the capital structure of swiss companies an empirical analysis
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FAME - International Center for Financial Asset Management and Engineering
HEC-University of Geneva
The capital structure of Swiss
Research Paper N 68
January 2003
Philippe GAUD
companies: An empirical analysis
using dynamic panel data
Elion JANIHEC-University of Geneva
Martin HOESLIHEC-University of Geneva, FAMEand University of Aberdeen (Business School)
Andr BENDERHEC-University of Geneva and FAME
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THE CAPITAL STRUCTURE OF SWISS COM
AN EMPIRICAL ANALYSIS USING DYNAMIC P
Philippe GAUD
Elion JANI
Martin HOESLIAndr BENDER
January 2003
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The capital structure of Swiss companies: an empi
using dynamic panel data
Philippe Gaud*, Elion Jani**, Martin Hoesli*** and Andr Be
This draft: 21 January 2003
Abstract
In this paper, we analyze the determinants of the capital structure for a
companies listed in the Swiss stock exchange. Both static and dynamic tethe period 1991-2000. It is found that the size of companies, the importan
and business risk are positively related to leverage, while growth
negatively associated with leverage. The sign of these relations suggest t
order theory and trade off hypothesis are at work in explaining the capit
companies, although more evidence exists to validate the latter theor
shows that Swiss firms adjust toward a target debt ratio, but the adjustm
slower than in most other countries. It is argued that reasons for this
institutional context.
JEL classification: G32
Keywords: Capital structure, dynamic panel data, trade-off theory, peckin
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Executive summary
One of the most important decisions in the field of corporate finance
policy. Using debt financing can have both positive and negative effect
firm. On the one hand, debt financing is value-enhancing for the firm bec
shield. Furthermore, debt allows to reduce the conflicts of interest bet
shareholders. On the other hand, the use of debt may increase bankruptc
the managers of firms with growth opportunities to accept sub
opportunities. In addition, debt often does not constitute an appropriat
highly innovative start-up companies. Empirical research in this area ha
the U.S market, and less evidence exists for European countries. The ai
contribute to the empirical literature by analyzing the determinants of th
Swiss companies. We analyze a panel of 106 firms for the period 1991-20
The capital structure decisions of firms can be explained by two alternati
off theory (TOT) and the pecking order theory (POT). The TOT posits th
off between the costs and benefits of debt financing that leads to an optim
In order to maximize the value of the firm, managers should determine t
then aim at reaching that level. In contrast, according to the POT, firms a
behavior: they first use internal financing, then debt and issue equity as a
is because of informational asymmetries between managers and outside in
The debate as to which theory better explains the capital structure
unresolved. Empirical research has shown that managers have a pre
sources of financing, but this does not imply that an optimal capital stru
Indeed, from a dynamic perspective, the preference for internal financing
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that the observed debt-to-equity ratio is not the optimal level and that t
over time.
Our results are often in contradiction with pecking order theory. Fir
theory, firms with few tangible assets should be more sensitive to inform
However, we observe a positive relationship between tangible assets and
suggest that firms use tangible assets as collateral when issuing debt. Seco
POT, informational asymmetries should be more severe for small size fir
positive correlation between size and leverage. This leads us to reject the
acts as an inverse proxy for informational asymmetries, but rather that siz
for the probability of bankruptcy which is consistent with the TOT. T
growth firms are less levered than non-growth firms, which suggests th
to debt to avoid bankruptcy costs.
In our sample, we find a negative relationship between profitability and d
is usually interpreted as evidence for the pecking order theory (POT
relationship is also consistent with the TOT in the short run. For exam
TOT, despite the fact that the contemporaneous profitability is a determi
cash-flow generated during the year can be used partly to decrease the lev
Overall, our results suggest that both the pecking order theory and trade
work in explaining the capital structure of Swiss companies, although mo
validate the latter theory. Our analysis shows that Swiss firms adjust
ratio, but the adjustment process is much slower than in most other c
explanation for this is that being in disequilibrium is not costly for Swi
that reasons for this can be found in the characteristics of Swiss firm
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The capital structure of Swiss companies: an empi
using dynamic panel data
1. Introduction
Since the seminal Modigliani and Miller (1958) paper showing th
conditions the impact of financing on the value of the firm is irreleva
capital structure has been expanded by many theoretical and empirical
emphasis has been placed on releasing the assumptions made by MM, in
into account corporate taxes (Modigliani and Miller, 1963), personal t
bankruptcy costs (Stiglitz, 1972; Titman, 1984), agency costs (Jensen a
Myers, 1977), and informational asymmetries (Myers, 1984). Two ma
currently the capital structure debate1: the trade off theory (TOT) and the
(POT).
