Annals of the University of North Carolina Wilmington...
Transcript of Annals of the University of North Carolina Wilmington...
Annals of the
University of North Carolina Wilmington
International Masters of Business Administration
http://csb.uncw.edu/imba/
THE EFFECT OF MACROECONOMIC FACTORS ON CAPITAL STRUCTURE DECISIONS
Sergey A. Chekanskiy
A Thesis Submitted to the University of North Carolina Wilmington in Partial Fulfillment
of the Requirements for the Degree of Master of Business Administration
Cameron School of Business
University of North Carolina Wilmington
2009
Approved by
Advisory Committee
Peter Schuhmann Ravija Badarinathi Nivine Richie
Chair
Accepted by
Dean, Graduate School
iii
TABLE OF CONTENTS
ABSTRACT ............................................................................................................................... v
LIST OF TABLES .................................................................................................................... vi
1. INTRODUCTION .............................................................................................................. 1
2. LITERATURE REVIEW ................................................................................................... 2
2.1. Tradeoff Theory ....................................................................................................... 2
2.2. Pecking Order Theory ............................................................................................. 3
2.3. Market Timing Theory ............................................................................................. 4
2.4. Macroeconomic influence ........................................................................................ 5
3. OBJECTIVES, RESEARCH QUESTIONS AND HYPOTHESIS ................................... 6
4. METHODOLOGY ............................................................................................................. 7
4.1. Target leverage estimation ....................................................................................... 8
4.1.1 Firm-specific target leverage variables .......................................................... 10
4.1.2 Macroeconomic target variables .................................................................... 11
4.2. Debt-Equity choice regressions ............................................................................. 12
5. RESULTS AND DISCUSSION ....................................................................................... 14
5.1. Target leverage estimation ..................................................................................... 14
5.1.1 Macroeconomic factors .................................................................................. 16
5.1.2 Lag vs. Lead ................................................................................................... 18
5.2. Financing Choice Regressions............................................................................... 19
5.2.1 Pure issue........................................................................................................ 22
5.2.2 Pure repurchase .............................................................................................. 26
iv
5.2.3 Mixed transactions ......................................................................................... 27
5.2.4 Debt maturity choice ...................................................................................... 29
6. CONCLUSION AND RECOMENDATIONS ................................................................. 31
REFERENCES ........................................................................................................................ 33
APPENDICES ......................................................................................................................... 35
v
ABSTRACT
I investigate the effect of macroeconomic factors on financing decisions. I support the
notion that companies have target debt ratios and rebalance their capital structure
accordingly. I estimate the target leverage using a set of firm-specific and macroeconomic
variables. I find that macroeconomic forecast plays a very important role in the target
leverage decisions. I show that such decisions are based on the medium-term economic
forecast to the greater extent than on the retrospective economic information. I introduce
mixed issue/repurchase choice regressions and illustrate the importance of macroeconomic
factors. I find that macroeconomic factors gain exceptional significance for ‘good’ and ‘bad’
decisions prior a change in the state of economy and prove that winners had a better
economic forecast. I highlight how the agency problem affects the capital structure of a
company and find further support for the market timing theory. Finally, I estimate the
maturity of debt regression and find how macroeconomic and firm-specific factors affect the
maturity of debt decision. My findings support the importance of the balance between short
and long term debt. I find that companies try to keep that balance and try to avoid short-term
debt during downturns.
vi
LIST OF TABLES
Table Page
1. Summary Statistics ........................................................................................................... 8
2. Determinants of target leverage ..................................................................................... 14
3. Descriptive statistics for debt-equity choice .................................................................. 19
4. Pure issue choice regression results ............................................................................... 22
5. Pure repurchase choice regression results ...................................................................... 26
6. Mixed transaction regression results .............................................................................. 27
7. Debt maturity choice regression results ......................................................................... 29
1. INTRODUCTION
Capital structure decisions are among the most important in financial management. As
shown in studies proving the relevance of capital structure (Ibrahimo and Barros, 2006). An
important question is which factors determine capital structure and what decisions are related
to it. Theoretical works explore the effect of managers' preferences of internal sources of
financing to external ones, the effect of the tax shield, and the costs of financial distress.
Empirical studies examine endogenous factors, such as firm size and asset tangibility. Despite
the fact that the importance of macroeconomic factors is recognized (e.g. Hackbarth, Miao,
and Morellec, 2006), few empirical studies test for the effect of exogenous factors,
particularly macroeconomic factors.
Recently in The Wall Street Journal, Michael Milken points out that “capital structure
significantly affects both value and risk” (April 21, 2009 p. A21) and defines six factors to
consider when making a financing decision. These factors include the state of capital markets
and the economy. Where a change in environment should signal a change in the optimal
capital structure. Mr. Milken argues that during the last forty years many companies suffered
because of the wrong capital structure. For example, looking back we can say that firms who
repurchased their stock in 2007 instead of decreasing leverage made a terrible mistake. They
entered a period of increasing credit constrains with low liquidity and extensive debt burden.
Moreover, their shares dropped by more than fifty percent a year later. This leads to a
conclusion that their current financial problems are most likely self-imposed.
