# Zero-Leverage Puzzle

MYKOLA PINCHUK Pinchuk Mykola is MSc student in the Sauder School of Business,
UBC

ABSTRACT

In this paper, I examine why some firms have zero leverage. I fail to find
evidence that firms are unlevered because of managerial entrenchment since
these firms do not have weaker corporate governance. I reject the hypothesis
that firms become zero-leverage after prolonged periods of high market
valuation, since before levering these firms do not suffer from declining
valuations and continue to issue large amounts of equity. I find strong
evidence in favor of the financial constraints explanation of the zero-
leverage puzzle. Zero-leverage firms appear to be financially constrained
using three different measures of financial constraints. I obtain mixed
evidence on the financial flexibility hypothesis since all-equity firms
increase investments and acquisitions after levering, but the probability of
their levering decreased during the financial crisis. My results suggest that
financial constraints are the first-order driver of zero-leverage behavior and
are more important than less obvious explanations such as managerial
entrenchment.

##  1 Introduction

Capital structure has always been at the heart of corporate finance
literature. During the recent 60 years, it has been subject to extensive
research, both theoretical and empirical. While this literature, without any
doubt, contributed to our understanding of capital structure decisions,
academic research produced as many answers as generated new questions. A low-
leverage puzzle is an example of such a challenge. It arose due to the
inability of traditional capital structure theories to explain observable
levels of leverage. This puzzle has been actively researched within the last
30 years and gave rise to both new theories of capital structure, such as
dynamic trade-off theory (Fischer, Heinkel, Zechner, 1989), and new approaches
to testing predictions of existing capital structure theories (Strebulaev,
2007). While extensive research offered numerous explanations of the low-
leverage puzzle, with recent papers proposing models and estimation
techniques, which generate predictions, consistent with data, a new phenomenon
of zero-leverage firms was unraveled.

#####

The zero-leverage puzzle is the most extreme form of a low-leverage puzzle. In
order to identify whether a firm is underlevered, we need to determine optimal
leverage using some capital structure model, which is subjective and can
generate significantly different results. Some capital structure theories can
produce high optimal leverage and consider a firm underlevered, while under
another theory a firm will have optimal leverage. However, the zero-leverage
firm is clearly underlevered, since no traditional capital structure model can
predict zero leverage. Therefore, the main research interest in zero-leverage
puzzle lies in the fact that zero-leverage puzzle is the most convenient way
to study low-leverage puzzle. There are other advantages of using zero-
leverage firms to examine low-leverage puzzle as well. Low leverage may often
be caused by high equity values rather than result from the low-leverage
policy. Using zero-leverage firms to study a low-leverage puzzle makes the
identification of low-leverage firms independent of the leverage denominator,
thus making results robust to equity value fluctuations. Another advantage of
using a zero-leverage sample to study a low-leverage puzzle emerges in
analyzing debt initiation decisions of zero-leverage firms, since any debt
issuance by zero-leverage firms unambiguously means a change in financial
policy, while for low-leverage firms it can be used to refinance previous
debts. Studying determinants of zero-leverage policy can shed light on
economic forces, which contribute to a firm‘s decision to become unlevered, so
it is important for better understanding capital structure decisions in
general.

#####

In this paper, I try to determine, which less traditional theories of capital
structure can explain zero-leverage behavior. Previous research offered
numerous explanations of this phenomenon, including managerial entrenchment,
financial constraints, financial flexibility and high equity market
valuations. In order to find a theoretical explanation, most consistent with
the data, I test predictions, generated by each of the aforementioned
explanations. Summary statistics show that zero-leverage firms compared to
positive-leverage firms are smaller, younger, have higher cash reserves, and
lower tangibility. Zero-leverage firms have higher valuations, grow faster,
pay higher dividends, issue more equity, have more volatile cash flows and
lower capital expenses. Results without matching indicate that zero-leverage
firms are less profitable and have lower cash flow, while after matching zero-
leverage firms appear more profitable and cash flow-rich.

#####

Analysis of the transitivity of zero-leverage behavior shows that firms are
more likely to be unlevered at the time of going public (26%) rather than at
any other moment in time (19.5%). The probabilities of firms transferring
between zero-leverage and positive-leverage groups are 19.9% and 3.46%
respectively, implying long-term distribution with only 14.8% of zero-leverage
firms. This means that to a significant extent, new firms, which usually lever
up as time passes by, drive zero-leverage puzzle.

#####

Results of the analysis clearly reject explanations of zero-leverage behavior
from the point of view of weak corporate governance, since firms with higher
board independence and institutional ownership are more likely to be
unlevered. Tests falsify a hypothesis, resulting from an explanation of zero-
leverage puzzle with high equity market valuation, since prior to levering,
zero-leverage firms do not suffer from declining market valuations and
continue to issue large amounts of equity.

#####

I find strong evidence for both financial constraints and financial
flexibility explanations. Zero-leverage firms are more financially constrained
than positive-leverage firms using the SA-index, cash flow sensitivity of
cash, and textual analysis of 10-K filings as measures of financial
constraints. All-equity firms dramatically increase capital expenditures as
well as acquisitions immediately after levering, consistent with the financial
flexibility hypothesis. To discriminate between the financial constraints
explanation and financial flexibility theory, I use the recent financial
crisis as an exogenous shock to the economic environment. Since a crisis is
unlikely to make previously financially constrained zero-leverage firms less
financially constrained, the percentage of zero-leverage firms, deciding to
issue debt, should decrease. The financial flexibility explanation predicts
that zero-leverage firms will use their stockpiled debt capacity to issue debt
in order to avoid cutting investments or acquiring distressed competitors. A
dramatic decrease in the proportion of levering zero-leverage firms in 2009
provides clear and statistically significant evidence in favor of the
financial constraints explanation and against the financial flexibility
explanation.

##  2 Hypotheses Development and Related Studies

###  2.1 Conceptual Framework

First mentions of zero-leverage firms logically emerged within research on the
low-leverage puzzle (Graham, 2000). Minton and Wruck (2001) were the first to
study the distinction between low-leverage firms and their high-levered
counterparts. They find that low-leverage firms have high market-to-book
ratios and utilize transitory low-leverage policies. To explain the financing
decisions of low-leverage firms, their paper proposes a modified pecking order
theory, implying that low-leverage firms stockpile their debt capacity to
finance large discretionary expenditures in the future. While this paper does
not address the issue of zero-leverage firms explicitly, it identifies basic
stylized facts about low-leverage firms, which hold for zero-leverage firms as
well, since the latter is a large subset of low-leverage firms. Furthermore,
the paper proposed an original theoretical explanation of the behavior of low-
leverage firms, which laid the foundation for financial flexibility theory.

#####

Literature on zero-leverage firms is young and limited in both scope and
depth. Recent papers propose 5 different explanations of the zero-leverage
phenomenon:

  1. 1.

Financial constraints.

  2. 2.

Financial flexibility.

  3. 3.

Underinvestment hypothesis.

  4. 4.

Managerial entrenchment

  5. 5.

High equity market valuation

Explanation of zero-leverage decisions by financial constraints usually uses
the theory, proposed by Faulkender and Petersen (2006). Their paper suggests
that access to the debt market is a significant determinant of capital
structure, since firms, unable to issue relatively cheap debt, will resort to
equity financing. In the context of zero-leverage firms, these explanation is
supported by several empirical research (Devos, 2012; Byoun, 2012; Bessler et
al, 2012; ).

#####

DeAngelo (2007) introduces the financial flexibility theory, though
conceptually similar to the theory of Minton and Wruck (2001). It suggests
that firms choose low leverage and large cash reserves in order to stockpile
debt capacity and preserve their borrowing capacity that can be used to fund
new large-scale investment opportunities in the future. Another variation of
financial flexibility theory predicts that during an economic downturn
underlevered firm will raise debt financing and use it for aggressive price
competition. These explanations of the zero-leverage puzzle are supported by
Minton and Wruck; Byoun, Besser et al.

#####

The underinvestment hypothesis is built upon the theory of agency conflicts
between stockholders and debtholders (Myers, 1977). This explanation suggests
that for the management of highly levered firms, it can be optimal to forgo
low-risk positive NPV projects since these projects are mainly financed by
equityholders, while their proceeds are mainly paid to debtholders. For young
fast-growing firms in highly competitive industries, underinvestment may be
very costly, causing them to decrease leverage to avoid agency problems and
underinvestment. Dang and Boroshko find evidence, supporting this view.

#####

The managerial entrenchment hypothesis of the zero-leverage puzzle is based on
previous research, suggesting an effect of CEO preferences on financing policy
(Jensen 1986, Yermack 1997). Strebulaev (2013) extends this explanation by
considering the attempts of managers with high stock ownership and
underdiversified portfolios to decrease the risk of their portfolios by
limiting their firms‘ leverage. While Strebulaev (2013) and Boroshko (2015)
find evidence in favor of this explanation, Devos (2012) and Byoun (2012)
report findings inconsistent with the managerial entrenchment explanation.

#####

The high equity market valuation hypothesis suggests that the firms, whose
stocks were significantly overpriced in the past, are more likely to be
unlevered due to managerial attempts to take advantage of this overpricing.
This explanation of the zero-leverage puzzle is supported by Byoun (2012).
However, the findings of Lee and Moon are inconsistent with the equity market
valuation hypothesis.

###  2.2 Hypotheses Development

####  2.2.1 Depletion of post-IPO cash reserves

The results above indicate that zero-leverage firms are usually small and
young. This fact suggests a possible explanation of the zero-leverage puzzle,
according to which small and young firms do not need any external financing,
and thus do not issue debt or equity. Since the sample consists of public
firms, firms enter the sample immediately after IPO, which lets them
sufficiently increase cash holding. This explanation suggests that for some
time after IPO firms have large cash reserves, sufficient to finance all their
activities and investments, so they have no reason to issue any additional
claims. As time passes by, their cash, raised after IPO, decreases, and firms
start to resort to debt financing. This theory explains why zero-leverage
firms are small and young and generate the following testable hypothesis.

#####

Hypothesis 1: As a ratio of current cash reserves to cash reserves immediately
after IPO decreases, firms become more likely to issue debt.