The TOT posits that firms maximize their value when the benefits that ste
shield, the disciplinary role of debt, and the fact that debt suffers less from
than outside equity) equal the marginal cost of debt (bankruptcy cost
between shareholders and bondholders). The POT, developed by
consequence of informational asymmetries existing between insiders of t
(i.e. the capital market). Thereafter, addressing the issue of how com
financing mix has been primarily an empirical question, and such studie
in the last decade. However, empirical studies dealing with capital stru
(Taggart, 1977; Marsh, 1982; Jalilvand and Harris, 1984; Titman and
latter authors made a significant contribution in formulating and testing
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leveraged than those in market-oriented countries. However, they
distinction is useful in analyzing the various sources of financing. Rajan
find that the determinants of capital structure that have been reported
growth, profitability, and importance of tangible assets) are important
well. They show that a good understanding of the relevant institutional
law, fiscal treatment, ownership concentration, and accounting standar
identifying the fundamental determinants of capital structure. The anal
(2001) suggests that the same determinants of capital structure preva
countries. These studies, however, do not shed any light on the adjust
capital structure.
Other studies, which have addressed the dynamic nature of capital struct
from some limitations also. For example, the results of Taggart (1977)
Jalilvand and Harris (1984) may be biased as they use future information
proxy of the optimal debt ratio. Moreover, the tests of the target adjustme
as they are unable to reject the target adjustment hypothesis even when fi
according to POT only (Shyam-Sunders and Myers, 1999). With resp
validation of pecking order theory, Chirinko and Singha (2000) show tha
Sunders and Myers (1999) may be misleading. In addition, Frank and Go
fact that the debt level is determined fundamentally by the financing defic
of mean reversion of leverage, the adjustment process being influenced b
by Rajan and Zingales (1995).
Recent work has benefited from the advances in econometrics. Krem
Miguel and Pindado (2001), and Ozkan (2001) focus on the dynamics of
decisions offering better insight on the adjustment process toward the
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important. They argue that adjustment costs are lower in Spain than in t
because of the major role of bank financing.
The aim of this paper is to analyze the determinants of the capital struc
We add to the relatively limited literature on the dynamics of the capital
examining the dynamics of the relationship between leverage and a
variables. The analysis is conducted using panel data pertaining to 106 S
the period 1991-2000. A total of 967 observations are available for analy
that both the pecking order theory and the trade off theory are at w
evidence exists to validate the latter theory. Also, we find that the speed o
slow in Switzerland as compared to other countries. We argue that
specificity of the Swiss institutional framework help explain why Swis
too much from being away from their target ratios.
The paper is organized as follows. In section 2, we provide an overview
the determinants of the capital structure. The models and the data are pr
while the results from using both the static and dynamic models are dis
Finally, section 5 contains some concluding remarks.
2. The determinants of capital structure
In this section, we provide a review of the six main variables that have be
studies examining the determinants of capital structure.
2.1 Growth opportunities
For companies with growth opportunities, the use of debt is limite
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and Meckling, 1976; Smith and Warner, 1979). From a pecking order
growth firms with strong financing needs will issue securities less subj
asymmetries, i.e. short-term debt. If these firms have very close relat
there will be less informational asymmetry problems, and they will be a
long term debt financing as well.
A common proxy for growth opportunities is the market value to book
Firms with growth opportunities should exhibit a greater market-to-book
growth opportunities, but Harris and Raviv (1991) suggest that this is not
This will typically occur when assets whose values have increased over
depreciated, as well as when assets with high value are not accounted for
(e.g. the brand name Nestl).
