Stuart Briers - Undergraduate Research Paper

Stuart Briers, 40040306, Size: The most important leverage determinant?
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Queen’s University Management School,
Queen’s University Belfast
Size: The most important leverage determinant?
An empirical analysis of the
U.S. Financial Crisis 2007-2009
Stuart Briers
40040306
BSc Finance
John Turner
Wed, 14th
May 2014
Abstract
The Financial Crisis of 2007-2009 was the worst since the 1929 Great Depression and
forced firms to reassess their capital structure and exposure to the market. Through
this study of the top 1,200 U.S. firms during the period, firm size is assessed as the most
important component in dictating leverage. The Pecking Order Theory is tested and
analysed as the underlying reason why firm size affects leverage when considering
retained earnings and debt. Existing evidence is compared on firm size and the Pecking
Order Theory, with conclusions and future areas of research given based on the results
of the sample.
Previous studies in capital structure have been “hampered by a lack of consistent accounting
and market information outside the United States” according to Rajan and Zingales (1995).
Therefore, this paper will review capital structure changes in the U.S.A. in order to gain a
broad understanding of how firms are structured; allowing inferences to be drawn on the
effect firm size has on leverage. The U.S.A. is an ideal country to analyse due to its access to
capital markets and as Myers (2001) points out; firms have the “broadest menu of financing
choices”.

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This paper investigates if firm size is the most important component in dictating
leverage and if it is positively correlated to it due to the Pecking Order Theory. This theory,
which was first introduced by Donaldson (1961) and modified by Myers and Majluf (1984),
states that firms use retained earnings until depleted, at which point they issue debt. Equity
will only be used when it is not feasible or sensible to issue more debt. The paper will analyse
if the Pecking Order Theory is relevant in the Financial Crisis or if other theories can be used,
such as the Trade-off Theory (Kraus and Litzenberger 1973), where the firm chooses its
mixture of debt and equity as a function of the present value of both tax shields and
bankruptcy costs. Most researchers in capital structure have acknowledged firm size as a
significant factor affecting leverage, but few have called it the most important leverage
determinant. This paper will analyse evidence to attempt to prove this thesis.
Other factors will also be considered including tangibility of assets, market-to-book
value and profitability. The Financial Crisis of 2007-2009 has been described as the worst
since the Great Depression by the IMF1
, but due to publishing lags, little is known about how
firm size affected capital structure during this time. Miglo (2013) points out its importance as
the crisis “forced financial economists to look critically at capital structure theory because the
problems faced by many companies stemmed from their financing policies”. This paper will
attempt to fill this gap in literature by showing that the size of a firm has the effect to
significantly reduce investment (i.e. leverage see fig. 7a) during a crisis when financial
institutions cut lending. This theory is very clear in the 2007-2009 Financial Crisis.
After the abstract and introduction, the following section discusses existing literature
on firm size and its importance in determining leverage. Reasons for its effect on leverage are
given by different researchers and also existing evidence on the strength of the Pecking Order
Theory. Other variables are also considered.
1
The Guardian: see http://www.theguardian.com/business/2008/apr/10/useconomy.subprimecrisis

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In the data section, the regressions to be investigated are listed along with important
points and omissions, such as excluding financial firms. Each variable is defined here
because no standardised approach exists to measure the variables, especially leverage.
In the results section, the Pecking Order Theory is shown to exist as when retained
earnings fell, leverage rose for each of the three periods. However it cannot explain why in
2009, the top 500 firms on average continued to accumulate retained earnings yet increase
their leverage ratios. In this scenario, bankruptcies more than doubled since 2007 (fig. 6) and
credit was severely restricted by lenders (fig. 7). Potentially, the trade-off theory is evident
here due to higher financial distress costs in the economy, lending increased to the very top
firms. These firms can select their leverage ratios as they see fit. The results also show that in
8/10 quintiles leverage has increased in 2012 beyond pre-crisis levels. This is a worrying sign
because the advent of cheap credit fuelled a boom in securitisation which in turn gave birth to
the crisis. Financial institutions and lawmakers must therefore ensure sound regulation exists
to avert another crisis so soon (a double-dip recession is still possible). Firm size has a greater
impact on leverage for smaller firms and an insignificant impact on the largest 200, showing
that other variables need to be considered. Tangibility of assets and market-to-book value are
highly significant for all periods, with beta and profitability being significant in only 2012.
The conclusion gives important results obtained by the analysis of the sample. In
particular, firm size is important depending on the industry, the stage of the business cycle
and its importance in the economy when analysing leverage. More research is required based
on the 2007-2009 Financial Crisis because researchers have yet to discover the majority of
the variables affecting capital structure, as measured by R2
.
I) Literature review
Many factors have been argued to affect capital structure and leverage since the initial
work of Modigliani and Miller in 1958. This literature review will focus on the importance of

