Redefining Financial Constraints: a Text-Based Analysis
Gerard Hoberg and Vojislav Maksimovic∗
August 16, 2013
ABSTRACT
We score 10-K text to obtain annual measures of financial constraints, with
separate measures for firms assessing equity and debt issues. Equity market
constraints have more severe consequences for the firm, relate to firms funding
growth opportunities, and are likely driven by informational asymmetries. We
find a significant population of firms that report equity market constraints
and declare proprietary information risks. Constraints in the debt markets are
distinct, relate to firms funding capital expenditures, and are likely driven by
factors relating to debt overhang. Our measures outperform others used in the
literature in predicting investment cuts following exogenous shocks.
∗
Both authors are from the University of Maryland.
Hoberg can be reached at
[email protected] and Maksimovic can be reached at [email protected]. We
especially thank Christopher Ball for giving us access to metaHeuristica software, which made the
technical aspects of this paper possible. We also thank Ran Duchin, Alex Edmans, Laurent Fresard,
Steve Kaplan, Tim Loughran, Raghu Rau, Denis Sosyura, Toni Whited, Luigi Zingales, and participants at seminars at Nanyang Technological University, The National University of Singapore,
the Securities and Exchange Commission, the SFS Cavalcade at Darden, Singapore Management
University, Stockholm School of Economics, UCLA, the University of Michigan, USC, the 2012
UBC Summer Finance Conference, and the Western Finance Association for their comments. We
thank Danmo Lin and Austin Starkweather for excellent research assistance. All errors are the
c
authors alone. Copyright 2011
by Gerard Hoberg and Vojislav Maksimovic. All rights reserved.
Researchers have identified several theories explaining why financial constraints
might exist (asymmetric information, moral hazard, cost of contract enforcement,
transaction costs, and debt overhang). A limitation of existing measures of financial constraints is that they are uni-dimensional, even as theory recognizes that the
binding constraint might separately relate to the ability to raise equity or to raise
debt. Moreover, existing constraint measures rely on predictive models estimated
using small samples of potentially unstable accounting ratios, which are then applied out of sample for materially different populations of firms. We develop a novel
methodology that overcomes these limitations.
Our results suggest that the most constrained firms are high growth firms that
desire external equity financing. We will refer to these firms as equity-market constrained firms, as their constraints are most binding in the equity market and not the
debt market. Despite their constraints, these firms have high Tobin’s Q relative to industry peers. These firms also significantly curtail R&D, CAPX, and equity issuance
policies following negative shocks, and we also find that equity market constrained
firms are more likely to mention the existence of proprietary information risks in
their 10-K. These findings provide some of the first direct evidence regarding potential informational asymmetries in equity market financial constraints. Our findings
strongly support the predictions of Krasker (1986)’s theoretical extension of Myers
and Majluf (1984). Krasker shows that asymmetric information can lead to “equity rationing”, where firms with the best investment opportunities face a bounded
issuance proceeds function (financial constraints), and are forced to underinvest.
Our results also show that firms funding capital expenditures using debt financing
can also be constrained. However, the impact of negative shocks for these firms is
less severe. We find that these constraints are likely not driven by informational
asymmetries and differ on many levels from equity market constraints. In particular,
these firms are not more likely to report proprietary information risks, and instead
they are more likely to discuss issues relating to covenant violations in their 10Ks. In all, the existence of viable investment opportunities for these firms, coupled
with their increased rate of covenant violations, suggests that issues relating to debt
overhang likely contribute to financial constraints for debt market constrained firms.
1
Our measures of the firm’s financial constraints are based on the analysis of
Management’s Discussion and Analysis (MD&A) section in 48,512 10-Ks. We focus
on mandated disclosures regarding each firm’s liquidity, as well as the discussion of
the sources of capital each firm intends to use to address its financing needs. We
calculate four direct measures of financial constraints for each firm in each year: those
due to broad liquidity challenges leading to potential under investment, and those
due to specific liquidity challenges pertaining to equity, debt and private placement
financing. We examine how the constraints for different forms of external finance
are related to firm characteristics, and show how the constraints are differentially
related to actual investment and issuance decisions.
Following KZ, we focus on the discussion of liquidity in the MD&A section of the
10-K. This section is context sensitive, and when managers indicate the potential
need to curtail or delay investment, the intended conclusion for the reader is that
the firm is investing less than what might be optimal due to the existence of challenges
to its liquidity.1 Our constraint indices differ substantially from those used following
KZ and Whited and Wu (WW) (2006). For example, our new constraint measures
are 15% or less correlated with the KZ and WW indices.
Our findings also extend those of Hadlock and Pierce (2010) (HP), who suggest
that firm size and age proxy for financial constraints. Consistent with HP, the topmost chart in Figure 1 shows that smaller and younger firms are more likely to
discuss issues relating to constraints than are larger and older firms according to our
measures. Going beyond HP, we also identify a large fraction of small and young
firms that are unlikely to be constrained, and a large number of medium sized and
middle aged firms that are likely constrained. We are also the first to show, as
depicted in the lower three charts, that constrained firms focusing on equity, debt,
and private placements are also distinct and likely cannot be identified systematically
using size and age. Equity focused constrained firms are likely to be smaller, and
debt focused constrained firms are likely to be larger, and private placement focused
constrained firms are likely to be middle sized or middle aged.
1
In the next section, we extend the theoretical overview in KZ and outline why this text query
directly measure the existence of binding financial constraints.
2
Our findings also extend those of Brown, Fazzari and Petersen (2009) (BFP), who
suggest that R&D plays a major role in financial constraints. Consistent with BFP,
Figure 2 shows that constraints are broadly higher for younger firms with higher
R&D. Going beyond BFP, we identify a significant population of firms that declare
proprietary information risks in their 10-Ks and show that this category of firms
also have higher constraints. As the figure shows, constraints are higher for younger
firms directly mentioning proprietary information risks, even holding R&D fixed.
Our results regarding a role for proprietary information are novel and highly robust.
Financing frictions related to proprietary information concerns exist across FamaFrench-48 industries where R&D is less pervasive: Personal Services, Retail, Transportation, and Meals. These informational concerns are especially strong among
younger firms, likely because it takes time for investors to build trust in opaque
firms with higher levels of proprietary information.
We take our constraint measures to the data and examine the responsiveness of
firms’ investment and issuance policies to shocks. In an Opler and Titman (1994)
style test, we examine whether constrained firms curtail these polices more than
unconstrained firms during the 2008 financial crisis. In a second test aimed at identifying a unique exogenous channel for liquidity, we consider the Edmans, Goldstein
and Jiang forced mutual fund selling shock. As their measure of forced mutual fund
selling is not industry-specific, and only affects equities, we view this shock as an
exogenous shock to a firm’s equity market liquidity. We expect that this supply-side
shock should especially affect equity market constrained firms. We find this to be the
case. These results are consistent with a causal link between financial constraints and
equity issuance policy curtailment through the channel of equity market liquidity.
We find that firms having an equity, debt, or private placement focus curtail
investment and issuance policies differently. Equity focused constrained firms (and,
more so, private placement constrained firms) most severely curtail their R&D, capital expenditures, and equity issues following negative shocks. In contrast, debt focus
constrained firms reduce their debt issuances and also their CAPX investment. Private placement focus constrained firms are similar to equity focus constrained firms,
but experience more severe curtailments.
3
We split our sample along several dimensions. We find that constraints have a
larger overall impact on firms that are younger and those that focus on R&D as their
primary investment. These differential responses to shocks are economically large.
For example, the highest tercile of delay constrained firms reduced their R&D by
14.8% of sales in the financial crisis of 2008, whereas the lowest tercile reduced R&D
by only 3.4% of sales. On the issuance side, the high constraint tercile firms curtailed
equity issuance by 5.4% of assets, compared to just 1.7% for low constraint tercile
firms. These differential reactions are roughly 15% to 20% of one cross sectional
standard deviation of each policy.
Our paper is most strongly related to existing articles that imply measures of
constraintedness including Fazzari, Hubbard and Petersen (FHP)(1988), and the
aforementioned articles by Kaplan and Zingales, Whited and Wu, and Handlock and
Pierce. Also related, a second strand of research uses surveys to measure financial
constraints. Graham and Harvey (2001) and Campello, Graham and Harvey (2011)
analyze the role of constraints for large firms and Beck, Demirguc-Kunt and Maksimovic (2006, 2008) for small firms. Our paper also draws upon a growing literature
that considers text-based analysis to test theoretical hypotheses in Finance.2
There is also a large literature on the roots of financial constraints. Myers and
Majluf (1984), Krasker(1986), Greenwald, Stiglitz, and Weiss (1984), Jensen and
Meckling (1976), Hart and Moore (1998) have all identified reasons for a wedge
between internal and external financing. Regarding equity market constraints, we
find strong support for a role for information (Myers and Majluf (1984), Krasker
(1986)). Regarding debt market constraints, our results are less conclusive, but
suggest that information is likely not the most important factor, and that debt
overhang may be a material factor.3
2
Early financial studies using text include Antweiler and Frank (2004) and Tetlock (2007). In
corporate finance, earlier work includes Hanley and Hoberg (2010), Hoberg and Phillips (2010),
and Loughran and McDonald (2011). See Sebastiani (2002) for an excellent review of text analytic
methods.
3
Hennessy and Whited (2005, 2007) also go beyond a uni-dimensional approach and consider
financial constraints in a setting where debt and equity are potential choices using structural estimation and moment matching. These articles further motivate our work as they also show that
bankruptcy and financing frictions affect investment, debt and equity issuance differently and in
ways not picked up by existing constraint indicators. Our studies are otherwise distinct as these
articles do not aim to provide direct firm-level empirical indicators of investment delay constraints
4
Our empirical framework has several advantages. First, we obtain information on
constraints for a large fraction of the Compustat universe directly from firm disclosures. Our variables thus have the advantage of low ambiguity due to direct textual
context, and we do not rely on the use of indirect proxies using non-transparent
accounting variables. Second, we avoid the difficulty of having to predict constraints
out of sample using unstable or potentially endogenous accounting variables. Third,
we can query the text further regarding related questions akin to a survey (but not
having to deal with issues of low response rates). This approach has the potential
to identify more specific channels through which constraints matter, and we use this
approach to assess the degree to which constraints are related to proprietary information and also to covenant violations. Fourth, the methodology we are using is
transparent, consistent, and can be applied out of sample with the same interpretation. We find a high degree of consistency throughout our sample and believe that
our methods can continue to be used in future years.
The rest of the paper is organized as follows. Section I discusses the prior literature, Section II describes our data and methods. Our results regarding constraint
variable attributes are in Section III and our results regarding corporate finance
policies and constraints are in Section IV. Section V explores the origins of financial
constraints and Section VI concludes.
I
Literature and Hypotheses
In early work, FHP argue that, holding investment opportunities constant, the capital expenditures of a financially unconstrained firm will depend on the Net Present
Value of its investment opportunities. Hence, if financial constraints are a first order issue, investment will become positively correlated with cash flow realizations
once the researcher controls for the value of investment opportunities. This simple
framework allows the authors to measure the degree of constrainedness by estimating
the coefficient on cashflow when CAPEX is the dependent variable and Tobin’s Q is
and separate measures of debt and equity focused constraints that can be used out of sample.
We also obtain an empirical measure of asymmetric information and show how this relates to our
financial constraint indicators.
5
included as a control variable.
KZ criticize FHP on the grounds that the relation between cashflow sensitivity
and the degree of constraints is not necessarily monotonic and depends on the firm’s
production function and its cost of external funds. KZ use expert evaluations of
10-K MD&A statements for 49 firms to measure financial constraints. The analysis
reveals that some of the firms classified as constrained by FHP, in fact, appear to
be unconstrained. Many later studies use the “KZ Index”, which is a linear sum of
the accounting variables (firm cash flow, long-term debt, dividend-to-asset ratio and
Tobin’s Q) shown by KZ to predict constraints.4
The KZ index is estimated using a very small sample of firms. HP consider a larger
random sample of 356 firms over 1995-2004. They read the management’s letter to
shareholders and MD&A and group firms into categories of constrainedness based
on the number and quality of statements indicating financial constraints. HP then
re-estimate the link between accounting variables and constraintedness. They find
that the KZ index is unstable and that only size and age robustly predict financial
constraints. These results cast serious doubt on any attempt to measure constraints
using essentially endogenous accounting variables.
A second approach, exemplified by Whited (1991) and WW, is to explicitly model
the relation between investment and the cost of external funds. A key advantage
is the possibility of directly estimating the shadow price of external capital. A
key limitation is theoretical assumptions can be restrictive, especially regarding the
functional form of the shadow price of external finance. WW generate a WW index
of constraints by relating this shadow price to firm and industry long-term debt to
total assets, a dividend payer dummy, firm and industry sales growth, firm size, liquid
assets to total assets, and cash flow to total assets. Later studies use the estimated
coefficients out of sample to measure financial constraints.5
Our approach is to use automated textual analysis of the MD&A section of the
10-K to construct direct indices of financial constraints. Our methodology allows full
4
Lamont, Polk, and Saa-Requejo (2001) were the first to apply the KZ index to measure constraints out of sample.
5
It should be noted that WW do not use estimates out of sample in this way.
6
coverage of all firms, and does not rely on any mapping to accounting variables as
an intermediate step. We focus on direct statements regarding the need to delay or
curtail investments as a result of issues relating to liquidity challenges. Hence, we
only consider statements of curtailed investment appearing alongside a discussion of
financial liquidity (the organization of 10-Ks makes this straight forward), and we
do not consider any other indications of investment delay in other parts of the 10-K.
Separately, ours is the first study to assess whether a firm views either debt or equity
capital as being more central, and related impact on constraints.
Our approach has several advantages. By reading MD&A text directly, we avoid
the use of out of sample coefficients from a potentially unstable predictive model.
The text is also scored in a way that does not rely on human judgment, and thus it
is easily applied in extended samples. Finally, by using direct statements of financial
constraints, we do not rely on data processed through a structural model, which
depend on assumptions regarding firm and market characteristics.
SEC Regulation S-K obligates firms to discuss (A) challenges to their liquidity,
(B) their demand for liquidity (investment plans), and (C) the sources of available
liquidity which are further specified as:6
(1) Liquidity. Identify any known trends or any known demands, commitments,
events, or uncertainties that will result in . . . the registrant’s liquidity increasing
or decreasing in any material way. If a material deficiency is identified, indicate
the course of action that the registrant has taken or proposes to take to remedy
the deficiency. Also identify and separately describe internal and external sources of
liquidity, and briefly discuss any material unused sources of liquid assets.
(2) Capital Resources. (i) Describe the registrant’s material commitments for
capital expenditures as of the end of the latest fiscal period, and indicate the general
purpose of such commitments and the anticipated source of funds needed to fulfill
such commitments . . .
(ii) Describe any known material trends, favorable or unfavorable in the registrant’s capital resources. Indicate any expected material changes in the mix and the
6
See, for example, http://taft.law.uc.edu/CCL/regS-K/SK303.html for the full text of Item 303.
7
relative cost of such resources.
”Liquidity” is clarified in Instruction 5: “refers to the ability of an enterprise to
generate adequate amounts of cash to meet the enterprise’s needs for cash...Liquidity
generally shall be discussed on both a long-term and short-term basis.”
We find that firms widely comply, and we find the relevant discussion in a machine
readable subsection of MD&A for roughly 85% of all firms. This subsection is often
titled “Liquidity and Capital Resource”7 .
