A Work Project, presented as part of the requirements for the Award of the Masters Degree in
Finance from Nova – School of Business and Economics
FINANCIAL CRISIS SPILLOVERS TO THE
CORPORATE SECTOR
BANK-DEPENDENT BORROWERS VERSUS BOND MARKET
PARTICIPANTS
ANA CATARINA HENRIQUES SIMÕES MARQUES #426
A Project carried out on the Financial Markets Area with the supervision of:
Prof. Miguel Ferreira and Prof. João Santos
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FINANCIAL CRISIS SPILLOVERS TO THE CORPORATE SECTOR
BANK-DEPENDENT BORROWERS VERSUS BOND MARKET PARTICIPANTS
Acknowledgements
I would like to express my deepest gratitude to my supervisor, Professor Miguel Ferreira, who
provided insightful comments and suggestions since the formative stages of this project, to this
final draft. His availability, guidance and advice were invaluable. I would also like to thank my
family and friends for the unconditional love and support they offered me, not only throughout
the course of this thesis, but during my entire life.
Abstract
This paper provides empirical evidence on the impact that shocks to capital providers have on
their borrowers’ performance. We use the recent financial crisis, which was originated in the
U.S. mortgage market, as reasonably exogenous shock to the performance of European
Union-based companies, therefore allowing us to disentangle credit supply and demand-side frictions.
Our results show that bank-dependent firms were more adversely affected by the financial crisis
in terms of stock market valuation, investment decisions and profitability, in comparison to
firms with access to public debt markets. Overall, we highlight the role that financial
intermediaries play in the propagation of financial shocks to the real economy, even across
distinct regions.
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“Credit availability has the ability to build a modern economy. Lack of credit has the ability to
destroy it, swiftly and absolutely.”
Ben Bernanke, Chairman of the Federal Reserve Board
September 18, 2008
1. INTRODUCTION
In 2007, the world witnessed the beginning of what many claim to be the worst financial crisis
since the Great Depression. It resulted in downturns in most equity markets, in the collapse or
bailout of financial institutions known as too big to fail, and in the freezing of credit and money
markets. After all, because banks’ balance sheets were tremendously affected, this financial crisis turned out to be a banking crisis characterized by a large lending tightening to the
corporate sector, followed by a consequent slowdown of economies worldwide.
Bearing this in mind, the recent financial crisis brought renewed interest in studying the role that
financial intermediaries play in the propagation of financial shocks to the real economy, which
will be our main focus in this paper. We provide new evidence on the impact of the recent
financial crisis on firms’ performance, in terms of: (1) stock market valuation; and (2) investment decisions and profitability. Particularly, we investigate if bank-dependent firms were
more adversely affected by this event when compared to firms with access to the public debt
markets1.
The remainder of the paper is organized as follows. Section 2 explains the theory behind our
study. Section 3 clarifies the relevance of the financial crisis. Section 4 proceeds with the
methodology. Section 5 describes the data sources and characterizes the sample. Section 6
presents our results. Section 7 concludes.
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2. THEORETICAL FRAMEWORK
The seminal investigation of Modigliani and Miller (1958) assumes that, in frictionless markets,
the supply of capital is perfectly elastic, so that firms can borrow as much as they want at the
same cost of capital. This implies that lending is purely a function of credit demand factors, such
as size, growth opportunities and profitability. Nevertheless, in the real world, market frictions
such as information asymmetry and moral hazard do exist, affecting both the availability and the
pricing of credit (see for example, Stiglitz and Weiss 1981; Faulkender and Petersen 2006;
Lemmon and Roberts 2010).
In fact, many researchers maintain that market imperfections in financial markets prevent firms
from easily and costlessly access alternative financing channels in case their providers of capital
unexpectedly curtail access to credit. For firms that are either financially constrained or heavily
reliant on external financing, this particularly results in suboptimal corporate decisions – such as not expanding inventories or foregoing positive investment opportunities –, with clear negative consequences for those firms.
For instance, in the context of the recent financial crisis, Campello, Graham, and Harvey (2010)
survey 1,050 CFOs worldwide and find that financially constrained firms planned deeper cuts in
employment, technology and capital expenditures; Almeida et al. (2012) show that firms whose
most long-term debt matured right after the onset of the crisis experienced greater reductions in
their investment than other firms; and Buca and Vermeulen (2012) find that, for small and
medium firms from Southern European countries, which are in general more bank-dependent,
there was an unprecedented drop in aggregate fixed capital formation in 2009.
Bearing all this in mind, in a rational market it is expected that any unanticipated shock to the
banking sector should result in a greater stock price decline for firms that are dependent on bank
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debt markets. For instance, Slovin, Sushka and Polonchek (1993) document negative effects of
Continental Illinois Bank’s failure on stock performance of the bank’s borrowers; Chava and
Purnanandam (2011) show that, during the Russian Crisis of 1998, bank-dependent firms
experienced larger stock market valuation losses than their bank-independent counterparts; and
Kang and Stulz (2000) find similar results for the Japanese banking crisis in 1990-1993. To the
best of our knowledge, the impact of the recent financial crisis on firm’s stock market valuation is not thoroughly investigated until date.