####  2.2.2 Financial Constraints

An important question as to the zero-leverage puzzle is whether firms choose
not to issue debt or their zero leverage is not a result of their decision
since they are not able to issue debt due to problems with access to debt
financing. Financial constraints explanation suggests that unlevered firms
would wish to issue debt to utilize tax and other advantages of debt, but do
not have access to debt financing on acceptable terms. This explanation is
consistent with the observation that zero-leverage firms are smaller and
younger than positive-leverage firms since small and young firms are less
likely to have established reputations on debt markets or relations with
banks. Another possible problem with the access of small and young firms to
debt financing is that these firms are considered riskier and are expected to
pay higher interest to compensate for high risk. Financial constraints
explanation generates testable predictions, formalized in hypothesis 2A.

#####

Hypothesis 2A: Zero-leverage firms are more likely than positive-leverage
firms to be financially constrained.

#####

To test this hypothesis, I use different measures of financial constraints:
financial constraints variables, derived from 10-K filings, SA–Index and cash
flow sensitivity of cash.

####  2.2.3 Financial Flexibility

In contrast to financial constraints theory, financial flexibility theory
suggests that zero leverage is result of management‘s decision rather than
condition, forced by circumstances. Zero-leverage firms are able to issue debt
on acceptable terms, but they decide to temporarily abstain from it, in order
to have more leeway in the future regarding financing options. This theory
presents debt issuance decision as a trade-off between raising debt financing
now and raising debt financing in the future. It the firm decide to issue debt
now, it means that it will not be able to issue as much debt in the future as
it would be able otherwise. Financial flexibility theory argues that zero-
leverage policy is transitive, since firms are going back and forth between
zero leverage and positive leverage. Firms use debt issuance to finance large-
scale investment projects or acquisitions. Thus, under financial flexibility
theory, firm waits until such projects arise and then undertakes debt
issuance. Within next years, firm repays debt promptly (either because it is
short-term debt or using early repayment) and becomes unlevered. Financial
flexibility theory underpins hypothesis 3A.

#####

Hypothesis 3A: After transition from zero leverage to positive leverage, firm
increases investments or acquisitions.

#####

To test this hypothesis, I calculate capital expenditures, R&D expenses and
acquisitions before and after levering of zero-leverage firm.

####  2.2.4 High market valuation

Market valuation theory of capital structure considers current capital
structure as a result of previous attempts of firm‘s management to take
advantage of market conditions. Under this theory, firm decides which claims
to use by comparing conditions on equity market and debt market. Since debt is
senior to equity, stock prices are more volatile than debt prices and it is
equity market conditions, which matter most in firms‘ attempts to time the
market. Thus market valuation theory predicts that zero-leverage firms had
overvalued stocks in the past and their managements undertook large stock
issuance in an attempt to take advantage of overvalued stocks. High market
valuation explanation generates testable predictions, formalised in hypothesis
4A.

#####

Hypothesis 4A: Before the levering, zero-leverage firms have decreasing
valuations and issue less equity than positive-leverage firms.

####  2.2.5 Managerial entrenchment

Another explanation of zero-leverage phenomenon is based on the assumption
that zero-leverage policy is suboptimal and is pursued by the managers due to
managers‘ incentives. Explanation of exact reasons for managers to pursue
zero-leverage policy may vary. For example, Strebulaev (2013) proposes
modified theory by Yermack (1997), arguing that managers with high ownership
in their firms are more likely to be underdiversified, since major part of
their portfolio of financial assets have the same source of risk as their non-
financial portfolio. Thus managers try to decrease their main source of risk –
firm they are running. One of the methods of doing this is decreasing
leverage, with zero-leverage policy being the most extreme form.

#####

Attempts to avoid disciplining effect of debt payment is another possible
reason for suboptimal zero-leverage policy, imposed by managers. This notion
is based on the assumption that managers prefer to have discretion over as
much cash reserves as possible, partially in order to be able to waste cash on
perquisites or suboptimal projects. I do not discriminate between these two
possible explanation and use one single hypothesis to test this explanation,
which does not depend on driving forces behind managerial entrenchment.

#####

Hypothesis 5: Firms with strong corporate governance are less likely to have
zero leverage.

#####

Since zero-leverage policy is suboptimal, managers are destroying value by
pursuing it. Therefore in firms with stronger corporate governance and
external control it will be harder for mangers to pursue suboptimal zero-
leverage policy.

####  2.2.6 Multiple hypotheses: Financial constraints, Financial flexibility
and High market valuation

All hypotheses, proposed above, are not mutually exclusive, so, in case all of
them are accepted, we need a way to discriminate between them. For this effect
I use recent financial crisis as a setting, providing a source of exogenous
shock for zero-leverage firms. Due to clearly exogeneous source of shock, I
can establish causal effect of changes in economic environment on zero-
leverage policy.

#####

Financial constraints explanation of zero-leverage puzzle entails that firms
are zero-levered because they are unable to raise debt financing, which would
increase their value. Financial and economic crisis in 2008 – 2009 was
accompanied by deep crisis on the debt markets. Thus I argue that firms which
were financially constrained in 2007, are highly unlikely to become
financially unconstrained and obtain access to debt markets in 2008 – 2009. On
the other hand, many firms with access to debt market in 2007, lost it and
became financially constrained in 2008 – 2009. Financially constrained zero-
leverage firms lack opportunities to raise funding from debt markets while,
with regard to equity market drop in 2008, it is very unlikely that they
decide to raise equity. Therefore, zero-leverage financially constrained firms
have only internal source of cash, which suffers from plunge in consumption,
and hence, in corporate sales. All mentioned facts mean that financially
constrained firms are more fragile and find it harder to adapt to rapidly
changing economic environment. Therefore, their performance is expected to be
worse than those of unconstrained firms.

#####

Hypothesis 2B: In 2008 - 2009 zero-leverage firms were less likely to issue
debt and become positive-leverage firms. Performance of zero-leverage firms
was worse than performance of positive-leverage firms.

#####

Financial flexibility explanation implies that firms choose zero-levrage
policy in order to be able to issue debt in case of contingencies. Financial
crisis is exactly type of event, during which firms, which choose zero-
leverage policy to retain ability to issue debt, should take advangtage of
this opportunity. These firms may use debt financing either to keep
investments at previous level (it is well-known fact that firms cut
investments during crisis) or to undertake cheap acquisitions of distressed
competitors. So financial flexibility theory predicts that percentage of zero-
levered firms, becoming positive-leverage firms, will increase. Zero-leverage
firms will have higher investments than positive-leverage firms and undertake
more acquisitions. These testable predictions are formalised into the
following hypothesis.

#####

Hypothesis 3B: In 2008 and 2009 zero-leverage firms were more likely to issue
debt and become positive-leverage firms. Investments and acquisitions of zero-
leverage firms are higher than those of positive-leverage firms.

#####

High market valuation explanation of zero-leverage puzzle entails that zero-
leverage firms became unlevered due to high prices of their shares and hence,
massive equity issuance in the past, enough to cover all financing needs.
Having regard to plunge in equity market in 2008, it is obvious that these
firms can not count on equity issuance any more (even if their shares are
still overpriced relative to competitors). Thus these firms will be forced to
gradually start issuing debt.

#####

Hypothesis 4B: In 2008 and 2009 zero-leverage firms were more likely to issue
debt and become positive-leverage firms.

#####

Therefore, answer on the question whether percentage of levering zero-leverage
firms increased or decreased in 2008 – 2009 will let us discriminate between
Hypothesis 2B and Hypotheses 3B with 4B.

###  2.3 Related Empirical Studies

Strebulaev and Yang (2013) conduct thorough empirical study of zero-leverage
and ultra-low leverage firms (firms with leverage below 5%). They find that
this phenomenon is persistent in the long term and identify key differences
between zero-leverage firms and their levered peers. Zero-leverage firms pay
substantially higher dividends, have higher profitability and cash balances,
pay more taxes and issue more equity. To emphasize inconsistency of zero-
leverage financial policy with dominant theories of capital structure, they
estimate increase in market value of zero-leverage firms by 7% in case of
levering up to a level of their matched proxies. Strebulaev and Yang explain
existence of zero-leverage firms by managerial entrenchment. They find that
firms with high CEO ownership and less independent boards are more likely to
pursue zero-leverage policy. These findings and explanation by the managerial
entrenchment are consistent with previous literature (Jensen, 1986; Yermack,
1997).

#####

Devos (2012) investigates zero-leverage firms and tests two alternative
explanations using managerial entrenchment and financial constraints. He
reports evidence that zero-leverage firms are young, small and have high cash
balances. He does not find support for managerial entrenchment hypothesis and
argues that zero-leverage firms are financially constrained, implying that
their financial structure does not result from their choice. Since zero-
levered firms lose market share in economic downturns, Devos argues that
financial constraints explain zero-leverage policy better than financial
flexibility theory.

#####

Byoun (2012) fails to find evidence in favor of managerial entrenchment
explanation and argues that zero-leverage firms are unlevered due to both
financial constraints and high equity market valuations. He notes that high
valuations of zero-leverage firms are likely to indicate overvaluation of
their stock, which cause them to resort to equity financing. While these
findings are in line with Devos (2012), in studying zero-leverage firms Byoun
goes further and proposes to divide them into two groups on the basis of their
characteristics and plausible explanations of their financial policy. He finds
that most zero-leverage firms are small and young firms, which actively raise
equity financing. Thus Devos argues that financing decisions of these firms
are mainly governed by financial constraints. Another group of zero-leverage
firms consists of large and profitable firms, which have positive cash flows
and sufficient internal funds. While reporting detailed evidence on this
group, he does not provide clear explanation for their financial decisions.

#####

Numerous papers investigate zero-leverage phenomenon in international scope.
Dang (2009) finds that zero-leverage phenomenon in the UK is even more
frequent that in the US. He shows that characteristics of British zero-
leverage firms are similar to those of zero-leverage firms in the US and finds
evidence in favor of underinvestment hypothesis and dynamic trade-off theory
to explain financial decisions of zero-leverage firms. His analysis rejects
explanation of zero-leverage puzzle from pecking order theory and finds mixed
evidence for financial flexibility theory.