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firm size and other major factors’ relationship with leverage, as well as analysing the strength
of the Pecking Order Theory in determining capital structure.
Rajan and Zingales looked at G7 countries during the late 1980s and found that firm
size was highly significant and positive as “informational asymmetries between insiders in a
firm and the capital markets are lower for large firms”. Marsh (1982) considered the U.K. (a
similar common-law economy) and agreed on the significance of firm size between debt and
equity issuers adding “smaller companies, those with few fixed assets [low tangibility of
assets], and those with greater bankruptcy risk are more likely to issue equity”. Chaplinsky
and Niehaus (1993) found firm size to be the only significant variable.
Opinions of researchers have been divided on the direction of the sign in the firm
size to leverage relationship. Friend and Hasbrouck (1988) find a positive relationship on
the grounds that larger firms have better access to credit markets, whilst Chaplinsky and
Niehaus find a negative relationship. The small firm effect is in existence according to
Titman and Wessels (1988), who state that small firms pay more than large firms to issue
equity and hence would prefer debt [a negative relationship]. Another reason could possibly
be due to “high transaction costs small firms face when issuing long-term financial
instruments”. The “size effect, if it exists, affects mainly the very small firms”. It should be
noted that some researchers find no significant difference for firm size, for example Kester’s
(1986) 1982-83 U.S. study.
Many analysts advocate other significant variables. Rajan and Zingales found that
firms moved towards debt financing and away from capital gains (retained earnings) due to
the tax advantage that debt has. The U.S. has the highest corporation tax rates in the world2
.
Whilst Harris and Raviv (1991) found the effect of management-friendly bankruptcy laws to
be important; when there is a high probability of bankruptcy, firm leverage will decrease. In
2
See http://www.kpmg.com/global/en/services/tax/tax-tools-and-resources/pages/corporate-tax-rates-
table.aspx

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relation to U.S.A, it is harder for firms to go bankrupt due to Chapter 11 (U.S. Code),
allowing these firms to take on more leverage with softer penalties. The U.S.A. has highly
diffuse ownership, as shown by La Porta et al. (1997). Therefore, according to Zwiebel
(1996), managers will take on debt to commit to paying out future cash flows, making the
firm unattractive to raiders in relation to the takeover market. He makes the important point
that “for debt to restrict managers credibly … cash in hand must not be large enough to pay
off debt when a bad investment is undertaken”. Myers (1997) has pointed out that growth
(market-to-book value) is an important factor; “highly levered companies are more likely to
pass up investment opportunities”, believing a correlation exists between growth rates and
equity financing (in turn decreasing leverage). Titman and Wessels note “firms with high
market values relative to their book values have higher borrowing capacities and hence have
higher debt levels relative to their book values.” Profitability has been suggested by Myers
and Majluf (1984) as imperative “because firms will prefer to finance with internal funds
rather than debt”, suggesting a negative relationship between leverage and profitability. This
is disputed by Jensen (1986), who finds a positive relationship determined by an effective
market for corporate control “which forces firms to commit to paying out cash by levering
up.”
Much debate has ensued about the extent and relevance of the Pecking Order
Theory in recent times. In a study by Myers (1984) on non-financials from 1973-1982,
“internally generated cash [retained earnings] covered on average 62% of capital
expenditures and net new stock issues were never more than 6% of external financing”. This
shows a clear reliance on internal finance and debt, supporting the theory. Managerial
capitalists agree, stating “firms’ reliance on internal finance as a by-product of the separation
of ownership and control” (Myers 1984). Berle and Means add professional managers do not
wish to be subject to the discipline of capital markets. Titman and Wessels find “increases in

