Testing the Stability in Corporate Capital
Structures of Public Dutch Firms
The Determinants of Public Dutch Firms
Master Thesis Finance
Faculty of Economics and Business Administration
Tilburg University
August 2011
Maarten Wesseloo (anr. 518127)
Supervisors:
Prof. Dr. F. Braggion
Prof. Dr. M. Da Rin
Department of Finance
Abstract
By testing the stability in leverage it is shown that the research techniques of Lemmon, Roberts and
Zender (2008) and of DeAngelo and Roll (2011) can both be applied on public Dutch firms. Research
has shown that a large part of the variation in leverage is caused by invisible time (in)variant factors,
which generate amazingly stable corporate capital structures over nine event years. A high degree of
instability is measured when the individual firms are tested on their individual movements in
leverage. A deviation from a stable leverage regime is strongly related to the growth in assets and to
the growth in external financing to sustain that growth. Combining the research techniques of
Lemmon, Roberts and Zender (2008) and DeAngelo and Roll (2011) ensures that 80% of the firms
appear to have a relatively stable capital structure over different time-series.
Table of contents
Introduction: ........................................................................................................................................... 4
Chapter 1: Brief description of the main capital structure theories ..................................................... 7
1.1 Modigliani & Miller (1958) ............................................................................................................ 7
1.2 Modigliani & Miller (1963) ............................................................................................................ 8
1.3 Trade-off theory............................................................................................................................. 8
1.4 Agency theory .............................................................................................................................. 10
1.5 Pecking order theory ................................................................................................................... 11
1.6 Market timing .............................................................................................................................. 12
Chapter 2: Stability in corporate capital structures ............................................................................ 13
2.1 Persistence and the cross-section of corporate capital structure (Lemmon, Roberts & Zender,
2008).................................................................................................................................................. 13
2.1.1 Convergence and persistency in the capital structure .......................................................... 13
2.1.2 Persistency in capital structure............................................................................................. 15
2.1.3 Variance decomposition ....................................................................................................... 16
2.2 The stability of corporate capital structure (DeAngelo & Roll, 2011) ....................................... 18
2.2.1 Stability in leverage ratios .................................................................................................... 18
2.2.2 Stability of leverage cross-section ........................................................................................ 21
2.2.3 Key factors of leverage instability ........................................................................................ 23
Chapter 3: Data selection and description........................................................................................... 25
3.1 Data selection and data testing .................................................................................................. 25
3.2 Analyzing the data....................................................................................................................... 26
Chapter 4: Testing the convergence effect for public Dutch firms ..................................................... 28
4.1 Portfolio decomposition .............................................................................................................. 28
4.2 Convergence effect ...................................................................................................................... 28
Chapter 5: Capital structure determinants .......................................................................................... 30
5.1 The importance of the initial leverage of a firm .......................................................................... 30
5.2 Variance decomposition .............................................................................................................. 35
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Chapter 6: (in)Stability in corporate capital structures ....................................................................... 39
6.1 Stability based on the inter-temporal variation of leverage for each individual firm ................. 39
6.2 Determinants that caused an instable leverage regime ............................................................. 42
6.3 Stability based on the mean movement in leverage for the individual firms over time.............. 44
Conclusion: ............................................................................................................................................ 46
Literature: ............................................................................................................................................. 48
Appendix: .............................................................................................................................................. 51
Appendix 1: Construction of the variables......................................................................................... 51
Appendix 2: Convergence effect for survivors ................................................................................... 52
Appendix 3: Persistency in capital structure for survivors ................................................................. 53
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Introduction:
One of the most interesting items in corporate finance nowadays is the choice of capital structure. A
mix of different sources of capital usually defines the capital structure of a firm. A mix of different
sources of capital consists for example of the following components: common equity, preferred
equity, long term debt, short term debt and hybrid securities. A firm’s capital structure is all about
how a firm finances its operation. Changes in capital structure can have an impact on a firm’s value,
which will be discussed in section 2.1, the trade-off theory. Since 1958 different capital structure
theories exist, but after several decades of research there is still no concrete answer on how firms
should optimally organize their capital structures. In the article by Myers the author came up with
the following statement: “How do firms choose their capital structures? We don’t know” (1984:575).
Therefore, researchers keep on searching. What is remarkable is that the main researchers have
been focusing on time-variant determinants only. According to Lemmon, Roberts and Zender,
henceforth LRZ, the traditional time-variant determinants account for 18% to 29% of the capital
structure of firms (2008:1576).
In 2008 LRZ made a breakthrough with their paper in which they found evidence for time invariant
factors based on a sample of US public firms between 1965 and 2003. In their paper the authors
tested the stability of firms over twenty event years. Testing the stability resulted in two important
factors for a firm’s capital structure, namely the convergence and the persistency effect. The authors
concluded that the: “majority of the variation in leverage is determined by an unobserved timeinvariant effect which generates relatively stable capital structures” (2008:1575). In contrast to these
findings DeAngelo and Roll published totally different results in 2011, when they studied the stability
in leverage. The authors used a sample for public US firms between 1950 and 2008. Apart from these
totally different results DeAngelo and Roll also contradicted LRZ’s results (2008) by arguing that firm
fixed effects are time-invariant. DeAngelo and Roll found evidence that firm fixed effects differ
significantly across decades (2011:13). The authors drew the following conclusion: “Capital structure
stability is the exception, not the rule” (2011:5).
The contrasting conclusions on stability for the US public firms makes it interesting to test stability in
a different country. The goal of this paper is to test the stability for the public Dutch firms. In case of
instability time-varying determinants in the financial policy for the public Dutch firms are extremely
important, but in case of stable capital structures the cross-sectional variation in leverage are the
interesting determinants. The limited sample for public Dutch firms, compared to the research by LRZ
and DeAngelo and Roll, may bias the results. To my knowledge no research has recently been carried
out on testing the stability of the public Dutch firms. Another interesting aspect is that US managers
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focus on maximizing shareholder value. When the US firms do not succeed in maximizing the
shareholder value, their companies may be taken over by other firms. “This situation does not
happen that much in the Netherlands, this because practically all firms have adopted multiple
takeover barriers”, says De Jong (2000:1862). To test the stability of firms the research techniques
used by LRZ and DeAngelo and Roll will be applied to the public Dutch firms.
After having applied LRZ’s research techniques on the public Dutch firms the convergence effect and
the persistency effect are clearly visible. Furthermore, it appears that the different portfolios never
crossed each other over time. This supports the fact that leverage ratios are characterized by
persistency and by transitory components. It has been shown that Initial leverage has some
explanatory power in determining the future leverage ratio of a firm. When firm fixed effects are
determined to be constant over time, as assumed by LRZ, firm fixed effects explain 54% (39%) of
variation in Book (Market) leverage, which is even more than the traditional determinants. Research
has shown that firm specific characteristics vary over time, this results in an increase in the adjusted
R-squared from 54% (39%) to 71% (62%) in Book (Market) leverage. The increase in explanatory
power of firm fixed effects makes that other determinants have no or little explanatory power. Firm
fixed effects are known to be invisible. When these invisible items vary over time it will become even
more difficult to solve the capital structure puzzle.
This paper further tested the stability of individual firms. It is assumed that firms are very stable
(relatively stable), when the deviation is less than 0.1 (0.2) from their initial Book leverages. The
actual Book leverage for individual firms deviates highly from their initial Book leverage.
Determinants that cause an instable leverage regime are mainly explained by the variable Asset
growth. For the public Dutch firms Asset growth is related to higher outstanding debt to finance
growth. Therefore the investment policy of a firm tends to impact the stability of firms. But when the
mean deviation for individual firms is observed over different time-periods, it appears that more than
80% of the firms over different time-series are relatively stable. This supports the results by Graham
and Harvey (2001), who found that target leverage ratios are important. Stable leverage regimes give
support to the trade-off theory. After doing research on the public Dutch firms, it seems too rigorous
to state that the capital structure stability is the exception and not the rule, like DeAngelo and Roll
assumed for the US public firms (2011:5).
This research is constructed as follow: chapter one will give a brief review of the most important
capital structure theories. Chapter two will be devoted to the most important findings by LRZ (2008)
and DeAngelo and Roll (2011), where different research techniques are briefly discussed. Chapter
three will describe how the dataset for public firms was obtained and which data samples will be
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used in this study. In chapter four the convergence and the persistency effects of LRZ (2008) will be
tested on the public Dutch firms. Chapter five will then test the importance of the initial leverage, as
a permanent component of a firm. This chapter will further examine the importance of each
determinant in relation to variance decomposition. Chapter six is devoted to stability in the capital
structure for individual firms and to the determinants that influence an instable leverage regime.
Finally, the main conclusions will be given in chapter seven.
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Chapter 1: Brief description of the main capital structure theories
Capital structure theories function as the basis to find new determinants in explaining the ideal
capital structure of a firm. The uncertainty in determining the capital structure creates the possibility
for several researchers to have different views on the capital structure of a firm and on how to
realize the optimal leverage ratio. Different theories about the capital structure are used to advise
managers how to optimize their firm in terms of finance. Before the traditional determinants on the
public Dutch firms will be examined, a brief description of the main capital structure theories that
have been developed since 1958 will be given, starting with the Modigliani Miller theory.
1.1 Modigliani & Miller (1958)
Modigliani and Miller (M&M) can be seen as the founders of the capital structure theory and
theorists of modern finance. Their theory’s key and most arguable assumption is that firms live in a
perfect market. Modigliani and Miller (1958) demonstrate that in a perfect market the firm’s value
does not depend on financial policy. This means that all the securities are fairly priced and that there
are no arbitrage opportunities. M&M assume that in a perfect market investors are indifferent about
the capital structure of a firm. Investors are able to gain the same results as in a leveraged firm, by
investing in an unlevered firm and by borrowing the percentage of debt the leveraged firm has. The
assumption is that investors can borrow at the same rate as firms, even when the average costs of
capital are independent of the firm’s capital structure. Therefore M&M came up with the following
proposition.
Proposition I: “the market value of any firm is independent of its capital structure and is given by
capitalizing its expected return at the rate pk appropriate to its class’ (1958:268).
The amount of net cash a firm has has no impact on the firm’s value. Firms are always able to obtain
funding for new debt in a perfect market at a constant rate. Therefore, firms never have a shortage
in cash. However, reality teaches us that higher leverage ratios result in higher risks and that
investors want to be compensated for that. Therefore, the authors came up with the following
proposition.
Proposition II: “the expected yield of share of stock is equal to the appropriate capitalization rate pk
for a pure equity stream in the class, plus a premium related to financial risk equal to the debt-toequity ratio times the spread between pk and r.”
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According to proposition II the expected rate of return is a linear function of the debt to equity ratio.
Propositions I and II make it able to take the right steps, in getting a better understanding of the
possible factors that create a well-established, fundamental base for firms to be financial healthy.
1.2 Modigliani & Miller (1963)
In their 1958 paper M&M did not take the tax benefit of debt into account. After the recognition that
tax benefits do have influence on the firm value, in 1963 M&M came with a correction on their
original paper of 1958 and amended the two propositions. M&M still use the assumption of a perfect
market, but with corporate taxes. The theorem supports firms to include as much debt as possible
within the firm. When interest expenses are tax deductible, the market value of a levered firm will
always surpass the market value of the unlevered firm. The benefit of leverage is most important for
firms that have a consistent taxable income.
Even though this theory of capital structure is described from the perspective of a perfect market,
the authors were aware of the imperfections in the real world. This theory has been developed to
give insight in the determinants that can contribute to solving the capital structure puzzle. In reality,
the ideal capital structure depends on market imperfections such as financial distress, agency
problems, taxes, and information asymmetry. Even if the assumptions made by M&M seem
unrealistic, a lot of researchers still use this paper as a starting point to create an optimal capital
structure. From here on different capital structures have been developed.