#####

Besser, Drobetz, Haller and Meier (2012) investigate international sample from
Compustat Global and find the same characteristics of zero-leverage firms.
They analyze recent upward trend in a fraction of zero-leverage firms and
explain it by higher IPO activity together with observation that new public
firms are more likely to be unlevered. A shift in industrial composition of
stock market towards industries with higher fraction of all-equity firms is
another plausible explanation. In explaining zero-leverage puzzle, they base
their analysis on Byoun (2012) and extend his attempt to divide zero-leverage
firms into two groups. They argue that majority of zero-leverage firms are
unlevered due to financial constraints. These firms are usually small, young,
do not generate enough cash flows and have small debt capacity. They remain
unlevered for a long time and actively raise equity financing. Another group
consists of large and profitable unlevered firms, which have sufficient debt
capacity, but choose zero-leverage policy. Besser, Drobetz, Haller and Meier
argue that financial flexibility is the main reason for their financial
decision to abstain from debt and prove it by lower persistence of zero
leverage policy of large unlevered firms.

#####

Boroshko and Kokoreva (2015) study zero-leverage firms in Russia and Brazil
and find support for managerial entrenchment explanation and underinvestment
hypothesis. They report mixed evidence in favor of financial constraints
hypothesis and argue that managerial entrenchment is the best explanation of
zero-leverage puzzle in Russia and Brazil. Li and Gao (2015) investigate
effect of zero-leverage policy on inefficient investment in China. They find
that zero-leverage firms undertake more inefficient investment than levered
firms and explain this effect by the lack of bank monitoring. Their evidence
suggests that effect of zero-leverage policy on propensity to undertake
inefficient investment is sufficiently lower for unlevered firms with strong
external monitoring.

#####

Lee and Moon (2011) examine stock returns of firms with persistent zero-
leverage policy in the US. They find that these firms have significantly
positive abnormal returns. This finding is remarkable, since it contradicts
equity market valuation hypothesis explaining zero-leverage puzzle by firms‘
attempts to benefit from overvalued stocks. Therefore, equity market valuation
hypothesis predicts that zero-leverage firms will have negative abnormal
returns.

#####

Lee and Moon (2015) extend their previous research by distinguishing between
financially constrained zero-leverage firms and zero-leverage firms with high
debt capacity. They find that both groups of unlevered firms have positive
abnormal returns, with unconstrained firms generating significantly higher
returns that their constrained counterparts. They suggest that this effect
shows that financial decision to abstain from debt financing indicates
disciplined and prudent management, which explains better performance of all-
equity firms.

##  3 Data and summary statistics

In this section, I describe the data and present summary statistics. The
sample is constructed from merged annual Compustat and CRSP databases for the
period 1996–2015. Other sources of data include BoardEx, Compustat Execucomp,
Thomson Reuters and 10-K SEC filings. In order to test managerial entrenchment
hypothesis, I retrieved data on institutional ownership, managerial ownership,
board composition and board independence from Execucomp, Thomson Reuters and
BoardEx respectively. I use 10-K filings to create additional measure of
financial constraint. To this effect, I parse ”Liquidity and Capital
Resources” subsection for all electronically filed 10-Ks for the period 1996 –
2015. This approach will be discussed in detail later.

#####

For the purposes of analyzing zero-leverage puzzle, difference between book
and market leverage is not significant, since for unlevered firms numerator in
both formulas is equal to zero. Thus further analysis uses only book leverage.
Zero-leverage firm is defined as a firm, which has neither current nor long-
term debt in the given year, while positive-leverage firms is defined as a
firm, having non-zero current or long-term debt. Book leverage ratio of firm i
in year t is defined as follows:

$BL_{it}=(DLTT_{it}+DLC_{it})/(AT_{it}),$ (1)

where DLTT is the value of long-term debt and DLC is the value or current debt
(debt with maturity below one year). This definition is compatible to recent
papers (Strebulaev, 2012; Lemmon, Roberts and Zender, 2008; Graham and Leary,
2011).

#####

Appendix A provides an overview of all variables as well as detailed
description of their calculation. The sample includes only US firms with
positive total assets and excludes regulated firms (Standard Industry
Classification codes 4900 – 4999) as well as financial companies (SIC codes
6000 – 6999). To make results robust to characteristics of small firms, sample
includes only companies with total book value of assets more than $10 million
in 2015 dollars. To adjust for inflation all nominal variables, I use Consumer
Price Index (CPI) data from Federal Reserve Bank of St. Louis. After excluding
duplicated firm-year observations there are 104688 firm-year observations
left. To mitigate effect of outliers, all variables are trimmed at 1% and 99%
percentile levels.

#####

Table 1 presents dynamics of zero-leverage policy for the sample period. From
1996 to 2015 number of zero-leverage firms as a fraction of all public firms
increased from 14.7% to 22%, showing recently increased significance of zero-
leverage phenomenon. 2000 – 2005 period accounts for the most rapid growth of
zero-leverage firms percentage, when fraction of zero-leverage firms surged
from 16% to 23%. Despite the fact that fraction of zero-leverage firms
decreased by 2% after the recent crisis, there is clear increasing long-term
trend in zero-leverage policy. Zero-leverage companies accounts for 19.5% of
the companies in the whole sample.

#####

Tables 2, 3 and 4 reports distribution of zero-leverage firms among broad and
narrow industries. Broad industry is defined based on 2-digit SIC code, while
3-digit SIC code is used to determine narrow industries. There is some
heterogeneity in occurrence of zero-leverage policy among broad industries.
Construction as well as Transportation, Communication and Utilities have the
lowest percentage of zero-leverage firms, which is 5.5% and 7.4% respectively.
Services is the only broad industry, where fraction of zero-leverage firms is
sufficiently higher than in the whole sample (28.4% compared to 19.5%). In
spite of obvious differences of propensity to use zero-leverage policy among
broad industries, unveiled distinctions are not large enough to explain all
the variation in the sample, since fraction of zero-leverage companies is
below 30% in all broad industries.

#####

Tables 3 and 4 describe heterogeneity in usage of zero-leverage policy among
narrow industries. Educational services, Apparel and Accessory Stores, and
Non-classifiable Establishments have the highest percentage of zero-leverage
firms – 32.6%, 31.8% and 31.6% respectively. The list of 10 industries with
the highest fraction of zero-leverage companies consists of different types of
services as well as manufacturing industries. Interesting fact is that even in
case of narrow industries there are no industries with very high proportion of
zero-leverage companies, since the ‘most unlevered’ industry has only 32.6%
debt-free companies compared to 19.5% in the whole sample. Local passenger
transit is the only industry where all the firms have positive leverage. Other
industries with the lowest percentage of zero-leverage firms include
Automotive Dealers, Tobacco products, Petroleum and Coal, Auto Services,
General Building Contractors, and Water Transportation. Fraction of debt-free
firms is below 3% in all aforementioned industries.

#####

Uncovered differences between narrow industries can be explained using basic
trade-off theory and cost of financial distress as the main distinctive
feature of narrow industries. Services has the highest percentage of zero-
leverage firms, since due to high relative importance of intangible assets,
they lose relatively more value in case of financial distress compared to
transportation companies, which are less prone to use zero-leverage policy.

#####

Table 5 and Figure 1 provide insight into mean profitability of zero-leverage
firms as well as positive-leverage firms. Within all the sample period,
levered firms always were more profitable than zero-leverage firms. Starting
from 1998 zero-leverage firms always on average had negative profitability,
while positive-leverage companies usually were profitable. Figure 1
illustrates that within 1996 – 2015 profitabilities of zero-leverage and
positive-leverage firms evolved in the similar way, but profitability of zero-
leverage firms exhibites much higher volatility. Another interesting fact is
that profitability of zero-leverage firms increased insignificantly during
recession and then exhibited sudden decrease during recovery and expansion
period.

#####

Table 6 presents summary statistic of zero-leverage firms and positive-
leverage firms. To test statistical significance of differences in means of
variables, Table 6 reports results of T-test as well. Zero-leverage firms on
average are smaller, younger and less profitable (by means of profitability,
returns on assets and returns on equity). These results are consistent with
previous studies (Devos, 2008; Byoun, 2012; Strebulaev, 2013). Zero-leverage
firms have less tangible assets and hold more cash than firms with positive
leverage. These differences are extremely significant with absolute value of
t-statistics being above 150. Obtained results are in accord with intuition,
since tangible assets are the most convenient collateral for bank loans, while
cash holdings are good source of liquidity which could be raised through
borrowings otherwise. Zero-leverage firms have higher market valuation,
captured by market-to-book ratio, price-to-sales ratio and Tobin‘s Q.
Interestingly enough, while having lower profitability, zero-leverage firms
pay higher dividends than positive-leverage firms. One of possible explanation
of this phenomenon is using dividends as substitution for debt payment in
order to discipline managers and mitigate shareholder-management agency
conflicts. Zero-leverage firms have higher R&D expenses, but lower capital
expenditures. On average, zero-leverage firms grow faster, have lower internal
cash flow, issue more equity and have much higher volatility of profitability
and cash flows.

#####

Many reported results are expected from extensive literature on leverage and
its determinants (Lemmon, Roberts, Zender, 2008), but some evidences, such as
lower profitability, cash flows and capital expenditures of zero-leverage
firms are rather unexpected. As to profitability of zero-leverage firms
compared to profitability of levered firms, the situation significantly
changed over time. Although this timeframe is beyond sample, used in the
current paper, zero-leverage firms had higher profitability compared to
levered firms before 1980, while after 1980 positive-leverage companies have
been more profitable than zero-levered firms with this gap constantly
increasing.

#####

Table 7 reports results of propensity-score matching, where each zero-leverage
firm was matched to three levered firms in the same 3-digit SIC industry,
fiscal year and with total assets within 66% - 150% of its total assets.
Summary statistics of zero-leverage firms and their matched levered peers
differs significantly from previous results. Main reason for this difference
is that size of the firm is extremely important variable, which is likely to
influence all other variable. Therefore, when comparing zero-leverage firms to
unmatched positive-leverage firms, the fact that zero-leverage firms are
smaller than positive-leverage firms will influence the results, since in fact
small firms are compared to large firms. Matching by size lets eliminate this
channel and argue that the differences, which are left, are mostly
attributable to leverage.