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the market value of equity, due to an increase in operating income, are not completely offset
by an increase in the firm’s borrowing” [showing reluctance to go external]. The modified
Pecking Order Theory recognises “as the firm goes up pecking order: it faces higher odds of
incurring costs of financial distress, and higher odds that future positive-NPV projects will be
passed by because the firm will be unwilling to finance them by issuing common stock”
(Myers, 1984). In Chaplinsky and Niehaus’ study, leverage is found to decrease as internal
funds increase [in favour of Pecking Order Theory as retained earnings are used to finance
capital expenditure prior to leverage]. Myers and Majluf (1984) also advocate the theory;
citing firms prefer internally generated projects when managers have greater information to
investors due to under-pricing new issues. Using internal funds decreases leverage by
increasing the value of existing equity; “thus, the pecking order hypothesis predicts a
negative relation between leverage and the availability of internal funds, ceteris paribus.”
Korajczyk et al. (1990) look at how equity issues affect stock prices but find no proof
of the pecking order theory. The theory suggests “one might expect the debt/equity ratio to
rise before an equity issue” [as debt is cheaper than equity], but Korajczyk et al. find that “the
debt/equity ratio, however measured, falls or remains constant in the two years prior to an
equity issue”. Myers criticises the theory in his 2001 paper citing the agency problem, as it
assumes managers act in the interests of existing shareholders to maximise the value of
existing shares. This does not always occur due to managers’ selfish gains and pet projects.
An alternative theory is the Trade-off Theory, which argues that leverage depends on
the present value of both non-debt tax shields and bankruptcy costs. “DeAngelo and Masulis
(1980) argue that the greater the level of non-debt tax shields, the lower is the tax benefit of
additional leverage. Thus, [ceteris paribus] firms with higher non-debt tax shields are
expected to receive lower tax benefits from issuing debt”.

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II) Data
The data has been sourced from Thomson One Banker using a sample at three time
periods: 2006 (pre-crisis), 2009 (mid-crisis) and 2012 (post-crisis). The rationale behind this
is to analyse how firm size and other variables influence leverage levels. The first two
regressions are leverage = β0 + β1(firm size) and leverage = β0 + β1(weight of debt)
respectively. To analyse other variables, three OLS regressions are used (one for each sample
period), defined as: leverage = β0 + β1(market β) + β2(tangibility of assets) + β3(market-to-
book value) + β4(firm size) + β5(profitability). This regression is similar to the Rajan and
Zingales regression. The data will be analysed using Microsoft Excel and Stata. Many other
variables appear to affect capital structure (for example, see Baxter and Cragg’s (1970) model
which examined over 14 independent variables) but a balance must be found between data-
mining and not omitting relevant variables. Before proceeding further, the variables need to
be defined due to non-standardised approaches of measurement. This is shown in Table I:
Table I: Variables & Measurement
Variable Definition Measure
Leverage Ratio of firm’s total debt to its total assets 𝑇𝑜𝑡𝑎𝑙 𝐷𝑒𝑏𝑡
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
Market
Beta
A measure of the sensitivity of a stock’s price
to the movement of S&P 500
β of regression:
y = α + βx
Tangibility
of Assets
Assets that have a physical form 𝑇𝑜𝑡𝑎𝑙 𝐹𝑖𝑥𝑒𝑑 𝐴𝑠𝑠𝑒𝑡𝑠
Market-to-
Book Value
A proxy for investment: under/overvalued
firm. Higher MtB = investor expectation of
higher value creation of assets
𝑀𝑎𝑟𝑘𝑒𝑡 𝑐𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑠𝑎𝑡𝑖𝑜𝑛
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠 − 𝑑𝑒𝑝𝑟𝑒𝑐𝑖𝑎𝑡𝑖𝑜𝑛
Firm Size Ln of net sales (proxy for firm size) Natural Log of Net Sales
Profitability Return on Assets 𝑁𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒
Source: Thomson One Banker
Many different measures of leverage exist, making it difficult to compare studies. For
example, Crutchley and Hansen (1989) measure leverage as long-term debt relative to outside
funds, whereas Friend and Hasbrouck use total debt divided by total assets, which this paper