This paper adds to the existing literature by trying to identify to which extent variations in the
supply of capital after the onset of the recent financial crisis had a greater impact on the
performance of bank-dependent firms in comparison to firms with access to bond markets, in
terms of stock market valuation, investment behaviour and profitability. However, investigating
the impact of financial distresses on firm’s performance poses a major challenge of properly disentangling the effect of supply-side shocks felt by banks from demand-side shocks faced by
firms, mainly because they usually occur simultaneously (e.g. common economic shocks).
Our identification strategy aims at overcoming this problem in different ways. First and
foremost, we use the recent financial crisis, which originated from the subprime mortgage
market, as a reasonably exogenous event to the performance of European Union firms, hence
making it possible to identify the effect of credit supply on corporate performance2. To further rule out any demand-driven effect, we control for firm-specific factors in all our market
valuation regressions and, in an alternative approach, a matched sample analysis is also
performed; when studying firms’ real outcomes, a firm fixed effect model is estimated, which accounts for observable and unobservable heterogeneity in firms’ characteristics. In the next section, we elaborate on the relevance of the recent financial crisis for this study.
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3. THE 2007-2008 FINANCIAL CRISIS
The most recent financial crisis had its origins in 2007 with the meltdown in the U.S subprime
mortgage market, which primarily resulted from the widespread of the so called
mortgage-backed securities. The risk mispricing of those securities became clear with the bursting of the
housing bubble, which generated several defaults and forced banks to large write-offs. By the
end of 2007, the largest U.S. banks had already announced write-downs in excess of $100bn.
During the following months, the U.S. government was forced to take over Fannie Mae,
Freddie Mac and AIG, and many banks either got bankrupt or were acquired by others. But it
was certainly the collapse of the Lehman Brothers on the 15th of September 2008 that triggered the whole panic in financial markets. Almost immediately, equity markets experienced a sudden
plummet, market volatility reached historical peaks, and credit markets seized up, as suggested
by Figures 1 to 3, in the Appendices.
This environment gradually caused lending in the interbank market to dry up which, associated
with high deleveraging pressures, ultimately resulted in a credit squeeze characterized by a
significant reduction in the supply of loans and in tightened lending standards to the corporate
sector. For instance, Ivashina and Scharfstein (2008) show that U.S. bank lending fell by 68%
during the peak of the crisis (2008Q4) relative to the peak of the credit boom (2007Q2); and
Campello et al. (2009) report that the financial crisis resulted in an increase on U.S.
commitment fees by 14 basis points, on mark-ups over the LIBOR rate by 69 basis points, and
in a decline of credit maturity by 2.6 months on average.
Summing up, this meltdown in financial markets and the associated losses to the banking sector
resulted in a sudden and severe capital rationing to the corporate domain, hence highlighting the
importance of studying the impact of credit supply shocks on corporate performance. In the
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4. EMPIRICAL METHODOLOGY
This section outlines the basic methodology adopted in order to test our key hypothesis.
Hypothesis: the recent financial crisis had a larger impact on the performance of firms that
relied on banks as their main source of capital, as opposed to firms with access to public debt
markets, after controlling for firm-specific factors.
In order to test this hypothesis, our methodology is divided in two main parts. Part I investigates
the impact of the recent financial crisis on firms’ stock market valuation during a short-term window, which is the core contribution of this paper. Part II investigates the associated
medium/long-term effects on firms’ real outcomes – investment decisions and profitability. In both parts, firm performance is modeled as a function of demand and supply-side frictions, in an
attempt to clearly disentangle the effect of the credit contraction of banks from correlated shocks
to the corporate realm.
4.1. Effects on Stock Market Valuation
Following Chava and Purnanandam (2011), we concentrate on stock returns during the crisis
period as the key outcome variable, since it allows to identify the unanticipated impact of the
financial crisis on firms’ market valuation, and it is unaffected by later interventions of policy-makers. Given such, the following cross-sectional regression model is estimated:
i K
k i k i
i BOND F
r
1 1
0 (1)
Where is the market model adjusted stock return of firm i, during the crisis period; is
a measure of bank-independence and our main goal is to estimate 𝛽 , the coefficient on this variable; is a set of firm-specific control variables. In the following sub-sections, additional
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4.1.1. Market Model Adjusted Returns
The construction of the dependent variable of our cross-sectional regression – i.e. abnormal returns – relies on a standard event-study methodology3, which imposes the selection of an event and an estimation window.
A 20-trading day event window is defined starting on the day of the Lehman Brothers’ bankruptcy – which represented an immediate and massive shock to the financial system (…)
larger than anything seen over nearly two decades (Sterling 2009) – and ending on October 10th – a day that marks the end of a black trading week4, and precedes the announcement of the
Troubled Asset Relief Program (TARP) by the U.S. Government.
Then, for every firm in the sample, the market-model beta is estimated using 250 trading days
for the year of 2007. By choosing this period, we aim at preventing the estimation window from
being contaminated by the turmoil already being felt in the markets between late 2007 and
2008.