#####

After matching by fiscal year, narrow industry and size, zero-leverage firms
are still younger, but difference becomes less significant. Probably it is
caused by close correlation between size and age. The most interesting result
is that after matching zero-leverage firms are more profitable (in terms of
profitability, ROA and ROE) than their matched levered peers are. Zero-
leverage firms have less tangible assets, hold more cash, pay higher
dividends, grow faster and have higher valuations. While unlevered firms still
have lower capital expenditures, difference in R&D expenses becomes
insignificant. Differences in mean standard deviations of profitability and
cash flow also become less significant.

#####

Although main objective of this section is to report basic evidence from the
data, it is possible to draw some preliminary conclusions as to validity of
the main explanations of the zero-leverage puzzle. Smaller size and age, lower
tangibility and profitability (in case without matching) of zero-leverage
firms are consistent with financial constraints explanation, although higher
asset growth of zero-leverage firms is a minor contradiction to this theory.
Financial flexibility explanation is not consistent with significantly higher
R&D expenses of zero-leverage firms. Summary statistics provides evidence for
high market valuation explanation too, since zero-leverage firms have higher
market valuation ratios.

#####

Table 8 reports evidences on transitivity of zero-leverage policy. While 19.5%
of all firms in the sample are zero-leverage firms, among entering firms 26.4%
are all-equity firms. This significant difference shows that fraction of zero-
leverage firms is much higher for firms at the moment they enter Compustat. At
each year, zero-leverage firm becomes levered with probability 19.9%, while
probability that levered firm becomes zero-leverage firm is only 3.4%. These
probabilities let us model system of firms, randomly moving between zero
leverage and positive leverage, as discrete time Markov Chain. Using these
transition probabilities, implied long-term distribution of firms must be
14.8% and 85.2%, which is significantly different from empirically observable
one. Therefore, data shows that fraction of all-equity firms among entering
firms is higher than the one among all firms in the sample, which in turn is
higher than long-term fraction of unlevered firms, implied by transition
probabilities. The most evident explanation of this phenomenon is that aging
zero-leverage firms slowly become positive-leverage firms. Alternative
explanation suggests that instead of transferring to positive-leverage firms,
zero-leverage firms are more likely to delist. Since probability of delisting
is 8.5% for zero-leverage firm compared to 9.8% for positive-leverage firm,
results suggest that high fraction of zero-leverage firm in the data is driven
by new firms, which are more likely to enter unlevered.

##  4 Empirical results

###  4.1 5.1 Depletion of post-IPO cash reserves

The first obvious test of post-IPO cash depletion explanation is to look
whether post-IPO zero-leverage firms issued equity. Since this explanation
suggests that young zero-leverage firms stay unlevered as long as they have
enough cash reserves left after IPO, these firms should not issue equity as
well as debt. However, in the sample of 4601 zero-leverage firm-year
observations, 60.11% of firm-year observation indicated positive equity issue.
Therefore, this basic test implies that explanation of zero-leverage puzzle
from the point of post-IPO cash depletion is not consistent with the data.

#####

In order to test Hypothesis 1, I create variable After-IPO Cash Ratio, equal
to ratio of current cash reserves to cash reserves immediately after IPO.
Thus, the theory predicts that young zero-leverage firm gradually depletes
cash holdings after IPO and After-IPO Cash Ratio will be gradually decreasing.
Therefore, zero-leverage firms issue debt when After-IPO Cash Ratio is low,
and we expect negative coefficient in regression of levering of zero-leverage
firms on After-IPO Cash Ratio.

#####

Table 9 reports results of regression of levering of zero-leverage firms on
After-IPO Cash Ratio. Second and third model specifications include industry
fixed effects at 2-digit and 3-digit SIC levels respectively. All three
regression models estimate significantly positive coefficients on After-IPO
Cash Ratio, implying then levering events of zero-leverage firms are
associated with high current cash holdings comparing to immediate after-IPO
cash reserves. These results falsify Hypothesis 1 and together with
unexpectedly high equity issuance of young zero-leverage firms imply that we
should reject explanation of zero-leverage puzzle from the point of view of
depletion of post-IPO cash reserves.

###  4.2 5.1 Financial constraints

In order to test financial constraints theory, I use the following measures of
financial constraints:

  1. 1.

Financial constraints from textual analysis of 10-K filings.

  2. 2.

SA-Index.

  3. 3.

Cash flow sensitivity of cash

Appendix B describes in detail measuring financial constraints from 10-K
annual filings. I use simplified approach, used by Hoberg and Maksimovic
(2014) to perform textual analysis and compute financial constraints variable.

#####

Table 10 reports results of textual analysis of 59 958 10-K filings. Only
10-K, 10-KSB, 10-K405 and 10-KSB40 were analyzed for non-financial companies.
For each fiscal year, fraction of firms, which appear to be financially
constrained, is 7 – 19%. Appendix B provides some examples of 10-K filings,
where financial constraints were identified. For the purposes of further
analysis, I take log of variable “Constraints”.

#####

SA-Index is another measure of financial constraints (Hadlock and Pierce,
2010). SA-Index is calculated as follows:

$SA\ index=(-0.737*Size)+(0.043*Size^{2})-(0.040*Age),$ (2)

where size was trimmed at $5B and age was trimmed at 37 years. Higher values
of SA-Index indicate higher level of financial constraint. The motivation
behind functional form of SA-Index is that young and small firms are
financially constrained more often, while dependence of constraint on size is
non-linear.

#####

Cash-flow sensitivity of cash is a measure of financial constraint, based on
assumption that more financially constrained firms tend to save cash out of
cash flows, since their cash flows are likely to be the only source of
liquidity. Since firm can not be sure as to future cash flows, it saves cash
from current cash flows to ensure against future cash flow disruptions.
Almeida (2004) show that financially constrained firms usually save 5-6% of
cash flows in cash, while unconstrained firms do not save any statistically
significant percentage of cash flows. To calculate this measure of financial
constraints, I run the same regression as Almeida (2004):

$\Delta{CashFlow}_{it}=alpha_{0}+alpha_{1}*CashFlow_{it}+alpha_{2}*MB_{it}+alpha_{3}*Size_{it}.$
(3)

Firms for which coefficient on Cash flow was at least 0.05, were classified as
financially constrained. This approach identifies 8352 firms out of 41223 as
financially constrained. Table 11 presents results of test of Hypothesis 2A.
When using measure of financial constraints, derived from 10-K filings,
regression of zero-leverage status on logarithm of financial constraints
estimates positive coefficient, implying that zero-leverage status is
associated with higher values of financial constraints. After adding 2-digit
and 3-digit industry fixed effects, coefficients decrease substantially, but
remain statistically significant. Therefore, under measure of financial
constraints from 10-Ks, Hypothesis 2A on zero-leverage firms being financially
constrained is accepted.

#####

Using SA-index as a measure of financial constraint gives similar results with
even higher statistical significance. Since higher SA-index means higher level
of financial constraint, positive and statistically significant coefficient in
regression of zero-leverage status on SA-index suggests that zero-leverage
status is associated with financial constraint. Adding traditional variables
in leverage regressions as extra covariates and including industry fixed
effects does not change results sufficiently. Coefficient on SA-index remains
positive and very significant, implying that using SA-index as a measure of
financial constraint provides strong evidence in favor of Hypothesis 2A.

#####

Finally, cash flow sensitivity of cash as a measure of financial constraint
returns similar results, as shown in the last three columns in Table 11. In
regression of zero leverage status on financial constraint, identified from
cash flow sensitivity of cash, coefficient on financial constraint dummy
variable is positive and statistically significant. Controlling for
traditional variables in leverage regressions and industry fixed effects
decreases value of coefficient more than by half, but it remains significantly
positive. Thus, all three measures of financial constraint provide strong
evidence in favor of Hypothesis 2A, and thus financial constraint explanation
of zero-leverage puzzle.

###  4.3 Financial flexibility

First obvious test of financial flexibility explanation of zero-leverage
puzzle is to examine transitivity of zero-leverage status. Table 12 reports
result of transitivity analysis. Since the sample includes 20 years of data,
the largest possible number of consecutive years with zero leverage is 20
years. Financial flexibility theory predicts that for most firms zero leverage
will be short-term phenomenon and unlevered firms will issue debt within
couple of years after becoming all-equity firms. Table 12 reports that 50.7%
firms stay unlevered for 2 years or less and 63.5% firms stay unlevered for
less or equal to 3 years. These results show that behavior of half of firms is
generally consistent with financial flexibility explanation, while the other
half does not utilise zero-leverage policy as a short-term measure. So these
results provide mixed evidence for financial flexibility theory.

#####

Table 13 presents results of test of Hypothesis 3A. Hypothesis 3A entails that
immediately after levering a firm increases investment or acquisition. Looking
at R&D expenses, no increase after levering takes place. Furthermore, there is
statistically significant decrease in R&D 1 year after levering. This is
exacerbated by the fact that immediately after levering R&D decreases too,
though decrease is statistically insignificant. However, these two fact
together imply that 1 and two years after levering R&D expenses are
significantly smaller than prior to levering.

#####

Dynamics of capital expenditures near levering looks completely different. It
is difficult to answer a question whether capital expenditures or R&D expenses
are better variables to test financial flexibility theory, since in capital-
intensive industries a role of capital expenditures is much higher than in
less capital-intensive industries. One year before levering there is
statistically significant increase in capital expenditures (from 4.4% to
5.3%), followed by another statistically significant increase immediately
after levering (from 5.3% to 6.2%). During the next two years, capital
expenditures gradually revert to its starting values. Significant increase in
capital expenditures 1 year before levering may look suspicious, but financial
flexibility theory entails that levering is planned decision of a firm, so
firm may start to increase capex, already knowing that it will raise funds by
issuing debt soon. So these results are consistent with predictions of
financial flexibility theory.

#####

Analysis of acquisition activities near levering provides strong evidence in
favor of Hypothesis 3A. During all years before levering acquisition levels
are small and never exceed 0.5%. In the years of levering dramatic increase in
acquisitions is registered, with acquisitions amounting to 6.3% of total
assets. For all subsequent years acquisition activities decrease substantially
but still remain significantly higher than before levering. Therefore analysis
of capital expenditures and acquisitions strongly accepts hypothesis 3A, while
analysis of R&D expenses does not provide evidence in favor of Hypothesis 3A.