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will use (where it is assumed assets are not offset by non-debt liabilities as Rajan and
Zingales note). The definition of leverage is so important that Rajan and Zingales devote an
entire section analysing it.
The sample is of the 1,200 largest U.S. firms ranked by market capitalization. An
important omission from the data is financial institutions (which have been excluded via their
GICS code – see A1), due to the existence of investor insurance schemes such as deposit
insurance (Rajan and Zingales) which would artificially distort the figures towards debt
prudence (also excluded by Friend & Hasbrouck). Bank bailouts would also have distorted
the figures. The sample considers the largest firms, so any statistical inferences may not be
relevant to the whole economy.
The sample data is limited as it does not distinguish between equity built through
retained earnings and equity obtained through stock offerings (used by Rajan and Zingales).
Thomson One Banker uses book values when market values may have been more suitable in
the sample. Titman and Wessels also encountered this problem. Bowman, however, has
demonstrated that “the misspecification due to book values is probably fairly small” so this
limitation is ignored. Rajan and Zingales find consolidated balance sheets are reported by
large firms to conceal debts in subsidiaries when they need to raise external finance. This
could indicate why it is easier for these firms to raise debt. A constant beta is assumed due to
data limitations.
The first hypothesis test asks if a positive relationship exists between firm size and
leverage. A regression between the two will be used to test this, with the significance and
sign direction being important factors. As shown in the literature review, empirical
researchers have had conflicting evidence on this.

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The second hypothesis test is that large companies have more debt than smaller
companies because they have lower information asymmetries. This will be tested by the
relationship between the weight of debt against firm size.
The final hypothesis investigates if the small firm effect exists in the data. Titman and
Wessels argue small firms prefer debt due to cost of equity. This will be tested using the top
200 firms in the same versus the bottom 200 firms. Finally, the effect of other variables and
industry will be examined.
III) Results & Analysis
Before the data is regressed, heteroskedasticity will need to be eliminated due to the
OLS assumption of constant variance. This is achieved by running robust regressions.
A. Summarising the Data
Table II: Summary Statistics (All 1,200 firms)
Variable Measure 2006 2009 2012
Leverage Mean 0.17 0.17 0.19
Minimum 0.00 0.00 0.00
Top 25% 0.01 0.00 0.02
Median 0.15 0.15 0.18
Top 75% 0.26 0.28 0.30
Maximum 0.80 0.76 0.79
Standard Deviation 0.15 0.16 0.16
Log Sales Mean 7.19 6.99 7.49
Minimum 0.00 2.18 2.46
Top 25% 6.06 5.72 6.39
Median 7.14 7.02 7.41
Top 75% 8.28 8.20 8.49
Maximum 12.76 12.92 12.95
Standard Deviation 1.69 1.81 1.55
Table II shows summary statistics for the data. Across the sample, leverage is similar
for 2006 and 2009, although post-crisis on average it increases. The standard deviation for all
three periods is essentially unchanged, meaning that the overall market is moving together
with leverage shifts, according to the mean and median figures. As is expected, at the height
of the crisis the mean of log sales falls then recovers post-crisis as the market recovers. It

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should be noted that firm leverage exceeds 2006 levels upon recovery, potentially showing
that investors demand safer securities as a result of a lack of market confidence and possible
contagion. These safer securities would be the top firms by market capitalisation (i.e. firms in
this sample) and would be expected to have lower return.
B. The Relationship between Firm Size and Leverage
Table III: Hypothesis 1: Robust Regression of Log Sales with Leverage
2006 2009 2012
Leverage Leverage Leverage
Log Sales 0.017*** 0.025*** 0.020***
(0.002) (0.002) (0.003)
Constant 0.045*** -0.005 0.035
(0.017) (0.016) (0.022)
Observations 1,200 1,200 1,200
R-squared 0.036 0.078 0.038
The first hypothesis shown in table III confirms a positive relationship exists between
firm size and leverage and is shown above. In this regression, it is assumed no other variables
affect leverage purely to isolate the effect of firm size. Inevitably a low R2
exists but each
coefficient of log sales is significant. The relationship is strongest in 2009 showing as log
sales increased by a unit, leverage increased by 0.025 on average. During the crisis, larger
firms may have taken on more leverage potentially because of greater availability of debt
when cheaper credit was available, coming from risk-averse investors. Large firms may also
conceal debts in subsidiaries, making it easier to raise debt compared to small firms. After
confirming the null of the first hypothesis, the second hypothesis tests log sales with weight
of debt. In particular, the question is asked if larger firms will have a greater proportion of
debt relative to smaller companies.
C. Comparing Firm Size with Weight of Debt
Table IV: Hypothesis 2: Robust Regression of Log Sales with Weight of Debt
2006 2009 2012
Weight of Debt Weight of Debt Weight of Debt
Log Sales 0.039*** 0.044*** 0.048***
(0.003) (0.003) (0.004)
Constant -0.002 -0.035 -0.054
(0.025) (0.025) (0.033)