Afterwards, using the beta estimates, we compute the abnormal returns – i.e. the difference between the observed and the expected returns – over the event window period. The cumulative abnormal returns from 15-Sep-2008 to 10-Oct-2008 represent the dependent variable of the
cross-sectional regression model to be developed.
3
Event studies attempt to measure the abnormal performance associated with an event, departing from the primary assumption that markets process information about the event in an efficient and unbiased manner. For further information, see Kothari and Warner (2005).
4 During this week, the Dow Jones Industrial Average dropped by 18.1%, and the S&P500 fell by roughly 20%.
01.01.07
Event Window Estimation Window
15.09.08 10.10.08 13.10.08
End of a black week
TARP announced Lehman Brothers’
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4.1.2. Measure of Bank-independence
A critical part of our methodology is the measure of firm’s bank-independence, which should
reflect whether a firm has access to public bond markets. Following Santos (2006), we
implement this through the inclusion of the dummy variable BOND, which takes the value of
one if the firm has ever5 issued a public bond, and zero otherwise. According to our hypothesis, this variable should present a positive coefficient, meaning that bank-independent firms
experienced greater stock market returns than bank-dependent firms during the crisis period.
In further analyses, we also break down the group of firms with access to public debt markets
into two distinct groups: (a) firms whose last bond issued was rated (RATED); and (b) firms that
most recently issued a non-rated bond (NRATED). Indeed, even though firms that issued
unrated bonds are classified as bank-independent in our first specifications, their lack of credit
rating may act to bond holders as a signal of poor reliability, especially when public debt
markets are under a considerable pressure – as it is evident by the uncommonly high levels of paper-bill spread during this period (Appendices, Figure 4) – and investors are shifting their
capital to safer assets –flight-to-quality. Consequently, we expect the coefficient on the dummy
variable RATED to be more positive than the respective one on NRATED.
4.1.3. Firm-specific control variables
The decision to obtain financing in public debt markets is likely to be related with various firm
characteristics and, therefore, in order to completely isolate the effect of bank-independence on firm’s performance we need to control for these factors in our regression, as they may help to
explain any returns’ differential between these two groups of firms.
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9
Since bank-dependent firms are, in general, considerably smaller than bank-independent firms,
we control for firm size using log of firm’s sales in million euros (LNSALES) as proxy. Indeed,
small firms are less likely to be known by analysts and investors, and are more likely to face
asymmetric information problems, which may limit their access to alternative sources of funds.
Additionally, because older firms may benefit from an established reputation, we include
LNAGE(the log of the firm’s age, computed as the difference between the year of the crisis,
2008, and the year the firm first appeared in Datastream). Similarly, we add EBITSALES (EBIT
divided by sales) as more profitable firms have a more comfortable cushion to service their debt.
Because of the aforementioned reasons, we expect these three factors to have a positive impact
on firms’ valuation during crisis.
Also, since firms with high levels of leverage are generally more sensitive to economic
downturns than their low-leveraged counterparts, we expect those firms’ valuation to be more adversely affected by the crisis6. A similar argument applies in terms of default risk. During crisis periods, high default-risk firms are expected to suffer larger valuation losses, not only due
to their increased probability of default but also because they are more exposed to the
risk-aversion and flight-to-quality effects widely noticed during crises. To control for leverage, we include firm’s debt-to-asset ratio in our regressions (LEVER); we control for default risk by
including the firm’s credit-worthiness percentile rank based on Altman Z-Score7 (ZSCORE).
Additionally, firm’s market-to-book ratio (MKTBOOK) is also incorporated, which proxies for
future value creation through growth opportunities. The effect of growth opportunities on firms’ valuation is twofold. First, it is expectable that firms’ growth opportunity set shrinks during crisis periods; second, even if growth opportunities remain, the shortage of credit may force
6
For further information, see Opler and Titman (1994).
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firms to forego some intended projects. In either case, market’s response to firm value is expected to be lower for high-growth firms.
We include shares turnover (SHARESTURN) to account for differences in stock liquidity across
firms, i.e. the degree to which an asset can be bought or sold in the market. During crisis
periods, the effect of stock liquidity on returns can be ambiguous. On the one hand, because
liquidity becomes particularly valuable at times of great turmoil in the markets, less liquid
stocks may experience a significant stock price decline. On the other hand, in order to obtain
immediate cash inflows, investors will generally get rid of their most liquid risky assets, leading
to larger price drops for these stocks.
We complement this set of firm controls with two variables related with stock market valuation,
STOCKVOL and STOCKRET, which are respectively the standard deviation and the average of
the firm’s stock return over the past twelve months. Since stocks that are either more volatile or have higher past returns should experience superior price declines during crisis, we expect both
variables to have a negative impact on firms’ valuation.
Finally, we include country and Fama-French industry fixed effects in all our regressions, as to
ensure that our results are not driven by any unobservable country and industry characteristics.