We posit a simple extended version of the model in KZ regarding MD&A disclosure and firm liquidity. First consider a firm with adequate cash to fully fund its
investments. This firm will maximize its objective function F (I) − I, where F (I) is
the firm’s net revenue function, which depends on the firm’s investment level I. Let
I ∗ denote the optimal investment that solves this problem.
Now suppose the firm’s cash is less than I ∗ and hence the firm must use external
financing. Further suppose that the cost of external capital might differ from the
opportunity cost of internal cash. We refer to the difference between the cost of
external and internal capital as a financing wedge. We extend the KZ framework
and further assume that the firm has a choice of financial instruments: equity and
debt. Let wE (I, θ) and wD (I, θ) be the financing wedges associated with debt and
equity financing respectively, where θ describes the firm’s growth options, and all
characteristics that affect its ability to raise capital.
The relative size of the equity and debt financing wedges depends on firm characteristics and market conditions. The firm prefers the form of financing which yields
the maximum of the quantities F (I) − wE (I, θ) and F (I) − wD (I, θ). In general
the existence of a financing wedge reduces the firm’s desired investment below the
optimal level I ∗ , to a constrained level I C .
We hypothesize that if I ∗ − I C is immaterial, then the firm does not disclose
this shortfall, and does not extensively discuss the financing wedges that is faces.
However, since firms face legal liability with respect to regulation S-K, if I ∗ − I C
7
The subsection can also appear with a number of related titles, which we discuss in our online
Appendix 1.
8
is material relative to the firm’s size and operations, the firm discloses a potential
delay or curtailment of investment statement. Pursuant to Regulation S-K, the firm
also comments on the nature of the wedge. If the wedge is smaller for equity (debt),
then the firm will also disclose its intention to try to issue equity (debt).
We thus score each Liquidity and Capital Resource subsection based on indications of broad financial constraints I ∗ − I C , and separately for discussion of equity
and debt financing wedges. Our analysis focuses on several questions:
First, following shocks to θ, are changes in the levels of R&D and CAPX greater
for for firms that report high expected investment delays I ∗ − I C ? Such a relation is
implied by a well-behaved wedge function w(I, θ) (See KZ for example).
Second, are changes in R&D and CAPX more likely to be associated with financing wedges involving equity or debt? The reporting requirements of our data set
are particularly well-suited to address this question, which is absent in the existing
literature. R&D expenditures are more likely to be financed using equity (Myers
(1977)), as is intellectual capital based on unproven proprietary information, and
debt is more likely to fund tangible investments such as CAPX. Thus, we would
expect a higher sensitivity of R&D for equity-focused firms, and greater sensitivity
of CAPX for debt-focused firms.
Third, we estimate the relationship between security issuance and financial constraints. We expect that following a shock, constrained firms will experience a greater
reduction in security issuance than unconstrained firms.
Fourth, we explore differential security issuance responses to shocks for equity
or debt-focused firms. We expect that equity (debt) focused firms will experience
greater reductions in equity (debt) issuance and investment policies following a shock.
Fifth, it is likely that the key theoretical drivers of financial constraints are different in the equity and in the debt markets. We investigate whether our measures
are sensitive to variables related to such a hypothesis. Specifically, we investigate
the relation between our equity and debt market constraint variables and measures
of proprietary information (both R&D-related and unrelated to R&D), growth opportunities, and financial distress.
9
Sixth, we examine whether firms focused on private equity respond to shocks differently. As shown by Gomes and Phillips (2006), private equity issuers are opaque
and might have greater informational asymmetries. We expect these firms’ investment and issuance policies to be particularly sensitive to shocks.
II
Data and Methods
The variables we create derive purely from 10-K text extracted from the “Management’s Discussion and Analysis” (MD&A) section. We utilize text processing
software provided by meta Heuristica LLC to web-crawl and parse the MD&A section from each 10-K for all electronically filed 10-Ks from 1997 to 2009. From each
MD&A, the software then extracts the Liquidity and Capital Resources subsection.
This subsection’s title is not fully uniform, and we explain in Appendix 1 of our
Online Appendix how we unify subsections of similar content. This key subsection,
which is also used by KZ contains the firm’s remarks concerning its financial liquidity
and its intentions regarding future capital market interactions. The software uses
natural language processing to parse and organize textual data, and its pipeline employs “Chained Context Discovery” (See Cimiano (2010) for details). This approach
leverages an empirical ontology to normalize the structure of the content and map it
to a standardized form. The software stores processed data in a high speed database.
From a research perspective, the technology enables fast and thorough querying,
while permitting analysis that is also easy to interpret. For example, many of the
variables used in this study are constructed by simply identifying which firm-year
filings (within a set of 40,000+ filings) specifically contain a statement indicating
that the firm may have to delay its investments due to financial liquidity issues.
The database supports advanced querying including contextual searches, proximity
searching, multi-variant phrase queries, and clustering. These tools are designed to
accelerate discovery of linguistic patterns that can further enrich content.
10
A
The CRSP and COMPUSTAT Sample
We start with 56,496 firm-years in the COMPUSTAT sample from 1997 to 2009
with sales of at least $1 million and positive assets excluding financials and regulated utilities (SIC 6000 to 6999 and SIC 4900 to 4949, respectively) with adequate
COMPUSTAT data. The years are chosen based on availability of SEC Edgar data.
B
The Sample of 10-Ks
Our sample of 10-K MD&A’s is extracted by web-crawling the Edgar database for
all filings that appear as “10-K,” “10-K405,” “10-KSB,” or “10-KSB40.” MD&A
generally appears as Item 7 in most 10-Ks. The document is processed for text
information, fiscal year, and the central index key (CIK).
We link each document to the CRSP/COMPUSTAT database using the central
index key (CIK), and the mapping table provided in the WRDS SEC Analytics
package. Of the 56,496 observations available in CRSP and COMPUSTAT database
noted above, we are left with 52,438 after requiring that same-year machine-readable
text data is available. This loss of these observations is due to two reasons: (1) a
mapping from CIK to gvkey is not available or (2) some MD&As are not filed in
the 10-K itself, but are incorporated by reference. Of the 52,438 machine readable
MD&A sections, 44,441 of these also have a machine readable Liquidity and Capitalization Resource Subsection (henceforth CAP+LIQ). We later show that the firms
that have a machine readable MD&A, but do not have a separable CAP+LIQ, are
generally healthy firms that have few if any liquidity issues to disclose.
C
Identifying Constraints in Different Markets
Our first query identifies firms that discuss the possibility of delaying investment
in CAP+LIQ. Because firms focus on liquidity issues in CAP+LIQ, it follows by
context that these firms are curtailing investment due to challenges to the firm’s
liquidity. Indeed some firms often state explicitly that poor financing options are to
blame. Importantly, we do not identify investment curtailment anywhere else in the
10-K to insure we focus solely on issues relating to financial liquidity.
11
Because there are many ways to verbally indicate the notion of curtailment, and
even more forms of investment, identifying a reliable set of firms that are curtailing
investment using text searches offers several challenges. Using the two-sided sentence
view features in the metaHeuristica software, we were able to identify key synonyms
to the word curtail, and a long list of words identifying types of investment. To
identify firms that are directly reporting potential delays in investment, we query
CAP+LIQ for instances in which one word from each of the following two lists exists
side by side (separated by no more than one stop word such as “the”, “of”, “and”,
etc). Note that “*” denotes a wildcard.
Delay List 1: delay* OR abandon OR eliminate OR curtail OR (scale back)
OR postpone*
Delay List 2: construction OR expansion OR acquisition* OR restructuring
OR project* OR research OR development OR exploration OR product* OR expenditure* OR manufactur* OR entry OR renovat* OR growth OR activities OR
(capital improvement*) OR (capital spend*) OR (capital proj*) OR (commercial
release) OR (business plan) OR (transmitter deployment) OR (opening restaurants)
We refer to firms that trigger this query as the “precise training set”, as these firms
are very directly indicating that issues relating to financial liquidity are resulting in
potential under investment. We find that 1.7% of all firm-year observations load
on this query. However, in reading many other CAP+LIQ examples, we also note
that a large number of firms exist that have words from both lists present, but they
are not necessarily side by side. This is a natural consequence of the need to delay
investment being a rather intricate issue to express verbally. Some writers add more
detail, whereas others are more Spartan.
As our objective is to identify as many firms as possible who indicate delay of
investment, we thus consider a query that relaxes the requirement that both words
from both lists must exist side by side in the text. Instead, we require that both
words appear within a twelve word window of one another. Indeed this identifies a
12
larger set of firms, as 5.5% of our sample firms load on this enhanced query. We refer
to this set of firms as the “12-word enhanced training set”.8
We also identify which firms are focused on issuing equity or debt. In using the
two sided sentence views available in metaHeuristica software, we identify a number
of commonly used phrases that identify the intent to issue equity as follows:
Equity Focused List: issuing equity securities OR expects equity securities OR
through equity financing OR sources equity financing OR seek equity investments
OR seek equity financings OR access equity markets OR raised equity arrangements
OR undertake equity offerings OR sell common stock OR issuing common stock
OR selling common stock OR use equity offerings OR offering equity securities OR
planned equity offering OR seek equity offering OR raise equity offering OR equity
offering would add OR additional equity offering OR considering equity offering OR
seek equity financing OR pursue equity offering OR consummates equity offering OR
raises equity capital OR raise equity offering OR sources equity offering.
Regarding debt focused firms, we consider the following commonly used phrases.
Debt Focused List: increased borrowings OR use line of credit OR expanded
borrowings OR funded by borrowings OR additional credit lines OR incur additional
indebtedness OR pursue lines of credit OR anticipates lines of credit OR through
loan financing OR borrowings bond issue OR increase line of credit OR provided by
credit facilities OR seek borrowing transaction OR raise borrowings OR additional
bank financing OR raises debt capital OR secure line of credit OR borrowing of
capital
For both equity and debt focus, we also consider a precise query identifying firms
that use these phrases directly, and an expanded query allowing these phrases to be
expressed in a twelve word window. For the precise query, we find that 1.0% and
3.4% of sample firms are equity and debt focused, respectively. When we use the
twelve word window, we find that 12.8% and 14.6% of sample firms are equity and
debt focused, respectively.
8
We have experimented with 6- and 8-word windows and they yield similar qualitative conclusions.
13
Finally, we also identify which firms are focused on issuing private placements of
equity. We require that one word from each of the following three lists be present
within a 12 word window in CAP+LIQ:
Private Placement List 1: private
Private Placement List 2: placement OR placements OR sale OR sales OR
offering OR offerings OR infusion OR infusions OR issued OR issuance OR financing
OR financings OR funding
Private Placement List 3: equity OR stock
We find that 9.4% of the firms in our sample load on this query.
D
Degrees of Financial Constraints and Scores
The methodology in the above section identifies a set of firms that directly indicate
constrainedness, or intent to issue debt or equity. However, we are concerned about
two potential sources of error in measuring the degree of constrainedness. First, some
firms may indicate constrainedness in their overall discussions, but they may not
conclude their discussion with a direct statement regarding investment curtailment.
Second, some firms may be partially constrained, and this would be overlooked using
all-or-nothing direct queries. We thus consider the methodology used in Hanley and
Hoberg (2010) to tap the CAP+LIQ vocabulary more fully, and to generate a more
informative degree of constrainedness metric.
We thus score each CAP+LIQ section based on how similar its vocabulary is to
those firms in a given training set of interest, while controlling for the presence of
standard (boilerplate) text. This method generates a continuous score for each firm,
and firms with higher scores are more constrained. Firms with lower scores are either
unconstrained or only partially constrained.
Where N denotes the number of unique words in the entire corpus, we define an
14
N -vector for each firm i in each year t as wordi,t . This vector is populated with the
number of times each word corresponding to each of the N elements is used in firm
i’s CAP+LIQ subsection in year t. We next define the normalized vector for each
firm year normi,t as being equal to wordi,t divided by the sum of its components.
This is the vector we seek to “explain” using various text factors related to boiler
plate content, delay investment constraint content, equity focus content, debt focus
content, and private placement focus content.
We define standard (boilerplate) content in year t stant as the average normi,t
over all firms in the corpus in the given year. We define the delay investment text
vector in year t using a two-step procedure. First we compute the average word usage
vector using all words in all paragraphs for paragraphs that are members of the “12word delay investment training set” discussed in the previous section. Second, we
regress this vector on the standard content vector stant and compute the residual.
This residual vector, which we label delayt approximates the non-standard (or nonboilerplate) content used by firms that indicate intent to possibly delay investment.
The main idea is that a firm that has content that is similar to this average training
set vocabulary is more likely to be more constrained.
We compute equity focus content using a similar procedure. However, we first
note that our objective is not just to identify firms that are focused on potentially
issuing equity, but more precisely those that are both constrained and focused on
issuing equity. This allows us to explore whether there is a difference between firms
that might be equity constrained or debt constrained. Our first step is to compute
the average word usage vector using all words in all paragraphs for firms that are
members of both the “12-word delay investment” training set, and the “12-word
equity focus” training set, but not members of the “12-word debt focus” training
set (all training sets are discussed in the previous section). Second, we regress this
average word usage vector on both the standard content vector stant and the delay
investment vector delayt and take the residual. This residual vector, which we label
equityf ocust approximates the marginal non-boilerplate content in these paragraphs
that is unique to firms that both indicate the need to delay investment, and the
potential desire to issue equity (but not the desire to issue debt). Any firm that
15
has content that is highly similar to this content is likely constrained, and it is also
focused on issuing equity rather than debt to meet its liquidity demands. We define
our debt focus constraint variable debtf ocust in a fully symmetric fashion.
With this set of word usage vectors, all of length N , we are able to decompose a
given firm’s CAP+LIQ section into components that load on each vocabulary factor,
and we run the following decomposition regression for each firm in each year:
normi,t = cstan,i,t stant + cdelay,i,t delayt + cequity,i,t equityf ocust +
(1)
cdebt,i,t debtf ocust + i,t
The coefficients from this regression are the “degree of constraint” scores for firm i
in year t. We therefore define our constraint variables as follows:
Delay Investment Score = 1 + cdelay,i,t ,
Equity Focus Delay Investment Score = (1 + cdelay,i,t ) · (1 + cequity,i,t ),
Debt Focus Delay Investment Score = (1 + cdelay,i,t ) · (1 + cdebt,i,t ).
These variables respectively measure the degree to which a firm faces binding financial constraints broadly, in the equity market, or in the debt market, respectively.
The use of the product functional form in the latter two expressions is due to our
desire to identify the existence of delay investment constraints and to quantify the
intensity of focus on the equity or the debt markets as a source of marginal financing.
The raw coefficients have coefficients that are never less than minus one, and this
form ensures that higher values equate to higher measures of constraints.
Figure 3 displays the empirical distribution of our text-based constraint scores.
The figure shows that these variables generally have a bell shaped distribution without extreme outliers. The distributions also have two additional features. First, all
three variables have a spike above the bell curve at zero. This occurs because roughly
4% of all firms that have a CAP+LIQ section that is very small. In these cases, the
loading on the constrained training set can be zero if none of the words in the small
CAP+LIQ section coincide with those in the constrained training sets. Due to the
existence of this small spike, we examine robustness to including a dummy variable
16
for firms with scores of exactly zero, and our inferences are unchanged. We also
consider a test in which we reclassify firms that have a very small CAP+LIQ section
as not having a CAP+LIQ section for the purposes of our study (inferences are also
unchanged). Henceforth we use the constraint score variables described above for
parsimony. The second feature of the distributions is that all three are right skew.