###  4.4 High market valuation

Table 14 reports results of testing Hypothesis 4A regarding predictions of
market valuation theory as to zero-leverage firms. Fisrt part of Hypothesis 4A
entails that zero-leverage firms have decreasing market valuation prior to
levering. In fact it is this decrease which makes them issue debt. Table 14
shows that neither market-to-book ration nor Tobin‘s Q exhibit any significant
decrease before levering: both variables are increasing, but this increase is
not statistically significant. Price/sales ratio 2 years prior to levering is
significantly higher than 3 year prior to levering, while 1 years before
levering price/sales ratio is again higher comparing to previous year, the
increase being even more statistically significant. Therefore, this test
provides evidence against first part of Hypothesis 4A.

#####

Second part of Hypothesis 4A predicts that decreasing valuations of zero-
leverage firms make them stop issuing equity and finally make them decide to
lever. Table 14 reports that for all years before levering zero-leverage firms
are issuing significantly more equity than levered firms. Thus results provide
evidence against second part of Hypothesis 4A as well. Ultimately, tests of
Hypothesis 4A give clear evidence against this hypothesis and against high
market valuation explanation of zero-leverage puzzle in general.

###  4.5 Managerial entrenchment

Hypothesis 5 states that firms with stronger corporate governance will be less
likely to be unlevered. I use board independence and institutional ownership
as proxies for quality of institutional governance. Furthermore, I use
plausibly exogenous source of treatment: delisting shock. This variable
calculates a change in percentage of firms, which were delisted due to mergers
in the same 2-digit SIC industry in the previous year. The motivation behind
this is a a notion that unusually high takeover activity in some industry
should scare managers of all firms in the industry and make them worry as to
possibility of being acquired. Poor management is known as one the most
frequent reasons for takeover, so, to decrease probability of hostile
takeover, managers will attempt to decrease suboptimal value-destroying
activities. Under managerial entrenchment theory, zero-leverage policy is
suboptimal, so it can cause hostile takeover.

#####

Table 15 reports results of test for managerial entrenchment explanation of
zero-leverage puzzle. Under hypothesis 5, higher board independence,
institutional ownership and delisting shock should cause lower probability of
zero-leverage policy, so coefficients on all variables should be negative. In
regression of zero-leverage dummy variable on only these three covariates
board independence have statistically significantly positive coefficient,
while institutional ownership have negative statistically significant
coefficient, as expected. For all possible model specifications, coefficient
on delisting shock is not statistically significant. Second, third and fourth
columns in Table 15 reports results of adding traditional capital structure
variables, 2-digit and 3-digit fixed effects. All these regressions estimate
significantly positive coefficients on board independence and institutional
ownership. Therefore, results of the test of managerial entrenchment
explanation clearly falsify Hypothesis 5 and suggest that managerial
entrenchment theory is not valid explanation for zero-leverage puzzle.

###  4.6 Multiple hypotheses

Table 16 reports results of tests of first parts of Hypotheses 2A, 3A and 4A.
In 2007 23% unlevered firms issued debt, in 2008 only 20.9% firms with zero
leverage become levered. While this decrease from 2007 to 2008 is not
statistically significant, dramatic decrease from 20.9% in 2008 to 13.5% in
2009 is obvious and statistically significant. Therefore, financial crisis
clearly negatively affected probability of levering of zero-leverage firms.
Thus we can see that this test falsifies Hypotheses 3A and 4A. Since high
market valuation explanation (Hypothesis 4A) had already evidences against, at
this point I dismiss this explanation of zero-leverage puzzle. Significant
decrease in probability of levering for zero-leverage firms is evidence in
favor of financial constraints explanation.

#####

Second parts of Hypotheses 2A and 3A are not mutually exclusive and relate to
changes of variables near transition from zero-leverage firms to positive-
leverage firm. Table 17 presents evidence on Hypothesis 2A. Here I utilise
difference-in-difference estimator with financial crisis as a source of
exogenous shock. Thus effects of treatment and zero-leverage status are
captured by coefficients on these variables, while effect of financial crisis
on zero-leverage firms compared to its effect on positive-leverage firms is
captured by Treatment*Zero-leverage coefficient. All indicators of performance
(profitability, ROA, ROE, stock returns, market share) decreased for zero-
leverage firms compared to positive-leverage firms after crisis, though this
decrease was statistically significant only for ROA and returns. So this test
provides mixed evidence in favor of financial constraints explanation.

#####

Table 18 reports findings on Hypothesis 3A (financial flexibility). As
expected, capital expenses, R&D and acquisitions decreased after the crisis.
While R&D expenses of zero-leverage firms were not affected more or less than
those of positive-leverage firms, capital expenditures and acquisitions for
zero-levered firms increased dramatically compared to positive-leverage forms.
These results provide evidence in favor of second part of Hypothesis 3A
(financial flexibility).

##  5 Conclusion

This paper attempts to determine which theories of capital structure can
explain zero-leverage behavior. Previous research offered numerous
explanations of this phenomenon, including managerial entrenchment, financial
constraints, financial flexibility and high equity market valuations. In order
to find a theoretical explanation, most consistent with the data, I test
predictions, generated by each of the aforementioned explanations.

#####

Results of the analysis clearly reject explanations of zero-leverage behavior
from the point of view of weak corporate governance, since firms with higher
board independence and institutional ownership are more likely to be
unlevered. Tests falsify a hypothesis, resulting from an explanation of zero-
leverage puzzle with high equity market valuation, since prior to levering,
zero-leverage firms do not suffer from declining market valuations and
continue to issue large amounts of equity.

#####

The paper finds strong evidence for both the financial constraints explanation
and the financial flexibility explanation. Zero-leverage firms are more
financially constrained than positive-leverage firms by three different
measures of financial constraints and dramatically increase capital
expenditures as well as acquisitions immediately after levering. To
discriminate between the financial constraints explanation and financial
flexibility theory, I use the recent financial crisis as an exogenous shock to
the economic environment. Since a crisis is unlikely to make previously
financially constrained zero-leverage firms less financially constrained, the
percentage of zero-leverage firms, deciding to issue debt, should decrease.
The financial flexibility explanation predicts that zero-leverage firms will
use their stockpiled debt capacity to issue debt in order to avoid cutting
investments or acquiring distressed competitors. A dramatic decrease in the
proportion of levering zero-leverage firms in 2009 provides clear and
statistically significant evidence in favor of the financial constraints
explanation and against the financial flexibility explanation.

#####

This paper shows that borrowing constraints explain zero-leverage behavior
better than more complicated explanations, such as weak corporate governance
or high equity valuations. My results suggest that financial constraints are
the key driving force behind zero-leverage policy and contribute to the
research of the zero-leverage and low-leverage puzzle. Furthermore, I unveil
the role of financial constraints in zero-leverage behavior and calls upon
theoretical studies of capital structure, accounting for borrowing
constraints. On the one hand, financial constraints are important determinants
of zero-leverage policy but are unlikely to be a key factor in the more
general theory of capital structure. On the other hand, traditional capital
structure theories, such as trade-off theory, explain capital structure
decisions relatively well except in cases of zero and ultralow leverage.
Therefore theoretical research, incorporating borrowing constraints as an
important friction into the dynamic trade-off model look specifically
promising.

##  6 References:

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cash. J. Finance 59, 1777–1804.

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Berger, P.G., Ofek, E., Yermack, D.L., 1997. Managerial entrenchment and
capital structure decisions. J. Finance 52, 1411–1438.

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Bessler, W., Drobetz, W., Haller, R., and Meier, I. (2013). The international
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structure of companies in the emerging capital markets. Working paper.

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Byoun, S. and Xu, Z. (2013) Why do some firms go debt free?, Asia-Pacific
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Dang, V., 2011. An empirical analysis of zero-leverage firms: evidence from
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DeAngelo, H., DeAngelo, L., 2007. Capital structure, payout policy, and
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Faulkender, Michael, and Mitchell A. Petersen, 2006, Does the source of
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and directions for the future. Annual Review of Financial Economics 3,
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Hoberg, Gerard and Vojislav Maksimovic, 2015, “Redefining Financial
Constraints: A Text-Based Analysis,” Review of Financial Studies, 28(5),
1312-1352.

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takeovers. Am. Econ. Rev. 76, 323–329.

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constraints: moving beyond the KZ index. Rev. Financ. Stud. 23, 1909–1940.

  17. 17.

Lee, H., Moon, G., 2011. The long-run equity performance of zero-leverage
firms. Managerial Finance 37, 872–889.

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term stock returns of debt-free firms, Applied Economics, 47:4, 333-345

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## Appendix A

### Definition of variables

Size | log(Total Assets)
---|---
Age | number of years the firm is in Compustat
Profitability | $\frac{EBIT}{TotalAssets}$
Tangibility | $\frac{Property,\ Plant\ andEquipment}{Total\ Assets}$
Cash | $\frac{Cash\ and\ Short-Term\ Investments}{Total\ Assets}$
R&D | $\frac{Research\ and\ Development\ Expense}{Total\ Assets}$
MB (Market-to-Book) | $\frac{Total\ Assets-Total\ Common\ Equity+Market\ Value}{Total\ Assets}$
P/S (Price/Sales) | $\frac{(Common\ Shares\ Outstanding)(Price\ Close)}{Sales}$
ROA | $\frac{Net\ Income}{Total\ Assets}$
ROE | $\frac{Net\ Income}{Total\ Common\ Equity}$
Asset Growth |  $\frac{Total\ Assets_{t}}{Total\ Assets_{t-1}}$ \- 1
Dividends | $\frac{Dividends\ Common}{Total\ Assets}$
Capex | $\frac{Capital\ Expenditures}{Assets\ Total}$
Cash Flow | $\frac{Income\ Before\ Extraordinary\ Items+Depreciation\ and\ Amortization}{Total\ Assets}$
Net Debt Issuance |  $\frac{Tota\l Debt_{t}}{Total\ Debt_{t-1}}$ \- 1
Equity Issuance | $\frac{Sale\ of\ Common\ and\ Preferred\ Stock-Purchase\ of\ Common\ and\ Preferred\ Stock}{Total\ Assets}$
Tobin‘s Q | $\frac{Total\ Assets+Market\ Value+Common\ Equity+Deferred\ Taxes}{Total\ Assets}$
Acquisitions | $\frac{Acquisitions}{Total\ Assets}$
Book Leverage | $\frac{LongTermDebt+ShortTermDebt}{Total\ Assets}$
After-IPO Cash Ratio | $\frac{Cash_{it}}{Cash_{i0}}$
SA-index | $(0.737*Size)+(0.043*Size^{2})-(0.040*Age)$
Board Independence | $\frac{Numberofexternaldirectors}{BoardSize}$
Delisting Shock | $\max{(\frac{Fraction\ of\ delistings\ due\ to\ merger\ in\ 2-digit\ SIC\ industry_{t}}{Fraction\ of\ delistings\ due\ to\ merger\ in\ 2-digit\ SIC\ industry_{t-1}}-1,0)}$