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R-squared 0.084 0.114 0.094
Once again log sales are highly significant across all three periods explaining around
10% on average of the variation of the weight of debt. The null hypothesis here is that firm
size and weight of debt should be significant and positively correlated. This indeed occurs in
all three periods indicating that the largest firms do take on greater debt. The main reason for
this is down to lower information asymmetries (and greater risk for smaller firms). Despite
being statistically significant, this relationship, however, may not be practically significant as
Apple (the largest sample firm) actually has no debt! Hypothesis 3 deconstructs firm size into
the top 200 and bottom 200 firms in the sample using to leverage as the dependent variable.
D. A Closer Analysis of Firm Size
Table V: Hypothesis 3: Robust Regression of Log Sales and Leverage at Extreme Points
2006 2009 2012
Top 200 Bottom 200 Top 200 Bottom 200 Top 200 Bottom 200
Log Sales -0.006 0.039*** 0.005 0.042*** -0.004 0.040***
(0.008) (0.007) (0.007) (0.012) (0.009) (0.013)
Constant 0.225*** -0.067* 0.143** -0.079 0.247*** -0.080
(0.074) (0.039) (0.065) (0.052) (0.086) (0.078)
Observations 200 200 200 200 200 200
R-squared 0.005 0.107 0.003 0.082 0.001 0.044
The new regression segregating larger and smaller firms yields very different results.
Firm size is statistically insignificant for the top 200 firms and extremely significant for firms
ranked 1000-1200 in the sample. For large firms, their size plays no real part in determining
leverage. Titman and Wessels argue smaller firms face higher transaction costs when issuing
equity so would prefer debt which occurs here as the coefficients are much higher for small
firms in each period also indicating the existence of the small firm effect. Incidentally, firm
size plays essentially no role in determining leverage for the top 200 firms as seen by the R2
which is a major finding.
E. Incorporating Industry Differences with Firm Size

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Table VI: Robust Regression of Log Sales with Leverage according to Industry
2006 2009 2012 2006 2009 2012
Leverage
GICS Codes Energy (10) Consumer Staples (30)
Log Sales -0.001 0.000 -0.012 0.012 0.029*** 0.011
(0.007) (0.007) (0.008) (0.008) (0.006) (0.012)
Constant 0.195*** 0.165*** 0.321*** 0.091 -0.020 0.124
(0.054) (0.061) (0.067) (0.071) (0.049) (0.101)
Observations 104 74 99 73 80 78
R-squared 0.000 0.000 0.026 0.023 0.170 0.015
GICS Codes Healthcare (35) Information Technology (45)
Log Sales 0.024*** 0.025*** 0.037*** 0.012*** 0.021*** 0.020***
(0.005) (0.005) (0.009) (0.004) (0.004) (0.005)
Constant -0.030 -0.022 -0.081 0.007 -0.056** -0.05
(0.033) (0.035) (0.061) (0.028) (0.025) (0.031)
Observations 112 151 133 219 218 239
R-squared 0.122 0.116 0.125 0.020 0.085 0.061
In the above regressions leverage is once again regressed with log sales. Only half of
the industries are reported purely to show industry differences. The number of observations
differs because the sample selects the largest firms each year, so some firms may fall out or
fall in to the sample. Firm size plays an extremely insignificant role in the Energy sector
possibly because they are typically large multinationals proving hypothesis 3 that large firms’
leverage are not influenced by firm size. This is compared to the Healthcare and IT sectors
where it is highly significant. Consumer Staples is a potential area for more investigation as
firm size is insignificant outside the crisis, but played a significant role in leverage during it.
These goods are generally necessities (e.g.
food) so could identify that these firms
should reduce price because of less
disposable income, using increased leverage
to finance this. At this time deflation was a
major concern for the U.S. government (see fig.
1). Bradley et al. (1984) cited in Chaplinsky and Niehaus indicates industry factors are
important in capital structure as firms choose it “on the basis of underlying costs and benefits
that are similar within each industry”. As demonstrated here, there are differences in how
Fig. 1: U.S. Inflation Rate 2008-2012