1.3 Trade-off theory
What has been described in the previous section, is that M&M’s theory only examines the benefits of
debt. Therefore, M&M claim that a higher debt ratio results in a higher firm value. They did not take
into account the financial distress costs and the bankruptcy costs when a firm is too highly leveraged.
As stated by Kraus et al. (1973:911). “The firms financing mix determines the states in which the firm
is insolvent, the value of the firm is not affected since bankruptcy penalties would exist in a perfect
market”. The assumption is that the perfect market does not exist in the real world, and therefore
Kraus and Litzenberger came up with the trade-off theory. This theory makes a trade-off between
the costs and the benefits of debt. The benefits are tax advantages that will appear when the interest
costs of debt are tax deductible. When a firm takes on debt, it is legally obligated to pay interest and
the principal to the debt holders. Debt holders have to be paid first, before the firm can spend the
funds to the residual claimholders. The costs of debt appear when a firm is not able to pay for their
debt obligations and end in bankruptcy.
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Figure 1 shows that the market value of a firm consists of the following components: the value of the
unlevered firm plus the present value of the difference between the tax advance of debt and the
bankruptcy costs. The slope of firm value creation must be equal to the present value of the interest
tax shield. This is in line with M&M’s theory (1963). The two theories are fairly similar, but the main
difference is that the trade-off theory not only focuses on the benefits of debt, but also takes into
account the bankruptcy costs when a firm is over leveraged. The horizontal red line in Figure 1
explains the firm value of an unlevered firm. When the firm takes on more debt, the value of the firm
will grow. The optimal firm value is at point *. When a firm decides to take on more debt after point
*, bankruptcy costs will be involved which will result in a decrease in the firm value.
Figure 1: Trade-off
The company value depends on the capital structure of a firm. When a firm takes on too much debt, bankruptcy costs arise.
When a firm issues more debt after point *, firm value will decrease.
Warner (1977:337) measured that the total bankruptcy costs count for one percent of the firm’s
value. Bankruptcy costs can be divided into direct and indirect costs. For example, legal fees,
accountancy costs, and other professional costs are placed in the category ‘direct bankruptcy costs’.
Indirect bankruptcy costs are much broader and are not always measurable. Examples of indirect
costs are: reduction in sales, loss of profits, a difficulty in obtaining new credit, ongoing support
services, customers switching to a competitor, and opportunity losses.
Warner states that when bankruptcy is expressed as a percentage of the firm value, firms with a
higher market value have a lower percentage of bankruptcy costs (1977:345). These results support
the fact that part of the bankruptcy costs are fixed. The trade-off theory appears to be somewhat
more realistic than M&M’s theory. Throughout the trade-off theory one can learn that in this case
the capital structure of a firm has influence on the value of a firm. When the financial leverage of the
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firm is too high, it will reduce the value of the firm. The theory supports firms to find their optimal
capital structure, where the cost and benefits of debt will be balanced.
1.4 Agency theory
Jensen and Meckling tried to explain the impact of the agency costs on the capital structure of the
firm. The agency theory tries to explain managerial behavior. Jensen and Meckling give the following
definition: “We define the agency relationship as a contract under which one or more persons (the
principal(s)) engage another person (the agent) to perform some service on their behalf which
involves delegating some decision making authority to the agent” (1976:5). If both parties are trying
to maximize their own utility, it is possible that the agent not always acts in the way the principal
wants to. This may result in an agency conflict. Agency conflicts can arise between stakeholders and
manager and also between debt holders and shareholders.
First, an explanation of a conflict between debt- and shareholders will be given. A conflict between
debt- and shareholders is mainly caused by the difference in risk-level between two parties that has
been taken. A conflict may arise when a manager is in line with the shareholders and wants to
maximize their equity value. For example: when a firm has a high debt to equity ratio and has a high
chance that it will be financially distressed, potential conflicts could rise between shareholders and
bondholders on what project they want to invest in. The main conflict in this situation is caused by
the fact that debt holders are risk-averse and equity holders are risk takers. In some cases equity
holders could even support negative NPV-projects. This makes that bondholders should be extremely
critical when they decide to invest in a project or a company. When the company’s risk profile is high,
bondholders can decide not to invest in a specific company or they can charge a higher interest rate.
Higher risk goes together with a higher return. For bondholders it is possible to reduce business risks
by setting up some debt-covenants. Debt-covenants are agreements between the owner of the firm
and the creditors. With debt-covenants creditors can force managers to act within certain limits.
Violating debt-covenants could lead to extreme reprimands. When a firm violates a debt contract,
creditors get more power, more influence and can demand more rights. Therefore, breaching a debt
contract has impact on the firm’s capital structure. It even has a big influence on the firm’s financial
policy and the creditor’s bargaining power. This will reduce the possibility that shareholders are able
to take on risky projects. Robert and Sufi (2009) discovered that the leverage ratios will decrease
after breaching a debt covenant. This chance is lower when a firm has a credit rating.
Agency conflicts could also arise between the manager and the shareholders of a firm. Agents are
able to act in their own interest. They could try to maximize their utility with pecuniary and nonpecuniary incomes. Non-pecuniary incomes can be seen as luxury items, for example office space,
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private jets, and exclusive cars. These luxury items have nothing to do with creating firm value. It
could be that an agent is risk-averse and will avoid positive NPV projects, which is not in line with the
outside investors. The total amount of agency costs will differ from firm to firm. These will depend on
the behavior of the manager, the costs of monitoring and advising the manager. An agency conflict
between managers and shareholders can be reduced when the managers are stock based
compensated. Furthermore, an agency conflict can be reduced when a firm decides to issue new
debt. In some situations shareholders like to increase the leverage ratios of the firm, because a
higher leverage ratio puts more pressure on the managers to run the firm in the most efficient way.
Eventually, when an increased efficiency results in a higher firm value shareholders can benefit from
a higher leverage ratio.
The agency theory has shown to take into account different kinds of behavior of several parties.
When a company wants to create an optimal capital structure, they should not only take into
account the tax benefit of debt, but also the agency problems that can arise when firms take on too
much or too little debt. The principal can put some pressure on this matter and could reduce a
manager’s freedom. Agency costs consist of monitoring the manager and set some debt covenants.
In bigger and more complex organizations there is always a part that is infeasible to monitor.
Therefore, stakeholders have to take into account the costs and benefits of solving these agency
problems.
1.5 Pecking order theory
The Pecking order theory is developed by S.C. Myers. He argued: ““How do firms choose their capital
structure? We don’t know”, (1984:575). Because of this the Pecking order theory was developed in
order to get new insights on a firm’s capital structure. The Pecking order theory focuses mainly on
financial distress costs and the asymmetric information between the in- and outsiders, when a firm
decides to enter the external market. The Pecking order theory does not give any advice about an
optimal target leverage ratio, but only claims to prefer internal funds over external funds when new
investment opportunities arise. This means that firms should only enter the external market when
the internal funds are insufficient. The external market can be roughly divided into two categories,
namely: debt and equity. Debt is preferred over equity, because interest is tax deductible. Another
reason why debt is preferred over equity is that equity issuing signals the market that the firm is
overvalued, which is asymmetric information.
When a firm decides to enter the external market it faces two different types of costs. The first is
taking on too much debt, which could lead to financial distress costs and the costs that pass by
positive NPV projects. The Pecking order theory takes the asymmetric information and financial
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distress into account. The general rule for the Pecking order theory is: “Issue safe securities before
risky ones” (1984:584).
1.6 Market timing
Baker and Wurgler discovered evidence for market timing. When a firm is overvalued it will issue
equity. When a firm is undervalued it will repurchase equity. This will influence the firm’s capital
structure. When the market is inefficient, firms can benefit from opportunities to time the market.
Baker and Wurgler did research on firms that took on an IPO between 1970 and 1999. Since the
period between 1990 and 1999 the market leverage decreased. Their explanation is that at the end
of 1990 market valuations were historically high. This resulted in a decrease in market value, increase
in equity issues and a decrease in internal finance (2002:7).
Baker and Wurgler also discovered that if the variable “Market to Book ratio is high, this will
decrease the book leverage of the firm. “The clear result is that market-to-book affects leverage
through net equity issues” (2002:8). Furthermore, firms will issue equity when the investors are too
enthusiastic about a firm’s future performance. They state that market timing is very important for a
firm’s financial policy. This is the exact opposite of Myers’ (1984) Pecking order theory: this theory
tries to avoid issuing equity. Baker and Wurgler also found evidence that fluctuation in market value
leads to permanent changes in a firm’s capital structure. Market timing can be concluded to have a
long-run impact on capital structure. When managers want to increase their firm value, they should
be aware of the market circumstances.
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Chapter 2: Stability in corporate capital structures
2.1 Persistence and the cross-section of corporate capital structure
(Lemmon, Roberts & Zender, 2008)
This section will present and explain the main results of Lemmon, Roberts and Zender’s paper (LRZ)
(2008). The central question in their paper is how to get closer to solving the capital structure puzzle.
A lot of research to find the optimal capital structure has already been done, but so far only a
fraction of the puzzle has been solved. The traditional determinants have an adjusted R-squared
ranging from 18% up to 29% (2008:1576). Therefore, LRZ tried to provide new insights to narrow the
gap in solving the capital structure puzzle.
A described in LRZ’s paper they used the CRSP-Compustat dataset, which is based on non-financial
US firms for the period between 1965 and 2003. Most of the results are present for the full sample as
well as for the subsample survivors. Survivors are firms that have at least 20 years of non-missing
value for the dependent variable “Book leverage”. This is done to create a robustness check for
survivorship bias and to examine whether firms that leave the dataset prematurely, for example
through bankruptcy, influence the results. The different research techniques by LRZ will be explained
briefly and tested on public Dutch firms, later in this paper.
2.1.1 Convergence and persistency in the capital structure
The authors start their research by explaining two interesting features, which were not explained
already by the existing determined variables for capital structure. These features are the persistency
and the convergence effect in capital structure. Convergence and persistency are determinants that
could narrow the gap in solving the next part of the capital structure puzzle. The two features are
illustrated in Figure 2. Book leverage is the ratio of total debt to total assets. Market leverage is the
ratio of total debt to total debt plus the market value of equity. Figure 2 is based on four different
portfolios, Low, Medium, High and Very High. The figure is built as follows: for each year the firms
are sorted in one of the four quartiles (i.e. four portfolios) based on their leverage ratios. Point zero
is the portfolio formation period. Then the average leverage is calculated for each portfolio in each of
the following 20 years, when holding the portfolio composition constant. The final step that has to be
made, is taking the average leverage for each quartile, based on the event time. The convergence
effect is tested on the dependent variables Book and Market leverage, which can be found in panel A
and C of Figure 2. The whole process is repeated for the subsample survivors, the results can be
found in the appendix.
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Figure 2: Convergence and persistency in capital structure
The figure is taken from Lemmon, Roberts and Zender paper (2008:1580). It presents the four portfolios for a period of
twenty event years. The results of the full sample of Book and Market leverage are presented in panel A and C. This
approach is repeated for the sample of survivors. Panel B and D give almost similar results and can be found in the
appendix.
Looking at figure 2 it is remarkable that there is a significant amount of convergence for the different
portfolios over a period of twenty years. For example, the average Book leverage (Market leverage)
ranges at point zero, between the Very High and the Low portfolio, amounts to 52% (60%). The
convergence effect is most noticeable in the first years. In panel A, the Very High portfolio decreased
from 55% to 35%, and the Low portfolio increased from 3% to 19%. At the end of the twenty event
periods, the range in the average Book leverage decreased to 16%. The average Book leverage after
twenty event years for the Low, Medium, High, Very High are 19%, 25%, 30%, 35% respectively. The
5% difference between these portfolios is both economic and statistically significant.