To control for dependence in the error terms for firms within the same country, we use robust
standard errors clustered at the country-level in all the analyses performed.
4.2. Effects on Real Outcomes – Investment and Profitability
In order to complement our market valuation analysis, we study the impact of the recent
11 iq iq iq iq q
i q
i q
q i i
iq BOND C C BOND C BOND C LEV SG CF
Y 1 1 2 2 1 * 1 2 * 2 (2)
Where measures firm’s performance in real terms, for firm i in quarter q. Two distinct
measures of performance are used: (1) capital expenditures scaled by lagged assets, as proxy for
investment; and (2) operating income scaled by lagged assets, as proxy for profitability.
𝛼 measures firm fixed-effects. is a measure of access to bond markets, constructed as
in the previous specifications8; it is time invariant in this regression model, and thus subsumed by the firm fixed-effects. equals one for quarters after 2008Q3, and zero otherwise (crisis
period). Additionally, we include , which equals one for quarters between 2007Q3 and
2008Q3, and zero otherwise, as to control for any variations occurring during this mixed period
(early stage of the crisis).
Since firm fixed effect models only capture firm-specific factors that are either time-invariant
(e.g. industry) or slow changing (e.g. size)9, we incorporate and , which are firm varying characteristics that control respectively for leverage, sales growth and operating cash flow, of firm i in quarter q. These variables are scaled by the amount of assets at the
beginning of the period, as to maintain a common scale factor.
This model is estimated using data from 2007Q2 to 2009Q4. We use standard errors that are
heteroskedasticity-consistent and clustered at the firm level. Our main goal is to estimate the
coefficient on , which measures changes in real outcomes during the crisis period,
for firms with access to bond markets in comparison to bank-dependent firms; therefore, we
expect it to be positive in both models (investment and profitability).
8 As in the previous part of our methodology, in some specifications we also segregate access to bond markets by
using the dummy variables RATED and NRATED.
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5. DATA, SAMPLE CONSTRUCTION AND DESCRIPTIVE STATISTICS
This section provides information on the data and sample construction. For further detail on the
definitions of the variables used, please refer to Table 1, present in the Appendices.
5.1. Data and Sample Construction
The data for this project has Datastream database as its main source. Our initial sample covers
all the European Union listed companies comprised in this database, having data on stock
returns for the crisis period, as well as for the year before. Afterwards, some adjustments were
performed to the sample selected.
Firstly, both financial firms (SIC codes between 6000 and 6999) and utilities (SIC codes
between 4910 and 4940) were eliminated from the sample, since their nature does not match
our interests in this study. To remove the effect of the bid-ask bounce from the analysis – which is more relevant for less liquid securities -, we also remove firms trading at less than 1€ stock price at the end of 2007. All-equity financed firms were also excluded from the sample since,
according to our classification, they would always be considered as bank-dependent and it is not
possible to distinguish whether these firms could have accessed the market for public debt if
they wished to. Furthermore, in order to insure that the results are not be driven by any
demand-side condemand-siderations, all firms reporting primary operations in the United States were also
eliminated from the sample. Finally, we remove all junk-rated firms as, due to their poor
credit-quality, they are not representative. This leaves us with a sample of 2,648 firms for our base
case analysis.
For the first part of this study, we obtained accounting and financial data as of December 2007;
for part II, quarterly data was gathered from 2006Q2 to 2009Q4. Information regarding bond
issuance up to September 2008 was also collected, as well as the respective bond rating. When
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used as proxy. To prevent outliers from affecting our results, the data is winsorized at 1% and
99% in all the analyses performed.
5.2. Descriptive Statistics
Table 2 illustrates our sample by comparing bank-dependent firms with firms that accessed
public debt markets, along some key characteristics.
First of all, it is worth mentioning that the sample is composed by approximately 85%
bank-dependent firms and only 15% firms with access to public bond markets, which clearly
illustrates the bank-based system prevailing in most European Union countries.
The results reported in Table 2 also confirm that, as previously discussed, there are clear
differences regarding key characteristics across the two groups of firms. In terms of size, the
average bank-independent firm has annual sales of 10,400 million euros, which is roughly 13.5
times more than the average bank-dependent firm. Also, firms with access to bond markets are
on average 8.14 years older then bank-dependent firms, and are likewise more profitable.
Concerning leverage and default risk, firms that issued bonds have on average greater
debt-to-asset ratios and present an increased probability of default. There are also other remarkable
differences in terms of shares turnover, past stock market returns and volatility.
Overall, we find that these two groups of firms present considerable disparities across various
key dimensions, which can per se explain any return differential between them during the crisis
period. To proceed with our analysis, we control for these differences in the next section, using
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6. RESULTS
In this section, we analyse stock market performance for the full sample of firms, followed by a
matched sample procedure. Afterwards, we provide our regression results concerning changes
on firms’ real outcomes – investment decisions and profitability.