Rajan and Zingales (1995) find a negative relationship between grow
leverage. They suggest that this may be due to firms issuing equity w
high. As mentioned by Hovakimian et al. (2001), large stock price i
associated with improved growth opportunities, leading to a lower debt ra
2.2 Size
Large size companies tend to be more diversified, and hence their cash fl
Size may then be inversely related to the probability of bankruptcy (T
1988; Rajan and Zingales, 1995). Ferri and Jones (1979) suggest that lar
access to the markets and can borrow at better conditions. For small
between creditors and shareholders are more severe because the manage
to be large shareholders and are better able to switch from one investme
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bankruptcy law and the Hausbanksystem which offer better protection to
case in other countries.
2.3 Profitability
One of the main theoretical controversies concerns the relationship b
profitability of the firm. According to the pecking order theory, firms
sources of financing first, then debt and finally external equity obtained
things being equal, the more profitable the firms are, the more interna
have, and therefore we should expect a negative relationship bet
profitability. This relationship is one of the most systematic findings in th
(Harris and Raviv, 1991; Rajan and Zingales, 1995; Boothet al., 2001).
In a trade-off theory framework, an opposite conclusion is expecte
profitable, they should prefer debt to benefit from the tax shield.
profitability is a good proxy for future profitability, profitable firms can
likelihood of paying back the loans is greater.
Dynamic theoretical models based on the existence of a target debt-to-e
that there are adjustment costs to raise the debt-to-equity ratio towards th
debt can easily be reimbursed with excess cash provided by internal sour
to have a pecking order behavior in the short term, despite the fact that th
their debt-to-equity ratio (Fischeret al., 1989; Leland, 1998).
2.4 Collaterals
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guarantee. Hence, firms have an incentive to do so, and one would expe
between the importance of tangible assets and the degree of leverage.
Based on the agency problems between managers and shareholders, Har
suggest that firms with more tangible assets should take more debt. This i
of managers who refuse to liquidate the firm even when the liquidation
the value of the firm as a going concern. Indeed, by increasing the levera
default will increase which is to the benefit of the shareholders. I
framework, debt can have another disciplinary role: by increasing the deb
flow will decrease (Grossman and Hart, 1982; Jensen, 1986; Stulz, 1990
former, this disciplinary role of debt should mainly occur in firms with
because in such a case it is very difficult to monitor the excessive expense
From a pecking order theory perspective, firms with few tangible assets
informational asymmetries. These firms will thus issue debt rather than eq
external financing (Harris and Raviv, 1991), leading to an expected nega
the importance of intangible assets and leverage.
Most empirical studies conclude to a positive relation between collaterals
(Rajan and Zingales, 1995; Kremp et al., 1999; Frank and Goyal, 2002)
are reported for instance by Titman and Wessels (1988).
2.5 Operating Risk
Many authors have included a measure of risk as an explanatory varia
(Titman and Wessels 1988; Kremp et al 1999; Booth et al 2001) Le
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2.6 Taxes
The impact of taxation on leverage is twofold. On the one hand, compan
to take debt because they can benefit from the tax shield. On the other h
from debt are taxed more heavily than revenues from equity, firms also
use equity rather than debt. As suggested by Miller (1977), the financia
are irrelevant given that bankruptcy costs can be neglected in equilib
Masulis (1980) show that if non-debt tax shields exist, then firms are li
debt tax shields. In other words, firms with large non-debt tax shields ha
to use debt from a tax shield point of view, and thus may use less deb
substitution effect is difficult to measure as finding an accurate proxy
that excludes the effect of economic depreciation and expenses is t
Wessels, 1988). According to Graham (2000), the tax shield accounts o
the firm value when both corporate and personal taxes are considered.