Furthermore, the authors state that after twenty event periods, there is a permanent component in
the different leverage ratios. The different portfolios converge over time to more moderate leverage
ratios, but the different portfolios are remarkably stable. The portfolios Low, Medium, High, Very
High, never crossed each other. The authors concluded that different components influence a firm’s
capital structure over time.
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2.1.2 Persistency in capital structure
After recognizing the persistency component, it is stated that the average leverage ratios are
relatively closely related to their initial leverages. To show the influence of the initial leverage ratios
on the future leverage ratios of a firm, the authors came up with the following formula, (2008:1585):
Leverageit= α + β Xt-1+γ Leveragei0+ Vt + εit
Xit-1 is the one-year lagged control variables in this formula. Y is the coefficient of the initial leverage.
Initial leverage is determined by the first non-missing book value of a firm, Vt are the year fixed
effects and εit are the random errors. The most important component in this regression is the
influence of initial leverage on the dependent variable leverage. The regression is done for both book
and market leverage. The results are presented in Table 1. The regression is repeated for the
subsample survivors, which is presented in the appendix.
Table 1: Influence of initial leverage on future capital structure ratios
(The table is directly taken from LRZ [2008:1586]). The coefficients of the table are obtained by doing an OLS regression and
scale the parameters by the standard deviation of that specific variable. The t-stats are obtained by clustering the standard
errors at firm level and controlling for heteroskedasticity. The interpretation of each coefficient is the change in leverage
with a one standard deviation change of that specific determinant. For example, the first column of Panel A indicates that a
one standard deviation change in initial leverage will result in a 7% change in the dependent variable, book leverage. Panel
A is based on the Full sample. The sample of Survivors can be found in the appendix.
15
Table 1 is created as follows: the first column represents the variable initial leverage only; in the
second column the year fixed effects and the traditional variables of Rajan and Zingales (1995) 1 are
added to the model. Column three also includes the determinants of Frank and Goyal (2007) 2. To be
able to make a better comparison the coefficient of each variable is scaled by its standard deviation.
The first column of panel A reveals that initial leverage is highly significant. A one standard deviation
increase in initial leverage will result on average in a 7% (11%) increase in the future book (market)
leverage ratio of the firm. This corresponds with the results of Figure 2. After controlling for the
seven determinants that were already explained by earlier research, the determinant initial leverage
has lost some explanatory power, but is still highly significant. It is also the second highest
explanatory variable in the model. The authors conclude that historical leverage ratios have
explanatory power for future leverage ratios. In the following part the authors will investigate the
importance of each variable.
2.1.3 Variance decomposition
To investigate the importance of the different determinants the authors decided to use the ANOVAmodel. They used the following formula (2008:1588):
Leverageit= α + β Xt-1+ŋi+ Vt +εit
The components of the regression are the same as defined before, only
ŋ
i
are firm fixed effects.
Table 2 presents the variance decomposition based on seven different models. To obtain the effects
of each determinant the authors look at the partial sum of squares and normalize this for each
determinant. Therefore, each column sums up to one. To illustrate this we will observe column (a). In
column (a) firm fixed effects (Firm FE) are the only determinant in the model, which explains the
number 1.00 in the model. At the bottom row the adjusted R-squared can be recognized. The
adjusted R-squared tests how much of the variation is explained by the variable(s). It shows that firm
fixed effects by itself already have an explanatory power of 60%. Another very interesting point is
that year fixed effects (year FE) have only 1% of explanatory power. The very low explanatory power
of the firm year fixed effects support the fact that most of the variation of capital structure is
explained by the time-invariant factors. Lemmon at al. made the following statement: “This finding is
important because it suggests that theories of capital structure based on volatile factors, in a timeseries sense, are unlikely explanations for capital structure heterogeneity. Rather, leverage ratios are
relatively stable”(2008:1589). Column (d) shows the impact of the four traditional determinants of
Rajan and Zingales (1995). Tangibility and Industry fixed effects have the highest explanatory power
in the model, but only the adjusted R-squared accounts for 18% (31%) of the Book
1
2
Market to book (growth measure), Log(Sales), Tangibility, Profitability.
Median industry leverage, Cash flow Volatility, Dividend.
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Table 2: Variance decompositions
(The table is directly taken from LRZ (2008:1589)). The table presents a variance decomposition for different models. To
obtain the effects of each determinant, they look to the partial sum of squares and normalize this to each determinant. This
will cause every column to sum up to one. The adjusted R-squared tests how much of the variation is explained by the
variable(s). For example, if we look at column (g), then we see that 65 % of the table is explained by determinants of model
(g). Firm FE are Firm fixed effects. Year FE are Year fixed effects.
and (Market) leverage. In column (e) the firm fixed effects are added to the traditional determinants
of Rajan and Zingales (1995). The adjusted R-squared is three and a half times higher than the
adjusted R-squared in column (d). Finally, when we examine column (g), it is remarkable that firm
fixed effects cause little explanatory power in the other determinants. There are two main results
which can be taken from the paper by LRZ. First, the unobserved firm specific determinants are not
captured by the already existing determinants. Second, time invariant components mainly cause the
change in leverage ratios. Therefore, the authors concluded that cross-sectional differences explain
the majority of the variation in leverage.
17
2.2 The stability of corporate capital structure (DeAngelo & Roll, 2011)
Some research has been done on the stability of corporate capital. Because the outcomes of these
studies were mixed, both DeAngelo and Roll were interested in studying this topic. Welch (2004)
concluded that mean-reversion in capital structure occurs very slowly in contrast with Lemmon,
Roberts and Zender’s (2008) results, who found evidence that capital structures are remarkably
stable over time. As stated by to Lemmon, Roberts and Zender (LRZ) (2008), they used average
leverage ratios in their research. DeAngelo and Roll want to examine the individual movements in
leverage of firms to see if individual firms are stable over different time periods. In this section
DeAngelo and Roll’s (2011) main results will be presented and explained.
When the assumption is made that instability of capital structure is a fact, then time-varying
components are extremely important. But when the opposite is true, cross-sectional variation
components are interesting to examine. To test the stability in leverage, the authors used a sample
of 15,096 industrial firms of CRSP/Compustat from 1950 until 2008. The “constant composition”
exists of 157 firms, which means that these firms are included in the sample for the whole
investigation period.
2.2.1 Stability in leverage ratios
The authors start their research by testing the stability of individual firms. They observed these firms
for a different time-series range to see how firms change from their initial leverage ratios. The timeseries ranges of Book (Market) leverage are presented in Table 3. The dependent variable Book
leverage is the ratio of total debt to total assets. Market leverage is the ratio of total debt to total
debt plus the market value of equity. The median range is the range between book leverage and the
initial book leverage of a firm. The median leverage is the median book leverage for all firms in that
specific range.
When analyzing Table 3 it becomes clear that the median range for public firms that have been listed
for 20 or more years is 0.392. Firms that deviate more than 0.5 from their initial book leverages
count for 29.8% of the firms. Only 2.3% of these firms are very stable and stay in the range of 0.1.
Eight point three percent of the 20-plus year sample stay in the range of 0.2. For the dependent
variable Market leverage the stability is even worse: the median range for 20-plus firms is 0.551 and
57.4% deviate even more than 0.5 from their initial leverage. The median leverage increases when
firms stay in the sample longer.
18
Table 3: Time-series range of leverage ratios of public US firms
The table is taken from the paper by DeAngelo and Roll (2011:38). Book leverage is the ratio of total debt to total assets.
Market leverage is the ratio of total debt to total debt plus the market value of equity. The constant composition contains
157 public industrial firms. The firm enters the sample when it has non-missing value for total assets and for the marketvalue. For example, 2 to 4 means that the firms stay in the sample for at least 2 years with a maximum of 4 years. It is
possible that after that the firms are delisted or that they have missing values. The median range is the median range
between book leverage and the initial book leverage of a firm. The median leverage is the median book leverage for all
firms in that specific range.
The results of Table 3 indicate that there is little evidence for a stable capital structure for the
different time-series ranges. To be able to see the magnitude of speed of departure from the initial
leverage ratio the authors developed Table 4. Table 4 uses a sample of firms that have been listed for
20 or more years. Table 4 is constructed as follows: At year zero the initial leverage ratio of all firms
has been determined. To see the stability of the firm the firms are tracked for the next 19 years. One
can see that even after 5 years 81.5% of the firms in the sample deviate more than 0.05 from its
original value. After ten years 79.7% of the firms deviate more than 0.100 and 47.9% deviate more
than 0.200 from their original value. Even after 19 years only 3.2% (8.6%) of the firms deviate less
than 0.050 (0.100). After a time-period of twenty years only a fraction of the firms stays in a relative
stable capital structure and that most of the firms vary wildly in their capital structure.
19
Table 4: Inter-temporal Variation in Leverage:
Magnitude of Speed of Departure from Original Leverage
The table is taken from the paper by DeAngelo and Roll (2011:40). The sample consists of 2,751 firms, which have available
data for book leverage for 20 years or longer. Book leverage is measured as the book value of total debt divided by the
book value of total assets. Column one gives an indication of firms that deviated more than 0.05 from the original leverage.
The remaining columns show the fraction of years that deviate in their Debt/TA ratio, given their range (+/- 0.100, +/- 0.200,
+/-0.300, +/-0.400). For example, in column one and row five, the number means that 81,5% of the firms deviate more than
0.05 from their initial leverage after five years.
The time-series variation that is created in Table 4 contrast greatly with the evidence found by LRZ
(2008), who concluded that the mean leverage ratios of the firms are remarkably stable over time. In
the next part the cross-section variation in leverage, which is determined by the firm fixed effect, will
be examined in LRZ.
20
2.2.2 Stability of leverage cross-section
As discussed in LRZ (2008) firm fixed effects have an enormous impact on the cross-section variation
in leverage, which produce an adjusted R-squared of 0.6 for the dependent variable book leverage.
DeAngelo and Roll (2011) found evidence that firm fixed effects are highly significant, but that firm
fixed effects deviate per decade. According to the authors: “firm-specific time-series variation in
leverage translates to substantial instability in the cross-section of leverage,” (2011:12). The results
are presented in table 5.
Through the time-series variation firm specific items have now an adjusted R-squared of 0.757, which
is more than double compared to the firm dummies, 0.353. The F-statics reject the hypothesis that
firm fixed effects are constant over time. Consistent with the results of LRZ firm dummies have more
explanatory power than year dummies, which indicates that there is weak evidence for crosssectional factors explaining more than time-series factor.
Table 5: Explanation power of firm fixed effect per decade
The table is taken from the paper by DeAngelo and Roll (2011:42). In Table 5 different regressions are done on three
different samples, namely the Constant sample, the sample of firm-listed 20 or more years and on the Full sample. The
dependent variable Book Leverage is measured as the book value of total debt divided by the book value of total assets. In
regression (1) the “Firm/decade dummies” are decade specific dummies. For example: the first firm/decade dummy scores
one if firm j falls in the decade of the 1950’s. If the firm does not belong to this decade or belongs to another firm it scores
zero. The second firm/decade dummy scores one if the firm j falls in the decade of the 1960’s and scores zero if the firm
does not belong to this decade or it belongs to another firm and so on. In regression (2) and (4) firm dummies are created,
firm j scores 1 for all observations that are corresponding to firm j, otherwise it will score zero. In regression (3) and (4) year
dummies are created, the variables score one for year t, and zero for all other years. In regression (5) initial leverage is the
first non-missing observation for book leverage of that specific firm.