## Appendix B

### Textual analysis of 10-K filings for financial constraints

I parse “Liquidity and Capital Resourses” subsection from 10-K, 10-K405,
10-KSB and 10-KSB40 filings for the period 1996 – 2015. This subsection
contains remarks regarding financial condition and liquidity sources as well
as intentions with regard to future capital market interactions.
To create measure of financial constraints I use approach similar to one, used
by Hoberg and Maksimovic (2014). This approach assumes that the firms, which
discuss in “Liquidity and Capital Resources” delaying investments are
financially constrained. Since firms discuss liquidity issues in this
subsection, it follows logically that firms decrease investments due to
liquidity concerns.
In order to identify delaying investments I use composition of words from 2
lists. To find firms which directly report potential delay in investments, I
search “Liquidity and Capital Resources” subsection for occurrences of words
from list 1, identify their locations and then search for occurrences of words
from list 2 in close neighbourhood to locations of words from list 1.
List 1 consists of verbs, synonymous to “delay”, while list 2 contains nouns
and phrases, standing for processes, suffering from delays due to liquidity
issues. Note that “*” denotes wildcard.

List 1: delay*, abandon*, eliminate*, curtail*, postpone*, curb*, suspend*,
defer*, discontinue*, cut* back, scale* down

List 2: construction*, expansion*, acquisition*, restructuring, project*,
research, development, exploration, product*, expenditure*, manufactur*,
entry*, renovat*, growth, activities, activity, capital improvement*, capital
spend*, capital proj*, commercial release*, business plan, transmitter
deployment, opening restaurants.

After word from list 1 is identified, I search for words from list 2 within 10
words after found word from list 1.

### Examples of 10-Ks with identified financial constraints

#### 10-K for 2014 fiscal year of Paratek Pharmaceuticals:

“… Prior to the Merger and recapitalization in October 2014 we were subject to
significant liquidity constraints. During 2014 and 2013 we significantly
curtailed our research and development and other operating activities as we
worked within financial constraints…”

#### 10-K for 2014 fiscal year of Nanoantibiotics:

“… However, there can be no assurance that we will be able to obtain required
funding. If we are unsuccessful in securing funding from any of these sources,
we will defer, reduce or eliminate certain planned expenditures in our
research protocols. If we do not have sufficient funds to continue operations,
we could be required to seek bankruptcy protection or other alternatives that
could result in our stockholders losing some or all of their investment in
us…”

#### 10-K for 2013 fiscal year by HKN Inc.:

“…There can be no assurances that, if needed, we will be able to raise
adequate funds from these or other sources or enter into licensing, partnering
or other arrangements to advance our business goals. Our inability to raise
such funds or enter into such arrangements, if needed, could have a material
adverse effect on our ability to develop our products. Also, we have the
ability to curtail discretionary spending, including some research and
development activities, if required to conserve cash. Because of our long-term
capital requirements, we may seek to access the public equity market whenever
conditions are favorable, even if we do not have an immediate need for
additional capital at that time…”

#### 10-K for 2013 fiscal year by Nanoantibiotics:

“…We continue to focus on improving our operating efficiency to increase
operating cash flows, since we are unlikely to have access to public markets.
Our actions have included implementing operational expense reduction
initiatives, delaying or eliminating certain capital spending and research and
development projects, focusing on timely customer collections and re-
negotiating longer payment terms with our vendors. We hope that we will be
able to satisfy our cash requirements for at least the next twelve months with
the liquidity provided by our existing cash, cash equivalents and marketable
securities…”

#### 10-K for 2013 fiscal year by Coronado Biosciences:

“…Adequate additional funding, particularly subsequent to the negative results
from our TRUST-I clinical trial, may not be available to us on acceptable
terms or at all. If adequate funds are not available to us when needed, we may
be required to delay, curtail or eliminate one or more of our research and
development programs and, potentially, delay our growth strategy…”

Table 1: Fraction of zero-leverage firms over time Year | Zero leverage | Normal leverage | Obs
---|---|---|---
$1996$ | 14.67% | 85.33% | $5795$
$1997$ | 14.89% | 85.11% | $5701$
$1998$ | 14.97% | 85.03% | $5830$
$1999$ | 15.28% | 84.72% | $5721$
$2000$ | 15.99% | 84.01% | $5360$
$2001$ | 16.57% | 83.43% | $4907$
$2002$ | 17.72% | 82.28% | $4656$
$2003$ | 19.73% | 80.27% | $4531$
$2004$ | 21.67% | 78.33% | $4411$
$2005$ | 22.92% | 77.08% | $4384$
$2006$ | 22.94% | 77.06% | $4264$
$2007$ | 22.92% | 77.08% | $4131$
$2008$ | 21.28% | 78.72% | $4050$
$2009$ | 22.14% | 77.86% | $3849$
$2010$ | 23.55% | 76.45% | $3702$
$2011$ | 23.45% | 76.55% | $3731$
$2012$ | 22.74% | 77.26% | $3822$
$2013$ | 22.84% | 77.16% | $3910$
$2014$ | 22.31% | 77.69% | $3801$
$2015$ | 21.92% | 78.08% | $3112$
Table 2: Fraction of zero-leverage firms in broad industries 2-digit SIC Industry | Zero | Normal | Observations
---|---|---|---
Agriculture, Forestry and Fishing | 9.61% | 90.39% | $461$
Mining | 14.59% | 85.41% | $6762$
Construction | 5.48% | 94.52% | $1419$
Manufacturing | 20.29% | 79.71% | $50722$
Transportation, Communication and Utilities | 7.42% | 92.58% | $9038$
Wholesale Trade | 10.2% | 89.8% | $4155$
Retail Trade | 14.37% | 85.63% | $7571$
Services | 28.37% | 71.63% | $23160$
All industries | 19.49% | 80.51% | $104688$
Table 3: Industries with the highest fraction of zero-leverage firms | 3-digit SIC Industry | Fraction
---|---|---
1 | Educational Services | $32.6\%$
2 | Apparel & Accessory Stores | $31.8\%$
3 | Non-Classifiable Establishments | $31.6\%$
4 | Business Services | $27.7\%$
5 | Instruments & Related Products | $24.9\%$
6 | Leather | $23.9\%$
7 | Pipelines, Except Natural Gas | $23.7\%$
8 | Transportation Services | $23.1\%$
9 | Chemical & Allied products | $22.5\%$
10 | Engineering & Management Services | $22.5\%$
Table 4: Industries with the lowest fracion of zero-leverage firms | 3-digit SIC Industry | Fraction
---|---|---
1 | Local Passenger Transit | $0.0\%$
2 | Automative Dealers & Service Stations | $1.8\%$
3 | Tobacco Products | $1.8\%$
4 | Petroleum & Coal | $2.0\%$
5 | Auto Services | $2.2\%$
6 | General Building Contractors | $2.5\%$
7 | Water Transportation | $2.7\%$
8 | Paper & Allied Products | $2.9\%$
9 | Transportation by Air | $3.8\%$
10 | Social Services | $3.9\%$
Table 5: Mean profitability of different leverage groups over time Years | Zero | Normal
---|---|---
$1996$ | $0.9\%$ | $3.1\%$
$1997$ | $0.5\%$ | $2.0\%$
$1998$ | $-5.3\%$ | $-1.5\%$
$1999$ | $-6.1\%$ | $-1.1\%$
$2000$ | $-10.8\%$ | $-1.1\%$
$2001$ | $-14.8\%$ | $-2.6\%$
$2002$ | $-9.1\%$ | $0.0\%$
$2003$ | $-4.7\%$ | $1.6\%$
$2004$ | $-3.3\%$ | $3.4\%$
$2005$ | $-3.7\%$ | $2.4\%$
$2006$ | $-4.4\%$ | $2.3\%$
$2007$ | $-4.2\%$ | $1.8\%$
$2008$ | $-5.1\%$ | $0.7\%$
$2009$ | $-2.7\%$ | $1.3\%$
$2010$ | $-1.3\%$ | $4.1\%$
$2011$ | $-3.6\%$ | $3.3\%$
$2012$ | $-6.7\%$ | $1.5\%$
$2013$ | $-8.4\%$ | $0.9\%$
$2014$ | $-11.0\%$ | $0.7\%$
$2015$ | $-17.5\%$ | $-1.1\%$
Table 6: Summary statistics Variable | Zero-leverage | Positive leverage | T-test
---|---|---|---
| Mean | Median | Mean | Median | Diff in means | T-stat
Size | $4.594$ | $4.589$ | $6.072$ | $6.147$ | $-1.478$ | $-120.190$
Age | $11.562$ | $9.000$ | $16.118$ | $11.000$ | $-4.557$ | $-45.524$
Profitability | $-0.013$ | $0.023$ | $0.049$ | $0.062$ | $-0.062$ | $-46.259$
Tangibility | $0.095$ | $0.067$ | $0.260$ | $0.216$ | $-0.165$ | $-161.476$
Cash | $0.421$ | $0.395$ | $0.087$ | $0.051$ | $0.334$ | $168.966$
R&D | $0.138$ | $0.110$ | $0.059$ | $0.029$ | $0.079$ | $69.297$
MB | $2.103$ | $1.675$ | $1.401$ | $1.241$ | $0.701$ | $58.718$
P/S | $3.500$ | $2.630$ | $0.978$ | $0.835$ | $2.522$ | $98.716$
ROA | $-0.029$ | $0.022$ | $0.003$ | $0.022$ | $-0.032$ | $-24.032$
ROE | $-0.032$ | $0.032$ | $0.022$ | $0.072$ | $-0.054$ | $-27.356$
Asset growth | $0.069$ | $0.040$ | $0.058$ | $0.026$ | $0.012$ | $6.208$
Dividends | $0.009$ | $0.000$ | $0.008$ | $0.000$ | $0.001$ | $5.165$
Capex | $0.028$ | $0.021$ | $0.045$ | $0.035$ | $-0.017$ | $-68.447$
Cash Flow | $-0.011$ | $0.055$ | $0.043$ | $0.068$ | $-0.054$ | $-31.865$
Net debt issuance | $-0.004$ | $0.000$ | $0.026$ | $0.005$ | $-0.030$ | $-72.829$
Equity issuance | $0.042$ | $0.003$ | $0.004$ | $0.000$ | $0.037$ | $37.451$
Profitability SD | $0.145$ | $0.118$ | $0.074$ | $0.054$ | $0.071$ | $73.737$
CF SD | $0.160$ | $0.125$ | $0.086$ | $0.060$ | $0.074$ | $59.824$
Tobin‘s Q | $3.595$ | $3.174$ | $2.132$ | $2.012$ | $1.463$ | $119.016$
Table 7: Summary statistics with matching Variable | Zero-leverage | Positive leverage | T-test
---|---|---|---
| Mean | Median | Mean | Median | Diff(means) | T-stat
Age | $9.953$ | $7.000$ | $10.225$ | $7.000$ | $-0.272$ | $-2.886$
Profitability | $-0.020$ | $0.018$ | $-0.052$ | $0.003$ | $0.032$ | $19.330$
Tangibility | $0.132$ | $0.073$ | $0.208$ | $0.136$ | $-0.077$ | $-49.699$
Cash | $0.456$ | $0.429$ | $0.222$ | $0.129$ | $0.234$ | $95.843$
R&D | $0.146$ | $0.108$ | $0.150$ | $0.090$ | $-0.003$ | $-2.061$
MB | $1.884$ | $1.596$ | $1.537$ | $1.343$ | $0.347$ | $31.120$
P/S | $8.794$ | $2.914$ | $2.340$ | $1.201$ | $6.454$ | $33.944$
ROA | $-0.037$ | $0.018$ | $-0.110$ | $-0.034$ | $0.073$ | $41.754$
ROE | $-0.042$ | $0.028$ | $-0.209$ | $-0.025$ | $0.167$ | $53.657$
Asset growth | $0.169$ | $0.046$ | $0.135$ | $0.025$ | $0.034$ | $7.424$
Dividends | $0.006$ | $0.000$ | $0.001$ | $0.000$ | $0.004$ | $33.097$
Capex | $0.040$ | $0.025$ | $0.050$ | $0.030$ | $-0.009$ | $-21.699$
Cash Flow | $0.001$ | $0.052$ | $-0.050$ | $0.020$ | $0.051$ | $29.920$
Net debt issuance | $-0.009$ | $0.000$ | $0.040$ | $0.011$ | $-0.048$ | $-67.593$
Equity issuance | $0.109$ | $0.004$ | $0.079$ | $0.003$ | $0.030$ | $12.683$
Profitability SD | $0.159$ | $0.122$ | $0.152$ | $0.110$ | $0.007$ | $4.829$
CF SD | $0.159$ | $0.122$ | $0.161$ | $0.125$ | $-0.003$ | $-1.906$
Tobin‘s Q | $3.366$ | $3.091$ | $2.289$ | $2.176$ | $1.078$ | $91.623$
Table 8: Transitivity of zero-leverage policy | Zero Leverage | Positive leverage
---|---|---
Fraction of firms in data | 19.5% | 80.5%
Fraction of entering firms | 26.4% | 73.6%
Probability of changing leverage group | 19.9% | 3.46%
Implied long-term fraction of firms | 14.8% | 85.2%
Probability of delisting | 8.5% | 9.8%

