0.002***
(5.10)
-0.003
(1.00)
0.112***
(10.14)
-0.377***
(36.30)
0.432***
(41.48)
-0.011
(1.30)
-0.042***
(3.69)
Yes
MA_Active[t+1,t+3]
0.126***
(5.30)
-0.104
(0.69)
0.157***
(44.31)
0.001
(0.54)
0.047***
(5.29)
0.002
(0.06)
-0.024
(0.85)
-0.079**
(1.97)
0.04
(1.18)
-1.543***
(11.40)
Yes
Year fixed effects
Industry fixed
effects
Yes
Yes
Observations
48808
48992
R-squared
0.52
Robust t statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
MA_Deals[t+1,t+3]
0.024***
(4.53)
-0.027
(0.76)
0.040***
(47.40)
0.000*
(1.79)
0.010***
(5.37)
0.011*
(1.90)
-0.002
(0.43)
-0.033***
(3.85)
0.011*
(1.70)
0.01
(0.35)
Yes
MA_Value[t+1,t+3]
0.029***
(7.38)
-0.028*
(1.67)
0.002***
(3.12)
0.001***
(3.25)
0.010***
(4.75)
0.006
(0.97)
-0.023***
(3.74)
0.038***
(5.03)
-0.012**
(2.13)
0.043***
(3.31)
Yes
Yes
48992
0.08
Yes
46081
0.03
Table 6 (continued)
Panel B: Regression Results Controlling for Lagged Dependent Variables
Comp_Rank
RD_Exp
RD_Exp[t+1,t+3]
0.016***
(4.99)
1.908***
(68.26)
MA_Active[t]
MA_Active[t+1,t+3]
0.112***
(4.69)
MA_Deals[t+1,t+3]
0.019***
(3.77)
0.594***
(27.95)
MA_Deals[t]
0.290***
(29.27)
MA_Value[t]
Herfindahl_Index
Size
MB_ratio
Sale_Growth
CFO_Vol
ROA
Cash_Holding
Leverage
Constant
MA_Value[t+1,t+3]
0.027***
(6.92)
-0.014
(1.34)
-0.003***
(6.72)
0
(1.19)
-0.020***
(8.05)
0.030***
(3.50)
-0.116***
(13.87)
0.173***
(19.43)
0.001
(0.17)
0.020***
(2.96)
Yes
-0.099
(0.65)
0.141***
(38.88)
0.001
(0.54)
0.025***
(2.82)
0.004
(0.11)
0.001
(0.04)
-0.028
(0.70)
0.034
(1.01)
-1.527***
(11.14)
Yes
Year fixed effects
Industry fixed
effects
Yes
Yes
Observations
48808
48992
R-squared
0.73
Absolute value of z statistics in
parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
44
-0.025
(0.69)
0.034***
(41.38)
0.000*
(1.72)
0.003*
(1.86)
0.011**
(2.03)
0.005
(0.93)
-0.017**
(2.07)
0.008
(1.31)
0.02
(0.69)
Yes
0.173***
(9.34)
-0.026
(1.50)
0.001***
(2.62)
0.001***
(3.16)
0.007***
(3.25)
0.006
(1.10)
-0.022***
(3.48)
0.039***
(5.09)
-0.011**
(2.05)
0.043***
(3.23)
Yes
Yes
48992
0.12
Yes
45249
0.03
Table 7
Perceived Competition and the Rate of Diminishing Marginal Returns Conditional on Financing Policies and Investments
This table presents estimated coefficients for regression model (3) for the overall sample as well as various subsamples formed based on firms’ financing
policies and investments in R&D and acquisitions. Firms with conservative financing policies are those with cash holdings above the median, leverage below
the median and dividend payout ratio below the median (measured for year t). High R&D firms are those with average R&D expenditure over 3 years t-2
through t above the 75th percentile. M&A active firms are those that undertake at least one acquisition over 3 years t-2 through t. The dependent variable is
∆RNOA[t+1]. See the Appendix for a description of variables.
RNOA
∆NOA
Comp_Rank
Baseline
Model
Conservative
Financing
Others
High R&D
Low R&D
M&A Active
M&A
Inactive
-0.179***
-0.252***
-0.156***
-0.200***
-0.178***
-0.159***
-0.183***
(12.90)
(7.69)
(10.54)
(6.19)
(11.39)
(5.20)
(11.78)
-0.104***
-0.129***
-0.110***
-0.123***
-0.116***
-0.064***
-0.128***
(12.15)
(3.56)
(12.97)
(4.30)
(12.64)
(5.32)
(10.84)
0.014***
0.018
0.005
0.006
0.004
0.034***
0.009*
(3.02)
(1.37)
(0.99)
(0.48)
(0.86)
(3.22)
(1.71)
-0.155***
-0.098**
-0.105***
-0.149***
-0.070**
-0.277***
-0.121***
(6.57)
(2.11)
(3.60)
(3.27)
(2.34)
(5.02)
(4.63)
-0.042***
-0.096*
-0.002
-0.089**
0.028*
-0.059***
-0.035*
(2.76)
(1.89)
(0.15)
(2.07)
(1.79)
(2.65)
(1.70)
0.030***
-0.164*
0.029***
0.134***
0.017
0.063**
0.020*
(2.92)
(1.67)
(3.12)
(5.82)
(1.59)
(2.57)
(1.82)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
41442
9847
30250
9657
28973
9262
32083
R-squared
0.14
0.15
Robust t statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
0.11
0.16
0.1
0.18
0.13
Comp_Rank*RNOA
Comp_Rank*∆NOA
Constant
Year fixed effects
Industry fixed effects
Observations
Table 8
Perceived Competition and Future Investments Conditional on Industry
Investment Intensity
The table presents selected coefficients for regression model (2) conditional on whether the firm is in
an R&D intensive or M&A intensive industry. For each industry-year, we calculate the average R&D
expenditure and number of acquisitions undertaken over the past 5 years. R&D-intensive industries are
those with the average R&D expenditure above the 75th percentile. M&A-intensive industries are those
with the average number of acquisitions above the 75th percentile. The dependent variable is
alternatively RD_Exp[t+1, t+3], MA_Deals[t+1, t+3] or MA_Value[t+1, t+3]. See the Appendix for a
description of variables. All continuous variables are winsorized at the 1 st and 99th percentiles.