3. Models and Data
3.1 Static Model
The static model tests the Modigliani and Miller (1958) hypothesis that
variable. More specifically, the leverage is regressed on a set of explana
MM holds, then these variables should not be significant from a statistic
use explanatory variables to proxy for the determinants of capital struc
section 2. We do not take into account the tax effect for two reasons. Fir
substantially the size of our sample due to the lack of data regardi
choosing the appropriate marginal tax rate of firms is crucial in determini
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correlations that may result from a discrepancy between one of our prox
the debt-to-equity ratio computed by the managers (Titman and W
important to obtain the same sign from the various explanatory variab
market capitalization or book value. To check further the robustness of
run the regressions by only considering long term debt. Although t
reported in the tables, they are discussed whenever necessary.
Growth opportunities (GROWTH) are proxied by the market-to-book v
proxies also have been used in the literature, such as R&D and marketing
expenditures (Titman and Wessels, 1988), but such items are difficu
published financial statements, and hence are not considered in this study
logarithm of sales as proxy for size (SIZE). This measure is the most com
(Titman and Wessels, 1988; Rajan and Zingales, 1995; Ozkan, 2001).
could be the natural logarithm of total assets, but it is subject to more a
Various proxies can be used to measure profitability (PROF). We choo
assets, which is calculated as the ratio of EBIT to total assets (Rajan
Booth et al., 2001). We use the ratio of the sum of tangible assets and
asset as a proxy for collaterals (TANG). Adding inventories to the tangibl
by the fact that debts are used partly to finance inventories, and in mo
maintain some value when the firm is liquidated (Krempet al., 1999).
Proxying for the operating risk is a difficult task because such a mea
expectations concerning a firms profitability as compared to that of th
also take into account the specific nature of the firms assets. Many auth
of operating profits of each company as a proxy (Titman and Wessels
2001) Kremp et al (1999) measure the operating risk as the squared dif
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changes in the institutional context have occurred in recent years. For
prefer panel data analysis, as it is possible to include time effects as well
heterogeneity of firms by including firm-specific effects, which may
However, the fixed effects model is costly in degrees of freedom becau
the use of a dummy variable for every firm (Greene, 1993). The ra
assumes the independence between error terms and explanatory variab
performed in this study to control for the presence of firm specific effect
Hausman test is then performed to validate the exogeneity of the firm
dependent variables (Hausman, 1978). If the two null hypotheses are rej
effect model will be retained. A Wald test of the joint significance of time
also used.
In order to ease comparison, we also report simple pooled ordinary lea
pooled ordinary least squares with dummy variable for time and sector
type estimations.
Our static model to analyze firms with panel data is as follows:
ittiitit uxy +++= '
withi = 1,.,Nandt=1,.,T
and
ity : the leverage of firm i in year t
itx : a K x 1 vector of explanatory variables
: a K x 1 vector of constants
i : firm effect assumed constant for firm i over t
t : time effect assumed constant for given t over iu : error term
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( )1*
1 =
itititit yyyy
with 10
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asymptotically distributed as
2 under the null hypothesis of no relations
of the joint significance of the capital structure determinants.
Arellano and Bond (1991) show that when the number of firms is lim
standard errors associated with the two-step estimates may be biased d
the one-step estimators are less efficient than the two-step estimators
homoskedasticity of the error terms. Since the standard errors associat
estimators are more reliable to make inferences, the results using both me
this study.
3.3 Data
Our data consist of Swiss firms listed on the Swiss stock exchange SWX
2000. We use annual data extracted from Worldscope
. Banks, insuran
companies, and some other companies, whose business is highly regula
companies, are excluded from the sample. This is motivated by the fact
have to comply with very stringent legal requirements pertaining to
sample thus contains primarily industrial, commercial and service co
managers have considerable leeway concerning financial decisions.
We deflate our data using 1992 as base year. Company financial statem
stock guide are used to fill any gaps in the data, and to check th
observations (i.e. a given company in a given year) for which we have ne
balance sheet, except for retained earnings and other assets3. Ou
applying a methodology similar to that of Kremp et al. (1999). We excl
hi h fit bilit ll t l d th t iti t id th
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consecutive years for a company to be included. This leaves us with a
and a total of 967 observations.