Table 9: Post-IPO cash depletion | Dependent variable:
---|---
| Levering dummy variable
| (1) | (2) | (3)
After-IPO Cash Ratio | 0.0004∗∗ | 0.0003∗∗ | 0.0003∗∗
| (0.0002) | (0.0002) | (0.0002)
Constant |  $-$1.654∗∗∗ |  $-$2.486∗∗ |  $-$2.486∗∗
| (0.040) | (1.041) | (1.041)
Industry Fixed Effects | No | 2-digit | 3-digit
Observations | 4,601 | 4,601 | 4,601
Log Likelihood |  $-$2,028.230 |  $-$1,977.937 |  $-$1,942.693
Akaike Inf. Crit. | 4,060.459 | 4,059.874 | 4,149.387
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Table 10: Financial Constraints from 10-K filings Constraints | Number of filings | Fraction, %
---|---|---
0 | $51535$ | $85.952$
1 | $3725$ | $6.213$
2 | $2074$ | $3.459$
3 | $873$ | $1.456$
4 | $466$ | $0.777$
5 | $306$ | $0.510$
6 | $231$ | $0.385$
7 | $148$ | $0.247$
8 | $119$ | $0.198$
9 | $103$ | $0.172$
10 | $78$ | $0.130$
11 | $48$ | $0.080$
12 | $54$ | $0.090$
13 | $36$ | $0.060$
14 | $24$ | $0.040$
15 | $28$ | $0.047$
16 | $20$ | $0.033$
17 | $19$ | $0.032$
18 | $19$ | $0.032$
19 | $8$ | $0.013$
20 | $12$ | $0.020$
21 | $10$ | $0.017$
22 | $3$ | $0.005$
23 | $4$ | $0.007$
24 | $4$ | $0.007$
25 | $2$ | $0.003$
26 | $1$ | $0.002$
27 | $2$ | $0.003$
29 | $1$ | $0.002$
32 | $1$ | $0.002$
34 | $2$ | $0.003$
35 | $1$ | $0.002$
47 | $1$ | $0.002$
Total | $59958$ | $100.000$
Table 11: Financial Constraints | Dependent variable:
---|---
| Zero leverage dummy variable
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9)
log (10-K Constraints) |  0.244∗∗∗ |  0.124∗∗∗ |  0.051∗∗ |  |  |  |  |  |
| (0.022) | (0.022) | (0.023) |  |  |  |  |  |
SA - Index |  |  |  |  0.539∗∗∗ |  0.378∗∗∗ |  0.381∗∗∗ |  |  |
|  |  |  | (0.016) | (0.028) | (0.029) |  |  |
Profitability |  |  |  |  |  1.079∗∗∗ |  1.090∗∗∗ |  |  0.946∗∗∗ |  1.322∗∗∗
|  |  |  |  | (0.077) | (0.079) |  | (0.164) | (0.171)
Tangibility |  |  |  |  |  $-$1.888∗∗∗ |  $-$1.822∗∗∗ |  |  $-$3.738∗∗∗ |  $-$3.636∗∗∗
|  |  |  |  | (0.132) | (0.141) |  | (0.131) | (0.142)
MB |  |  |  |  |  $-$0.002 |  $-$0.003 |  |  0.179∗∗∗ |  0.146∗∗∗
|  |  |  |  | (0.009) | (0.009) |  | (0.012) | (0.012)
Cash |  |  |  |  |  4.690∗∗∗ |  4.608∗∗∗ |  |  |
|  |  |  |  | (0.086) | (0.090) |  |  |
Dividends |  |  |  |  |  5.072∗∗∗ |  5.096∗∗∗ |  |  |
|  |  |  |  | (0.463) | (0.484) |  |  |
CF SD |  |  |  |  |  $-$0.281∗∗∗ |  $-$0.321∗∗∗ |  |  |
|  |  |  |  | (0.091) | (0.092) |  |  |
Cash Sensitivity Constraints |  |  |  |  |  |  |  0.972∗∗∗ |  0.421∗∗∗ |  0.384∗∗∗
|  |  |  |  |  |  | (0.030) | (0.037) | (0.037)
Age |  |  |  |  |  |  |  |  0.003∗∗ |  0.006∗∗∗
|  |  |  |  |  |  |  | (0.002) | (0.002)
Size |  |  |  |  |  |  |  |  $-$0.277∗∗∗ |  $-$0.286∗∗∗
|  |  |  |  |  |  |  | (0.011) | (0.011)
Constant |  $-$1.584∗∗∗ |  $-$2.402∗∗∗ |  $-$2.395∗∗∗ |  0.140∗∗∗ |  $-$1.597∗∗∗ |  $-$1.581∗∗∗ |  $-$1.873∗∗∗ | 0.052 | 0.060
| (0.012) | (0.290) | (0.290) | (0.048) | (0.477) | (0.476) | (0.017) | (0.395) | (0.395)
Industry Fixed Effects | No | 2-digit | 3-digit | No | 2-digit | 3-digit | No | 2-digit | 3-digit
Observations | 59,958 | 59,958 | 59,958 | 57,292 | 35,211 | 35,211 | 38,826 | 31,190 | 31,190
Log Likelihood |  $-$27,878.210 |  $-$26,034.700 |  $-$24,795.690 |  $-$26,688.520 |  $-$13,103.910 |  $-$12,682.080 |  $-$16,927.690 |  $-$12,084.430 |  $-$11,545.720
Akaike Inf. Crit. | 55,760.420 | 52,201.400 | 50,083.390 | 53,381.030 | 26,347.810 | 25,860.160 | 33,859.380 | 24,310.870 | 23,589.440
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Table 12: Transitivity of zero-leverage policy Consecutive years | Number of firms | Fraction, %
---|---|---
1 | $6061$ | $31.231$
2 | $3799$ | $19.575$
3 | $2513$ | $12.949$
4 | $1764$ | $9.090$
5 | $1273$ | $6.559$
6 | $949$ | $4.890$
7 | $727$ | $3.746$
8 | $528$ | $2.721$
9 | $403$ | $2.077$
10 | $313$ | $1.613$
11 | $256$ | $1.319$
12 | $189$ | $0.974$
13 | $151$ | $0.778$
14 | $121$ | $0.623$
15 | $96$ | $0.495$
16 | $72$ | $0.371$
17 | $61$ | $0.314$
18 | $55$ | $0.281$
19 | $51$ | $0.260$
20 | $41$ | $0.209$
Table 13. Financial Flexibility Hypothesis:
Mean variables around transition from zero leverage to positive leverage | -3 years | -2 years | -1 year | 0 year | +1 year | +2 years
---|---|---|---|---|---|---
R&D | $0.120$ | $0.121$ | $0.127$ | $0.123$ | $0.101$ | $0.092$
R&D t-stat |  | $0.15$ | $0.96$ | $-0.66$ | $-3.35$ | $-1.364$
Capex | $0.045$ | $0.044$ | $0.053$ | $0.062$ | $0.051$ | $0.042$
Capex t-stat |  | $-0.29$ | $3.39$ | $2.93$ | $-3.57$ | $-1.62$
Acquisitions | $0.004$ | $0.004$ | $0.005$ | $0.063$ | $0.015$ | $0.018$
Acquisitions t-stat |  | $0.44$ | $1.21$ | $14.88$ | $-11.65$ | $0.98$
Table 14. High Market Valuation Hypothesis:
Mean Variables around Transition Event111‘levering‘ means transition of the
firms from zero-leverage firm to positive-leverage firm.
‘unlevering’ stands for transition from positive-leverage firm to zero-
leverage firm
| -3 years | -2 years | -1 year | 0 year | +1 year | +2 years
---|---|---|---|---|---|---
MB levering | $1.875$ | $1.895$ | $1.979$ | $1.624$ | $1.619$ | $1.479$
MB t-stat |  | $0.25$ | $1.16$ | $-5.934$ | $-0.11$ | $-3.06$
P/S levering | $3.335$ | $3.958$ | $5.284$ | $2.918$ | $2.418$ | $2.040$
P/S t-stat |  | $2.98$ | $4.70$ | $-9.33$ | $-4.13$ | $3.54$
Tobin‘s Q levering | $3.298$ | $3.352$ | $3.419$ | $2.623$ | $2.575$ | $2.441$
Tobin‘s Q t-stat |  | $0.63$ | $0.86$ | $-12.53$ | $-0.92$ | $-2.57$
Equity iss. levering | $0.118$ | $0.144$ | $0.152$ | $0.072$ | $0.080$ | $0.052$
Equity iss. unlevering | $0.061$ | $0.062$ | $0.107$ | $0.215$ | $0.055$ | $0.035$
Equity iss. t-stat | $4.895$ | $7.127$ | $3.622$ | $-10.942$ | $2.982$ | $3.636$
Debt iss. levering | $-0.006$ | $-0.003$ | $0.000$ | $0.215$ | $0.026$ | $0.018$
Debt iss. unlevering | $-0.013$ | $-0.013$ | $-0.026$ | $-0.158$ | $0.000$ | $0.013$
Debt iss. t-stat | $1.460$ | $2.217$ | $6.264$ | $42.549$ | $7.050$ | $1.044$