This is primarily due to members of each training set receiving higher scores than
firms that are not in the training set. We view this behavior as appropriate and
consistent with scoring direct statements higher.
We also compute a “Private Placement Focused Score”, where the objective is to
identify the focus on selling equity to private investors, holding fixed the degree of
unconditional delay constraints, and holding fixed the focus on selling equity or debt
unconditionally. Our first step is to compute the average word usage vector using all
words from all firms that are members of both the “12-word delay investment” training set, and the “12-word private placement focus” training set. Second, we regress
this word usage vector on the standard content vector stant , the delay investment
vector delayt , the equity focus vector equityf ocust , the debt focus vector debtf ocust ,
and take the residual. This residual vector, which we label privatef ocust approximates the non-boilerplate content that is unique to firms that are both indicating the
need to delay investment, while also mentioning the potential desire to issue a private
placement, holding fixed the desire to issue equity or debt. This private placement
variable is then added to the model in equation 1, and the resulting coefficient on
privatef ocust (cpriv,i,t ) is the given firm’s degree of focus on private placements, and
we compute the following additional constraint variable:
Debt Focus Delay Investment Score = (1 + cdelay,i,t ) · (1 + cequity,i,t ) · (1 + cpriv,i,t ).
We also compute a “Covenants Violation” score variable using a similar two-step
procedure. First we identify a training set based on firms that mention the words
covenant and violation within a 12-word window. We then compute the average
word usage of paragraphs that match on this query in each year and thus define
the N -vector covenantt . Next we run the following decomposition regression (which
17
controls for standard content as before):
normi,t = cstan,i,t stant + ccovenant,i,t covenantt + i,t
(2)
We define “Covenant Violation Score” as the coefficient ccovenant,i,t , and this variable
measures how close firms are to satisfying the conditions generally necessary to trigger
covenant violations.
E
Investment Style Measures
A key foundation of our framework is that a firm must have viable investments before
it can possibly report potentially having to delay them. As a result, it is natural
to explore the link between financial constraints and investment activity. We thus
construct three measures of investment style based on vocabularies associated with
three classes of investment. In later analysis, these variables will enable us to examine
financial constraints holding fixed each firm’s investment opportunities.
We separately consider the following text queries:
R&D Focused List: research OR development OR new product OR new products.
CAPX Focused List: construction OR expenditure OR renovation OR capital
improvement OR capital spending OR capital project OR opening restaurants.
Unlike our earlier analyses, which were limited to CAP+LIQ, discussion of investment opportunities is generally in the MD&A but generally not in the CAP+LIQ
subsection. Hence we use the entire MD&A to identify which firms are most focused
on various investments, and we do not rely on the CAP+LIQ section alone as we did
in earlier variable constructions.
We compute the fraction of paragraphs in each firm’s MD&A that score on each
query above. This generates two measures of investment focus that are a function of
the entire MD&A: MD&A R&D Focused Score, and MD&A CAPX Focused Score.
Each variable is bounded in the interval [0,1].
18
F
Measuring Shocks
We also use the text in the Management’s Discussion and Analysis (MD&A) to
identify firms that might be facing shocks. For this analysis, we query the entire
MD&A, as we find that firms mention potential shocks in various locations of this
section. We consider competitive, demand, and cost shocks. In each case, we identify
shocks by requiring that two words from two separate lists exist side by side in the
text of a given firms’ MD&A in a given year. These variables are relevant to exploring
which firms move from an unconstrained to a constrained status in time series.
Our first variable is the “Higher Competition Dummy”. This variable is set to one
when a firm-year MD&A has the word increasing or one of its synonyms (increasing
OR increased OR higher OR greater OR intensified OR intensification) appearing
beside the word competition in its MD&A in the given year.
We next define the “Increasing Cost Dummy”. This variable is set to one when
a firm-year MD&A has the word increasing or one of its synonyms (increasing OR
increased OR higher OR greater OR intensified OR intensification) appearing beside
the word cost in its MD&A in the given year.
We next define the “Lower Demand Dummy”. This variable is set to one when
a firm-year MD&A has the word lower or one of its synonyms (decreasing OR decreased OR lower OR reduced OR reduction) appearing beside the word demand in
its MD&A in the given year.
Beyond using text data, we also explore the impact of the 2008 the financial crisis
on constrained versus unconstrained firms. We also explore the role of forced mutual
fund selling as in Edmans, Goldstein and Jiang (2011).9 This measure is based on
mutual fund redemptions, and can be seen as an exogenous shock to equity market
liquidity that is not attributable to firm policies.
9
We thank Alex Edmans for providing this data on his website. Phillips and Zhadanov (2011)
also use this variable as an instrument for a firm’s equity valuation that is unrelated to fundamentals.
19
G
Compustat Variables
The four key policy variables we examine are R&D/sales, CAPX/sales, equity issuance/assets, and debt issuance/assets. All four are constructed directly from
Compustat data. Regarding equity and debt issuance, we only consider newly issued
equity (SSTK) and newly issued long term debt (Compustat DLTIS), both scaled
by assets. All four policy variables are winsorized at the 1/99% level to control for
outliers. In all cases, we winsorize financial variables that are defined as ratios at the
1/99% level in each year to reduce the impact of outliers. We also include controls
for firm age and size (log assets) in many specifications.
III
Financial Constraints
In this section, we first examine the properties of our constraint variables in time
series and cross section. We then compare them to measures used in the literature.
We also note that the online appendix to this paper contains three detailed appendices outlining (1) the heterogeneity in subsection names regarding subsections that
are analogous to Liquidity and Capital Resource, (2) ten sample firms from each of
our sample years that scored highly on our financial constraint measures along with
their Fama-French-48 industries, and (3) sample paragraphs from the CAP+LIQ
section of the firms with high constraint scores. Based on these online appendices,
we note that the firms that are most constrained are often, but not always in the
drug and technology industries. An interesting exception is that home builder Toll
Brothers was financially constrained in 2005 and 2006 exactly when valuations were
high, but not in later years when, arguably, this industry was more in distress than
it was financially constrained. Regarding the sample paragraphs, the examples show
that the text queries are highly reliable in terms of identifying firms that acknowledge
the possibility of delaying their investments in CAP+LIQ. However, the results also
show that the queries are not fully perfect, as occasional “difficult to parse” 10-Ks
such as Gliatech’s 1997 10-K can add some noise to our calculations. However, because we focus on average word distributions across thousands of firms to form the
vocabulary of the training set, we do not believe this low level of noise is problematic.
20
A
Summary Statistics and Correlations
Table I displays summary statistics for our 1997 to 2009 panel of 52,438 firm-year
observations having machine readable MD&A sections in their 10-K. Panel A shows
that 85.1% of these firms also have a machine readable CAP+LIQ subsection. We
also find that 10.5% of MD&A paragraphs mention R&D, but only 1.9% mention
capital expenditures. Because we require a CAP+LIQ section to compute our key
variables, we conduct our empirical analysis in two stages: (1) we examine which
firms have a distinct CAP+LIQ section, and (2) among those that do, we explore
our key hypotheses relating to financial constraints. In later sections, our results will
suggest that the firms that do not include a CAP+LIQ section are primarily unconstrained firms. As a result, the fact that we focus on firms that do have CAP+LIQ
is unlikely to be problematic because the primary challenge in the literature is to
identify constrained firms rather than unconstrained firms.
[Insert Table I Here]
Panel B reports summary statistics regarding the membership of our precise
training sets, and Panel C reports summary statistics regarding the members of
our 12-word expanded training sets. Not surprisingly, expanding the text queries to
12 words greatly improves coverage. For example, coverage increase from 1.7% to
5.7% for delay investment constraints. The larger training sets offer improved power.
Panel D reports summary statistics for our score-based constraint variables, and
Panel E reports summary statistics related to covenant violation score and the size
of the CAP+LIQ section. Because the construction of these variables controls for
boiler plate content, intuitively, these scores are relative rankings and have means
near zero. These variables measure the degree to which the given firm’s CAP+LIQ
section is similar to that of firms in each respective training set. This approach
provides a measure of the degree to which a firm is constrained.
Panel F reports the summary statistics for our text-based shocks, which have
averages of less than 15%. Panel G reports summary statistics for the four corporate
finance policies we examine. These results suggest that firms in our sample spend an
21
average of 19.1% of their sales on R&D and 11.7% of their sales on CAPX. Equity
and debt issuance average 8.1% and 10.9% of assets, respectively.
[Insert Table II Here]
Table II reports Pearson correlation coefficients between our financial constraint
variables and other constraint variables including the KZ index and the WW index.
The three text constraint variables are strongly correlated. This is by construction
as all three have a common component requiring the firm has text that is similar
to firms reporting that they may have to delay their investments in their discussion
of financial liquidity. For example, our equity market constraint variable is 65.8%
correlated with our debt market constraint variable.
In comparing our constraint variables to existing measures, we find that our delay
investment constraint is positively correlated with both the KZ index (1.7%) and the
WW index (9.5%). However, readers may find it surprising that these correlations
are low. The finding indicates that these variables are quite distinct. The KZ index
and the WW index primarily load on investment opportunities, firm size, and age.
Because firms must have viable investment opportunities before they might be
forced to delay them, our three text-based constraint variables also correlate positively with investment opportunities. Firms that are delay investment constrained
tend to be more focused on R&D than CAPX, likely because the former investment
type is more prone to informational asymmetry. Equity focused constrained firms
load even more on R&D relative to CAPX, whereas debt focused firms load more on
CAPX. The results for debt focused constrained firms are consistent with a role for
asset tangibility, collateral, and lower uncertainty.
The table also shows that the KZ index and the WW index correlate in an opposite way with our equity and debt market constraint variables. The WW index
correlates more with equity-focused constrained firms, and the KZ index correlates
more with debt-focused constrained firms. Also relevant, our debt market constraint
variable correlates positively with firms reporting covenant violations (32.7%). This
suggests that these firms (not surprisingly) struggle to meet some contractual obligations. However, our other two constraint variables (delay score and equity focused
22
delay) are somewhat negatively correlated with covenant violations text, indicating that they contain information potentially linked to asymmetric information or
market frictions, and not distress.
Although not reported to conserve space, we note that our financial constraint
variables are somewhat persistent, but not overwhelmingly so. Our three key textual constraint variables have autocorrelation coefficients that range from 0.4 to 0.6.
Hence, a firm that is constrained in a given year is quite likely to be constrained next
year, but unlikely to still be constrained two to three years later.
[Insert Figure 4 Here]
Figure 4 displays the time series average for our delay investment constraint. We
display results for all firms (upper figure), and for firms with December fiscal year
endings (lower figure). The figures show a jump in reported constraints in 2008, the
year of the financial crisis. Despite the small number of annual observations, the
jump is large and it is statistically significant at the 1% level. Also noteworthy, we
observe a large decline in reported constraints in 2003. This decline is likely due to
either (A) the SEC’s release of the new MD&A guidance in late 2003, or (B) the
fact that economic conditions were improving rapidly from 2002 to 2003 as financial
markets rebounded following the technology collapse of 2000 and the terrorist attacks
in September 2001. In all, the persistence levels and the aggregate time series results
illustrate that our constraint measures are dynamically managed by firms as their
liquidity changes, and they are not a form of boiler plate content that is held fixed
in filings from year to year.
B
Firm Characteristics and Constraints
We begin our analysis by assessing which firms have a machine readable CAP+LIQ
subsection in their MD&A in a given year. Our summary statistics indicate that
85.1% of firms have this subsection, and we conjecture that only unconstrained firms
would omit this content. This conjecture is based on the fact that disclosure of
liquidity is required by law, and only unconstrained firms might have sufficiently
little to disclose to warrant not doing so in a separate subsection.
23
Table III formally tests this conjecture using a logistic regression in which the
dependent variable is a dummy equal to one when the firm has a CAP+LIQ subsection. We include industry and year fixed effects (Panel A), or firm and year fixed
effects (Panel B), and all standard errors are adjusted for clustering at the firm level.
[Insert Table III Here]
The table confirms that firms disclosing a CAP+LIQ subsection indeed are more
likely to be constrained. Consistent with HP, these firms have fewer assets in place
and are younger. Firms having a CAP+LIQ section are also less likely to pay dividends, and tend to be more R&D oriented. The focus on R&D rather than CAPX
by these firms, supports a possible link between liquidity challenges and asymmetric
information, which we test later. Because we are primarily interested in examining
constrained firms, the remainder of this paper focuses only on the sample of firms
that has a machine readable CAP+LIQ subsection.
In Table IV, we consider panel data regressions in which we explore the association between various firm and industry level characteristics, and our four constraint variables (delay investment constrained, equity-market constrained, debtmarket constrained, and private placement constrained) plus controls for the size
of the CAP+LIQ section, firm size, and firm age. Although we report results for
all four constraint variables in columnar form on the same table, in order to characterize each variable independently, we run regressions separately for each constraint
variable. In all regressions, we include the same controls, and we report the control variable coefficients for the first regression that includes the delay investment
constraint as these coefficients do not change materially when different constraint
variables are included. We use this compact format to conserve space.
[Insert Table IV Here]
The results of Table IV are consistent with our delay investment constraint variable indeed being a valid measure of financial constraints. We highlight three major
pieces of evidence, but also note other nuances. First, the table shows that firms
with high delay investment scores operate in industries with high Q. This is consis24
tent with these firms having good investment opportunities, a necessary requirement
for binding financial constraints (a firm must have a promising investment before it
can delay it). This result also confirms that financial constraints and distress are
distinct concepts, as distressed firms intuitively have low Q. Second, we find that
the delay investment score is negatively correlated with profitability, consistent with
their having little in the way of free cashflow.
The third major finding regarding the delay investment variable is that it is
positively related to both capital expenditures and R&D at the industry level. This
again supports the existence of good investment projects as a necessary condition
for financial constraints to be binding. More striking, the delay investment variable
is negatively related to own-firm R&D. This supports another direct prediction of
financial constraints: the firm has low investment relative to what would be optimal
if it had perfect liquidity (so the constrained firm in an industry invests less than its
industry peers). Finally, we note that these findings are statistically significant. In
all cases, standard errors are adjusted for clustering by firm.
Table IV also shows that equity market and debt market constraints are different.
For example, some coefficients (such as those for cash and leverage) have opposite
signs for these two variables. The overall coefficient patterns also show that equity
market constrained firms are similar but more extreme variants of delay investment
constrained firms. These firms have high Q and little in the way of current profits,
and hence they are more growth oriented. Regarding investment policy, they also
reside in product markets with high levels of R&D, although their own-firm R&D is
lower than the industry average. These firms also reside in product markets where
firms tend to hold high levels of cash, consistent with precautionary savings relating
to financial constraints. The link to R&D and high growth is consistent with a role
for asymmetric information in financial constraints for equity market constrained
firms, a link we find more evidence for later.
In contrast, firms that are debt market constrained are in product markets with
average or slightly below average Q, and these firms have a slightly lower Q relative
to their industry peers. These firms also hold less cash and have higher leverage.
In all, these firms, unlike equity market constrained firms, have some resemblance
25
to distressed firms given their higher leverage. However, the fact that they are
in industries with high levels of investment (both CAPX and R&D) suggests that
debt constrained firms differ from distressed firms due to their better than average investment opportunities (our definition of delay constraints ensures these firms
should indeed have some investment opportunities as is required by the definition of
a binding wedge resulting in under investment). This provides some initial evidence
suggesting that debt constrained firms potentially suffer from debt overhang, and
find it difficult to fund their investment opportunities due to the excess liabilities
that any new provider of capital would have to absorb.