6.1. Effects on Stock Market Valuation – Full Sample Analysis
We begin our analyisis by examining the impact of bank-independence on stock market returns,
ignoring all other factors. From the analysis of Table 2, we conclude that, during the crisis
period, the mean (median) firm with access to public debt markets experienced market-model
abnormal returns of -6.85% (-6.73%), in comparison to -10.76% (-9.15%) grossed by
bank-dependent firms. The differences in both the mean (3.91%) and median (2.42%) returns across
the two groups of firms are statistically significant at the 1% level of significance.
Table 3 reports the OLS regression results from regressing market-model adjusted returns
(ABNRET) on the measure of bank-independence and on the control variables previously
discussed. In Model 1, we determine bank-independence by whether a firm has ever issued a
public bond (BOND). In Model 2, we segreggate bond issuers according to whether the last
bond issued by the firm was rated (RATED) or not (NRATED), due to the previously mentioned
reasons. Both models include country and Fama-French industry fixed-effects. The t-statistics
reported are computed using robust standard errors clustered at the country-level.
Model 1 shows that, during the crisis period, firms with access to public debt markets registered
on average 2.71% higher returns than bank-dependent firms, after controling for firm-specific
factors. Even though the magnitude of the return differential between the two groups of firms
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Methodology section. They confirm that stocks of bigger and older firms performed better
during the crisis period; whilst liquid stocks, stocks with high past returns, and stocks of
highly-leveraged firms experienced a larger drop.
In Model 2, the public debt market access is decomposed through the introduction of the
dummy variables RATED and NRATED, which represent the marginal impact of contemporary
public issuances of rated and unrated bonds, respectively. The results are in accordance with the
expectations. In this specification, the estimate on RATED equals 5.68% and is statistically
significant at the 1% significance level. This indicates that firms that most recently issued rated
bonds earned on average 5.68% higher returns than bank-dependent firms, after controlling for
firm-specific risks. Still, there is no evidence that stocks of firms that had issued unrated bonds
(NRATED) performed better than those of bank-dependent firms during the crisis period.
All in all, the results suggest that, after a shock that affects the health of the banking system,
bank-dependent firms suffer greater market valuation losses than firms that have access to
alternative capital sources. Furthermore, after distinguishing between rated and unrated bonds
our results become stronger, which highlights the greater trust that markets deposit upon bonds
that possess a credit rating, especially at times when financial markets are under great pressure.
6.2. Effects on Stock Market Valuation – Matched Sample Analysis
Although our results in the full sample analysis do support our hypothesis, some objections can
be raised, namely regarding the assumption on the exogenous access to bond markets. In fact,
given the large dissimilarities across bank-dependent and bank-independent firms, it is possible
that our results are driven by unobservable factors, which may lead to biased estimators. We
address this potential problem using a matched sample analysis based on a matching of
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The first step of the matching procedure is to estimate a probit model of the dependent binary
variable indicating whether a firm has accessed or not the market for public debt. We consider
as determinants of this choice10: firm’s size, age, growth opportunities, profitability, leverage, default risk, stock market liquidity, past stock return and volatility. Additionally, we include the
Fama-French industry classification and the country dummies, as to control respectively for
industry and country-specific factors.
The first two columns of Table 4 Panel A, denoted Pre-Match, present the estimation results of
the probit model. Through the analysis of the aforementioned table, it is evident that various
factors that we used to explain abnormal stock market returns in the previous regressions are
determinant to the access of public debt markets. Larger and older firms are better established,
which boosts their access to public debt markets. Moreover, firms that are more profitable and
have greater growth opportunities are also more attractive to investors in bond markets. The
probability of accessing bond markets is also positively correlated with firm leverage, since
those firms have eased-access to credit, and with volatility of returns, which may be only a
result of the unique conditions of the markets at the time. Firms with high risk of bankruptcy are
less attractive to bond holders, hence less likely to access credit markets. We obtain a pseudo
R-square of 36.14%, suggesting a reasonable fit of the model.
Afterwards, we obtain the propensity score i.e. probability of accessing bond markets for each
firm in the sample, which allows us to find, for every independent firm (treated), the
bank-dependent firm (control) that is the most similar in terms of the observable characteristics (i.e.
the closest propensity score). We match firms without replacement which, despite sacrificing
the number of firms in the sample, maximizes the statistical power of the tests performed. The
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matching procedure yields a sample of 812 firms (406 bank-independent firms matched with
406 bank-dependent firms).
The second two columns of Panel A, Table 4 (Post-Match), present the results of estimating the
probit model on the matched sample, which is only carried out for validity purposes. From the
analysis of this informstion, we can confirm the effectiveness of our matching technique, since
none of the variables is significant in this specification – this indicates that, after the match, there are no pair wise statistically significant differences across the two groups of firms.
Table 4, Panel B, provides the difference-in-difference estimates of the abnormal returns, before
and after matching bank-independent and bank-dependent firms along key observable
dimensions. From the matched sample analysis we conclude that, during the crisis period, the
mean bank-dependent firm (control) experienced a cumulative abnormal return of -8.92% in
comparison to -6.89% for firms with access to public debt markets (treated). The difference
(2.03%) is significant at the 1% significance level, though smaller than for the Pre-Match
analysis (3.53%). Additionally, when performing the same analysis but using firms that issued
rated bonds as the treatment group, our results become stronger, with a difference-in-difference
estimate of 6.54%, statistically significant at the 1% significance level11.