Table I contains summary statistics for selected years of the time period
and 2000), as well as for the total period 1991-2000. The importance of
over the period, with a sharper decrease when market values of equity
when book values are used. Whereas both proxies for leverage are rough
leverage ratio amounts to 54.2% in 2000 when market values are used
book values are used. Over the period, companies on average have gro
increased the relative importance of intangible assets in their balance sh
book ratio has increased from 1.10 in 1991 to 1.87 in 2000, but remains
the fact that the Swiss market is a value market rather than a g
profitability of Swiss companies has increased over the period, and not su
return levels are accompanied by higher levels of risk.
The correlation coefficients between variables are reported in Table
generally low, except the correlation between profitability and risk. In fa
between these two variables would be expected. To check whether the
collinear, we perform a VIF test. Our VIF tests are substantially lower th
should not constitute a problem (Chaterjee and Price, 1977).
4. Results
4.1 Static Analysis
In table III we present the regression results for the static analysis. The le
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macroeconomic events and changes in the institutional context play a si
borrowing decision of Swiss firms.
SIZE plays a positive role and the coefficients are significant for all
profitability variable (PROF), we find a negative relationship with l
coefficients are again significant at the 1% level. For the other three varia
more across specifications. The RISK variable is positively related t
leverage and in all cases significant at the 1% level when the market
considered, but not when book values are considered. The GROWTH
impacts on leverage and is significant at the 1% level when market valu
is significant in all cases with panel data estimations. Tangible assets (TA
impact that is significant at the 1% level for all panel data estimatio
estimations with market values. In summary, when panel data are used, th
RISK variables have a positive impact on leverage, whereas GROWT
negative impact. All these relations are significant at the 1% level when
or book values of equity are used, except for the SIZE variable, whic
when market values are used.
When using long-term debt in lieuof total debt to compute the leverage,
firm-specific effects exist and that the random effects models give the b
obtain the same sign and the same significance for all coefficients, e
variable, which is now significant at the 5% level when using book val
SIZE, which looses its significance.
The positive impact of size on leverage is consistent with the result
studies (Rajan and Zingales 1995; Booth et al 2001; Frank and Goyal
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being not well developed in Switzerland, this allows banks to select bet
they will prefer large firms to small ones, the sign of the SIZE coefficient
As reported in several other studies, the PROF variable is negative and si
(Rajan and Zingales, 1995; Boothet al., 2001; Frank and Goyal, 2002). T
support for the pecking order theory. However, caution has to be e
dynamic nature of the relation between leverage and profitability is e
Goyal, 2002; Titman and Wessels, 1988). The positive impact of RISK
estimation when using market data implies that firms, which perform be
levered. In other words, companies with high operating risk try to c
limiting financial risk.
The coefficient of the TANG variable is positive and significant
estimations, and this result is similar to those reported in previous
Zingales, 1995; Kremp et al., 1999; Frank and Goyal, 2002). This resul
use tangible assets as collateral when negotiating borrowing, especially l
The observed sign of the relationship does not confirm the sign that wou
using the pecking order theory framework. In such a framework, firm
assets are more subject to informational asymmetries, and are more
principally short term debt - when they need external financing.
The negative sign of GROWTH confirms the hypothesis that firms with
are less levered. To analyze further this relationship, we divide our samp
using the median growth as cut-off. The negative sign and significan
remains irrespective of the leverage measure for the high growth firms.
growth firms which are typically no growth firms as the market to boo
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earnings will increase also, leading to a lower leverage ratio. Moreover,
Porta et al. (1998), these companies are often family-owned (such as L
Bucher), and have a strong tax incentive not to distribute any remaining
repurchase of shares is too costly for this type of company. For companie
(such as Axantis, and WMH), banks are reluctant to lend funds, as t
encouraging. In some cases, these companies have been forced to sell som
often with a capital gain, making it possible to reduce the debt-to-equi
market values are used, the above-mentioned financial operations will ha
impact on the market value of equity4. Therefore, the debt-to-equity ratio
4.2 Dynamic Analysis
The dynamic analysis makes it possible to study the financing behavior
time and whether there are adjustment costs. As the model is estimated in
one or more lagged variables are used as explanatory variables, our sam
967 to 755 observations. To examine the impact of profitability ov
borrowing, we add the lagged profitability in equation (3). This is motiv
we want to test the persistence of a pecking order financing, which w
model. Models such as those of Fischer et al. (1989) and Leland
existence of an optimal debt-to-equity ratio, but find pecking order beha
due to the adjustment costs. Despite the fact that the contemporaneo
determinant of the importance of leverage, the cash flow generated du
used partly to decrease the level of debt.