The results for the private placement score are similar overall to those for the
equity focused score. Intuitively, equity private placement focused constrained firms
are essentially more extreme versions of equity focused constrained firms. In all,
our results support the conclusion that our delay investment variable measures the
degree of constraints, and our equity and debt constraint variables affirm that both
forms of capital play unique roles in financial constraints.
IV
Investment Policy and Financial Constraints
In this section, we examine whether firms with higher levels of financial constraints
react to negative shocks differently than do unconstrained firms. We consider two
types of investment expenditures – CAPX and R&D expenditures – and two forms of
external financing — equity and debt financing. While historically the literature on
constraints has focused on examining financing of CAPX expenditures, the financing
of R&D is more likely to be subject to the constraints, both because R&D is less
tangible and less certain, and because R&D focused firms are often small and young
and lack a well-established track record.10
We consider two shock variables as independent variables in our initial policy
regressions. The first is the aggregate sum of the three text-based shocks (high
competition, low demand, and high cost). Our second is the 2008-2009 financial
10
Brown, Fazzari and Petersen (2009) discuss the factors that make financing of R&D expenditures more susceptible to constraints.
26
crisis dummy. Our shock framework parallels Opler and Titman (1994), who examine
the effect of a macro-economic shock on distressed firms. Later in this section, we
also consider an exogenous equity market liquidity shock, forced mutual fund selling
in response to redemptions, as in Edmans, Goldstein and Jiang (2011). For ease of
comparison, we scale all shock variables to be positive when the shock arrives (mutual
fund selling is scaled to be negative in Edmans, Goldstein and Jiang (2011)).
A central hypothesis in the literature is that constrained firms should more aggressively scale back issuance and investment activities following negative shocks, as
their constraints likely become more binding in times of hardship. Table V displays
the economic magnitudes regarding the degree of policy curtailment experienced by
firms in various groups when they experience each of the three shocks discussed
above. For the financial crisis, the value after the shock is the firm’s value of the
policy in 2009, and the pre-shock value is the firm’s value of the given policy in 2007.
For the text shock, the value for shocked firms is the average policy for firms experiencing at least one of the three text shocks (low demand, high competition, or high
cost) and the value for non-shocked firms is the average policy for firms experiencing
none of the three text shocks. For forced mutual fund selling, the value for shocked
firms is the average policy for firms with above median forced selling and the value
for non-shocked firms is the average policy for firms that have below median mutual
fund selling. Because each of the policy variables is scaled by sales or assets, the
reported mean changes are in fractional units of sales or assets, respectively.
[Insert Table V Here]
The table shows that constrained and unconstrained firms react differentially to
shocks, and that these differences are economically large. For example, the highest
tercile delay constrained firms curtailed R&D by 14.8% of sales in the financial
crisis of 2008-2009, whereas the lowest tercile firms reduced R&D by only 3.4%
of sales. This inter-tercile range of 11.4% is an economically large 14.6% of one
standard deviation (the cross sectional standard deviation of R&D/sales is 78%).11 .
11
This estimate is conservative because economic magnitudes are even larger if we consider the
subsample of firms with strictly positive R&D. This result is also not influenced by outliers as we
winsorize R&D/sales at the 99% level.
27
On the issuance side, high delay constrained firms curtailed equity issuance by 5.4% of
assets during the financial crisis, and low delay constrained firms by just 1.7%. This
inter-tercile difference of 3.7% is an economically meaningful 18.4% of one standard
deviation. For equity focus delay, this inter-tercile range is a larger 4.2% of assets,
and is 5.9% for private placement focused firms. The difference in equity issuance
response to forced mutual fund selling is equally large.
A
Panel Regressions
We now examine the link between constraints and financial policies using a panel
data approach. We first consider firm fixed effect regressions. We then consider the
Arrellano-Bond Differences GMM estimator that allows for consistent estimation of
models with lagged dependent variables. We consider several tests from the existing
literature to assess the validity of the Arrellano-Bond method in our setting.
In Table VI, we examine four firm policies: R&D/sales, CAPX/sales, equity
issuance/assets and debt issuance/assets in year t as a function of year t − 1 characteristics, year t − 1 constraint measures, and year t − 1 shocks. For each policy
we estimate a simple model that predicts the firm’s policy as a function of the shock
that the firm experiences and several control variables. Control variables include log
firm age, log Assets, Tobin’s Q and log document size. The independent variables of
interest are our text-based shock variables (high competition, low demand, technological change, and high cost), and the 2008-2009 financial crisis. Our tests focus on
the cross terms between our constraint variables and the shock variables. For both
shocks, we expect to observe a negative and significant cross term coefficient indicating that a firm that scores high on our constraint indices curtails a given investment
or issuance policy more than a less constrained firm.12 The equations are estimated
using OLS firm fixed effect regressions with year fixed effects. Standard errors are
adjusted for clustering at the firm level.
[Insert Table VI Here]
12
We do not have direct predictions regarding the coefficients for the observed levels of the shock
or constraint variables since deriving such a relation requires further assumptions about equilibrium
outcomes.
28
Panel A of Table VI shows that firms with high delay investment scores significantly scale back both R&D and CAPX investments following negative shocks.
These coefficients are significant at the 5% or 1% level. Regarding issuance policy,
we also find that delay constrained firms also curtail equity issuance in Panel A.
Overall, our results support the conclusion that our delay constraint variable is associated with treated firms reacting more negatively to negative shocks, consistent
with a constraint interpretation. We view this evidence as suggestive, and we defer
a discussion of causality until the next section.
Additional results in Panel A are also in line with expectations. Firms with higher
Tobin’s Q invest more in both R&D and CAPX. They also issue more stock. Finally,
the delay constraint variable level coefficient is positively related to investment policy
and issuance policy. This finding is consistent with Krasker (1986) and the conclusion
that firms seeking very aggressive investment strategies might not be able to issue
securities at a rate that would facilitate full funding of all their opportunities.
Panels B to E of Table VI repeat this test for four additional constraint variables:
equity focused delay and debt focused delay, the KZ index, and the WW index.
The panel only shows the shock cross terms (the other variables are included in the
regressions but not displayed to conserve space).
Panel B thus shows that firms with high equity focused delay scores curtail investment and equity issuance as do baseline delay constrained firms in Panel A. Hence,
the average firm reporting constraints is likely to be an opaque high growth firm
attempting to issue equity to fund growth opportunities.
Panel C shows that debt focused delay firms are different. These firms curtail
R&D investment and and CAPX investment following shocks, but do so less aggressively relative to standard delay constrained firms. These firms do not curtail
issuance in a statistically significant way in the financial crisis, but in later tests that
account for causality and persistence, we do find that they curtail debt issuance in
a significant way. In all, we find broadly that debt market constrained firms are less
affected by their constraints relative to equity constrained firms.
The results in Panel D do not support a constraint based interpretation of the
29
KZ Index. Firms with a high KZ index do not appear to change any of their policies
more or less than control firms during the financial crisis or following text shocks. For
the WW Index, Panel E provides mixed results. There is no support for a constraint
based interpretation for the text shock, but the results for the financial crisis are
supportive of the WW index containing some information that is consistent with
constraints. Firms with a high WW index curtail R&D, CAPX, and equity issuance
during the crisis. However, these results are not as strong as those are for the textbased constraint measures, and results presented in the next section suggest that the
WW index is less robust to controls for endogeneity.
We also perform unreported placebo tests as in Duchin, Ozbas, and Sensoy (2010).
In particular, we run analogous specifications to those in this section, except that
we replace the financial crisis dummy (indicating the years 2008 and 2009) with a
placebo crisis (2006 and 2007) from a nearby time interval. We find in this test that
all of our negative and significant cross term results for all policy regressions entirely
vanish. Moreover, all of the financial crisis cross terms are insignificant, with the
exception of equity issuance, which is 10% level positive and significant in some tests
(note that in the real financial crisis that this cross term is negative as predicted. This
may be consistent with constrained firms taking advantage of the strong economy in
2006 and 2007, and viewing this as an opportunity to issue high levels of equity.
B
Alternative Econometric Specification
The use of fixed effect panel regressions is standard in many studies. However, it
is likely that the process driving corporate investment policies has both a firm fixed
effect and an autoregressive term. In this case, such panel regressions will be biased.
Disentangling the impact of firm fixed effects and the lagged dependent variable is
especially critical in our setting because our panel is short in time duration at 13
years even though we have a large number of firms in cross section (See Roodman
(2009)). To address this issue we re-estimate our model using the Arrellano-Bond
estimator, which eliminates this bias, while also addressing potential endogeneity
concerns by using lagged variables as instruments.
30
The Arrellano-Bond estimator allows for added flexibility to specify which variables are endogenous, pre-determined, and truly exogenous. The quality of these
designations can then be examined using standard tests. These tests allow us to
evaluate whether the relationships we find can be linked to a causal interpretation
in the sense that the variables of interest are independent of the error term.
To take full advantage of the Arrellano-Bond estimators, we follow Roodman’s
(2009) recipe. We first designate three variables as exogenous: firm age, firm size,
and the 2008-2009 crisis dummy. Second, we designate financial constraint variables
as potentially endogenous, and we use an additional lag for these variables. We
classify all other variables such as Tobin’s Q as being predetermined by past data,
but not fully exogenous. Our specification is conservative because we use differences
GMM instead of system GMM, and we avoid the orthogonal deviations transform.
Both choices avoid the need for additional assumptions. We also include year fixed
effects and we adjust standard errors for clustering by firm.
We assess the stability of our regressions in five ways. First, we test if the
idiosyncratic disturbance is autocorrelated at the second lag following Arrellano and
Bond (1991). If second order autocorrelation is significant, the instruments may
not be valid. Second, we examine whether the coefficient for the lagged dependent
variable falls within the two reasonable OLS bounds indicated in Bond (2002) and
illustrated by example in Bazzi and Clemens (2009). Third, we examine the Hansen
J-statistic of overidentifying restrictions. A significant J-statistic indicates improper
instrumentation for endogeneity. Fourth, we consider the Hansen Differences statistic
based on our two shock cross terms we draw inferences from. A significant differences
statistic indicates improper instrumentation for endogeneity directly relating to these
key variables. We report the results of the four tests discussed here in a row titled
“regression diagnostics”, where we indicate each test is passed using “a”, “b”, “c”,
and “d”, respectively. Finally, we note that the number of instruments used by the
Arrellano-Bond model outnumber groups by a factor of roughly five to ten across
our specifications, far exceeding the minimum 1:1 ratio below which Roodman (2009)
indicates that problems are most likely. Hence, it is unlikely that our results suffer
from problems relating to too many instruments.
31
Table VII displays the results using the Arrellano and Bond (1991) model. The
lagged dependent variable in Panel A is significant in each case indicating that investment policies do contain a relevant autoregressive component, although this component is smaller for equity issues which are known to be lumpier. With one exception,
the signs of the cross-term coefficients are negative and thus consistent with those in
in Table VI. As expected, when instrumental variables are used, significance levels
are somewhat lower, although our results are highly robust.
[Insert Table VII Here]
For investment policies, delay constrained firms are significantly more likely to
curtail R&D and CAPX policies following shocks. These results are significant at
the 1% or 5% level, and the critical R&D specification passes all four regression
diagnostic tests and thus is consistent with a causal conclusion. Panel B shows that
firms that are equity focused constrained curtail both forms of investment and equity
issuance in the financial crisis. Analogously, Panel C shows that firms that are debt
focus constrained curtail both forms of investment and debt issuance in the financial
crisis. These results especially support the expected interpretations of each variable.
We find notably weaker and mixed results for both the KZ index or the WW index
for investment and issuance policies in Panels D and E respectively, suggesting that
a similar conclusion is not equally warranted for these variables.
The Arrellano-Bond causality diagnostics are more conclusive for investment policies than for issuance policies. The Hansen J test (denoted by the letter “c” in the
regression diagnostics) uniformly suggests that the instruments are not sufficiently
strong to draw a causal conclusion for both the equity and debt issuance regressions,
but are sufficiently strong for the R&D regression. However, the models do uniformly
pass the critical Hansen differences test (denoted by the letter “d”), which examines
the issue of exogeneity for the two shock cross terms alone. Overall, the results are
least conclusive for the debt issuance model and most conclusive for the R&D model.
32
C
Subsample Results
We next explore key subsamples based on above or below median size, age, R&D
focus, and CAPX focus. Firms are sorted into these subsamples based on their value
of the given characteristic relative to the median in the year when the given firm
enters the sample. This ensures time-series continuity for each firm, an important
consideration given the nature of the Arrellano-Bond model and its use of lags.
HP find that financial constraints are most relevant for smaller and younger firms.
Panels A and B of Table VIII display results for small and large firm subsamples,
and Panels C and D display results for young and old firms. We find that results are
significant for both small and large firms, but are stronger for young firms relative
to older firms. These results suggest that among the two HP variables, age is likely
more important than size in determining the extent to which constraints bind.
Our results extend those of HP along three dimensions. First, we find that only
some small and young firms are constrained, and many are likely not constrained.
We conclude that financial constraints are best measured using textual measures and
that the sample of small and young firms is instead only useful in finding a sample
where variation across constrained and unconstrained firms is somewhat stronger.
Second, we find that constraints do have significant impact even on larger and older
firms, as both for example significantly curtail CAPX and R&D policy relative to
peers. Third, we show that equity and debt focus constraints (not considered in
any prior work) matter in unique ways, and are likely driven by different economic
primitives such as informational asymmetry and debt overhang.
[Insert Table VIII Here]
Panels E and F of Table VIII display analogous results for low and high R&D
Focused firms. Results for these subsamples are especially striking. We find only
one significant coefficient in Panel E for low R&D focused firms, and we find strong
results (five significant coefficients) in Panel F for high R&D firms. Panels G and H
of Table VIII display analogous results for low and high CAPX Focused firms, and
results not surprisingly are strongest for low CAPX firms. Overall we conclude that
33
investment type is important in determining when financial constraints are binding.
Investors are less willing to provide liquidity when investments are informationally
sensitive (R&D), and more willing when investments are tangible (CAPX).
D
Equity and Debt Focused Constraints
In this section, we further consider the hypothesis discussed earlier that equity and
debt focused constrained firms are fundamentally different, and respond to shocks
in different ways. We further propose that equity focused constrained firms likely
face the most severe constraints when they invest in R&D projects and when they
attempt to issue equity, whereas debt-focused constrained firms face more severe
constraints when they invest in CAPX and when they attempt to issue debt.
[Insert Table IX Here]
Table IX considers the equity and debt focused constraint variables for the high
R&D focused subsample (Panels A and B) and the high CAPX focused subsample
(Panels C and D). The results in Panel A show that firms with high equity focused
delay constraints curtail both equity issuance and R&D spending significantly more
than other firms during the crisis years and following text shocks. These firms also
significantly curtail CAPX investments. We observe similar but weaker results for
debt focused constrained firms in Panel B. As before, results are also weaker for
CAPX based subsamples in Panels C and D relative to R&D based subsamples in
Panels A and B.