The authors also tried to find persistency with another test. In table 6 they tried to find persistency
for firms that stay in the same quartile right from the start. At year 19 the groups Lowest,
Low/Medium and Medium/High, deviate very little from 0.250. For the Low/Medium Leverage group
the deviation from 0.250 is only 0.008. The authors conclude that for these three groups there is no
question of persistency. When at the end the different portfolios are observed, there is only weak
evidence of persistency for the Highest Leverage, 49% of the firms stay in their initial group. It is
21
weak in the sense that in this cross section 51% of the firms are now ranked in a lower leverage
group.
The authors found that the data from different tables have shown instable capital structures for
individual firms. In the following part the authors tried to come up with key factors that determine
the instability in a firm’s capital structure.
Table 6: Fraction of firms that stay in their initial quartile
The table is taken from the paper by DeAngelo and Roll (2011:43). The model is based on firms that have been listed for 20
years or more. The model is built as follows: For each calendar year, starting from 1950, firms have been sorted in four
groups (Lowest Leverage, Low/Medium Leverage, Medium/High Leverage, Highest Leverage) based on their leverage ratio.
This sorting process is repeated for each calendar year to determine the persistency of firms that stay in their quartile. The
group formation started at point zero, with the result that the fraction of firms at t=0 is 1. When firms are sorted randomly
then the fraction of firms in each group is 0.250.
22
2.2.3 Key factors of leverage instability
In this section the key factors of an instable leverage ratio will be investigated. Table 7 shows that
asset growth and external financing are determinants which have a significant impact on instable
leverage ratios. When looking at the variable “Asset growth”, the conclusion can be made that the
“Asset growth” is 0.204 at t=0, compared to 0.1 at t=-1. It is remarkable that the “Asset growth” is
more than twice as high when the firm deviates from its stable capital structure. The expectation was
for growth in assets to result in a lower leverage ratio. On the other hand growth in assets needs
more funding, which could cause an increase in the leverage ratio.
Table 7: Departure from a stable leverage regime
The table is taken from the paper by DeAngelo and Roll (2011:46). Table 7 presents the mean leverage values for firms that
have been listed 20 years or more. Firms are determined as stable if their leverage ratio deviates less than 0.1 over a time
period of ten years. Point t=0 is where the firm departs from the stable leverage regime, t=-1 indicates the last year of the
stable regime of the firm.
Capital expenditures is another determinant that increases significantly when a firm deviates from its
stable leverage regime, but the increase is small compared to the asset growth and has therefore less
economic power. The last three years before the point of instability, the mean value of the net
financing deficit was around zero and increased significantly at t=0 to 0.114. The net financing deficit
is comparable to the results of “Change in debt”. Three years before instability “Change in debt” was
around zero and increased significantly at t=0 to 0.116.
The authors also tested the impact of the traditional determinants (EBITDA, Log(Sales), Market-tobook and Tangibility) on the stability of capital structures. What is remarkable is that traditional
determinants have no exact explanatory power for determining the departure of a stable leverage
ratio.
This table leads to the conclusion that the operational side of a firm has great impact on the
instability of a firm’s capital structure. According to DeAngelo and Roll (2011:22): “instability is
23
significantly related to asset growth and the magnitude of external financing, with no detectable
connection to variation in traditionally posited determinants of leverage” (2011:22).
DeAngelo and Roll’s main conclusion is that evidence was found for time-series variation on
individual firms. These results play an important role in determining the future capital structure of a
firm. DeAngelo and Roll (2011) found evidence that investment policy has a strong impact on the
capital structure of a firm and leverage ratios are only stable temporarily. Firms deviate from their
modern leverage ratio as time passes. The instability is higher associated with the asset growth and
the external funding. Graham and Harvey (2001) did a CFO survey in which managers answered that
target leverage ratios are important. Empirical evidence on the listed firms in the US gives opposite
evidence. There is little support that firms hold on to their target ratio. These findings are slightly
similar to the finding by Modigliani and Miller (1958), which support that firms do not have a target
leverage ratio.
24
Chapter 3: Data selection and description
After analyzing the main capital structure theories and the main findings by LRZ (2008) and by
DeAngelo and Roll (2011), this section will briefly describe how the data for public Dutch firms have
been obtained.
3.1 Data selection and data testing
The dataset made for public Dutch firms has been obtained from the database Datastream and
Compustat. For this research non-financial active and inactive public Dutch firms between 1989 and
2010 have been investigated. After having obtained the data from the two databases there were still
9 observations based on financial firms (6000-6999) and 6 observations based on Utilities (49004949). Furthermore, there were 4 duplications in the dataset, which were eliminated.
The obtained sample has been tested on the OLS-assumption. To protect for outliers different tests,
such as scatter plots and Lvr2plots, were performed. The sample for public Dutch firms is relatively
small compared to LRZ (2008) and DeAngelo and Roll’s (2011) samples, therefore the outliers of the
dataset were winsorized instead of eliminated. The variables were trimmed at a maximum 1
percentile upper and lower level. When testing for linearity the variable Market-to-book proved to be
non-linear. Therefore the growth measure Market-to-book was replaced by a new proxy for growth.
The variable growth is the ratio of intangible assets scaled by the total assets of a firm. Furthermore,
the panel data were tested for heterogeneity and autocorrelation. The Modified Wald test gave
evidence for heteroskedasticity in the data and the Wooldridge test showed that there is also
evidence for autocorrelation. In order to control for heteroskedasticity and autocorrelation the
command cluster was used for different regressions. Furthermore, the Hausman-test supported
using fixed effects.
The full sample consisted of 233 firms and 2,750 firm-years observations. A subsample was made to
control for survivorship bias. The subsample survivors consisted of firms which had at least ten years
of available data. The subsample consisted of 139 firms and 2,263 firm-years observations. Most of
the tests are based on the full sample as well as on the subsample, in order to analyze if the short
lifespan of firms influences the statistics. Most of the results of the tables and figures will be
compared with the results of LRZ (2008) and DeAngelo and Roll (2011). LRZ used a dataset based on
US public firms between 1965 and 2003. They characterize survivors as companies that have at least
twenty years of non-missing book values. The final dataset of Lemmon, Roberts and Zender contains
225,839 observations. DeAngelo and Roll (2011) analyzed US public firms between 1950 and 2008.
Their sample consisted of 15,096 non-financial firms.
25
3.2 Analyzing the data
After obtaining the data and doing the right statistical test the main variables will be presented in
Table 8, which will be used for different tests in this research. In the summary statistics the mean,
median and the standard deviation (SD) are presented for the full sample as well as for the
subsample survivors.
Table 8: Summary Statistics
The sample is based on non-financial public Dutch firms, obtained from the database Compustat and Datastream between
1989 and 2010 (excluding utilities and financial firms). The table presents the main variable that will be used for different
tests in this research. It presents averages, median (between brackets) and the standard deviation (in parentheses) on both
the full sample and the survivors. The definition of the variables can be found in the appendix.
Variable
Book Leverage
All Firms
Mean
[Median]
0.22
(SD)
Survivors
Mean
[Median]
(SD)
(0.17)
0.22
(0.16)
[0.21]
Market Leverage
0.28
[0.21]
(0.26)
[0.23]
Firm Size
5.70
(2.05)
[5.69]
Growth
0.09
0.12
(0.15)
0.28
(0.13)
0.08
(0.19)
0.20
Observations
0.55
(0.15)
0.13
(0.10)
0.28
(0.19)
0.07
(0.10)
[0.04]
(0.10)
[0.21]
Dividend Payer
0.09
[0.26]
(0.11)
[0.04]
Median Industry Book Leverage
(2.03)
[0.13]
[0.25]
Cash Flow Volatility
5.89
[0.00]
[0.13]
Tangibility
(0.24)
[5.85]
[0.00]
Profitability
0.27
[0.23]
0.21
(0.10)
[0.22]
(0.50)
0.59
[1.00]
[1.00]
2.750
2.263
(0.49)
When analyzing Table 8 the survivors tend to be larger and more profitable, compared to the full
sample. Furthermore, the table provides evidence for survivors to pay out more dividend and to be
less volatile (lower cash flow volatility). A possible reason for survivors paying more dividend could
be that survivors on average are more profitable, which can make it easier for these companies to
pay out dividend. Another plausible sign is that survivors on average are less volatile. Firms which
26
leave the dataset earlier have a higher risk of bankruptcy which could therefore influence the
statistics.
What is remarkable in this table is that survivors have a lower (market) leverage ratio compared to
the full sample. Titman and Wessels (1988) found evidence that the transaction costs for smaller
firms are higher, which result in a lower leverage ratio for smaller firms. Therefore the expectation
was that survivors would have a higher (market) leverage ratio. Another finding that conflicts with
intuition is that survivors would be expected to have a stronger bargaining power for granting new
loans, which therefore influences the leverage ratio positively.
When the summary statistics are compared with the results found by LRM (2008) the following
conclusions can be made. The movement between the full sample and the survivors is slightly similar
to the results obtained by LRM (2008). In Table 8 the variable Tangibility is slightly positive, but it is
not noticable in this table because of the rounding effect. This effect is much bigger for LRM. When
we focus on the survivors only, then Dutch firms tend to have a lower leverage ratio, are larger, have
a lower ratio of tangible assets and are slightly more profitable and more risky than US public firms.
27
Chapter 4: Testing the convergence effect for public Dutch firms
In this chapter the convergence effect for public Dutch firms will be tested. In section 2.1 we saw that
LRZ (2008) found evidence of a convergence effect for non-financial US firms over twenty event
years. It would be interesting to see whether this effect can be found in public Dutch firms as well.
First the portfolio decomposition will be described before the convergence effect will be analyzed.
The convergence effect will be tested on the full sample as well as on the survivors.
4.1 Portfolio decomposition
In Figure 3 the average leverage ratios of the four portfolios are presented in “event years”. The
portfolio formation period starts at point 1989. For each calendar year firms are sorted in one of the
four quartiles (i.e. four portfolios) based on their leverage ratio, which are labeled as Low, Medium,
High and Very High. Point zero is the portfolio formation period. After that the average leverage is
calculated for each portfolio in each year of the following ten years, while holding the portfolio
composition constant. This process is done from 1989 to 1999. In this way eleven portfolios are
created. The final step to be made, is taking the average leverage for each quartile, based on the
event time. The convergence effect is tested on the dependent variables Book and Market leverage,
which can be found in panel A and C of Figure 3. The whole process is repeated for the subsample
survivors, presented in panel B and D.
4.2 Convergence effect
Figure 3 presents the evolution of leverage for the four different portfolios. This figure shows two
interesting effects. The first visible effect is the different portfolios converging over the event time.
At event time zero the initial portfolio formation gives a wide range between the different portfolios.
The range of the average Book (Market) leverage is 39% (65%). After eleven event years the Very
High portfolio converges from 44% (70%) to 33% (37%) for Book (Market) leverage. The Low portfolio
increases from 5% (4%) to 20% (23%). The convergence plays the highest roll in the first few event
years. This effect is noticeable because of the slope of the lines. The second interesting effect is the
different portfolios never crossing each other over the whole event time. This effect is called the
persistency effect, which means that the leverage ratios of the firms might have a permanent
component. The two effects are visible for the dependent variables Book and Market Leverage, in
panel A and C. The tests are repeated for the subsample survivors to make a robustness check for
survivorship bias. It is possible that in the first ten years firms merged with each other or that banks
went bankrupt, which would cause some biases in the sample. Survivors are presented in panel B and
D. It is still possible to see that the different portfolios converge over time. The very high portfolio
decreases with 11% (31%), the low portfolio increases with 16% (19%).