All in all, we find evidence that, even for a sample of homogenous dependent and
bank-independent firms across observable characteristics, the stock market differential for these two
groups of firms during the crisis remains, with bank-dependent firms being clearly more
adversely affected than their bank-independent counterparts.
6.3. Effects on Investment and Profitability
The estimation results concerning the impact of the financial crisis on firms’ real outcomes are provided in Table 5, Models 1 to 4.
11 For space saving purposes, the results of the
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Through the analysis of this table, it is evident that the financial crisis had a strong negative
impact on firm’s real outcomes, since C2 and C1 are negative and highly significant in both
models. Regarding the control variables, we confirm that sales growth and operating cash-flow
have a positive effect on investment and profitability, whilst leverage has the opposite impact on
both measures.
More importantly, Model 1 reports that firms with access to public debt markets were able to
navigate the crisis with better levels of capital spending than their bank-dependent counterparts.
In fact, the mean bank-dependent firm experienced a drop of roughly 1.70% in their capital
expenditures during the crisis, as compared to 1.30% faced by firms that issued public bonds
(1.09% for issuers of rated bonds –Model 2)
Additionally, Model 3 also documents a significantly positive coefficient on BOND*C2,
indicating that the profitability of bank-independent firms was less affected by the financial
crisis than that of their bank-dependent peers. Indeed, whilst the profitability of bank-dependent
firms decreased on average 5.35% between the fourth quarter of 2008 and the homologous
period of 2009, that reduction was of only 4.55% for bank-independent firms (4.18% if we
consider firms that issued rated bonds –Model 4).
Notwithstanding, it is worth mentioning that these results have some clear limitations. Firstly,
we assume that the two groups of firms remained unchanged over the period analysed, which is
not completely accurate, as some firms that previously financed themselves uniquely with bank
financing, may have started to issue public bonds during this period. Secondly, because this
analysis comprises a vast period, we are not able to assure that the contraction experienced by
firms in terms of capital expenditures and profitability was due to variations in external
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7. FINAL REMARKS
The financial crisis that hit the U.S. in 2007-2008 was a significant shock to the global financial
system, resulting in several defaults and huge losses of equity capital to the banking system.
This ultimately resulted in a credit squeeze characterized by a significant reduction in the supply
of loans and in tightened lending standards to the corporate sector. Because this natural event
had its origins in the U.S. mortgage market, it can be considered as a reasonably exogenous
shock to the performance of European Union-based firms, therefore allowing us to study the
impact of banks’ health on borrowers’ performance. Particularly, we compare the performance of firms dependent on bank financing with that of firms with access to public debt markets.
We find evidence that, during the 20 trading days after the Lehman Brothers’ collapse, firms with access to public debt markets registered on average 2.71% higher adjusted returns than
bank-dependent firms, after controling for firm-specific factors (2.03% using a matched sample
analysis). Furthermore, bank-independent firms were able to navigate the crisis with better
levels of capital expenditures (0.4%) and profitability (0.8%) than their bank-dependent
counterparts. Our results become stronger when we compare bank-dependent firms with firms
that issued rated bonds, which stresses the importance of a credit rating in providing firms with
a greater visibility and reliability in capital markets, especially during crisis periods.
Due to the particular effects of contagion present in this crisis, we also bring new evidence on
the role that the global integration of financial markets plays in the propagation of shocks across
regions, through the banking channel. Most importantly, our findings have strong implications
for the current sovereign-debt crisis in Europe and for policy-makers and monetary authorities
in general, highlighting the importance of efficient and well-developed corporate bond markets
in helping companies and the economies to better resist to financial and banking crises, and to
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8. REFERENCES
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9. APPENDICES
Figures 1 and 2 –Equity Prices12 and Implied Volatilities13, during 2006-2008. Source: Federal Reserve Bank of St. Louis.
Figures 3 and 4 –LIBOR-OIS and TED Spreads14, andPaper-Bill Spread15, during 2006-2008.
Sources: Bloomberg; Federal Reserve Bank of St. Louis.
12
Equity indices are expressed in local currency; 1 January 2006 = 100.
13 Volatility implied by the price of at-the-money call option contracts of the indices; expressed in percentage points. 14
The LIBOR-OIS spread reflects the difference between the rate at which banks will lend to each other (LIBOR), compared with the overnight indexed swap (OIS) rate; the TED spread equals the difference between the U.S. Treasury Bill and the Eurodollar rate. Both are expressed in percentage points.