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firm size affects capital structure depending on industry.
F. Analysing Leverage Ratios
Considering all firms again, figure 2 shows a
polynomial trend for each period (excluding outliers
to analyse the main sample). Leverage ratios appear
to follow a wave-like cycle with firms in the 200-400
category using the highest leverage. It is interesting
to note that ratios have risen for the top 600 firms meaning they are taking on more debt or
retiring equity through share repurchases which is becoming increasingly common since
2009 (figure 33
).
Looking historically, Myers (2001) has similar
findings in that “more shares are extinguished in
acquisitions and share repurchase programs than
are created by new stock issues”. For the
remaining firms leverage fell sharply in 2009 before returning to pre-crisis levels in 2012.
These firms reduced debt or found it was unavailable to them due to lending reductions by
financial institutions (see fig. 6).
G. Does the Pecking Order Theory exist in the sample?
According to figure 44
, the difference in
retained earnings is largest in the top 200
firms, where it increased during the periods
due to capital adequacy increases by firms
who can afford it. A wave-like pattern exists
3
Share Repurchases by firms listed on major exchanges with S&P 500 Index: see http://opesforge.com/?p=340
4
Line of best fit omits extreme observations for visual reference
Fig. 2: Smoothed Leverage
Ratios for all sample firms
Fig. 3: Normalized Share
Repurchases v S&P Price (1996=1)
Fig. 4: Retained Earnings and
Leverage Ratios (2006, 2009, 2012)
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
1 201 401 601 801 1001
Leverage
Firm ID
2006
2009
2012
Levels(1996=1)

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similar to leverage. The general trend for the Pecking Order Theory appears to exist as
leverage and retained earnings are inversely related across the sample. However in taking a
snapshot for example in looking at the 200th
firm, leverage and retained earnings both grow
during each period. Looking at smaller firms, retained earnings for the firms in the 400-600
category fell sharply (making losses) coupled with the largest sample increase in leverage,
supporting the theory. In 2009, leverage ratios fell sharply for firms outside the top 400
although retained earnings remained steady. This could be seen to represent a large fall in
capital expenditure during the crisis.
H. Considering Other Variables Affecting Leverage
Table VII: Robust Regressions using major determinants of Leverage
2006 2009 2012
Beta 8.50e-05 0.015 0.020**
(0.008) (0.010) (0.010)
Tangibility of Assets 0.237*** 0.333*** 0.331***
(0.019) (0.019) (0.020)
Market-to-Book Value -0.033*** -0.027*** -0.028***
(0.004) (0.005) (0.004)
Log Sales 0.004** 0.010*** 0.008***
(0.002) (0.002) (0.003)
Profitability ROA -0.001 0.001* 0.002**
(0.001) (0.000) (0.001)
Constant 0.073*** -0.067*** -0.055*
(0.025) (0.021) (0.030)
R-squared 0.319 0.396 0.342
Rajan and Zinagles’ variables (and also market beta) will now be considered to
determine their effect on leverage. This regression is used to show that firm size is not the
only important variable. On first glance, the R2
’s seem consistent with previous studies where
Friend & Hasbrouck found the “overall explanatory power of the cross-sectional models is
quite low”. For example Carlton and Silberman (1977) report an unadjusted R2
of 0.3 and
Marsh’s is 0.37. Comparisons of R2
can be used the studies use broadly similar variables. The
model may explain greater variation in 2009 possibly because investors placed more
emphasis on firm size (highest of the three periods) shown by log sales (positive and