28
Figure 3: Average leverage of portfolios in event time
The sample is based on non-financial public Dutch firms, obtained from the database Compustat between 1989 and 2010.
The portfolio formation period starts at point 1989. For each calendar year firms are sorted in one of the four quartiles (i.e.
four portfolios) based on their leverage ratio, which are labeled as Low, Medium, High and Very High. Point zero is the
portfolio formation period. After that the average leverage is calculated for each portfolio in each year of the following ten
years, while holding the portfolio composition constant. This process is done from 1989 to 1990. In this way eleven
portfolios are created. The last step is to take the average leverage for each quartile, based on the event time. The results
for book leverage are presented in panel A (Full Sample) and panel B (Survivors). The results for Market leverage are
presented in Table C (Full Sample) and D (Survivors). Book leverage is the ratio of total debt to total assets. Market leverage
is the ratio of total debt to total debt plus the market value of equity.
.6
Book Leverage
.2
.3
.4
.5
.1
0
0
.1
Book Leverage
.2
.3
.4
.5
.6
.7
Panel B: Book Leverage Portfolios (Survivors)
.7
Panel A: Book Leverage Portfolios
0
1
2
3
4
5
6
Event Time (Years)
Low
High
7
8
9
10
0
1
2
3
Medium
Very High
4
5
6
Event Time (Years)
Low
High
7
8
9
10
Medium
Very High
Panel D: Market Leverage Portfolios (Survivors)
0
0
.1
.1
Market Leverage
.2
.3
.4
.5
Market Leverage
.4
.5
.2
.3
.6
.6
.7
.7
Panel C: Market Leverage Portfolios
0
1
2
3
4
5
6
Event Time (Years)
Low
High
7
Medium
Very High
8
9
10
0
1
2
3
4
5
6
Event Time (Years)
Low
High
7
8
9
10
Medium
Very High
According to LRZ (2008) the different portfolios that were created never crossed each other. In the
final year the difference between the leverage ratios was 5% (2008:1581). For the public Dutch firms
comparable results are received for the portfolios Very high, High, Medium, Low (33%, 28%, 25%,
20%). The average difference between the portfolios lies around 5% as well. From this chapter it can
be concluded that the two effects, convergence and persistency, can be attributed to public Dutch
firms. In the next section the time invariant factor will be tested, which is able to explain persistency
over time.
29
Chapter 5: Capital structure determinants
5.1 The importance of the initial leverage of a firm
The most interesting observation from the previous section is that a possible permanent component
may exist for public Dutch firms. This could be interesting for solving another part of the capital
structure puzzle for public Dutch firms. In this section we focus mainly on the variable Initial
leverage.
Initial leverage is defined as the first non-missing observation of the leverage of a firm. In Table 9 the
importance of the variable initial leverage has been tested. The test is performed for the full sample
as well as for the subsample survivors for the time-period from 1989 to 2010. The regression is as
follows:
Leverageit= α + β Xt-1+γ Leveragei0+ Vt + εit
α is the constant, X are the one-year lagged control variables, β is the coefficient of the control
variables; i indexes the different firms; v are the year fixed effects; ε is the error random term and Y
is the coefficient for the main variable Initial leverage. The coefficient Y helps to explain the future
leverage ratio of a firm, based on the initial book leverage of a firm.
The table consists of the dependent variables Book and Market leverage. Each panel consists of three
different models, including different control variables. In model 1 only the variable Initial leverage is
taken into account. In model 2 and 3 Initial leverage is controlled by existing determinants based on
capital structure. In the second model the four traditional determinants of Rajan and Zingales are
used and in the third model the determinants of Frank and Goyal are added to the model as well. The
control variables are included in the model to notice the effect of time-varying variables on the
variable Initial leverage. Panel A (B) gives insight in the full (survivors) sample. The coefficients which
are obtained from an OLS regression are scaled by the standard deviation of that specific
determinant to make it easier to compare the two different samples. Each coefficient measures the
change in Book (Market) leverage when there is one standard deviation change in that specific
determinant. For example, the first column of panel A measured only the determinant initial
leverage, a one standard deviation change in initial leverage will result in a future leverage change of
9% (8%) in book (market) leverage. The Adjusted R-squared shows the importance of this variable.
Initial leverage has an Adjusted R-squared of 25%, this means that 25% of the variation in Book
leverage is explained by the variable Initial book leverage, which is quite high compared to the results
of LRZ (2008). Lemmon, Roberts and Zender’s results are 7% (11%) for Book (Market) leverage and
show an adjusted R-squared of 13% (20%). This suggests that the initial leverage of firms contain a
30
certain permanent component. A positive effect is in line with Figure 3, where on average a higher
initial leverage also results in high future leverage ratios and vice versa.
Year fixed effects in model 2 and 3 are used to control for unmeasured items, like a financial crisis,
that could have influenced the model. After interpreting panel A Initial leverage has still shown to be
the most important determinant for the dependent variable Book Leverage, even after controlling
for time-varying determinants.
Table 9: The effect of initial leverage on future leverage
The sample is based on non-financial public Dutch firms, obtained from the database Compustat and Datastream between
1989 and 2010 (excluding utilities and financial firms). The coefficients of the table are obtained by doing an OLS regression
and scale the parameters by the standard deviation of that specific variable. The t-stats are obtained by clustering the
standard errors at firm level and control for heteroskedasticity. The interpretation of each coefficient is the change in
leverage with one standard deviation change of that specific determinant. For example, the first column of Panel A
indicates that one standard deviation change in initial leverage will result in a 9% change in the dependent variable Book
leverage. Panel A is based on the full sample. Panel B is based on survivors. The definition of the variables can be found in
the appendix.
Panel A: All Firms
Variable
Initial Leverage
Book Leverage
Market Leverage
0.09
0.07
0.05
0.08
0.05
0.04
(9.05)
(7.55)
(5.96)
(6.01)
(4.01)
(3.21)
Firm Size
Growth
Profitability
Tangibility
0.02
0.02
0.04
0.03
(2.70)
(2.30)
(3.10)
2.79
0.04
0.03
0.01
0.00
(5.68)
(4.88)
(0.92)
(0.42)
-0.01
-0.01
-0.05
-0.04
(-1.82)
(-1.42
(-4.15)
(-3.13)
0.04
0.02
0.05
0.02
(4.67)
(2.57)
(3.83)
(1.60)
Median Ind lev
Cash Flow volatility
Dividend Payer
0.05
0.07
(5.57)
(6.33)
-0.01
-0.02
(-0.71)
(-1.12)
-0.02
-0.04
(-2.74)
(-5.00)
Year Fixed Effects
No
Yes
Yes
No
Yes
Yes
Adj. R2
0.25
0.30
0.36
0.11
0.19
0.28
Obs
2.724
2.465
2.46
2.328
2.133
2128
(continued)
31
Panel B: Survivors
Variable
Initial Leverage
Book Leverage
0.07
0.06
0.04
(6.96)
(6.24)
(4.03)
0.02
FirmSize
Growth
Profitability
Tangibility
Market Leverage
0.07
0.04
(3.05)
(2.11)
0.02
0.04
0.03
(2.33)
(2.01)
(3.30)
(3.72)
0.04
0.04
0.01
0.01
(6.19)
(5.42)
(0.90)
(0.62)
-0.02
-0.01
-0.06
-0.04
(-2.46)
(-1.24)
(-5.08)
(-3.72)
0.05
0.03
0.05
0.02
(4.65)
(2.98)
(3.64)
(1.81)
Median Ind lev
Cash Flow volatility
Dividend Payer
(4.28)
0.02
0.05
0.07
(5.53)
(5.80)
0.00
-0.01
(-0.31)
(-0.67)
-0.02
-0.04
(-2.69)
(4.07)
Year Fixed Effects
No
Yes
Yes
No
Yes
Yes
Adj. R2
0.22
0.33
0.39
0.08
0.23
31.00
Obs
2.192
2.032
2.03
1.976
1.838
1.836
Many researchers studied the time variant factors in leverage. What is interesting now is that capital
structure is partly explained by a time-invariant component. Only Initial leverage lost some influence
at the cost of adding time-variant determinants to the model.
For the dependent variable Market Leverage, Initial Leverage is the strongest explanation variable,
apart from Profitability. When taking into account the significance of the control variables it is
remarkable that profitability is insignificant for Book Leverage and significant for Market Leverage.
Titman and Wessels (1988) also found that profitability is not significant for the dependent variable
Book leverage but is significant for the dependent variable Market leverage. This can be interpreted
as follows: the increase in market value of equity through an increase in the operating income is not
totally offset by the increase in borrowing (1988:14). This is consistent with the pecking order theory.
A reason for profitability to be insignificant for book leverage is that the increase in book value of
equity through operating income leads to the same increase in issuing new debt. This ensures that a
lot of firms have a target debt to equity ratio.
32
The variable Firm Size is a proxy for size and is significant at a 5%-level for panel A as well as for panel
B. The coefficient in panel A is (0.02), which means that when the independent variable Firm Size
increases one standard deviation, the future Book leverage ratio on average will increase with 0.02. A
positive relation between firm size and leverage makes sense for the following reason: On the whole
larger firms have a better financial and administrative staff, are more diversified and have better
access to derivative instruments for reducing their volatility in earnings. A firm’s lower risk level is an
indication for banks that firms are healthy. For this reason larger firms generally have a higher
bargaining power towards lenders, which results in an increase in the leverage ratio of a firm.
The variable Profitability is negative and (not) significant at a five percent level for Market (Book)
Leverage. A negative sign for profitability is an indication of the pecking order theory. Profitable firms
prefer internal funds to debt. Therefore high profits should be associated with lower leverage. Chen
et al. (1998) and Degryse et al. (2010) found evidence for the pecking order theory for a Dutch Panel.
Possible reasons for management decisions to support internal funds are asymmetric information,
lower credit rating or through higher costs of external financing.
Tangibility gives a positive sign and is statistically significant at a five percent level. A positive sign is
an indication that the leverage ratio will increase in case of a standard deviation increase in
Tangibility. Creditors prefer firms with more tangibility, which means that firms have more collateral,
giving creditors more security.
Growth also gives a positive sign but is only significant for the dependent variable Book Leverage. In
other research growth is normally a determinant for the variable Market-to-book. It is expected to be
negative. A negative sign for Market-to-Book is an indication for mispricing. When share prices are
high the preferred action is to issue equity, after which leverage will decrease. In this research the
intangible assets scaled by the total assets of a firm are a proxy for growth. Intangible assets, such as
goodwill and research and development, are indicators for future payoffs. Intangible assets can be
seen as collateral and can help firms in granting new loans by banks. A positive sign for growth is also
found by Degryse et al. (2010), who researched Dutch small and medium-sized enterprises. The
authors concluded and found evidence that firms use their profits to reduce their leverage ratios and
that growing firms need more funds which result in an increase in their leverage ratios (2010:1),
these results are in line with result found for public Dutch firms. However, is it important to take into
account the economic sense of growth, because there are a lot of firms in the dataset which have no
intangible assets on their balance sheets. The coefficients of growth and tangibility are almost the
same, but tangibility has a higher economic significance.
33
In the third column the determinants by Frank and Goyal (2007) are added to the model. What is
remarkable is that the Cash flow volatility is economically weak and insignificant. The expectation
was that a firm’s higher cash flow volatility would certainly have an effect the leverage ratio of a firm.