15 Paper-Bill spread represents the difference between commercial paper and Treasury-bill rates, in percentage points.
0 25 50 75 100 125 150 175 200 225 Ja n-0 6 Ap r-0 6 Ju l-06 Oc t-0 6 Ja n-0 7 Ap r-0 7 Ju l-07 Oc t-0 7 Ja n-0 8 Ap r-0 8 Ju l-08 Oc t-0 8 Equity Prices
DJ Eurostoxx Nikkei 225
S&P500 0 10 20 30 40 50 60 70 80 90 100 Ja n-0 6 Ap r-0 6 Ju l-06 Oc t-0 6 Ja n-0 7 Ap r-0 7 Ju l-07 Oc t-0 7 Ja n-0 8 Ap r-0 8 Ju l-08 Oc t-0 8 Implied Volatilities
VXJ VSTOXX VIX
-6 -5 -4 -3 -2 -1 0 1 2 3 4 Ja n-06 A pr -06 Ju l-06 O ct -06 Ja n-07 A pr -07 Ju l-07 O ct -07 Ja n-08 A pr -08 Ju l-08 O ct -08
Libor-OIS and TED Spreads
Libor-OIS Spread TED Spread
0,0 0,3 0,5 0,8 1,0 1,3 1,5 Ja n-06 A pr -06 Ju l-06 O ct -06 Ja n-07 A pr -07 Ju l-07 O ct -07 Ja n-08 A pr -08 Ju l-08 O ct -08 Paper-Bill Spread
23
Table 1 – Variable Definitions
Panel A: Effects of the Financial Crisis on Stock Market Valuation
ABNRET Stock Market Adjusted Returns between 15-Sep-2008 and 10-Oct-2008.
BOND Dummy variable that takes the value of one for firms that have ever publicly issued
a bond.
RATED Dummy variable that takes the value of one if the firm most recently issued a rated
bond.
NRATED Dummy variable that takes the value of one if the firm most recently issued an unrated bond.
LNSALES Natural logarithm of the firm’s sales (sales measured in million euros).
LNAGE Natural logarithm of firm’s age; age being computed as the difference between the
crisis year (2008) and the year the firm first appeared in the Datastream database.
MKTBOOK Market value of equity divided by book value of equity.
EBITSALES EBIT divided by sales.
LEVER Ratio of total debt to total assets.
ZSCORE ZSCORE = 1.2Z1 + 1.4Z2 + 3.3Z3 + 0.6Z4 + 0.999Z5.
Z1 = Working Capital / Total Assets; Z2 = Retained Earnings / Total Assets; Z3 =
Earnings Before Interest and Taxes / Total Assets. Z4 = Market Value of Equity /
Book Value of Total Liabilities. Z5 = Sales/ Total Assets.
STOCKRET Annualized mean of daily stock returns.
STOCKVOL Annualized standard deviation of daily stock returns.
SHARESTURN Total number of shares traded over the past 12 months, divided by the average
number of shares outstanding for the period.
Panel B: Effects of the Financial Crisis on Investment and Profitability
BOND, RATED,
NRATED
Defined as aforementioned.
CAPEXASSETS Capital expenditures divided by lagged assets.
EBITASSETS EBIT divided by lagged assets.
SG Sales growth divided by lagged assets.
CF Operating cash-flow divided by lagged assets.
24
Table 2 – Descriptive Statistics
Mean 25th pctl 50th pctl 75th pctl Std. Dev.
Panel A: Bank-Dependent firms (N=2238)
SALES 773.193 32.688 121.588 436.162 2,557.455
AGE 16.346 10 14 22 9.3857
MKTBOOK 2.7246 1.2341 2.0115 3.2699 2.8518
EBITSALES 0.1308 0.0622 0.1116 0.184 0.1675
LEVER 0.2158 0.0787 0.1975 0.3189 0.1604
ZSCORE 0.5071 0.256 0.513 0.761 0.2903
SHARESTURN 1.4541 0.0758 0.2581 0.697 6.2066
STOCKRET 0.0115 -0.2323 -0.0162 0.2232 0.4361
STOCKVOL 0.407 0.2808 0.3572 0.4587 0.2051
ABNRET -0.1076 -0.217 -0.0915 0.0155 0.1929
Panel B: Firms with access to public debt markets (N=410)
SALES 10,400 727.619 3,220.278 11,200 16,200
AGE 24.4829 15 23 39 12.14
MKTBOOK 2.9121 1.471 2.2759 3.4737 2.7758
EBITSALES 0.1791 0.0981 0.1714 0.1828 0.1601
LEVER 0.2756 0.1941 0.2766 0.3409 0.1321
ZSCORE 0.4216 0.203 0.4035 0.604 0.2525
SHARESTURN 1.7047 0.1864 0.8605 1.7583 5.1062
STOCKRET -0.2014 -0.1836 -0.001 0.157 0.2805
STOCKVOL 0.3161 0.2361 0.2875 0.3542 0.1256
ABNRET -0.0685 -0.1705 -0.0673 0.0281 0.1681
This table reports summary statistics of key variables used in our analysis, for bank-dependent firms and firms with access to public debt markets (Panel A and B, respectively). The sample consists of 2,648 firms, for which we were able to obtain financial and market information in Datastream. This sample does not include: (1) Financial borrowers and utilities; (2) zero-debt firms; (3) firms trading at less than 1€ at the end of