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significant throughout), because they wanted to retain earnings due to market conditions (as a
capital buffer) and therefore issue more debt. This potential explanation contradicts the
Pecking Order Theory. Debt issuers would be prudent and favour larger firms due to
increased credit risk from more bankruptcies in 20095
.
The Trade-off Theory is
supported as leverage could be
affected by the present value of
bankruptcy costs. These results
differ from Rajan and Zingales as
they find with greater bankruptcy costs comes greater equity issuance. The regressions’
findings disagree with Harris and Raviv’s idea that greater bankruptcy will mean decreased
leverage. Looking at all firms in the sample, the mean leverage in 2006 and 2009 is 0.17
compared to 2012 at 0.19 as seen in table II. It is no surprise that firm size is positively
correlated considering Friend and Hasbrouck’s findings that larger firms have better access to
credit markets. This is because the U.S.A. has potentially the easiest access worldwide to
these markets for example the largest stock exchange in the world; NYSE6
.
Market Beta values are only significant in 2012. As beta values increase by a unit,
the increase in leverage grows each period. Firms with higher betas (more risky) will take on
greater leverage. The significance should be treated cautiously due to only having 2006 data.
Tangibility of assets is highly significant, possibly acting as a proxy for increased
collateral required during and post crisis in order to fulfil stricter lending requirements due to
lower bank lending7
(fig. 6). This may also be seen as an opportunity to diversify, for
example, by purchasing land due to increased mortgage defaults.
5
Bankruptcy Statistics 2006-2012: see http://www.tradingeconomics.com/united-states/bankruptcies
6
New York Stock Exchange:
see http://www.investopedia.com/financial-edge/1212/stock-exchanges-around-the-world.aspx
7
Commercial & Industrial Loans 2006-2012: see https://research.stlouisfed.org/fred2/series/BUSLOANS/
Fig. 5: U.S. Bankruptcies
No.ofBankruptcies

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Market-to-book values are consistently
significant and negative adding to the argument that
“highly levered companies are more likely to pass up
investment opportunities” as noted earlier by Myers.
These firms may have high market values in some cases
and hence higher borrowing capacities (Titman and Wessels) but the restricted lending during
the crisis by financial institutions may have meant debt levels did not rise accordingly.
The effect of profitability on leverage is somewhat ambiguous, because pre-crisis it
is insignificant and negative compared to post-crisis being significant and positive. Myers
and Majluf found a negative relationship suggesting firms prefer to use retained earnings
before debt however like Jensen a positive relationship exists in this sample although it is
quite small.
I. Visual Analysis of Main Leverage Determinants8
Fig. 7a-f: Variables from Table VII in graphical form
Fig. 7a: Leverage Fig. 7b: Beta
Fig. 7c: Tangibility of Assets Fig. 7d: Market-to-Book Value
8
Figure Title is the vertical axis label and Firm ID Decile (Largest to Smallest) is the horizontal axis label
0
0.05
0.1
0.15
0.2
0.25
1 2 3 4 5 6 7 8 9 10
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1 2 3 4 5 6 7 8 9 10
0
0.2
0.4
0.6
0.8
1 2 3 4 5 6 7 8 9 10
0
0.5
1
1.5
2
2.5
1 2 3 4 5 6 7 8 9 10
Fig. 6: U.S. Commercial and Industrial Loans

17
Fig. 7e: Firm Size (Log Sales) Fig. 7f: Profitability (ROE9)
Leverage is discussed in the previous section but importantly it hits a trough
during the crisis for smaller firms (deciles 6-10) because they have greater systematic risk
(due to the small firm effect) as seen with higher betas in fig. 7b. Due to data limitations, only
the 2006 beta is shown and as expected for the top decile (making up the greatest percentage
of the market) their beta is roughly 1.0.
The tangibility of assets increased during the crisis due to stricter lending
requirements on collateral, but for the top decile it was effectively unchanged. Apart from
these firms, tangibility of assets is greatest after the crisis, as a measure to prevent another
securitisation crisis as these are intangible products which can be price sensitive.
Market-to-book value levels are extremely high pre-crisis; due to the cheap supply of
credit available for investment or perhaps due to over-valuations, for example in property.
They fall during the crisis as expected because the outlook is more pessimistic and due to
lower lending figures there is less chance of obtaining this credit to invest. Firms in the 4th
,
5th
and 10th
decile categories have negative retained earnings so the only method of
investment for these firms is through equity.
Log sales (firm size) stayed the same for the top 120 firms showing price inelastic
firms. Smaller firms (10th
decile) see sales drop during the crisis but have recovered by 2012.
Profitability fell sharply for all firms in 2009 and has not recovered since. Again, the
smallest firms in the sample suffer the greatest decline in return on assets because of risk-
9
Return on Equity
0
2
4
6
8
10
12
1 2 3 4 5 6 7 8 9 10
0
2
4
6
8
10
12
1 2 3 4 5 6 7 8 9 10