Comparing these results with LRZ’s results (2008), the variable Cash flow volatility is also
economically weak but it is significant for the dependent variable Market Leverage. The
determinants Median Industry leverage and Dividend payer have a large influence in the model. The
median industry leverage can be seen as a kind of target ratio for managers. It is important for
managers to benchmark their own leverage ratio with similar companies. Frank and Goyal (2007)
found evidence that this determinant should have a positive effect on leverage. The median industry
leverage is highly significant, which gives a signal to the trade-off theory.
After adding all the control variables, there is still evidence that historical leverage is an important
determinant to take into account in determining the future leverage of a firm. Initial Leverage is the
most important determinant for the dependent variable Book Leverage. For Market Leverage only
the Median Industry leverage and Dividend payer have more explanatory power than the
determinant Initial leverage.
To make a robustness check these tests are also done for the subsample survivors. Panel B shows
that there are only small differences between the two samples. This does not come as a surprise
because the dataset obtained from the two databases (Datastream and Compustat) mainly consists
of firms which stay in the sample for a very long time. This is also notable in panel A and B, when the
observations of the different models are taken into account. The variable Initial Book Leverage has a
slightly lower explanatory power when the survivors are observed, which is shown by the Adjusted Rsquared. In model 3, the variables Profitability and Cash flow volatility are still insignificant for the
dependent variable, Book Leverage. The t-statistics are higher for the variables growth and the
median Industry Leverage compared to the variable Initial leverage, based on the dependent variable
book leverage. For the dependent variable Market Leverage, Initial Leverage is now fifth in range of
how significant a variable is. The signs of the coefficients, of all variables are similar to the full
sample.
When the results are compared to LRZ (2008), it is remarkable that all the outcomes of the
coefficients are in line. Moreover, the t-values in Table 9 are much lower compared with the t-values
of LRZ (2008). The different results can be explained by the limited dataset which is available for the
public Dutch firms. A great similarity between the research of LRZ and Table 9 is that Median Industry
Leverage is the most explaining determinant in explaining the future leverage ratio.
34
Even though some variables have little economical impact on their own, they have a good
economical impact on the capital structure of a firm when we add all the determinants. It is
interesting to see that initial leverage is a time-invariant factor which is able to explain a part of a
firm’s future leverage ratios. In the next section we will discuss the importance of each variable
through a variance decomposition with the dependent variables Book and Market Leverage.
5.2 Variance decomposition
The goal of this section is to determine the importance of each of the determinants, in capturing the
variation in leverage. Variance decomposition (ANCOVA framework) makes it possible to decompose
the variation in leverage through the different determinants. Therefore, the following regressions
will be used:
Leverageit= α + β Xt-1+ŋi+ Vt +εit
Vt are firm fixed effects, the other factors are already explained in the previous section. In Panel A
and B of Table 10 the importance of the determinants will be tested on the dependent variable Book
(Market) leverage. It will be interesting to see how much of the variation in leverage is captured by
the existing determinants. The total sum of squares (SST) consists of the following two components,
namely (SSE) and (SSR). The SSE is the variation which is explained by the model, SSR is the residual
which is not explained by the model.
To obtain the effects of each determinant we look at the partial sum of squares and normalize this to
each determinant. The effect will be that each column sums up to one. Each column contains a
different model, which will provide insight in the importance of each determinant. For example,
when looking at column (g) of Panel A of Book leverage it is interesting to notice that the
independent variable Firm Size determines 2% of the model. When a model includes only one effect,
as in column (a), then the whole effect is attributed to that effect. How much of the variation is
captured by each model can be found in the bottom row of the model, which is the Adjusted Rsquared. Because of the firm fixed effects the variable Initial leverage is invisible in the model. Firm
fixed effects and industry fixed effect are included in the table to control for the individual
characteristics of firms and industries.
At first panel A will be analyzed. Panel A shows that Firm FE have an Adjusted R-Squared of 54%.
When looking at model (b) for Book leverage one can conclude that Year FE (year fixed effects)
capture only 1% of the variation in the capital structure.
35
Table 10: Variance decomposition
The sample is based on non-financial public Dutch firms, obtained from the database Compustat and Datastream between
1989 and 2010 (excluding utilities and financial firms). The table presents variance decomposition for different models. To
obtain the effects of each determinant, we look at the partial sum of squares and normalize this to each determinant. The
effect will be that every column sums up to one. For example, when looking at column (a) we see that Firm FE is the only
determinant, which explains the number 1.00 in the model. The adjusted R-squared tests how much of the variation is
explained by the variable(s). For example, when looking at column (g) we find that 62% of the table is explained by
determinants of model (g). Firm FE are Firm fixed effects. Year FE are Year fixed effects. 5yr Firm FE are five years dummy
variables for each firm. For example, the firm i takes the value 1 if the firm falls in the period 2006 to 2010 and otherwise
takes zero.
Panel A
Book l evera ge
Va ri a bl e
(a )
(b)
Firm FE
1.00 .
(c)
(d)
Ma rket Levera ge
(e)
0.97 .
(f)
0.90 .
(g)
(a )
0.86
1.00 .
(d)
(e)
0.94 .
(f)
0.86 .
(g)
0.80
.
Firm Size
.
.
.
0.02 0.01 0.05 0.02
.
.
.
0.04 0.02 0.07 0.02
Growth
.
.
.
0.14 0.03 0.23 0.03
.
.
.
0.02 0.02 0.02 0.02
Profitability
.
.
.
0.07 0.02
0.1 0.02
.
.
.
0.14 0.02 0.04 0.01
Tangibility
.
.
.
0.10 0.03 0.16 0.03
.
.
.
0.06 0.01 0.05 0.01
Med Ind Lev
.
.
.
.
.
0.00 0.00
.
.
.
.
Cash Flow vol
.
.
.
.
.
0.01 0.00
.
.
.
.
.
0.01 0.01
Dividend Payer
.
.
.
.
.
0.11 0.01
.
.
.
.
.
0.25 0.04
Industry FE
.
.
.
0.25 .
.
.
.
0.63 .
.
(c)
Year FE
Adj. R²
1.00 0.03 0.04 0.02 0.08 0.02
(b)
0.54 0.01 0.55 0.30 0.61 0.32 0.62
Panel B
1.00 0.06 0.12 0.08 0.15 0.09
(a )
(b)
5yr Firm FE
1.00 .
(c)
(d)
0.40
Ma rket Levera ge
(e)
0.97 .
(f)
(a )
0.97
1.00 .
(d)
.
(e)
0.98 .
1.00 0.12 .
(f)
0.97
.
.
.
0.02 0.01 0.05 0.01
.
.
0.04 0.00 0.07 0.00
Growth
.
.
0.14 0.00 0.23 0.00
.
.
0.02 0.01 0.02 0.01
Profitability
.
.
0.07 0.00
0.1 0.01
.
.
0.14 0.00 0.04 0.00
Tangibility
.
.
0.10 0.01 0.16 0.01
.
.
0.06 0.01 0.05 0.01
Med Ind Lev
.
.
.
.
0.00 0.00
.
.
.
Cash Flow vol
.
.
.
.
0.01 0.00
.
.
.
.
0.01 0.00
Dividend Payer
.
.
.
.
0.11 0.00
.
.
.
.
0.25 0.01
Industry FE
.
.
0.25 .
.
.
0.71 0.01 0.30 0.73 0.32 0.73
.
(c)
Firm Size
0.63 .
0.08 .
(b)
Year FE
Adj. R²
1.00 0.04 .
0.61 .
0.39 0.03 0.42 0.24 0.47 0.26 0.48
Book l evera ge
Va ri a bl e
0.00 0.01
0.15 .
0.00 0.00
0.61 .
0.40
0.62 0.03 0.24 0.65 0.26 0.65
In column (d) the four traditional determinants of Rajan and Zingales (1995) are added to the model.
In this model Growth is the main determinant of the four traditional determinants. Industry fixed
effects explain almost all the variation in this model, which indicates that the characteristics of
industries are relevant for determining the leverage ratio. A high competition level could be a
36
characteristic for an industry. Firm FE (firm fixed effects) have a huge influence on the importance of
the other determinants. It is noteworthy that column (a), including only Firm FE, has an Adjusted Rsquared of 54% (39%) in Book (Market) leverage compared to column (d) 30% (24%), which indicates
that firm-specific items such as entrepreneurial skills are very important in determining the future
leverage ratio of a firm. Column (e) uses the same determinants but now firm fixed effects are used
instead of industry fixed effects. Table 10 shows that the adjusted R-squared has grown a lot, 89% of
the model is explained by the firm fixed effects. Column (f) and column (g) included the determinants
of Frank and Goyal’s paper (2004). The same trends as in model (d) and (e) appear in model (f) and
(g). In column (g) 62% of the Book leverage is explained by these factors, without firm fixed effects
only 32% of the variation in Book leverage is explained in column (f). When firm fixed effects are not
included in the model, industry fixed effects are the most important factors. This is in line with LRZ’s
findings (2008). What contradicts the results of LRZ is the high explanatory power of industry fixed
effects. The industry fixed effects for LRZ are 37% (2008:1589) which are not in line with the results
of Table 10. The industry fixed effects in Table 10 account for 63% of the variability of this model.
Span (2010) did similar tests based on a dataset for public French firms. He found evidence that
industry fixed effects count for 84%, including the traditional determinants (2010:51). A possible
explanation for the industry fixed effect in this research to be that high is that the sample consists of
233 firms which are divided in 41 different industries. This means that there are less than six firms
per industry. There are even industries included which consist of one firm only. Because of this
industry fixed effects are comparable to firm fixed effects and capture almost the same variation.
This causes the Adjusted R-squared for model (d) to be quite high, namely 30%, which is much higher
compared to the results of 18% for LRZ (2008:1589). When doing a regression for the four traditional
variables, including year fixed effects, the regression explains around 16% (results are not included in
the paper). When focusing on the Adjusted R-squared only the results obtained for Book leverage are
comparable with the results found by LRZ. However, this is not the case when the dependent
variable Market leverage is analyzed. The total Adjusted R-squared in model (g) for LRZ accounts for
70%, compared with 48% in this study. This is mainly caused by the differences in explanatory power
for firm fixed effects, where 61% is explained by firm fixed effects in LRZ and only 39% in this study.
Panel A shows that firm fixed effects are time-invariant and that they are the most important factors
in the model. Furthermore the adjusted R-squared is much higher for firm fixed effects than for year
fixed effects. This is weak evidence for saying that cross-sectional factors explain more variation in
leverage than time-series will do.
DeAngelo and Roll provide the following explanation: “The reason is that calendar time-dummies
rule out any notion of firm-specific time variation in expected leverage and assumed that all firms
37
experience an identical shift in expected leverage in any given year”(2011:14). This means that year
fixed effects do not take into account the firm-specific time varying factors. Therefore one cannot
assume that firm fixed effects are stable over different time-series. Because of this firm fixed effects
will be tested on different time-periods to determine whether firm fixed effects are stable. In panel B
regressions are done including firm specific dummy variables for every five years (5yr Firm FE). It will
be interesting to see whether firm-specific fixed effects differ for each period of five years. Panel B
gives evidence that the adjusted R-squared for the “firm five years” fixed effects explains much more
than the “normal” firm fixed effects. Apart from this it is noteworthy that when the 5yr Firm Fe is
added to the model, other determinants in the model have almost no explanatory power. For
example, when looking at column (f) in panel B for the dependent variable Book leverage the 5yr
Firm FE explains 97% of the variation of the model, the other 3% are attributable to the other seven
determinants in the model. Similar results arise when looking at the dependent variable Market
leverage in panel B. The results are in line with the findings of DeAngelo and Roll (2011), about firm
fixed effects. DeAngelo and Roll tested the firm-specific time-series variation in leverage for each
decade. The authors came to the conclusion that firm fixed effects significantly differ across decades
(2011:14).