To further examine where debt focused constrained firms experience the most
severe constraints, we consider the young firm sample in Panel E, and the young
and high CAPX firm sample in Panel F. Because younger firms typically have more
growth options than older firms, we consider these samples due to the possibility that
debt focus constraints relate to debt overhang, as debt overhang requires the potential
for investment and growth in order for it to imply binding financial constraints. We
find in both panels that debt focused constrained firms curtail both CAPX and debt
issuance following shocks in these samples in Panels E and F. We conclude that debt
focus constrained firms are indeed different from equity focused constrained firms,
34
and are most binding among younger firms with good investment options relating
to CAPX funded by debt issuance. These results are further consistent with the
possibility of debt overhang relating to financial constraints among debt focused
constrained firms.
E
Private Placements
In this section, we explore the role of private placement focus constraints. This
test is in part motivated by the finding in Gomes and Phillips (2011) that private
placements are more likely to be used when asymmetric information is high.
[Insert Table X Here]
Table X displays the results for our private placement focused constraint variable
for the overall sample (Panel A) and for various subsamples in later panels. The
findings are similar to our equity focused constraint variable and are also consistent
with a role for asymmetric information. Firms that are private placement focused
are significantly more likely to curtail R&D and equity issuance following negative
shocks (R&D and equity are often viewed as more informationally sensitive than
CAPX and debt). As was the case for our earlier analysis, these findings are also
especially strong for the high R&D focused subsample, for younger firms, and for
smaller firms.
F
Shocks to the market for the firm’s equity
In this section, we add forced mutual fund selling, and corresponding cross terms
with our constraint variables, to the specifications in Table VII. We hypothesize,
consistent with Edmans, Goldstein and Jiang (2011), that this variable represents
an exogenous shock to a firm’s equity-market liquidity. Unlike the 2008 crisis and the
text shock, which likely have both supply and demand side effects, as in Opler and
Titman (1994), the forced mutual fund shock variable is uniquely a supply side shock.
Our hypothesis is that shocks to this variable should result in greater curtailment of
equity issuance for constrained firms relative to unconstrained firms.
35
[Insert Table XI Here]
Panel A of Table XI shows that the mutual fund selling shock does induce delay
constrained firms to curtail equity issuance policy more than their peers, but this
result is only significant at the 10% level. Panels B and D show that equity focus
constrained firms, and private placement focus constrained firms especially curtail
equity issuance when forced mutual fund selling is high. These results are significant at the 1% level, and illustrate that our findings regarding this equity-based
supply shock are strongest where we expect them to be strongest: for equity market
constrained firms. We only find weak results for debt constrained firms in Panel C.
Because forced mutual fund selling is exogenous and also supply-side based, these
results are particularly supportive of a causal link between financial constraints and
policy curtailment through the channel of equity market liquidity. In particular, we
would expect this supply side shock to matter most for firms that are indeed equity
focus constrained.
These results clarify that our constraint measures relate to financial liquidity.
Going further, three key findings help to rule out a possible concern that our constraint measures are picking up disclosures by managers with low expected value
projects, who may have incentives to blame the markets for their failure to invest.
First, as shown in Table V, firms that report high delay investment and equity delay
constraints have high Tobin’s Q, and thus are likely to have good investment opportunities. Second, in unreported tests, we also find no evidence that high constraints
predict low equity returns, indicating that the high Tobin’s Q of these firms indeed
reflects their fundamentals. Third, the results in this section for the exogenous mutual fund selling shock uniquely imply a liquidity channel for our results. All three
of these results run counter to the predictions of the alternative hypothesis.
V
Origins of Constraints
The literature has proposed many potential origins of financial constraints, including
asymmetric information, moral hazard, cost of contract enforcement, transaction
36
costs, and debt overhang. It is outside the scope of this paper to examine all of these
issues. We instead explore the potential role played by asymmetric information,
which is perhaps the most heavily discussed potential source of constraints.
To examine the role played by asymmetric information, we first scan the text
of all 10-Ks for mentions of proprietary information or trade secrets. This issue is
typically discussed in one of two contexts: a 10-K might mention a risk factor noting
the potential damages that might arise if proprietary information is leaked, or a 10K might mention the existence of employment contracts that note the existence of
proprietary information and that forbid its leakage. Because in both cases, the discussion notes the need to protect this information, we identify firms with proprietary
information risks as firms mentioning one protection word (“protect”, “protection”,
or “safeguard”) and one information phrase (“trade secret” OR “trade secrets” OR
“proprietary information” OR “confidential information”) within a five word window. Our results are not highly sensitive to the width of this window. 28.5% of
the firms in our sample have such text and are thus coded as having high levels of
asymmetric information.
Because asymmetric information is generally taken as a primitive in theoretical
models, we consider regressions in which we examine if constraints in a given year
t are related to whether or not the firm has high levels of asymmetric information
in year t − 1, and we include controls for size, age, and R&D in order to further
illustrate that our results expand our understanding of constraints beyond the work
of Hadlock and Pierce (2010) and Brown, Fazzari and Petersen (2009).
[Insert Table XII Here]
The results of this test are reported in Panel A (with industry and year fixed
effects) and Panel B (with firm and year fixed effects) of Table XII. The table shows
that our delay investment constraint is indeed related to higher levels of asymmetric
information, and also that this result is amplified for equity focused constrained firms
and especially for private placement focused constrained firms. In contrast, we find
no link to asymmetric information for debt focused constrained firms. These findings
are robust in both panels, and the positive links to information for equity and private
37
placement focused constraints are highly significant at the 1% level despite the firm
fixed effects and the fact that standard errors are clustered by firm.
We conclude that asymmetric information is likely a strong driver of financial constraints among firms attempting to issue equity, but not for debt market constrained
firms. This adds further evidence regarding our initial hypothesis and earlier results
suggesting that firms facing constraints in either market are different, as these results
suggest that the constraints themselves likely have different theoretical origins.
We also note that our results are robust to controls for size, age and R&D. In
unreported tests, we also find that the link between constraints and asymmetric
information is especially strong among younger firms, and in a more extreme test,
that our results regarding information and constraints remain significant even in the
subsample of young firms that have zero reported R&D. As our earlier discussion of
Figure 2 suggests, these results are also economically relevant in size. Finally, we
further note that proprietary information concerns even exist in Fama-French-48 industries where R&D is less pervasive: Personal Services, Retail, Transportation, and
Meals. In these industries, more firms report proprietary information than report
non-zero R&D.13 The fact that these purely informational concerns are especially
relevant for constraints among younger firms is likely because it takes time for investors to build trust in opaque firms with higher levels of proprietary information.
We thus conclude that the issue of asymmetric information is more pervasive than
what one might expect by only considering small, young firms conducting R&D.
In a final test, we consider one additional prediction regarding the debt overhang
hypothesis for debt market constrained firms. Our results thus far are consistent
with debt overhang and illustrate that these firms have higher leverage, slightly
below average Tobins Q, and that these firms have viable investment opportunities
they wish to pursue. One final prediction is that such firms should also experience
difficulties repaying their large debt burdens. We thus examine if our constraint
variables are related to ex-post outcomes where firms report covenant violations. We
thus consider regressions in which a dummy indicating whether the firm mentions
13
Thus, for example, we find that in Personal Services almost twice as many young firms declare
risks pertaining to proprietary information as there are firms reporting engaging in R&D. This ratio
is higher still for Retail, Transportation, and Meals.
38
the words “covenant” and “violation” in a twelve word window in the capitalization
and liquidity section of their 10-K is the dependent variable. This variable is equal
to one for 1.5% of our firm year observations. These regressions are predictive as all
RHS variables are measured in the year prior to the dependent variable.
The results of this final test are reported in Panels C (industry and year fixed
effects) and D (firm and year fixed effects). The table shows that debt focus constrained firms are more likely than average to experience covenant violations. This
finding is significant at the 5% level or the 1% level.14 This result is not only robust to industry or firm fixed effects, it is also robust if an additional control for
Tobin’s Q is added, and hence it is robust to controls for investment opportunities.
In all, we note four empirical findings that lead us to conclude that debt market
constraints are most likely explained by issues relating to debt overhang and not
informational asymmetries: (1) Panels A and B show no relationship between debt
market constraints and our proprietary information variable, (2) Panels C and D
show that debt market constraints are related to ex-post covenant violations, (3) our
constraint variables are scored from a training set of firms which declare that they
have an opportunity to fund, (4) Table IV confirms that these firms are in industries with above average investment rates and that these firms have higher leverage
than average and especially relative to their industry peers.15 These findings are also
consistent with Moyen (2006), who also suggests that debt overhang issues are important. We do not find reliable links to covenant violations for the other constraint
variables.
In all, we conclude that equity market constraints and debt market constraints are
quite different, not only in the impact that the constraints have on firm investment
14
We report results using a linear probability model in order to control for the large number of
fixed effects and to avoid potential bias this may arise in a logistic model. These results are robust
to using a logistic model instead.
15
We also note that in an unreported test, we re-ran the model in Panels C and D of Table XII
for the subsamples with above and below median Tobin’s Q. We find that the results in both Panels
are robust at the 5% level in the high Tobin’s Q subsample, but are only robust in the low Tobin’s
Q subsample with industry effects (not with firm effects). This test helps to rule out the hypothesis
that debt constraints are driven by managers of failing firms noting delay investment disclosures in
the 10-K solely to try to raise capital to preserve their job or postpone bankruptcy. In particular,
our results are strongest in the subsample of firms where the market itself acknowledges that these
firms have reasonably good growth options, as required by a debt overhang interpretation.
39
and issuance policies, but also regarding the likely theoretical origins driving both.
VI
Conclusion
We develop a methodology for identifying financial constraints using mandated disclosures in firm 10-K statements. We focus on discussions in which management
summarizes their firm’s ability or inability to obtain financing for planned investments, and the marginal form of external financing (debt, equity, or private placements of equity) their firm is actively considering. We use this text to construct
indices of delay investment financial constraints, and financial constraints with a
debt-, equity-, or private placement- financing focus. These disclosures are required
by law, and contain a great deal of information concerning these issues. Our indices
allow us to directly investigate financial constraints without having to rely on the
assumptions of a specific model. Our text-based measures also have advantages over
surveys: they are available for the entire COMPUSTAT panel from 1997 forward,
are in response to regulation, and we do not have to extend our measures from a
small survey to the whole population using statistical models based on potentially
endogenous variables.
We find that text-based measures of constraints correlate with firm characteristics
in an intuitive way. For example, equity focused constrained firms are in industries
with high investments, higher Tobin’s Q, lower profits, lower leverage, and higher
cash. Hence, they are in industries where planned investment levels and opportunities
are high, and many rivals are cash rich. However, the relative position of constrained
firms within these industries is such that they have lower than average investment
levels (R&D and CAPX) than their peers despite having a higher average Tobin’s Q.
These firms are focused more on R&D than CAPX. Debt-focused constrained firms
are diametrically opposite in many ways.
Firms that score high on our constraint measures curtail R&D, capital expenditures, equity issuance and debt issuance more than less constrained firms when they
receive negative shocks. We also find that these curtailments are larger for firms
that are equity-focus constrained and private placement-focus constrained relative
40
to those that are debt-focus constrained. Because the market perceives these equity
focused constrained firms as having good investment opportunities (high Tobin’s Q),
these results strongly support the predictions of Krasker (1986) regarding equity
rationing (a form of financial constraints due to information asymmetry).
We further examine the theoretical origins of constraints and find some direct
evidence that constraints in the equity markets are more prevalent when firms report risks relating to proprietary information in their 10-K. This further supports
Krasker’s theory, and suggests that constraints in the equity market are driven at
least in part by informational asymmetries. However, we find no link between debt
market constraints and informational asymmetry, suggesting that debt market constraints are different. Our results for debt market constraints are most consistent
with debt overhang, as we find that these firms appear to have viable investment
opportunities, high leverage, and they frequently report issues relating to covenant
violations in their 10-Ks. Our results broadly suggest that existing variables such as
size, age, and R&D are informative yet inadequate to fully understand constraints.
Future research on constraints should consider a research design that allows for separate constraints in equity and debt markets.
We also document that our constraint measures outperform other measures used
in the literature regarding their ability to predict policy curtailments, and we also
document that simple proxies for constrained firms such as size, age, and R&D do
not fully capture issues relating to informational asymmetries, which appear to be
more pervasive. They also do not capture debt market constraints which especially
tend to affect firms outside the set of small firms conducting R&D. We believe our
constraint measures, which consider differential constraints in different markets, can
benefit future research in this area.
41
Antweiler, Werner; Frank, Murray; 2004, Is all that talk just noise? The information content of internet stock message boards, Journal of Finance 52, pp. 1259-1294
Arellano, M.; Bond, S.; 1991, Some tests of specification for panel data: Monte
Carlo evidence and an application to employment equations. Review of Economic
Studies 58, pp. 277-297
Bazzi, Samuel; Clemens, Michael; 2010, Blunt Instruments: A Cautionary Note
on Establishing the Causes of Economic Growth. Center for Global Development
Working Paper
Beck, Thorsten; Demirguc-Kunt, Asli; Maksimovic, Vojislav; 2005, Financial and
Legal Constraints to Growth: Does Firm Size Matter? Journal of Finance 60, iss. 1,
pp. 137-77
Bond, Stephen; 2002, Dynamic panel data models: a guide to micro data methods
and practice. Portuguese Economic Journal 1, iss. 2, pp. 141-162.
Brown, James R.; Fazzari Steven M.; Petersen Bruce C.: 2009, Financing Innovation and Growth: Cash Flow, External Equity, and the 1990s R&D Boom, Journal
of Finance 64, 151-185.
Campello, Murillo; Graham, John R.; Harvey, Campbell R.; 2010, The Real Effects of Financial Constraints: Evidence from a Financial Crisis, Journal of Financial
Economics 97, iss. 3, pp. 470-87.
Cimiano, Phillip; 2010, Ontology Learning and Population from Text: Algorithms, Evaluation and Applications, Springer, New York.
Duchin, Ran; Ozbas, Oguzhan; Sensoy, Berk; 2010, Costly External Finance, Corporate Investment, and the Subprime Mortgage Credit Crisis, Journal of Financial
Economics 97, 418-435.
Edmans, Alex; Goldstein, Itay; and Jiang, Wei, 2011, The Real Effects of Financial Markets: The Impact of Prices on Takeovers, Journal of Finance, Forthcoming.
Fazzari, Steven M., R. Glenn Hubbard, and Bruce P. Petersen, 1988, Financing
Constraints and Investment, Brookings Papers on Economic Activity 1, 141-195.
42
Gomes, Armando; Phillips, Gordon, 2011, Why Do Public Firms Issue Private
and Public Securities? University of Maryland Working Paper
Graham, John R.; Harvey, Campbell R.; 2001, The Theory and Practice of Corporate Finance: Evidence from the Field, Journal of Financial Economics 60, iss.
2-3, pp. 187-243
Greenwald, Bruce; Stiglitz, Joseph; Weiss, Andrew; 1984, Informational imperfections in the capital carket and macroeconomic fluctuations, American Economic
Review Papers and Proceedings 74, pp. 194-199
Hadlock, Charles J.; Pierce, Joshua R.; 2010, New Evidence on Measuring Financial Constraints: Moving beyond the KZ Index, Review of Financial Studies 23,
pp. 1909-40.
Hanley, Kathleen Weiss; Hoberg, Gerard; 2010, The Information Content of IPO
Prospectuses, Review of Financial Studies 23, iss. 7, pp. 2821-641.
Hart, Oliver; Moore, John; 1998, Default And Renegotiation: A Dynamic Model
Of Debt, Quarterly Journal of Economics 113, pp. 1-41
Hennessy, Christopher; Whited, Toni; 2005, Debt dynamics, Journal of Finance
60, pp. 112965.