18
averse investors who favour safer (larger) firms; although due to financial amnesia10
the
situation will probably soon return to pre-crisis levels. Zwiebel finds firms with better
investment opportunities (high market-to-book values) and high profitability to have less
leverage due to requiring less debt to avert a takeover. This generally occurs in this sample
particularly towards the top firms.
IV) Conclusion
The results show that firm size is the most important leverage determinant however it
depends on market capitalisation and industry ranking. Firm size was found to be positively
correlated with leverage and the weight of debt, in each case being highly significant. Log
sales were relatively sticky during the three periods for the top decile showing price inelastic
firms. Smaller firms (10th
decile) seen log sales drop during the crisis but recovering by 2012.
The firm needs to be considered within its relative placing in the economy as the very top
firms find firm size is insignificant in affecting leverage, whereas the bottom firms of the
sample find firm size plays a highly significant role in affecting leverage.
The type of industry a firm is placed in will also matter greatly as energy firms find
firm size to be insignificant, whereas in healthcare and IT
it is highly significant. In Consumer Staples, firm
size was only significant during the crisis
potentially due to changing consumer trends for
example a shift to discount dollar stores from
traditional stores (fig. 811
). Firm size plays a more
important role in a crisis as documented by R2
in all regressions.
10
Financial Amnesia: see CFA July-Aug 2012 Publication
http://www.cfapubs.org/doi/pdf/10.2469/cfm.v23.n4.7
11
Wal-mart v Dollar Stores (Note that Wal-Mart’s sales numbers were divided by a factor of 10 to allow for a
growth comparison). See: https://www.toydirectory.com/monthly/article.asp?id=4900
Fig. 8: Sales History:
Wal-mart v Dollar Stores

19
The Pecking Order Theory holds in that for the top 400 firms, retained earnings and leverage
are inversely related. However for smaller firms the
pattern is less clear including some firms who have
negative retained earnings (made losses) and are therefore
forced to use debt. Other variables such as market-to-book
values are significant and are high pre-crisis showing
strong investment but dropped during the crisis, showing a
pessimistic view for firms in the smallest quintile as lending decreased12
(fig. 9) coupled with
increased bankruptcies during crisis. Leverage ratios on the whole have increased since the
crisis but fell sharply during the crisis for smaller firms as the availability of credit dried up.
Greater research is required on this matter due to the low explanatory power of
empirical models so potential inclusions for future models could be research and
development, availability of internal funds (both which Chaplinsky and Niehaus found
significant), bank lending and also use of bankruptcy statistics.
V) Appendix
A1. GICS Codes
GICS Code Industry GICS Code Industry
10 Energy 35 Healthcare
15 Material 40 Financials
20 Industrials 45 Information Technology
25 Consumer Discretionary 50 Telecommunications Services
30 Consumer Staples 55 Utilities
A2. Notations
NB: In all regressions, coefficients are listed on the top line followed by Standard Errors
(in brackets) below. Statistically significant values denoted by P-values as follows:
*** p<0.01, ** p<0.05, * p<0.1. Those coefficients with p-values of <0.05 are coloured in
red.
Firm ID: Sample firms ranked from 1-1,200 from highest to lowest market capitalization
12
Lending Gap (in billions $): see http://www.cnbc.com/id/101009116
Fig. 9: 2007-2012 Lending Gap

20
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Carlton, W. and Silberman, I. (1977) 'Joint Determination of Rate of Return and Capital Structure: An Econometric
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Journal of Business and Economics, 32(4), pp. 51-65.
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Corporate Dividends', Financial Management,18(4), pp. 36-46.
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Financial Economics, 8, pp. 3-29.
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