The ANOVA-analyses lead to the conclusion that there are main unobserved firm characteristics
which are not captured by the existing determinants, but which have an enormous impact on a firm’s
capital structure. It is known that the “normal” firm fixed effects capture firm-specific items which
are invisible. The results of panel B support the fact that it will become even more complex to
determine the firm-specific items, because the invisible firm-specific items change over time.
Another conclusion that can be made is that firm-specific time-series variation in leverage can cause
instability in leverage to occur. The instability of visible items will be tested in the last chapter of this
paper.
38
Chapter 6: (in)Stability in corporate capital structures
6.1 Stability based on the inter-temporal variation of leverage for each
individual firm
In chapter 4 the convergence effects for public Dutch firms were discussed. The results found by LRZ
led to the conclusion that leverage ratios are relatively constant over time, even for twenty event
periods. De Angelo and Roll (2011) had some criticisms on the paper by LRZ and came up with totally
different results. The main difference between the research techniques of LRZ and DeAngelo and Roll
is LRZ being interested in the cross-sectional mean leverage of firms over different portfolios and
DeAngelo and Roll being only interested in interpreting the inter-temporal variation of leverage for
each individual firm. This leads to different results for the stability of public US firms. DeAngelo and
Roll drew the following conclusion: “Capital structure stability is an exception, not a rule” (2011:5).
This chapter is devoted to the stability in leverage over time, with a focus on individual firms.
For Table 11 the subsample survivors will be used to test the stability of firms for the first ten
observing years. DeAngelo and Roll (2011) used a sample in which firms stayed in the sample for at
least 20 years. The authors found evidence that the results are almost the same when firms are
analyzed for a period of ten years (2011:10). Table 11 consists of five different ranges (+- 0.05, +0.100, +- 0.200, +-0.300, +-0.400). The actual leverage ratio has been tracked for the next 9 years.
The firms which move beyond each range will be recorded. For example, after five years 88.81% of
the firms differs more than 0.05 from their initial book leverages.
Table 11: Inter-temporal variation in leverage:
Magnitude of speed of departure from original leverage.
The sample consists of 139 firms that have 10 years or more available data for book leverage. Book leverage is measured as
the book value of total debt divided by the book value of total assets. Column one gives an indication of firms that deviate
more than 0.05 from their original leverage ratio. The remaining columns show the fraction of firms that deviate from their
initial book leverages, over a time-period of ten years. For example, in column one and row 5, the number means that after
five years 88.81% of the firms deviate more than 0.05 from their initial leverages.
Percentage of firms which leverage has differed from its original value by at least:
Year
+--0.05
+--0.100
+--0.200
+--0.300
+--0.400
1
31.47%
13.29%
5.59%
2.10%
0.00%
2
61.54%
30.07%
13.99%
8.39%
3.50%
3
76.22%
39.86%
18.18%
11.19%
6.99%
4
83.22%
47.55%
20.28%
13.99%
8.39%
5
88.81%
54.55%
26.57%
14.69%
8.39%
6
92.31%
59.44%
30.07%
16.78%
10.49%
7
95.80%
63.64%
31.47%
18.88%
10.49%
8
97.90%
68.53%
35.66%
22.38%
11.19%
9
98.60%
73.43%
40.56%
24.48%
11.89%
39
At the last year observation almost 100% of the firms moved beyond the range of 0.05 from their
initial leverages. When one assumes that firms are very stable (relatively stable) if the deviation is
less than 0.1 (0.2) from their initial book leverages, then one can conclude that 26% (61%) of these
firms are very stable (relatively stable) over a period of 10 years. In the case of public Dutch firms
very few firms have a very stable leverage ratio. Substantial time-series variation is in great contrast
with the results of LRZ (2008), which shows that the mean leverage ratio for the different portfolios
is very constant over time. In relation to the results found by DeAngelo and Roll (2011) this study
produces similar results compared to public US firms after a time period of ten years (2011:10).
When the last year observation in Table 11 is compared with year 9 in Table 6 (evidence from
DeAngelo & Roll (2011)) a small difference can be seen. The largest difference of 8% is for the range
+/- 0.050. For the other ranges the differences are only 3.57%, 3.84%, 2.27% and 0.39%. This table
provides evidence that firms over a time-period of 10 years regularly change from their initial book
leverages.
Table 12 used the subsample survivors to see how stable the different firms are within their quartile.
The table gives an indication of how many firms remain in their current quartile, based on their initial
Table 12: Fraction of firms that remain in their current quartile
The table starts with calendar-year 1989. The subsample survivors (at least 10 years of data) is used to test for stability.
Each firm is sorted in one of the four quartiles based on their initial leverages. This sorting process is done for every
calendar-year till 2010. After this sorting process firms are tracked for the next nine years, based on their actual book
leverage ratios for each year. The fraction of firms that stay in their quartile are recorded. For example, after eight years the
Highest leverage group records still 20% of the firms. This means that on average 20% of the firms that started in the
Highest leverage group in year t=0 are still in the same quartile in t=8. In the first period, the construction period, the
fraction of the firms that stay in their current quartile in year t = 0 is 1.00. When firms are sorted randomly at the end the
expected percentage in each quartile will be 0.250.
Fraction of firms that remain in initially assigned leverage group.
Lowest
Year elapsed
Low/Medium
Leverage
Medium/High
Leverage
Highest
Leverage
Leverage
0
1
1
1
1
1
64.10%
59.38%
60.61%
68.57%
2
56.41%
46.88%
33.33%
54.29%
3
43.59%
34.38%
27.27%
54.29%
4
33.33%
21.88%
18.18%
45.71%
5
30.77%
18.75%
15.15%
37.14%
6
28.21%
15.63%
15.15%
28.57%
7
28.21%
15.63%
12.12%
22.86%
8
28.21%
15.63%
9.09%
20.00%
9
28.21%
9.38%
6.06%
14.29%
40
leverages. The table starts with calendar-year 1989. Each firm is sorted in one of the four quartiles
based on their initial leverages. This sorting process is done for every calendar-year until 2010. After
this sorting process firms are tracked for the next nine years, based on their actual book leverage
ratios for each year. The fraction of firms which stay in their quartile are recorded. In the first period,
the construction period, the fraction of the firms which stay in their current quartile in year t = 0 is
1.00. When firms are sorted randomly at the end the expected percentage in each quartile will be
0.250. Therefore, a decline from 1 to 0.250 means that there is clearly no question of persistency
over time, DeAngelo and Roll (2011:16).
Table 12 shows that even after year t=1 a lot of firms moved beyond their current quartiles. At t=1
around 30% of the firms deviated from their current quartiles. The fraction of firms that stay in their
current quartiles drops dramatically over time. In comparison to the other quartiles the Highest
quartile is the most persistent after four years, 54% of these firms stay in their current quartiles.
After a time-period of 10 years there is no question of persistency for all of the different quartiles.
For the Low portfolio the fraction of firms that stay in the current quartile from t=6 till t=9 is only
3.21% above the random term of 0.25.
In sum, there is no tendency that firms stay in their current quartiles over a time period of ten years.
Even though persistency cannot be found, interesting observations can still be made. If firms deviate
from their current Low/High quartiles, their actual Book leverage ratios increase/decrease. Firms
which deviate from their current Low/High quartiles can therefore be seen as a small effect of
convergence for the Highest and the Lowest quartiles.
At the end of this section it can be concluded that a large part of individual firms have a huge timeseries variation in leverage. Since it will be interesting to test which factors cause a company to move
beyond their stable leverage regimes, we will look into this in the following section.
41
6.2 Determinants that caused an instable leverage regime
The tables in the previous section have shown that a lot of firms which are observed on an individual
basis moved from their stable leverage regimes. This section focuses on finding out which factors
influence the instability in leverage for public Dutch Firms.
The “Variance Decomposition” in chapter 5 made it already clear that firm-specific factors vary over
time and could have an influence on the stability of the firm. Table 13 will indicate which visible
factors possibly caused a deviation from a stable leverage regime. The sample made for table 13 is
based on firms which deviate less than 0.1 from their initial book leverages for the first seven years.
Table 13 presents three years before and three years after the moment of deviation from a stable
period. T=0 indicates the moment of deviation from a stable leverage period. The final year of a
stable regime is denoted as t=-1.
Table 13: Deviation from a stable leverage period
For this table firms are selected when their actual book leverages deviate less than 0.1 from their initial book leverages, for
a time-period of at least seven years. Point zero indicates the moment when a firm departures from a stable leverage
period. The last year of a stable capital structure is denoted as t=-1. The table range from t=-3 to t=3 and it presents the
mean values of each item. For the following variables we used a one-year lag period: Asset growth, Capital expenditures,
Financing deficit, Change in Debt, EBITDA, and Tangible assets. The variables are winsorized at a 1% level. Change in debt is
determined as the change in the total outstanding debt for each period deviated by their total assets. It is calculated in this
way because Compustat offered no information to calculate the change in debt for public Dutch firms.
Event year to depature in year 0 from stable leverage regime
Mean value of
t=-3
t=-2
t=-1
t=0
t=1
t=2
t=3
1.Debt/Total Assets
0.18
0.20
0.17
0.24
0.27
0.25
0.24
2.Asset growth
0.10
0.08
0.10
0.23
0.15
0.10
0.06
3.Capital expenditures
0.19
0.21
0.19
0.03
0.00
0.21
0.23
4.Financing deficit
0.03
0.06
0.05
0.16
0.07
0.03
0.02
5.Change in debt
0.02
0.03
0.03
0.09
0.06
0.02
0.01
6. EBITDA
0.07
0.07
0.08
0.15
0.08
0.08
0.09
7.Log(sales)
6.53
6.59
6.35
6.66
6.73
7.02
7.07
8. Market-to-book
1.63
1.62
1.51
1.58
1.42
1.30
1.32
9. Tangible Assets
0.32
0.32
0.32
0.28
0.27
0.26
0.25
Analyses of the table produced interesting results. At t=0 the variable Asset growth has more than
doubled in size, from 0.1 in t=-1 to 0.23 in t=0. Remarkably capital expenditures decreased
dramatically at t=0 compared with t=-1, which is not in line with what was expected. This evidence is
also in contrast with the findings by DeAngelo and Roll (2011:23), where the capital expenditures
increased significantly. When the assets of a firm increase the capital expenditures of the firm will be
42
expected to increase as well. One possible explanation could be that the asset growth is caused by
acquisitions, which has no influence on the capital expenditures of a firm. Further research should be
done to investigate acquisitions in relation to asset growth and capital expenditures. This is not the
focus of this paper, but it could be interesting for further research. Another possible answer is the
limited observations of firms that could have influenced the results of table 13.
The growth in the variable asset growth is in line with what was expected. The instability can be
assigned to the asset growth of a firm, which is related to the financing deficit of a firm. Furthermore,
row one shows that an average increase in the leverage ratio reveals that firms more often move
away from their stable leverage regimes through attracting new debt instead of deleveraging their
debt position.
Furthermore, DeAngelo and Roll found evidence that the traditional determinants: EBITDA, Log of
sales, MTB and Tangible assets stay equal over time and have no influence on the instability of the
leverage of a firm (2011:22). Table 13 shows that the variable EBITDA contradicts the evidence of
DeAngelo and Roll. In Table 13 the variable EBITDA almost doubles in size when the firm moves away
from its stable leverage period. For the variable EBITDA it is remarkable that before and after t=0 the
average EBITDA is around 0.08. These results were not expected. To determine the exact cause of
this effect further research should be done.