Table 15: Managerial Entrenchment | Dependent variable:
---|---
| Zero-leverage dummy variable
| (1) | (2) | (3) | (4)
Board Independence | 0.803∗∗∗ | 0.297∗∗ | 0.423∗∗∗ | 0.468∗∗∗
| (0.104) | (0.147) | (0.154) | (0.160)
Institutional Ownership |  $-$0.799∗∗∗ | 1.231∗∗∗ | 1.058∗∗∗ | 1.035∗∗∗
| (0.050) | (0.086) | (0.091) | (0.094)
Delisting Shock | 0.267 |  $-$0.042 |  $-$0.053 |  $-$0.008
| (0.396) | (0.539) | (0.648) | (0.661)
Age |  |  $-$0.004∗∗ |  $-$0.001 |  $-$0.002
|  | (0.002) | (0.002) | (0.002)
Size |  |  $-$0.571∗∗∗ |  $-$0.552∗∗∗ |  $-$0.551∗∗∗
|  | (0.017) | (0.018) | (0.019)
Profitability |  | 2.311∗∗∗ | 1.660∗∗∗ | 1.699∗∗∗
|  | (0.111) | (0.115) | (0.117)
Tangibility |  |  $-$0.932∗∗∗ |  $-$1.128∗∗∗ |  $-$1.110∗∗∗
|  | (0.119) | (0.166) | (0.184)
MB |  | 0.002 | 0.022 | 0.027∗
|  | (0.013) | (0.014) | (0.014)
Cash |  | 4.176∗∗∗ | 4.638∗∗∗ | 4.455∗∗∗
|  | (0.101) | (0.113) | (0.119)
Dividends |  | 4.539∗∗∗ | 4.879∗∗∗ | 4.436∗∗∗
|  | (0.494) | (0.520) | (0.532)
CF_SD |  |  $-$0.268∗∗ |  $-$0.228∗ |  $-$0.307∗∗
|  | (0.119) | (0.120) | (0.121)
Constant |  $-$1.511∗∗∗ | 0.119 |  $-$1.267 |  $-$1.250
| (0.073) | (0.131) | (1.030) | (1.031)
Industry Fixed Effects | No | No | 2-digit SIC | 3-difit SIC
Observations | 31,054 | 26,366 | 26,366 | 26,366
Log Likelihood |  $-$15,453.070 |  $-$9,620.751 |  $-$9,016.584 |  $-$8,596.348
Akaike Inf. Crit. | 30,914.140 | 19,265.500 | 18,177.170 | 17,670.700
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Table 16: Probability of zero-leevrage firm becoming positive-leverage firm Year |  Number of zero-levrage firms |  Number of levering zero-leverage firms |  Fraction of zero-leevrage firms, which become unlevered, % |  X2-stat
---|---|---|---|---
$1996$ | $699$ | $182$ | $26.0$ |
$1997$ | $777$ | $212$ | $27.3$ | $0.232$
$1998$ | $754$ | $210$ | $27.9$ | $0.036$
$1999$ | $750$ | $210$ | $28.0$ | $0.000$
$2000$ | $804$ | $194$ | $24.1$ | $2.824$
$2001$ | $783$ | $167$ | $21.3$ | $1.615$
$2002$ | $781$ | $145$ | $18.6$ | $1.699$
$2003$ | $842$ | $139$ | $16.5$ | $1.050$
$2004$ | $879$ | $144$ | $16.4$ | $0.000$
$2005$ | $927$ | $161$ | $17.4$ | $0.246$
$2006$ | $899$ | $180$ | $20.0$ | $1.946$
$2007$ | $867$ | $199$ | $23.0$ | $2.078$
$2008$ | $799$ | $167$ | $20.9$ | $0.905$
$2009$ | $801$ | $108$ | $13.5$ | $14.947$
$2010$ | $806$ | $129$ | $16.0$ | $1.836$
$2011$ | $786$ | $140$ | $17.8$ | $0.801$
$2012$ | $763$ | $155$ | $20.3$ | $1.415$
$2013$ | $796$ | $140$ | $17.6$ | $1.714$
$2014$ | $776$ | $178$ | $22.9$ | $6.643$
$2015$ | $638$ | $140$ | $21.9$ | $0.146$
Table 17: Financial Crisis and Financial Constraints Hypothesis | Dependent variable:
---|---
| Profitability | ROA | ROE | Returns | Marketshare
| (1) | (2) | (3) | (4) | (5)
Treatment |  $-$0.006 |  0.020∗∗∗ |  0.100∗∗∗ |  1.116∗∗∗ |  0.010∗∗
| (0.004) | (0.005) | (0.020) | (0.017) | (0.004)
Zero-leverage |  $-$0.044∗∗∗ |  $-$0.007 | 0.006 |  0.144∗∗∗ |  $-$0.079∗∗∗
| (0.007) | (0.008) | (0.012) | (0.027) | (0.007)
Treatment*Zero-leverage |  $-$0.004 |  $-$0.007 |  $-$0.107∗∗ |  $-$0.154∗∗∗ |  $-$0.007
| (0.010) | (0.012) | (0.046) | (0.039) | (0.010)
Constant |  0.036∗∗∗ |  $-$0.044∗∗∗ |  $-$0.124∗∗∗ |  $-$0.803∗∗∗ |  0.094∗∗∗
| (0.003) | (0.004) | (0.014) | (0.012) | (0.003)
Observations | 7,415 | 7,415 | 7,415 | 6,659 | 7,461
R2 | 0.009 | 0.003 | 0.003 | 0.425 | 0.038
Adjusted R2 | 0.009 | 0.002 | 0.003 | 0.425 | 0.037
Residual Std. Error | 0.171 (df = 7411) | 0.204 (df = 7411) | 0.780 (df = 7411) | 0.633 (df = 6655) | 0.167 (df = 7457)
F Statistic |  22.192∗∗∗ (df = 3; 7411) |  6.378∗∗∗ (df = 3; 7411) |  8.210∗∗∗ (df = 3; 7411) |  1,638.844∗∗∗ (df = 3; 6655) |  97.511∗∗∗ (df = 3; 7457)
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Table 18: Financial Crisis and Financial Flexibility | Dependent variable:
---|---
| Capex | R&D | Acquisitions
| (1) | (2) | (3)
Treatment |  $-$0.023∗∗∗ |  $-$0.006 |  $-$0.018∗∗∗
| (0.001) | (0.004) | (0.001)
Zero-leverage |  $-$0.025∗∗∗ | 0.075∗∗∗ |  $-$0.015∗∗∗
| (0.002) | (0.006) | (0.002)
Treatment*Zero-leverage | 0.010∗∗∗ |  $-$0.010 | 0.020∗∗∗
| (0.003) | (0.008) | (0.003)
Constant | 0.067∗∗∗ | 0.063∗∗∗ | 0.026∗∗∗
| (0.001) | (0.003) | (0.001)
Observations | 7,417 | 4,523 | 7,125
R2 | 0.051 | 0.061 | 0.033
Adjusted R2 | 0.051 | 0.060 | 0.032
Residual Std. Error | 0.058 (df = 7413) | 0.119 (df = 4519) | 0.045 (df = 7121)
F Statistic | 133.454∗∗∗ (df = 3; 7413) | 97.594∗∗∗ (df = 3; 4519) | 79.964∗∗∗ (df = 3; 7121)
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01