Hennessy, Christopher; Whited, Toni; 2007, How Costly Is External Financing?
Evidence from a Structural Estimation, Journal of Finance 62, pp. 170545.
Hoberg, Gerard; Phillips, Gordon; 2010, Product Market Synergies and Competition in Mergers and Acquisitions: A Text-Based Analysis, Review of Financial
Studies 10, pp. 3773-3811
Hoberg, Gerard; Phillips, Gordon; 2010, Dynamic Text-Based Industry Classifications and Endogenous Product Differentiation, National Bureau of Economic
Research, Inc, NBER Working Papers: 15991
Jensen, Michael; 1986, Agency Costs of Free Cash Flow, Corporate Finance and
Takeovers, American Economic Review 76, pp. 323-329
Jensen, Michael; Meckling, William; 1976, Theory of the Firm: Managerial Be-
43
haviour, Agency Costs and Ownership Structure, Journal of Financial Economics 3,
pp. 305-360
Kaplan, Steven; Zingales, Luigi; 1997, Do Investment-Cash Flow Sensitivities
Provide Useful Measures of Financial Constraints, Quarterly Journal of Economics
112, 159-216.
Krasker, William; 1986, Stock Price Movements in Response to Stock Issues
Under Asymmetric Information, Journal of Finance 41, pp. 93-105.
Lamont, Owen; Polk, Christopher; Saa-Requejo, Jesus; 2001, Financial Constraints and Stock Returns, Review of Financial Studies 14, iss. 2, pp. 529-54
Loughran, Tim; McDonald, Bill; 2011, When is a liability not a liability? Textual
analysis, dictionaries, and 10-Ks, Journal of Finance 66, pp. 35-65.
Moyen, Nathalie; 2007, How Big Is the Debt Overhang Problem? Journal of
Economic Dynamics and Control 31, pp. 433-472.
Myers, Stewart; 1977, The determinants of corporate borrowing, Journal of Financial Economics 5, pp. 147-175
Myers, Stewart C.; Majluf, Nicholas S.; 1984, Corporate Financing and Investment Decisions When Firms Have Information That Investors Do Not Have Journal
of Financial Economics 13, iss. 2, pp. 187-221
Opler, Tim C.; Titman Sheridan: 1994, Financial Distress and Corporate Performance, Journal of Finance 49, 1015-1040.
Phillips, Gordon M.; Zhadanov, Alexei; 2011, R&D and the Incentives from
Merger and Acquisition Activity, University of Maryland Working Paper
Roodman, David; 2009, How to do xtabond2: An introduction to difference and
system GMM in Stata, Stata Journal 9, iss. 1, pp. 86-136
Sebastiani, Fabrizio; 2002, Machine Learning in Automated Text Categorization,
ACM Computing Surveys, iss. 34, pp. 1-47
Tetlock, Paul; 2007, Giving content to investor sentiment: The role of media in
the stock market, Journal of Finance 62, pp. 1139-1168
44
Whited, Toni M.; 1992, Debt, Liquidity Constraints, and Corporate Investment:
Evidence from Panel Data, Journal of Finance 47, iss. 4, pp. 1425-60
Whited, Toni and Guojun Wu, 2006, ”Financial Constraints Risk,” Review of
Financial Studies 19-2, 531-559.
45
Table I: Summary Statistics
Summary statistics for firms with machine readable 10-Ks, and having machine readable Management’s Discussion
and Analysis. Observations are from 1997 to 2009. We discard all financials, and require COMPUSTAT data
coverage in the current year and the past year. We also discard firms with zero assets or zero sales, and require
valid text data to be available. Please see Section II for a description of variables. All non-binary variables are
winsorized at the 1/99% level.
Variable
Mean
Std.
Dev.
Min.
Median
Max.
# Obs.
1.000
1.000
1.000
52,438
52,380
52,380
Panel A: MD&A Content (Firms with MD&A Section)
Has Cap+Liq Subsection Dummy
MD&A R&D Focused Score
MD&A CAPX Focused Score
0.851
0.105
0.019
0.356
0.113
0.043
0.000
0.000
0.000
1.000
0.071
0.000
Panel B: Precise Text Constraint Declarations (Firms with Cap+Liq Section)
Delay Investment Text
Equity Focused Text
Debt Focused Text
0.017
0.010
0.036
0.129
0.100
0.185
0.000
0.000
0.000
0.000
0.000
0.000
1.000
1.000
1.000
44,441
44,441
44,441
Panel C: 12-Word Text Constraint Declarations (Firms with Cap+Liq Section)
Delay Investment Text
Equity Focused Text
Debt Focused Text
Private Placement Foc. Text
0.057
0.132
0.146
0.161
0.232
0.339
0.353
0.368
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
1.000
1.000
1.000
1.000
44,441
44,441
44,441
44,441
Panel D: Text Constraint Scores (Firms with Cap+Liq Section)
Delay Investment Score
Equity Focused Score
Debt Focused Score
Private Placement Foc. Score
0.997
0.995
0.997
0.986
0.109
0.127
0.139
0.178
0.648
0.558
0.524
0.476
0.994
0.985
0.998
0.964
1.331
1.595
1.628
1.835
44,441
44,441
44,441
44,441
Panel E: Other Text Variables (Firms with Cap+Liq Section)
Covenant Violation Score
Log # Words in Cap+Liq Section
0.036
8.280
0.105
0.865
-0.487
3.951
0.029
8.394
0.525
11.315
44,441
44,441
Panel F: Text-Based Shock Variables (Firms having valid MD&A)
High Competition Dummy
Low Demand Dummy
High Cost Dummy
0.146
0.123
0.145
0.353
0.329
0.352
0.000
0.000
0.000
0.000
0.000
0.000
1.000
1.000
1.000
52,438
52,438
52,438
0.000
0.035
0.006
0.000
11.218
5.243
1.576
1.669
52,438
52,438
52,438
52,438
Panel G: Corporate Policy Variables
R&D/Sales
CAPX/Sales
Equity Issuance/Assets
Debt Issuance/Assets
0.191
0.117
0.081
0.109
0.779
0.328
0.201
0.229
0.000
-0.000
-0.000
0.000
46
47
Equity Focused Delay Score
Debt Focused Delay Score
Private Place. Foc. Delay Score
Covenant Violation Score
Log Assets
KZ Index
WW Index
MD&A R&D Focused Score
MD&A CAPX Focused Score
Row Variable
0.855
0.801
0.753
-0.039
-0.058
0.017
0.095
0.162
0.024
Delay
Invest.
Score
0.658
0.910
-0.084
-0.106
-0.046
0.133
0.235
-0.011
Equity
Focused
Score
Private
Placement
Focused
Score
0.519
0.327
0.049
0.099
-0.014
-0.017
0.056
-0.245
-0.141
-0.093
0.180
0.281
-0.036
Correlation Coefficients
Debt
Focused
Score
0.038
0.164
-0.062
-0.230
0.041
Covenant
Viol.
Score
0.078
-0.903
-0.188
0.155
Size
(Log
Assets)
-0.012
-0.163
0.061
KZ
Index
0.267
-0.152
WW
Index
-0.046
MD&A
R&D
Focused
Score
The table displays Pearson Correlation Coefficients between various measures of financial constraints and financial distress. The first six variables are text-based measures of
investment opportunities (the first three being derived from the entire MD&A, and the latter three derived from the Capitalization and Liquidity subsection (“CAP+LIQ”). We also
include our three constraint variables based on investment delay, equity focused delay, and debt focused delay. Finally, we include the covenant violation score. In addition to the text
variables, we include correlations with firm size, the KZ index, the WW index, and the Altman Z-score. Please see Section II for a description of variables. Finally, we consider firm
size and three existing measures of financial constraints and distress used in the existing literature: the KZ index, the WW index, and the Altman Z score.
Table II: Pearson Correlation Coefficients (Investment and Constraint Measures)
48
–0.1346
(–2.950)
–0.1232
(–2.670)
–0.1014
(–2.160)
–0.0990
(–2.100)
(5)
(8)
(7)
(6)
(4)
(3)
–0.2898
(–4.040)
–0.2835
(–3.950)
–0.2666
(–3.700)
–0.2745
(–3.780)
–0.1635
(–5.730)
–0.1549
(–5.410)
–0.1252
(–4.360)
–0.1236
(–4.330)
–0.1730
(–9.500)
–0.1627
(–8.800)
–0.1399
(–7.280)
–0.1353
(–7.020)
(1)
(2)
Log
Age
Log
Row Assets
–0.0117
(–0.360)
–0.0127
(–0.390)
–0.0055
(–0.170)
–0.0232
(–1.040)
–0.0203
(–0.920)
–0.0081
(–0.380)
oi/
Sales
0.4183
(1.750)
0.2885
(1.180)
0.2847
(1.160)
0.5838
(3.080)
0.5068
(2.600)
0.4311
(2.220)
Cash
Assets
Book
Leverage
Dividend
Payer
–0.1028
(–0.790)
–0.0400
(–0.300)
–0.3353
(–4.660)
–0.3190
(–4.420)
0.0376
(2.400)
0.0345
(2.180)
–0.1936
(–1.080)
–0.1679
(–0.930)
–0.0529
(–0.480)
–0.0461
(–0.420)
Panel B: Firm and year fixed effects
0.0139
(1.170)
0.0073
(0.630)
Panel A: SIC-3 industry and year fixed effects
Tobin’s
Q
1.5357
(4.250)
1.2616
(3.350)
MD&A
R&D
Focus
–5.1655
(–7.230)
–3.3154
(–5.430)
MD&A
CAPX
Focus
46,021
46,021
46,021
46,021
46,021
46,021
46,021
46,021
#obs
Panel data Logistic regressions with firm and year fixed effects. The sample includes all firm years from 1997 to 2009. The dependent variable is a dummy equal to one when the given
firm’s MD&A has a distinct Capitalization and Liquidity subsection. Standard errors are adjusted for clustering at the firm level. The independent variables include various firm
characteristics. Please see Section II for a full discussion of variable definitions.
Table III: Capitalization and Liquidity Subsection and Firm Characteristics
49
Tobin’s Q (Firm Specific)
Dividend Payer (Industry Specific)
Dividend Payer (Firm Specific)
oi/sales (Industry Specific)
oi/sales (Firm Specific)
oi/assets (Industry Specific)
oi/assets (Firm Specific)
cash/assets (Industry Specific)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(18) R+D/sales (Firm Specific)
(17) R+D/sales (Industry Specific)
(16) CAPX/sales (Firm Specific)
(15) CAPX/sales (Industry Specific)
(14) Market Leverage (Firm Specific)
(13) Market Leverage (Industry Specific)
(12) Book Leverage (Firm Specific)
(11) Book Leverage (Industry Specific)
(10) cash/assets (Firm Specific)
Tobin’s Q (Industry Specific)
(1)
Dependent
Row Variable
1.145
(14.68)
0.449
(2.92)
-0.069
(-4.71)
-0.019
(-0.64)
-0.270
(-15.68)
-1.345
(-11.19)
-0.152
(-14.32)
-0.200
(-10.93)
0.072
(10.35)
-0.005
(-0.43)
-0.016
(-2.22)
0.071
(3.94)
-0.034
(-5.15)
0.028
(2.25)
0.290
(16.27)
-0.024
(-1.01)
1.532
(12.18)
-0.556
(-4.89)
Delay
Invest
Score
1.429
(20.96)
0.698
(5.25)
-0.109
(-8.34)
0.028
(1.19)
-0.324
(-21.43)
-1.668
(-15.81)
-0.191
(-20.45)
-0.262
(-15.94)
0.101
(16.58)
0.007
(0.65)
-0.034
(-5.52)
0.061
(4.05)
-0.055
(-9.66)
0.010
(1.00)
0.301
(19.45)
-0.034
(-1.73)
1.861
(16.46)
-0.661
(-6.59)
Equity
Focused
Score
-0.102
(-1.89)
-0.229
(-2.17)
0.033
(3.03)
-0.138
(-5.95)
-0.049
(-4.29)
-0.430
(-5.60)
-0.019
(-2.70)
-0.102
(-7.91)
-0.020
(-3.98)
-0.075
(-8.48)
0.047
(8.18)
0.183
(13.88)
0.038
(7.36)
0.135
(14.38)
0.111
(8.86)
-0.000
(-0.03)
0.450
(5.73)
-0.171
(-2.36)
Debt
Focused
Score
1.341
(25.64)
0.817
(8.03)
-0.120
(-12.34)
0.052
(3.01)
-0.292
(-26.28)
-1.429
(-17.80)
-0.174
(-25.48)
-0.220
(-17.50)
0.100
(22.24)
0.033
(4.40)
-0.047
(-10.19)
0.008
(0.71)
-0.063
(-14.90)
-0.029
(-3.78)
0.260
(22.51)
-0.022
(-1.46)
1.544
(18.44)
-0.535
(-7.41)
Private
Placement
Score
0.000
(0.02)
-0.014
(-0.99)
-0.009
(-4.45)
-0.017
(-4.18)
-0.011
(-5.92)
-0.048
(-5.09)
-0.007
(-5.89)
-0.015
(-7.94)
0.003
(3.37)
-0.007
(-5.97)
0.001
(1.23)
0.026
(11.55)
0.001
(0.69)
0.020
(12.09)
0.011
(5.72)
-0.001
(-0.36)
0.046
(3.70)
-0.013
(-1.16)
Log #
Words in
CAP+LIQ
-0.141
(-15.42)
-0.110
(-6.94)
0.028
(12.61)
0.048
(11.41)
0.024
(12.49)
0.105
(10.91)
0.016
(13.96)
0.008
(4.28)
-0.012
(-13.83)
-0.007
(-5.96)
0.005
(5.40)
-0.005
(-2.21)
0.006
(7.58)
-0.004
(-2.27)
-0.025
(-12.35)
-0.010
(-3.55)
-0.091
(-7.14)
0.027
(2.83)
Log
Firm
Age
-0.024
(-4.15)
-0.053
(-4.72)
0.015
(11.50)
0.042
(16.43)
0.012
(9.92)
0.092
(15.91)
0.009
(11.44)
0.044
(28.03)
-0.005
(-9.28)
-0.012
(-15.37)
0.006
(10.22)
0.015
(11.78)
0.005
(8.87)
0.011
(11.93)
-0.002
(-1.80)
0.011
(7.61)
-0.050
(-6.20)
0.021
(3.08)
Log
Assets
35,262
0.383
35,262
0.014
35,262
0.384
35,262
0.086
35,262
0.474
35,262
0.105
35,262
0.456
35,262
0.137
35,262
0.476
35,262
0.038
35,262
0.463
35,262
0.044
35,262
0.472
35,262
0.046
35,262
0.450
35,262
0.078
35,262
0.475
35,262
0.286
# Obs. /
RSQ
The table displays the results of OLS regressions in which the dependent variable is specified by row. This panel only includes firms (85.1% of all firms) that have a machine readable
CAP+LIQ Section. Industry specific variables are computed using TNIC industries as in Hoberg and Phillips (2010, 2011). Firm specific components are computed as a firm’s raw
value for the specific characteristic minus the industry value. All regressions include time and three digit SIC fixed effects. Although we report results for all four constraint variables in
columnar form on the same table, we run regressions separately for each constraint variable due to their non-trivial correlations. In all regressions, we include the same control
variables, and we report the control variable coefficients for the first regression that includes the delay investment constraint variable as these coefficients do not change materially
when different constraint variables are included. We report results using this compact format to conserve space. All standard errors are adjusted for clustering by firm.