For the dynamic model, we test various specifications concerning the
explanatory variables. Only the results of the model that posits th
endogenous are reported in this paper Not surprisingly this is the b
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test for the one-step estimator does not confirm the validity of our ins
However, as Arellano and Bond (1991) note, the Sargan test has an te
often in the presence of heteroskedasticity.
The size of the coefficient of the lagged leverage is high and it is in all ca
1% level. For the Swiss market, the size of the coefficient is in the 0.708
one-step estimations. The coefficient is smaller when we use market
managers use market values when adjusting their leverage toward the targ
to be exercised when cross-country comparisons are made, but su
interesting however. The adjustment process is slow in Switzerland com
for other countries as reported in many studies: De Miguel and Pindado (
0.21 for Spain, Shyam-Sunder and Myers (1999) a value of 0.41 for th
(1999) a value of 0.47 for Germany, and Ozkan (2001) a value of 0.
France, the speed of adjustment is comparable to that for Switzerlan
reported by Krempet al. (1999).
The adjustment process is a trade off between the adjustment costs towa
the costs of being in disequilibrium. If the costs of being in disequilibrium
adjustment costs, then the estimated coefficient should be close to zero
example, De Miguel and Pindado (2001) explain the small coefficient t
Spanish market by the importance of bank credit. They argue that Span
low transaction costs when borrowing funds from banks, and that suc
lower agency costs between creditors and shareholders. It could seem a
same explanation for the Swiss market because companies in both coun
banks for their long term borrowing needs. According to the World Ban
debt ratio is 5 7% for Spain and 7 9% for Switzerland as compared e g t
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consider the use of debt in this case5. There is also a purely mechan
explain the lower level of debt within the framework of a dynamic TOT
prices increase due to a market boom without an increase in the size of a
of the growth options, this will lead to a decrease of the leverage, even be
Some institutional factors tend to lead to a high level of debt rather than
Hertig (1998) shows that Swiss firms have benefited from relatively ea
period under study. This is because loans were often granted base
relationships, than based upon objective criteria. The large banks were th
organization by splitting credit analysis from credit decisions, and by us
company expected earnings. In contrast, cantonal banks, in part due to p
been more inclined to continue granting loans based on the old sy
providing Swiss firms with relatively inexpensive financing given the
difficult, however, to measure the effect of the cantonal bank behavior on
policy of listed companies as they predominantly finance non-listed SM
some indirect effects however.
As far as corporate governance is concerned, Hertig (1998) shows that th
in Switzerland during the period under review is quite limited. For examp
of large banks giving them more than 10% of the voting rights in industr
than 5% of their total assets (BNS, 1992-2001). One of the consequence
of bank representatives on the boards of Swiss companies, the hi
ownership (La Porta et al., 1998), and the lack of control in bank credi
agency costs stemming from the conflicts of interest between lender
mainly borne by lenders. This will lead to an increase in the use of debt.
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of the stock market that leads companies to find themselves with a low
target. On the other hand, easy credit policy has enabled companies to bo
investments. Such firms will often have an above-target leverage. Such b
the risk premium on bank loan interest rates is too low.
An additional result from the dynamic analysis is the coefficient of the
variable (PROFit-1), which is positive and significant at the 1% leve
considered. The impact of lagged profitability on leverage, however,
current profitability. The coefficient on lagged profitability is not signifi
debt only is used. This result confirms a short-term pecking order beha
One possible reason could be that Swiss banks have made use of historica
loans; in such a context, one would expect past profitability to play an im
5. Concluding Remarks
This paper presents a study of the determinants of capital structure for Sw
analyses are performed using data pertaining to 106 firms for the peri
static and dynamic tests are conducted, and panel data specifications ar
analysis is conducted using a combination of the GMM approach and in
to check for endogeneity in variables.