25
Table 3 – Effects of the Financial Crisis on Stock Market Valuation: Full Sample
Model 1 Model 2
Estimate t-val Estimate t-val
BOND 0.0271 2.43**
RATED 0.0568 3.84***
NRATED 0.1039 0.79
LNSALES 0.0057 2.55** 0.0046 1.99**
LNAGE 0.0248 2.99*** 0.0252 3.03***
MKTBOOK 0.0029 2.09** 0.0028 1.95*
EBITSALES -0.0006 -0.47 -0.0005 -0.38
LEVER - 0.0756 -2.45** -0.0724 -2.35**
ZSCORE 0.0130 0.70 0.0154 0.83
SHARESTURN - 0.0018 -2.25** -0.0018 -2.30*
STOCKRET - 0.0958 -8.23*** -0.0966 -8.34***
STOCKVOL 0.0441 1.55 0.0448 1.57
R2 0.1485 0.1506
N 2,648 2,648
Industry Fixed Effects IN IN
Country Fixed Effects IN IN
This table presents regression results from regressing firm’s market adjusted stock returns (ABNRET) on a
measure of bank-independence and on a set of firm-specific control variables. Model 1 measures
bank-independence by the dummy variable BOND; Model 2 segregates bond issuances though the dummy
26
Table 4 – Effects of the Financial Crisis on Stock Market Valuation: Matched Sample
Panel A: Matching Estimation Results
Pre-Match Post-Match
Estimate t-val Estimate t-val
LNSALES 0.4224 12.45*** 14.774 -1.77
LNAGE 0.1951 2.68*** 3.0443 0.4
MKTBOOK 0.0213 1.97** 2.8342 0.82
EBITSALES 1.1667 3.67*** 0.1780 0.66
LEVER 0.8638 2.49** 0.2550 1.34
ZSCORE -0.6628 -2.95*** 0.4207 0.4
SHARESTURN 0.0099 1.07 1.7080 -0.46
STOCKRET 0.0044 0.05 -0.0211 -1.3
STOCKVOL 1.0670 3.89*** 0.3169 0.59
Pseudo R2 0.3614 0.013
N 2,273 812
Industry Fixed Effects IN IN
Country Fixed Effects IN IN
Panel B: Abnormal Returns for Treatment (Treat=1) and Control (Treat=0) groups
This table presents the results of the matched sample analysis concerning stock market adjusted returns of bank-dependent and bank-inbank-dependent firms around the crisis period (15-Sep-2008 to 10-Oct-2008). Panel A
presents the results of the probit regression, with access to public debt markets (BOND) as the binary dependent variable. In the Pre-Match model all the firms with non-missing observations are used, whilst in the
Post-Match model only firms that could be matched based on the propensity score from the Pre-Match model are
considered. Panel B presents the results from comparing bank-independent (treatment) and bank-dependent
firms (control), before and after the match. * Significant at 10%; ** significant at 5%; *** significant at 1%.
Treatment = BOND Treatment = RATED
Pre-Match Post-Match Pre-Match Post-Match
ABNRETTREAT=0 -0.1042 -0.0892 -0.1007 -0.0996
ABNRETTREAT=1 -0.0689 -0.0689 -0.0342 -0.0342
ABNRETTREAT=1-
ABNRETTREAT=0
0.0353 0.0203 0.0665 0.0654
t-test ABNRET 3.45*** 2.26** 4.53*** 3.50***
27
Table 5 – Effects of the Financial Crisis on Real Outcomes: Investment and Profitability
Models 1 and 2: Investment Models 3 and 4: Profitability
Estimate t-val Estimate t-val Estimate t-val Estimate t-val
C1 -0.0026 -3.86*** -0.0026 -3.85*** -0.0215 -14.60*** -0.0215 -14.60***
C2 -0.0170 -25.19*** -0.0170 -25.19*** -0.0535 -35.85*** -0.0535 -35.85***
BOND*C1 0.0006 0.40 0.0045 1.20
BOND*C2 0.0040 2.36** 0.0080 2.16**
RATED*C1 0.0019 0.78 0.0076 1.40
NRATED*C1 -0.0003 -0.13 0.0021 0.45
RATED*C2 0.0061 2.51** 0.0117 2.16**
NRATED*C2 0.0023 1.08 0.0053 1.11
SALESG 0.0010 16.05*** 0.0010 16.05*** 0.0016 11.42*** 0.0016 11.42***
OPERCF 0.0170 5.59*** 0.0170 5.60*** 0.4992 74.33*** 0.4992 74.34***
LEVER -0.0116 -3.96*** -0.0116 -3.97*** -0.1238 -19.10*** -0.1239 -19.11***
R2 0.0319 0.0319 0.1978 0.0319
N 39,246 39,246 39,246 39,246
Firm Fixed Effects IN IN IN IN
This table presents the firm fixed effect regression results for the effect of the crisis on firm’s investment and profitability. This regression model is estimated using quarterly data for 2,648 firms, from 2006Q2 to 2009Q4. The dependent variables are: CAPEXASSETS, quarterly capital expenditures scaled by lagged assets (in Models