It is interesting to see that the other traditional determinants, such as Log(sales), Market-to-book
and Tangible Assets are relatively stable over time. This gives an indication that the traditional
determinants have no influence on the stability in leverage. The table leads to the conclusion that
the variable Asset Growth has an enormous impact on the stability of a firm. When the equation,
debt to total assets, is taken into account the expectation is that higher asset growth will result in a
lower leverage ratio. For the public Dutch firms Asset Growth is related with higher outstanding debt
to finance the growth. The increase is that high that the leverage ratio increased instead of
decreased. DeAngelo and Roll, suggest that the capital structure problem is more about access to
capital to fund investment than about optimizing the leverage ratio (2011:31). The investment policy
of a firm tends to impact the stability of firms. But before we can draw this conclusion, it will be
interesting to observe how the stability in leverage will change when the research techniques of LRZ
and DeAngelo and Roll will be combined. This will be done in the next section.
43
6.3 Stability based on the mean movement in leverage for the individual
firms over time
The reason why LRZ found evidence for a stable leverage is mainly because the authors only
observed the mean leverage ratios for each portfolio, which consisted of thousands of firms.
Therefore big shocks on an individual basis will not be noticed. According to the technique used by
DeAngelo and Roll when firms are observed on an individual basis it seems intuitive that a lot of firms
move beyond their initial leverages. Big investments could cause a firm to move beyond their stable
leverage regimes. This section will test the stability in leverage, based on the combined research
techniques of LRZ and DeAngelo and Roll. The choice has been made to take into account the
average movements of the individual firms for different time-periods.
Table 14, which tests for stability in leverage, analyses 233 public Dutch firms over a time-period of
21 years. This table presents the time-series range for Book leverage (Panel A) and Market leverage
(Panel B). The table provides insight in stability of individual firms over time. In table 14 firms are
sorted in five different time periods (2-4, 5-9, 10-14, 15-19, 20-plus). The next sorting process is
based on the stability of each firm. Firms are sorted in one of the six ranges based on the mean
Table 14: Time-series range of leverage ratios of public Dutch firms
Book leverage is the ratio of total debt to total assets. Market leverage is the ratio of total debt to total debt plus the
market value of equity. The sample contains 233 public industrial firms. The firm enters the sample when it has non-missing
values for total assets and non-missing values for market-value. For example, 2 to 4 means that firms stay in the sample for
at least 2 years and up to 4 years. It is possible that after that period firms are delisted or merged with other companies.
The median range is the median range between book leverage and the initial book leverage of a firm. The median leverage
is the median book leverage for all firms in that specific range.
Percent of firms with a range of leverage ratios in the interval:
Median
0.000
0.100
0.200
0.300
0.400
Above
Median
Number
range
0.091
to 0.100
50.00%
to 0.200
38.89%
to 0.300
8.33%
to 0.400
2.78%
to 0.500
0.00%
0.5
0.00%
Leverage
0.206
of firms
36
15 to 19
0.095
48.72%
38.46%
10.26%
0.00%
0.00%
2.56%
0.253
38
10 to 14
0.072
49.09%
32.73%
12.73%
3.64%
0.00%
1.82%
0.220
55
5 to 9
0.077
53.33%
30.00%
10.00%
3.33%
0.00%
3.33%
0.244
60
2 to 4
0.064
68.57%
22.86%
2.86%
2.86%
0.00%
2.86%
0.206
35
1
---
---
---
---
---
---
---
---
9
20-plus
0.149
11.11%
47.22%
13.89%
8.33%
5.56%
13.89%
0.224
36
15 to 19
0.141
2.63%
57.89%
15.79%
7.89%
2.63%
13.16%
0.286
38
10 to 14
0.108
29.63%
33.33%
9.26%
5.56%
3.70%
18.52%
0.211
54
5 to 9
0.125
28.26%
13.04%
10.87%
17.39%
8.70%
21.74%
0.273
46
2 to 4
0.010
51.72%
20.69%
6.90%
0.00%
6.90%
13.79%
0.120
29
1
---
---
---
---
---
---
---
---
6
A. Bookleverage
20-plus
B. Marketleverage
44
deviation from their initial leverage over the whole time-period (0.0-0.1; 0.1-0.2; 0.2-0.3; 0.3-0.4; 0.40.5; above 0.5). For example, Table 14 shows that 50% of the firms, which stayed in the dataset for
20 or more years, have a mean range between 0 and 0.1. Zero percent of these firms deviates on
average more than 0.4 from their initial book leverages. It is noteworthy that for all the different
time periods none of the firms fall in the range of 0.4 to 0.5. It seems to be that when firms surpass
the mean range of 0.3 to 0.4, the deviation becomes extremely large. When looking at the median
time-series range for firms that stay in the sample for 20 or more years the median range is 0.091
(0.149) for Book leverage (Market leverage).
It can be concluded that almost 80.9% of the firms that stayed for 20 years or longer have a relatively
stable capital structure. Almost the same conclusions can be drawn for firms which are listed for 15
to 19 year, 10 to 14 years, 5 to 9 years and 2 to 4 years (87.18%, 81.82%, 83.33%, 91.43%). This
evidence is partially consistent with Figure 3, where some stability has been found. Notice that more
than 50% of the firms are very stable, where they seem to follow a target leverage ratio. This
confirms the conclusion from a study by Cools (1993). The author interviewed public Dutch firms
only, and came to the conclusion that 54% of the public firms have a target leverage ratio.
Different conclusions can be drawn from the dependent variable Market leverage. Market leverage is
much more volatile and therefore the median range is higher and much more firms deviate from
their initial leverage ratios. Nineteen point forty-four percent of the firms that stayed in the dataset
for 20 years or longer deviates more than 0.4 from their initial market leverages. Forty seven point
twenty-two percent of the firms that stayed in the sample for 20 years or longer have a relatively
stable capital structure. For firms listed for 15 to 19 year, 10 to 14 years, 5 to 9 years and 2 to 4 years
the following numbers are presented: 60.52%, 62.96%, 41.3%, 72.41%. For every time period the
dependent variable Book leverage is much more stable than the dependent variable Market
leverage. Therefore it can be concluded that the dependent variable Market leverage is much more
volatile, which is in line with the results of DeAngelo and Roll.
From this section we can draw the following conclusion: when the different research techniques are
combined it is interesting to notice that it tends to be that more than 50% of the firms seem to have
strict target leverage ratios. More than 80% of the firms tends to have a relative stable leverage
regime. This indicates that most of the public Dutch firms have a leverage zone in which the firm can
deviate, which is consistent with research results of Graham and Harvey (2001). Therefore we can
conclude that the statement made by DeAngelo and Roll (2011:5), “capital structure is the exception
not the rule”, does not hold for the public Dutch firms.
45
Conclusion:
This research aims to test the stability of public Dutch firms, based on the research techniques of
Lemmon, Roberts and Zender (LRZ) (2008) and of DeAngelo and Roll (2011). For this study only nonfinancial public Dutch firms are taken into account.
After doing research on the stability of public Dutch firms, it is clear that the traditional determinants
have almost no explanatory power. Therefore, after several decades of research there is still very
limited information available on constructing the optimal capital structure. Where firm fixed effect
have the most explanatory power in explaining the variation in leverage. Research has shown that
firm fixed effects even vary over different time-periods. This means that we are further away than
expected from solving the capital structure puzzle. A positive sign however, is that we now know
where to look at in the future.
It is safe to say that the research techniques used by LRZ (2008) and DeAngelo and Roll (2011) can be
applied to the public Dutch firms successfully. The persistency and the convergence effect are
created when the mean leverage ratios of all firms are taken into account. Research carried out on
individual firms, based on individual variation in leverage, showed that firms deviated widely from
their initial leverage ratios. Based on the latter technique, it appears that deviation from a stable
leverage regime is mainly caused by the growth in assets and the need of external funding.
The two different research methods of these authors look at two extremes. LRZ (2008) look at the
average deviation for all available firms in the sample for each specific year. Because of this big
outliers, both positive and negative, can be ticked off together. This makes it extremely difficult to
conclude whether firms are actually stable over time. The results of firm fixed effects in LRZ’s paper
(2008) are inconsistent by saying that firm fixed effects are time invariant. Research has shown that a
part of the firm specific items vary over different time periods, which makes that instability can arise
through variation in invisible items. DeAngelo and Roll (2011) look at the other extreme, where only
the individual firm observations are taken into account. When firms are observed on their individual
movements, it makes sense that companies are not stable over time. Firms that need new funding
for their investments can cause the firm to deviate from its current stable leverage regime.
Deviating from the two extremes, it is interesting to combine the techniques of LRZ and DeAngelo
and Roll. Which results in the fact that for individual firms, the mean deviation in leverage is
observed over different time-periods. Research has shown that 80% of the firms over different timeseries are relatively stable. Therefore firms tend to have some target zones, as is supported by
Graham and Harvey (2001). From these findings it seems too rigorous to state that the capital
structure stability is the exception and not the rule, like DeAngelo and Roll assumed (2011:5).
46
DeAngelo and Roll suggest that the capital structure problem is more about access to capital to fund
investment than about optimizing the leverage ratio (2011:31).
The combining technique ensures that it tends to be that public Dutch firms are quite stable over
time. Therefore it will be interesting to do further research on the cross sectional variation in
leverage. The firm specific components determine almost all the variation in leverage. It would also
be interesting to test the determinants that caused an instable leverage regime in other countries,
which have a larger dataset compared with the dataset of this research.
Although that the two research techniques can be applied to the public Dutch firms successfully, an
enormous part of the capital structure puzzle is still unexplained. It will be a challenge to get more
grip on the firm specific items, to come closer in determining the optimal capital structure of
individual firms. Therefore I am looking forward to future research aimed at solving the capital
structure puzzle.
47
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50
Appendix:
Appendix 1: Construction of the variables
(The abbreviations in the parentheses are codes referring to the CRSP-compustat database.)
Book leverage: Total debt / Total Assets (AT).
Capital expenditures: Capital Expenditures (CAPX)/ Assets Total (AT).
Cash flow volatility: Standard deviation of Operating income before depreciation (OIBDP), for firms
that have at least three years of data.
Dividend payer: Dummy variable, one if firm pays out dividend, zero if the firm does not pay
dividend.
Firm Size: Log (AT), the assets are deflated by the GDP deflator.
Growth: Intangible Assets (INTAN)/ Total Assets (AT).
Initial leverage: Is the first non missing value for Book leverage/ Market leverage.
Market leverage: Total debt/ (Total debt + Market value of Equity (MEV) ).
Market to book: (Market value of Equity (MEV) + Assets Total (AT) – Common/Ordinary Equity
(CEQ))/ Assets Total (AT).
Profitability: Operating income before depreciation (OIBDP)/ Total Assets (AT).
Tangibility: Net Property, Plant and Equipment (PPENT))/ Total Assets (AT).
Total debt: Long term debt (DLTT) + Total debt in Current Liabilities (DLC).
51
Appendix 2: Convergence effect for survivors
Figure 3b: Convergence and persistency in capital structure
The figure is directly taken from the paper by Lemmon, Roberts and Zender (2008:1580). It presents the four portfolios over
twenty event years. The results of the survivors of Book and Market leverage are presented in Panels B and D. The survivors
show almost similar results as the full sample does.
52
Appendix 3: Persistency in capital structure for survivors
Table 1b: Influence of initial leverage on future capital structure ratios
(The table is directly taken from LRZ (2008:1586)). The coefficients of the table are obtained by doing an OLS regression and
scale the parameters by the standard deviation of that specific variable. The t-stats are obtained by clustering the standard
errors at firm level and controlling for heteroskedasticity. The interpretation of each coefficient is the change in leverage
with a one standard deviation change of that specific determinant. The results of the survivors of Book and Market leverage
are presented in Panel B.
53