Our results show that the size of companies, the importance of tangible
risk are positively related to leverage, while growth and current profita
associated with leverage. The dynamic analysis suggests that there ex
equity ratio. Lagged profitability has a positive impact on leverage,
prediction of a short term pecking order behavior towards the target
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institutional framework, such as the impact of taxation and that of the re
the various sources of credit (securitized debt vs. bank debt).
From an empirical perspective, emphasis should be placed on construct
that enable to discriminate between the various factors that impact on the
impact on the speed of adjustment. Finally, focus should be placed on the
of Swiss companies to examine how firms make their financing decisions
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Table I: Descriptive statistics
This table presents descriptive statistics for the variables used in our estimatio
Worldscope
and the sample contains 106 Swiss firms listed on the Swiss stock excha
have a minimum of six consecutive years of data for the period 1991-2000. DTAB is
total assets where the total assets are measured with book values. DTAM is the ratio of
where the total assets is the sum of the book value of debt plus the market value of equSIZE is the natural logarithm of sales in real terms (base year = 1992). TANG is the ra
inventories to total assets using book values. GROWTH is the ratio of market value
assets plus market value of equity less book value of equity) to book value of assets. P
total assets. RISK is the squared difference between the firms profitability (PROF) an
of profitability for year t. To this squared measure we add the sign of the differ
profitability and the cross section mean. Summary statistics include the mean and th
years 1991, 1994, 1997, and 2000. For the total period (1991-2000), we also report the
Year 1991 1994 1997 2000
Mean Std Mean Std Mean Std Mean Std Mean
DTAB 0.573 0.135 0.575 0.153 0.563 0.151 0.542 0.157 0.566
DTAM 0.569 0.186 0.518 0 .196 0.468 0.197 0.402 0 .191 0.497SIZE 13.356 1.660 13.523 1.611 13.645 1.672 13.895 1.611 13.58
TANG 0.569 0.202 0.571 0.188 0.549 0.193 0.465 0.178 0.548
GROWTH 1.095 0.392 1.245 0.500 1.456 0.808 1.874 1.645 1.370
PROF 0.066 0.046 0.077 0 .056 0.082 0.059 0.092 0 .066 0.077
RISK 0.016 0 .401 0.053 0.783 0.011 0 .926 0.097 1.065 0.037
N 85 100 105 86
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Table II: Pearson correlation coefficients between variables and VIF
This table presents the Pearson correlation coefficients for the variables used in o
(variance inflation factor) tests between dependent variables. The data are from Worl
contains 106 Swiss firms listed on the Swiss stock exchange (SWX) for which we
consecutive years of data for the period 1991-2000. DTAB is the ratio of total debt to to
assets are measured with book values. DTAM is the ratio of total debt to total assets wh
sum of the book value of debt plus the market value of equity at the end of the year. SIZE
of sales in real terms (base year = 1992). TANG is the ratio of tangible assets plus in
using book values. GROWTH is the ratio of market value of assets (book value of ass
equity less book value of equity) to book value of assets. PROF is the ratio EBIT to
squared difference between the firms profitability (PROF) and the cross section mean
To this squared measure we add the sign of the difference between the firms profitabilmean.