Table IV: Firm Characteristics and Constraint Variables
Table V: Investment Policies versus Constraint Cross Terms (Economic Magnitudes)
Economic Magnitudes of Interactions between policy variables and constraint variables during each of the two
shocks considered in this paper. We consider the two shocks as noted in Panels A and B. For each shock, we
consider the difference in each policy for treated firms minus the policy level for non-treated firms. For the financial
crisis, the treated value is the firm’s value in 2009, and the untreated value is the firm’s value of the given policy in
2007. For the text shock, the treated value is the average policy for firms experiencing at least one of the three text
shocks (low demand, high competition, or high cost) and the untreated value is the average policy for firms
experiencing none of the three text shocks. Note that because each of the policy variables is scaled by sales or
assets, that the reported mean changes are in fractional units of sales or assets, respectively.
Constraint Variable
and Tercile (1 to 3)
Change in
R&D/Sales
Change in
CAPX/Sales
Change in
Equity
Issuance
/Assets
Change in
Debt
Issuance
/Assets
Panel A: Financial Crisis Change from 2007 to 2009
delay constraint 1
delay constraint 2
delay constraint 3
equity focus delay 1
equity focus delay 2
equity focus delay 3
debt focus delay 1
debt focus delay 2
debt focus delay 3
private placement focus delay 1
private placement focus delay 2
private placement focus delay 3
-0.034
-0.026
-0.148
-0.019
-0.016
-0.173
-0.056
-0.071
-0.081
-0.013
-0.025
-0.176
-0.026
-0.050
-0.118
-0.030
-0.060
-0.105
-0.030
-0.066
-0.099
-0.024
-0.051
-0.123
-0.017
-0.036
-0.054
-0.019
-0.027
-0.061
-0.028
-0.034
-0.045
-0.015
-0.020
-0.074
-0.022
-0.045
-0.040
-0.030
-0.031
-0.046
-0.014
-0.038
-0.055
-0.031
-0.036
-0.040
-0.015
-0.019
-0.041
-0.006
-0.016
-0.050
-0.018
-0.022
-0.034
-0.008
-0.012
-0.053
-0.014
-0.013
0.003
-0.011
-0.017
0.004
-0.017
-0.011
0.001
-0.007
-0.016
-0.000
Panel B: Text Shock (high minus low)
delay constraint 1
delay constraint 2
delay constraint 3
equity focus delay 1
equity focus delay 2
equity focus delay 3
debt focus delay 1
debt focus delay 2
debt focus delay 3
private placement focus delay 1
private placement focus delay 2
private placement focus delay 3
-0.012
-0.044
-0.254
0.008
-0.026
-0.282
-0.042
-0.079
-0.186
0.004
-0.027
-0.279
-0.009
-0.015
-0.022
-0.002
-0.013
-0.027
-0.012
-0.021
-0.013
-0.005
-0.012
-0.025
Panel C: Mutual Fund Selling (high minus low)
delay constraint 1
delay constraint 2
delay constraint 3
equity focus delay 1
equity focus delay 2
equity focus delay 3
debt focus delay 1
debt focus delay 2
debt focus delay 3
private placement focus delay 1
private placement focus delay 2
private placement focus delay 3
-0.015
-0.036
-0.046
-0.011
-0.015
-0.040
-0.022
-0.048
-0.042
-0.012
-0.009
-0.048
0.002
0.003
0.008
0.008
0.005
0.006
-0.002
-0.007
0.018
0.008
0.001
0.010
50
-0.034
-0.039
-0.061
-0.020
-0.034
-0.073
-0.045
-0.047
-0.046
-0.017
-0.034
-0.075
-0.011
-0.007
-0.008
-0.011
-0.004
-0.011
-0.012
-0.003
-0.009
-0.010
-0.007
-0.009
Table VI: Investment Policies versus Constraint Cross Terms (Firm Fixed Effects)
Panel data OLS regressions with industry and year fixed effects. The sample includes all firm years from 1997 to
2009 that have a machine readable Capitalization and Liquidity subsection. The dependent variable is firm level
equity issuance/assets, debt issuance/assets, R&D/sales, or CAPX/ sales as noted by column. All independent
variables are lagged one year. We examine a different constraint index and its interactions as noted in each Panel
header. All panels are based on regressions that include all of the variables shown in Panel A. However, Panels B
through E only display the cross terms we draw inferences from in order to conserve space. Standard errors are
adjusted for clustering at the firm level. The independent variables include our key text-based investment and
constraint variables, and our text-based shock variables (high competition, low demand, technological change, and
high cost). We also consider cross terms including our constraint variables and the shock variables, in addition to a
2008-9 dummy. We do not directly include a 2008-9 dummy because we control for time fixed effects. Please see
Section II for a full discussion of variable definitions. ***, **, * denote significance at the 1%, 5%, 10% levels,
respectively.
Variable
Name
Dependent
Variable=
R&D/Sales
Dependent
Variable=
CAPX/Sales
Dependent
Variable=
Equity
Issuance
Dependent
Variable=
Debt
Issuance
Panel A: Constraint Index = Delay Investment Score
Text Shock
Constraint Index
Log Firm Age
Log Size of MD&A
Log Assets
Tobins Q
Text Shock x Index
2008–9 Dummy x Index
0.0034
0.2431∗∗∗
–0.0395∗∗∗
0.0000
0.0193∗∗
0.0095∗∗∗
–0.1167∗∗∗
–0.3882∗∗∗
–0.0001
0.0584∗∗∗
–0.0157∗∗
0.0000∗∗∗
0.0247∗∗∗
0.0101∗∗∗
–0.0367∗∗
–0.1001∗∗∗
–0.0009
0.0715∗∗∗
–0.0130∗∗∗
0.0000∗∗∗
–0.0479∗∗∗
0.0135∗∗∗
–0.0379∗∗∗
–0.0644∗∗∗
–0.0009
–0.0008
0.0028
0.0000∗∗∗
–0.0134∗∗∗
0.0029∗∗∗
–0.0086
–0.0224
Panel B: Constraint Index = Equity Focus Delay
Text Shock x Index
2008–9 Dummy x Index
–0.1006∗∗∗
–0.4808∗∗∗
–0.0313∗∗
–0.1083∗∗∗
–0.0372∗∗∗
–0.1028∗∗∗
–0.0077
–0.0324
Panel C: Constraint Index = Debt Focus Delay
Text Shock x Index
2008–9 Dummy x Index
–0.0650∗∗∗
–0.1296∗∗
–0.0177∗
–0.0391∗∗
–0.0150∗∗
–0.0151
–0.0082
–0.0287
–0.0035
–0.0028
0.0005
–0.0111
–0.0050
–0.1848∗∗∗
0.0087
–0.0402
Panel D: Constraint Index = KZ Index
Text Shock x Index
2008–9 Dummy x Index
0.0089
0.0999
0.0137
0.0199
Panel E: Constraint Index = WW Index
Text Shock x Index
2008–9 Dummy x Index
0.0126
–0.2572∗∗
0.0166
–0.0544∗
51
Table VII: Investment Policies versus Constraint Cross Terms (Arrellano-Bond)
The table displays the results of Arrellano-Bond regressions using differences GMM including year fixed effects.
The dependent variable is firm level equity issuance/assets, debt issuance/assets, R&D/sales, or CAPX/ sales as
noted by column. All independent variables are lagged one year. We examine a different constraint index and its
interactions as noted in each Panel header. All panels are based on regressions that include all of the variables
shown in Panel A. However, Panels B through E only display the cross terms we draw inferences from in order to
conserve space. The independent variables include our key text-based investment and constraint variables, and our
text-based shock variables (high competition, low demand, technological change, and high cost). We also consider
cross terms including our constraint variables and the shock variables, in addition to a 2008-9 dummy. Please see
Section II for a full discussion of variable definitions. ***, **, * denote significance at the 1%, 5%, 10% levels,
respectively. We also report the results of four regression diagnostic tests. A specification includes the letter “a” if
the idiosyncratic disturbance is not autocorrelated at the second lag at the 5% level, following Arrellano and Bond
(1991). A specification includes the letter “b” if the lagged dependent variable falls within the two reasonable OLS
bounds indicated in Roodman (2009). A specification includes the letter “c” if the Hansen J statistic of
overidentifying restrictions is not significant at the 5% level. A specification includes the letter “d” if the two shock
cross terms have a Hansen Differences statistic that is not significant at the 5% level, consistent with a failure to
reject their being exogenous. A specification including all four letters passes all four tests. We also note that our
instruments outnumber groups by a factor of roughly ten across specifications, far exceeding the minimum 1:1 ratio
below which Roodman (2009) indicates problems are most likely.
Variable
Name
Dependent
Variable=
R&D/Sales
Dependent
Variable=
CAPX/Sales
Dependent
Variable=
Equity
Issuance
Dependent
Variable=
Debt
Issuance
Panel A: Constraint Index = Delay Investment Score
Lagged Dep. Var.
Text Shock
Constraint Index
Log Firm Age
Log Size of MD&A
Log Assets
Tobins Q
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
0.3262∗∗∗
0.0053
0.0676
–0.0157
0.0016
0.0554∗∗∗
0.0070∗∗
–0.1331
–0.3841∗∗
a, b, c, d
0.3034∗∗∗
–0.0035∗
0.1355∗∗
–0.0106
–0.0006
–0.0113∗
0.0057∗∗∗
–0.1082∗∗∗
–0.2283∗∗∗
a, b, d
0.0589∗∗∗
0.0046∗∗
–0.0246
–0.0125
0.0005
–0.1032∗∗∗
0.0085∗∗∗
–0.0593∗∗
–0.0720∗∗
a, b, d
0.0416∗∗∗
0.0023
0.0149
–0.0106
0.0004
–0.0639∗∗∗
–0.0051∗∗∗
–0.0125
–0.0770∗
b, d
Panel B: Constraint Index = Equity Focus Delay
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.2270∗∗
–0.1162∗∗∗
–0.0788∗∗∗
–0.5026∗∗∗
–0.1815∗∗∗
–0.1525∗∗∗
a, b, c, d
a, b, c, d
a, b, d
Panel C: Constraint Index = Debt Focus Delay
–0.1029
–0.1303∗
a, b, c, d
Panel D: Constraint
–0.0361∗
–0.0204
–0.1611∗∗∗
–0.0186
a, b, c, d
a, b, d
Index = KZ Index
0.0220
–0.0095
0.0275∗
–0.0028
–0.0504∗∗
0.0398∗∗
a, b, c, d
a, b, c, d
a, b, d
Panel E: Constraint Index = WW Index
0.0558
–0.4276∗∗∗
a, b, c, d
0.0090
–0.0541
a, b, c, d
52
0.0656∗
–0.1467∗∗∗
a, b, d
–0.0299
–0.0327
b, d
–0.0142
–0.0649∗
b, d
–0.0052
–0.0145
b, d
0.0584
–0.0239
b, d
Table VIII: Investment Policies versus Constraint Cross Terms (Arrellano-Bond)
(Various Subsamples)
The table displays the results of Arrellano-Bond regressions using differences GMM including year fixed effects for
subsamples based on firm size, firm age, R&D Focus, and CAPX Focus. The dependent variable is firm level equity
issuance/assets, debt issuance/assets, R&D/sales, or CAPX/ sales as noted by column. All independent variables
are lagged one year. Each Panel displays the cross terms for a regression model that is the same as in Panel A of
Table VII (see header for details), except that we examine a different constraint variable in each Panel as noted in
the Panel header. ***, **, * denote significance at the 1%, 5%, 10% levels, respectively. We also report the results
of four regression diagnostic tests, as also summarized in Table VII.
Dependent
Variable=
R&D/Sales
Variable
Name
Dependent
Variable=
CAPX/Sales
Dependent
Variable=
Equity
Issuance
Dependent
Variable=
Debt
Issuance
Panel A: Constraint Index = Delay Investment Score [SMALL FIRMS]
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.1958
–0.4411∗
a, b, c, d
–0.0984∗∗
–0.2697∗∗∗
a, c, d
–0.0456
–0.2068∗∗∗
a, b, d
0.0037
–0.0380
a, d
Panel B: Constraint Index = Delay Investment Score [LARGE FIRMS]
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.1570∗∗
–0.4084∗∗
d
–0.1358∗∗∗
–0.1834∗∗∗
a, c, d
–0.0228
0.0181
b, c, d
–0.0352
–0.0345
d
Panel C: Constraint Index = Delay Investment Score [YOUNG FIRMS]
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.1094
–0.3837∗
a, b, c, d
–0.1365∗∗∗
–0.2763∗∗∗
a, b, c, d
–0.0776∗∗
–0.1120∗∗
b, d
–0.0132
–0.0751∗∗
a, c, d
Panel D: Constraint Index = Delay Investment Score [OLD FIRMS]
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.0788
–0.4433∗∗
a, d
–0.0426
–0.1698∗∗∗
a, b, d
–0.0212
–0.0199
b, c, d
0.0076
0.0256
d
Panel E: Constraint Index = Delay Investment Score [LOW R&D FOCUS FIRMS]
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
0.0085
0.0073
a, d
–0.1007∗∗
–0.0349
a, b, c, d
–0.0066
–0.0037
a, b, d
0.0000
–0.0156
a, c, d
Panel F: Constraint Index = Delay Investment Score [HIGH R&D FOCUS FIRMS]
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.1404
–0.7320∗∗∗
a, b, c, d
–0.1494∗∗∗
–0.3184∗∗∗
b, c, d
–0.0669∗
–0.1537∗∗∗
a, b, d
–0.0282
–0.0078
a, c, d
Panel G: Constraint Index = Delay Investment Score [LOW CAPX FOCUS FIRMS]
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.2053∗
–0.5053∗∗
b, c, d
–0.0754∗∗
–0.2732∗∗∗
a, b, c, d
–0.0572
–0.0519
a, b, d
–0.0268
–0.0364
a, d
Panel H: Constraint Index = Delay Investment Score [HIGH CAPX FOCUS FIRMS]
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.0540
–0.1461
d
–0.1314∗∗
–0.0908
a, b, c, d
53
–0.0442
–0.0930∗∗
a, b, c, d
0.0193
–0.0254
a, c, d
Table IX: Investment Policies versus Constraint Cross Terms (Arrellano-Bond) (Equity and Debt Focus Constraints)
The table displays the results of Arrellano-Bond regressions using differences GMM including year fixed effects for
subsamples based on R&D Focus, and CAPX Focus subsamples. The dependent variable is firm level equity
issuance/assets, debt issuance/assets, R&D/sales, or CAPX/ sales as noted by column. All independent variables
are lagged one year. Each Panel displays the cross terms for a regression model that is the same as in Panel A of
Table VII (see header for details), except that we examine a different constraint variable in each Panel as noted in
the Panel header. ***, **, * denote significance at the 1%, 5%, 10% levels, respectively. We also report the results
of four regression diagnostic tests, as also summarized in Table VII.