RD_Exp[t+1, t+3]
Comp_Rank
RD_Exp
R&D
Intensive
0.024***
(4.10)
1.854***
(62.37)
MA_Deals[t+1, t+3]
Others
0.004**
(2.20)
2.145***
(18.35)
MA_Deals
M&A
Intensive
0.032***
(3.33)
Others
0.011*
(1.90)
0.300***
(19.49)
0.274***
(21.36)
MA_Value
Controls
Year and Industry FE
Observations
R-squared
MA_Value[t+1, t+3]
M&A
Intensive
0.048***
(6.14)
Others
0.016***
(3.69)
0.135***
(5.77)
Yes
Yes
30,380
0.03
Yes
Yes
Yes
Yes
0.205***
(7.16)
Yes
Yes
24,935
0.69
Yes
23,873
0.61
Yes
16,266
0.13
Yes
32,726
0.10
Yes
14,869
0.04
Table 9
Perceived Competition, Financing Policies and Future Investments Conditional
on Market Power
This table presents selected coefficients for regression models (1) and (2) conditional on firms’ market
power. We define market leaders as firms with at least a 5 percent market share (for each Fama-French
industry year) and market followers as those with less than 5 percent market share. In Panel A, the
dependent variable is alternatively Cash_Holding, Leverage or Div_Payout_Ratio. In Panel B, the
dependent variable is alternatively RD_Exp[t+1, t+3], MA_Deals[t+1, t+3] or MA_Value[t+1, t+3]. See the
Appendix for a description of variables. All continuous variables are winsorized at the 1 st and 99th
percentiles.
Panel A: Perceived Competition and Financing Policies
Cash_Holding
Leaders
Comp_Rank
Cash_Holding[t-1]
Leverage
Div_Payout_Ratio
Followers
Leaders
Followers
Leaders
Followers
0.002
0.010***
(0.57)
(6.45)
0.761*** 0.731***
(32.15)
(220.83)
0.013**
(2.08)
-0.006***
(3.10)
-0.024
(1.03)
-0.022***
(7.12)
Leverage[t-1]
0.883*** 0.756***
(50.25)
(131.57)
Div_Payout_Ratio[t-1]
0.487*** 0.522***
(12.91)
(50.69)
Controls
Yes
Yes
Yes
Yes
Yes
Yes
Year and Industry FE
Observations
R-squared
Yes
Yes
Yes
Yes
Yes
Yes
2822
0.8
66311
0.78
2820
0.87
66231
0.73
2549
0.42
62938
0.45
Panel B: Perceived Competition and Future Investments
RD_Exp[t+1, t+3]
Comp_Rank
RD_Exp
Leaders
0.003
(1.09)
1.422***
(4.46)
MA_Deals[t+1, t+3]
MA_Value[t+1, t+3]
Leaders
-0.012
(0.39)
Followers
0.021***
(4.01)
Leaders
-0.009*
(1.91)
Followers
0.028***
(6.79)
0.257***
(7.09)
0.284***
(27.64)
0.172***
(9.21)
Yes
Yes
43,434
0.03
Followers
0.017***
(5.20)
1.907***
(68.07)
MA_Deals
MA_Value
Controls
Year and Industry FE
Observations
R-squared
Yes
Yes
Yes
Yes
0.104*
(1.76)
Yes
Yes
2,318
0.74
Yes
46,490
0.73
Yes
2,322
0.23
Yes
46,670
0.11
Yes
1,815
0.12
47
Table 10
Perceived Competition and Rate of Diminishing Marginal Returns after
Adjusting for R&D Expenditure
The dependent variable is ∆RNOA[t+1]. In column (2), RNOA and ∆NOA are adjusted by
adding back R&D expenditure to operating income and net operating assets. See the
Appendix for a description of variables.
(1)
(2)
Results Based on
Reported Data
Results Based on
Adjusted Data
-0.181***
-0.184***
(13.17)
(15.43)
-0.105***
-0.107***
(12.28)
(12.83)
0.011**
0.013***
(2.45)
(3.20)
-0.140***
-0.076***
(6.08)
(4.25)
-0.038**
-0.073***
(2.52)
(5.21)
0.030***
0.028***
(2.93)
Yes
(2.72)
Yes
Yes
Yes
41,324
41,324
0.13
0.15
RNOA
∆NOA
Comp_Rank
Comp_Rank*RNOA
Comp_Rank*∆NOA
Constant
Year fixed effects
Industry fixed effects
Observations
R-squared
Robust t statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
48