DTAM DTAB SIZE TANG MTB
DTAM
DTAB 0.6985SIZE -0.0113 0.1533
TANG 0.2920 0.0581 -0.3573
MTB -0.6725 -0.2519 0.0738 -0.3056
PROF -0.6295 -0.4145 0.0748 -0.3200 0.5413
RISK -0.4480 -0.3615 0.0305 -0.2326 0.4155
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Table III: Static results
In this table we present various static estimations of the determinants of leverage. The d
stock exchange (SWX) for which we have a minimum of six consecutive years of data fo
assets are measured with book values. DTAM is the ratio of total debt to total assets wh
at the end of the year. SIZE is the natural logarithm of sales in real terms (base year =
values. GROWTH is the ratio of market value of assets (book value of assets plus mark
EBIT to total assets. RISK is the squared difference between the firms profitability (PR
add the sign of the difference between the firms profitability and the cross section mean
For the Fama-McBeth approach, we report the average of the time series of coefficien
and sector dummy variables use a dummy for each year 1992-2000 and a dummy for ea
in brackets. When appropriate, standard errors are White (1980) corrected for heteros
5% level. * indicates significance at the 10% level. Wald 1 is a test of the joint significavariables. Wald 1 and 2 are asymptotically distributed as
2under the null hypothesis o
The Hausman test is a test with H0: random effects are consistent and efficient, versus H
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OLS Fama-McBeth Estimati
Year
DTAM DTAB DTAM DTAB DTAM
Intercept 0.652 0.445 0.718 0.410
(0.051)*** (0.053)*** (0.054)*** (0.045)***
SIZE 0.010 0.017 0.010 0.018 0.010
(0.003)*** (0.003)*** (0.002)*** (0.002)*** (0.003)*** (
TANG 0.051 -0.020 0.021 -0.019 0.047 (0.027)*** (0.027) (0.025) (0.021) (0.027)*
MTB -0.105 -0.009 -0.165 0.012 -0.099
(0.018)*** (0.006) (0.023)*** (0.014) (0.018)***
PROF -2.263 -1.083 -2.054 -1.253 -2.208
(0.253)*** (0.158)*** (0.144)*** (0.249)*** (0.246)*** (
RISK 0.074 0.004 0.083 0.019 0.068
(0.015)*** (0.010) (0.016)*** (0.020) (0.015)***
R2
ajusted 0.580 0.205 0.598 0.210 0.588
R2 within
R2
between
R2
overall
Wald 1 21.1(10)
Wald 2
Chow
Hausman
N 967 967 10 10 967
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Table IV: Dynamic results
In this table we present Arellano and Bond one-step and two-step GMM estimatWorldscope
and the sample contains 106 Swiss firms listed on the Swiss stock excha
have a minimum of six consecutive years of data for the period 1991-2000. DTAB is
total assets where the total assets are measured with book values. DTAM is the ratio of
where the total assets is the sum of the book value of debt plus the market value of equ
SIZE is the natural logarithm of sales in real terms (base year = 1992). TANG is the rainventories to total assets using book values. GROWTH is the ratio of market value
assets plus market value of equity less book value of equity) to book value of assets. P
total assets. RISK is the squared difference between the firms profitability (PROF) an
of profitability for year t. To this squared measure we add the sign of the differprofitability and the cross section mean. Robust standard deviations are reported in
significance at the 1% level. ** indicates significance at the 5% level. * indicates signi
Wald 1 is a test of the joint significance of time dummy variables. Wald 3 is a test of the
estimated coefficients. Wald 1 and 3 are asymptotically distributed as 2
under th
relationship. The Sargan test of over-identifying restrictions is asymptotically distribut
instrument validity. The m2 test is a test for second order autocorrelation of residu
N(0,1).
Arellano-Bond Estimator (two-step) Arellano-Bond E
DTAM DTAB DTAM DTAB DTAM DTAB
DTit-1 0.609 0.759 0.726 0.889 0.708 0.734
(0.059)*** (0.045)*** (0.063)*** (0.050)*** (0.108)*** (0.101)***
SIZE 0.066 0.075 0.060 0.078 0.069 0.052
(0.011)*** (0.013)*** (0.011)*** (0.013)*** (0.017)*** (0.021)**
TANG 0.119 0.020 0.126 0.017 0.176 0.114
(0.042)*** (0.044) (0.045)*** (0.046) (0.092)* (0.137)
MTB -0.092 0.000 -0.095 -0.002 -0.070 -0.002
(0.011)*** (0.004) (0.012)*** (0.005) (0.019)*** (0.009)
PROF -0.989 -0.838 -0.979 -0.854 -0.954 -0.745
(0.126)*** (0.098)*** (0.118)*** (0.105)*** (0.196)*** (0.154)***
PROFit-1 0.331 0.406
(0.074)*** (0.063)***
RISK 0.025 0.012 0.028 0.016 0.025 0.006
(0 005)*** (0 005)** (0 005)*** (0 006)*** (0 008)*** (0 009)
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