Dependent
Variable=
R&D/Sales
Variable
Name
Dependent
Variable=
CAPX/Sales
Dependent
Variable=
Equity
Issuance
Dependent
Variable=
Debt
Issuance
Panel A: Constraint Index = Equity Focus Delay Investment Score [High R&D Firms]
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.2273∗
–0.7371∗∗∗
a, b, c, d
–0.1553∗∗∗
–0.2382∗∗∗
b, c, d
–0.0847∗∗
–0.2171∗∗∗
a, b, d
–0.0145
–0.0037
a, c, d
Panel B: Constraint Index = Debt Focus Delay Investment Score [High R&D Firms]
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.1428
–0.3523∗∗
a, b, c, d
–0.0849∗∗∗
–0.2853∗∗∗
b, c, d
–0.0200
–0.0662∗
a, b, d
–0.0341∗
–0.0159
a, c, d
Panel C: Constraint Index = Equity Focus Delay Investment Score [High CAPX Firms]
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.1536
–0.3209
d
–0.1461∗∗
–0.1430
a, b, c, d
–0.0665∗∗
–0.1708∗∗∗
a, b, c, d
0.0332
0.0085
c, d
Panel D: Constraint Index = Debt Focus Delay Investment Score [High CAPX Firms]
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
0.0319
0.0321
d
–0.0255
0.0153
a, b, c, d
–0.0247
–0.0292
a, b, c, d
0.0224
–0.0345
a, c, d
Panel E: Constraint Index = Debt Focus Delay Investment Score [Young Firms]
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.1851∗∗
–0.0575
a, b, c, d
–0.0670∗∗
–0.1809∗∗
a, b, c, d
–0.0180
–0.0261
b, d
0.0196
–0.0631∗∗
a, c, d
Panel F: Constraint Index = Debt Focus Delay Investment Score [Young High CAPX Firms]
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.1645
0.0449
a, b, c, d
–0.0689∗
–0.1978∗
a, b, c, d
54
–0.0462
–0.0282
a, b, d
0.0120
–0.0762∗∗
a, c, d
Table X: The Role of Equity Private Placements (Arrellano-Bond)
The table displays the results of Arrellano-Bond regressions using differences GMM including year fixed effects.
The dependent variable is firm level equity issuance/assets, debt issuance/assets, R&D/sales, or CAPX/ sales as
noted by column. All independent variables are lagged one year. The key independent variables of interest in all
panels are the cross terms between the two economic shock variables and the private placement focused delay
constraint variable. Each Panel displays the results for a regression model based on the whole sample (Panel A) or
based on various subsamples (all other panels). All regressions include the full set of variables used in Panel A of
Table VII (see header for details), except that we only display the key cross terms to conserve space. ***, **, *
denote significance at the 1%, 5%, 10% levels, respectively. We also report the results of four regression diagnostic
tests, as also summarized in Table VII.
Variable
Name
Dependent
Variable=
R&D/Sales
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.1414∗∗
–0.4059∗∗
a, b, c, d
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.1116
–0.4743∗∗
a, c, d
Dependent
Variable=
CAPX/Sales
Dependent
Variable=
Equity
Issuance
Dependent
Variable=
Debt
Issuance
–0.0663∗∗∗
–0.1395∗∗∗
a, b, d
–0.0178
–0.0058
b, d
–0.0440
–0.2402∗∗∗
a, b, d
–0.0051
–0.0111
a, c, d
–0.0253
–0.0155
b, d
–0.0144
–0.0455
d
–0.0710∗∗∗
–0.1566∗∗∗
b, d
0.0072
–0.0221
a, c, d
–0.0371
–0.1136∗∗∗
b, c, d
–0.0168
–0.0213
a, c, d
–0.0005
–0.0113
a, b, c, d
–0.0113
–0.0198
a, c, d
–0.0750∗∗∗
–0.1890∗∗∗
a, b, d
–0.0049
–0.0103
a, c, d
–0.0527∗∗
–0.1116∗∗∗
a, b, d
–0.0170
–0.0252
a, d
–0.0578∗∗
–0.1642∗∗∗
a, b, c, d
0.0243
–0.0092
c, d
Panel A: Whole Sample
–0.0813∗∗∗
–0.1421∗∗∗
a, b, c, d
Panel B: Small Firms
–0.0634∗∗
–0.1447∗∗
a, c, d
Panel C: Large Firms
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.1951∗∗
–0.3799∗
d
–0.0994∗∗∗
–0.1400∗∗
a, c, d
Panel D: Young Firms
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.1677∗
–0.4239∗
a, b, c, d
–0.0835∗∗∗
–0.1626∗∗
a, b, c, d
Panel E: Old Firms
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.0540
–0.4804∗∗
a, d
–0.0465∗∗
–0.1200∗∗
a, b, c, d
Panel F: Low R&D Focus Firms
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
0.0133
0.0096
a, d
–0.0381
–0.0123
a, b, c, d
Panel F: High R&D Focus Firms
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.1376
–0.6167∗∗∗
a, b, c, d
–0.1064∗∗∗
–0.1912∗∗
b, c, d
Panel F: Low CAPX Focus Firms
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.1671∗∗
–0.5125∗∗∗
c, d
–0.0632∗∗∗
–0.1311∗∗
a, b, c, d
Panel F: High CAPX Focus Firms
Text Shock x Index
2008–9 Dummy x Index
Regression Diagnostics
–0.1276
–0.1802
d
–0.1090∗∗
–0.0899
a, b, c, d
55
Table XI: Investment Policies versus Mutual Fund Selling Cross Terms (ArrellanoBond)
The table displays the results of Arrellano-Bond regressions using differences GMM including year fixed effects.
The dependent variable is firm level equity issuance/assets, or debt issuance/assets as noted by column. All
independent variables are lagged one year. We examine a different constraint index and its interactions as noted in
each Panel header. Each Panel displays the cross terms for a regression model that includes all variables displayed
in Panel A of Table VII (see header for details), but with two exceptions. First, we examine a different constraint
variable in each Panel as noted in the Panel header. Second, we add forced mutual fund selling from Edmans,
Goldstein and Jiang (2011) along with a cross term based on this variable and the given constraint index. Please
see Section II for a full discussion of variable definitions. ***, **, * denote significance at the 1%, 5%, 10% levels,
respectively. We also report the results of four regression diagnostic tests, as also summarized in Table VII.
Dependent
Variable=
Equity
Issuance
Variable
Name
Dependent
Variable=
Debt
Issuance
Panel A: Constraint Index = Delay Investment Score
Text Shock x Index
2008–9 Dummy x Index
Mutual Selling x Index
Regression Diagnostics
–0.0575∗∗
–0.0885∗∗∗
–0.0047∗
a, d
–0.0167
–0.0739
–0.0018
b, d
Panel B: Constraint Index = Equity Focus Delay
Text Shock x Index
2008–9 Dummy x Index
Mutual Selling x Index
Regression Diagnostics
–0.0671∗∗∗
–0.1626∗∗∗
–0.0075∗∗∗
a, b, d
–0.0289
–0.0373
–0.0023
b, d
Panel C: Constraint Index = Debt Focus Delay
Text Shock x Index
2008–9 Dummy x Index
Mutual Selling x Index
Regression Diagnostics
–0.0188
–0.0271
–0.0032∗
a, d
–0.0117
–0.0652∗
–0.0018
b, d
Panel D: Constraint Index = Private Placement Focus Delay
Text Shock x Index
2008–9 Dummy x Index
Mutual Selling x Index
Regression Diagnostics
–0.0543∗∗∗
–0.1478∗∗∗
–0.0067∗∗∗
a, b, d
–0.0154
–0.0078
–0.0018
b, d
56
Table XII: Proprietary Information, Outcomes, and Financial Constraints
Panel data OLS regressions with firm, industry and year fixed effects as noted in panel headers. Panels A and B
examine the link between proprietary information and our constraint variables. Panels C and D examine the link
between our constraint variables and outcomes in which the firm reports covenant violations in its Capitalization
and Liquidity Section of its 10-K in the following year. The sample includes 37,024 firm years from 1997 to 2009
that have a machine readable Capitalization and Liquidity subsection. All independent variables are lagged one
year. Standard errors are adjusted for clustering at the firm level. Please see Section II for a full discussion of
variable definitions. t-statistics are reported in parentheses.
Dependent
Row Variable
Log
ProprietaryFirm
InformationAge
Log
Assets
Log
Document
Size
R2
R&D/
Sales
#
Obs
Panel A: Constraints vs Proprietary Information [Ind. and Year Fixed Effects]
(1)
Delay Constraint
(2)
Equity Focused
Delay Constraint
Debt Focused
Delay Constraint
Priv Place Foc
Delay Constraint
(3)
(4)
0.011
(5.01)
0.018
(7.11)
0.002
(0.59)
0.033
(9.75)
-0.005
(-5.91)
-0.006
(-6.56)
-0.007
(-6.99)
-0.011
(-8.68)
0.001
(2.14)
-0.001
(-2.00)
0.006
(7.24)
-0.003
(-3.14)
0.014
(8.67)
0.024
(12.63)
0.008
(4.24)
0.039
(14.39)
0.006
(4.77)
0.007
(4.96)
0.014
(9.23)
0.021
(11.77)
0.145
37,084
0.160
37,084
0.119
37,084
0.204
37,084
Panel B: Constraints vs Proprietary Information [Firm and Year Fixed Effects]
(5)
Delay Constraint
(6)
Equity Focused
Delay Constraint
Debt Focused
Delay Constraint
Priv Place Foc
Delay Constraint
(7)
(8)
Dependent
Row Variable
0.006
(3.07)
0.008
(3.51)
0.003
(1.41)
0.011
(3.52)
Delay
Constr.
0.000
(0.14)
-0.001
(-0.36)
-0.001
(-0.53)
-0.002
(-0.94)
Equity
Delay
Constr.
-0.000
(-0.18)
-0.001
(-0.62)
0.003
(2.02)
0.001
(0.71)
Debt
Delay
Constr.
0.003
(2.06)
0.007
(4.01)
0.002
(1.53)
0.013
(5.05)
Priv.
Delay
Constr.
0.008
(5.47)
0.011
(6.46)
0.017
(9.35)
0.023
(11.06)
Log
Firm
Age
0.005
37,084
0.008
37,084
0.012
37,084
0.019
37,084
Log
Assets
Log
Doc.
Size
R2
Panel C: Covenant Violations vs Constraints [Ind. and Year Fixed Effects]
(1)
Covenant Violation
(2)
Covenant Violation
(3)
Covenant Violation
(4)
Covenant Violation
(5)
Covenant Violation
(6)
Covenant Violation
(7)
Covenant Violation
(8)
Covenant Violation
-0.011
(-1.26)
-0.018
(-2.48)
0.021
(3.18)
-0.022
(-4.01)
0.001
(1.38)
0.001
(1.29)
0.001
(1.66)
0.001
(1.10)
-0.003
(-5.70)
-0.003
(-5.74)
-0.003
(-5.94)
-0.003
(-5.80)
0.013
(12.24)
0.013
(12.26)
0.013
(12.19)
0.013
(12.41)
0.030
0.030
0.031
0.031
Panel D: Covenant Violations vs Constraints [Firm and Year Fixed Effects]
0.005
(0.53)
-0.001
(-0.15)
0.018
(2.31)
-0.008
(-1.23)
57
-0.000
(-0.15)
-0.000
(-0.15)
-0.000
(-0.12)
-0.000
(-0.17)
-0.004
(-2.39)
-0.004
(-2.38)
-0.004
(-2.40)
-0.004
(-2.35)
0.013
(9.12)
0.013
(9.12)
0.013
(9.09)
0.013
(9.14)
0.005
0.005
0.005
0.005
Figure 1: 3-D Plots of the likelihood of being highly constrained versus size and age.
One figure is displayed for each constraint variable as noted on the left-hand side of
each figure. The variable being plotted is the fraction of firms with a level of the
constraint variable that is more than two standard deviations above the mean based
on annual standard deviations.
Delay Investment Constraint
0.1
Large Firms
0.05
Medium Firms
0
Young
Small Firms
Mid‐Age
Old
0.1
Equity‐Focused
Constraint
Variable
Large Firms
0.05
Medium Firms
0
Young
Debt‐Focused
Constraint
Variable
Small Firms
Mid‐Age
Old
0.05
Large Firms
Medium Firms
0
Young
Small Firms
Mid‐Age
Old
0.1
Private Placement Focused Constraint
Large Firms
0.05
Medium Firms
0
Young
Small Firms
Mid‐Age
Old
58
Figure 2: 3-D Plots of the likelihood of being highly constrained versus R&D and
whether or not the firm mentions proprietary information risks in its 10-K. The
calculations are based only on firms with below median age (we expect and observe
in unreported tests that results are similar but weaker for the overall sample). One
figure is displayed for each constraint variable as noted on the left-hand side of each
figure. The variable being plotted is the fraction of firms with a level of the constraint
variable that is more than two standard deviations above the mean based on annual
standard deviations.
Delay Investment Constraint
0.1
High R&D Firms
Medium R&D Firms
0
Low R&D Firms
No Prop. Info
Has Prop. Info
Equity‐Focused
Constraint
Variable
0.1
High R&D Firms
Medium R&D Firms
0
Low R&Dl Firms
No Prop. Info
Has Prop. Info
Debt‐Focused
Constraint
Variable
0.05
High R&D Firms
Medium R&D Firms
0
Low R&D Firms
No Prop. Info
Has Prop. Info
Private Placement Focused Constraint
0.1
High R&D Firms
Medium R&D Firms
0
Low R&D Firms
No Prop. Info
Has Prop. Info
59
60
0.27
0.29
0.31
0.29
0.31
0.29
0.31
0.19
0.17
0.15
0.13
0.11
0.09
0.07
0.05
0.03
0.01
‐0.01
‐0.03
‐0.05
‐0.07
‐0.09
‐0.11
‐0.13
‐0.15
‐0.17
‐0.19
‐0.21
‐0.23
‐0.25
‐0.27
‐0.29
‐0.31
‐0.33
‐0.35
‐0.37
0.25
0.1
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
0.27
marginal debt‐focused delay constraint variable
0.27
0
0.23
0.02
0.25
0.04
0.25
0.06
0.21
0.08
0.23
0.1
0.23
marginal equity‐focused delay constraint variable
0.21
0.19
0.17
0.15
0.13
0.11
0.09
0.07
0.05
0.03
0.01
‐0.01
‐0.03
‐0.05
‐0.07
‐0.09
‐0.11
‐0.13
‐0.15
‐0.17
‐0.19
‐0.21
‐0.23
‐0.25
‐0.27
‐0.29
‐0.31
‐0.33
‐0.35
‐0.37
0.12
0.21
0.19
0.17
0.15
0.13
0.11
0.09
0.07
0.05
0.03
0.01
‐0.01
‐0.03
‐0.05
‐0.07
‐0.09
‐0.11
‐0.13
‐0.15
‐0.17
‐0.19
‐0.21
‐0.23
‐0.25
‐0.27
‐0.29
‐0.31
‐0.33
‐0.35
‐0.37
Figure 3: Frequency distribution of text-based constraint measures.
delay constraint variable
0.1
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
Figure 4: Firms mentioning delay investment constraints over time based on the 12
word window. The upper figure displays the fraction of firms mentioning delay investment constraints for the entire sample, and the lower figure displays this fraction
only for firms with December fiscal year endings. The increase in constraints in the
year 2008 is statistically significant at the 1% level in both figures.
Delay Constraint (All Firms)
7
6.5
6
5.5
5
4.5
4
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2008
2009
Delay Constraint (All Firms)
Delay Constraint (Firms With December FY Ends Only)
9
8.5
8
7.5
7
6.5
6
5.5
5
1997
1998
1999
2000
2001
2002
2003
2004
2005
Delay Constraint (Firms With December FY Ends Only)
61
2006
2007