In this study I look at the decisions made prior to the huge fall of the market in the
end of 2008. This paper aims to answer the following questions: First, do firms take into
account macroeconomic factors when they make capital structure decisions? Second, are
right and wrong (historically speaking) decisions differently affected by macroeconomic
factors? To answer these questions, I first define “right” and “wrong” decisions, divide the
2
data sample accordingly and use a Logit regression model to identify determinants of that
choice following Hovakimian et al., (2001).
2. LITERATURE REVIEW
For the last fifty years, since the proposition of the irrelevance of capital structure
(Modigliani and Miller, 1958) many studies focused on financial policy. Three main theories
were developed concerning capital structure decisions: the trade-off theory, the pecking order
theory and the market-timing hypothesis. There is also an agency cost theory, but its'
concepts are very close to the trade-off theory and thus I do not look at it separately in this
study.
2.1. Tradeoff Theory
The trade-off theory focuses on the balance between the tax benefit of debt and costs
of financial distress. Evidence supporting the trade-off theory is mixed. The trade-off theory
suggests that large and profitable firms should issue more debt to decrease their tax burden.
However, many studies find the opposite that higher profitability leads to lower target
leverage (see Fama and French, 2002). Graham (2000) estimates the cost and benefits of debt
and finds that large and profitable firms with low cost of financial distress use debt with
cautious. A classic example is Microsoft, which while being very profitable, maintained zero-
debt policy for years. Moreover, some evidence suggests that the target debt to equity ratio, if
it exists, is not important. A survey of 392 CFOs by Graham and Harvey (2002) found that
approximately half of them have a flexible leverage target or have none at all. Fama and
French (2002) show that the speed of adjustment toward target leverage is slow.
Nevertheless, some studies support the idea of the trade-off theory. Hovakimian, Opler, and
Titman (2001), Korajczyk and Levy (2003), Hovakimian (2004), and Hovakimian,
3
Hovakimian, and Tehranian (2004) find evidence supporting the role of the target capital
structure in security issuance and repurchasing.
2.2. Pecking Order Theory
The pecking order theory is proposed by Myers (1984) and Myers and Majluf (1984).
In their theoretical framework, there is no optimal capital structure. And even if there is an
optimum, the costs of deviating from it are insignificant in comparison with costs of raising
external funds. Investors are willing to buy risky securities only at a discount because of
information asymmetry between managers and outside investors. To avoid that problem,
managers prefer internal financing. When internal funds are exhausted, managers prefer
straight debt, then convertible debt, and finally equity as a measure of last resort. However,
information asymmetry is not the only possible reason for a pecking order. In 1961,
Donaldson talks about transaction costs. Another reason might be the managerial optimist
concerning company’s stock price (Heaton, 2002). Optimistic managers always think that
their stock is undervalued and thus are reluctant to issue equity. Empirical tests of the pecking
order theory have mixed results. Shyam-Sunder and Myers (1999) test pecking order theory
against the trade-off theory. Using a sample of 157 firms from 1971 to 1989, they find that
the pecking order theory has much more explanatory power. However several researchers
have questioned their sample, arguing that tests may provide misleading results when
evaluating plausible patterns of external financing. Fama and French (2002) find that
profitability is negatively related to leverage, consistent with the pecking order model. Seifert
and Gonenc (2008) test how well pecking order behavior applies to US, UK, German and
Japanese firms, using a sample of firms from 1980 to 2004. Their results are incon sistent
with the pecking order model, with the exception of Japanese firms in the period from 1980
to 1997. However, later the support for the pecking order hypothesis has diminished.
4
Korajczyk and Levy (2003) find that firms are more likely to issue equity when the
announcement effects are less negative.
2.3. Market Timing Theory
The main difference between the trade-off theory, pecking order theory and the
market timing theory is that first two theories assume semi-strong market efficiency. When
market timing theory does not require market to be efficient at all. However, market timing
hypothesis doesn’t say that market is inefficient. Windows of opportunities exist when
relative cost of equity varies over time. Market-timing theory says that managers try to time
the market, which is the critical assumption for this model. In practice, it seems that CFOs are
actively engaged in market timing in their financing decisions. In the survey by Graham and
Harvey (2001), managers admit to trying to time the market. Two-thirds of those that
considered issuing common stock agree that how much their stock is undervalued or
overvalued was an important consideration. Baker and Wurgler (2002) define market-timing
theory as that “capital structure evolves as the cumulative outcome of past attempts to time
the equity market.” (p. 23) They find evidence that external finance-weighted average of
historical market-to-book ratios is negatively related to current market leverage, which is
interpreted as a support of market timing theory. In other words low-levered firms tend to be
those that raised funds then their valuations were high. High-levered firms raised capital
when their valuations were low. Moreover, the effect of fluctuations in market valuations on
capital structure persists for at least a decade. Kayhan and Titman (2007) confirm that firm
histories strongly influence their capital structure, though they argue with Baker and Wurgler
on the persistence of the effect of market timing on capital structure over long horizons. They
find evidence that over time firms tend to balance capital structures towards target debt ratios.
That is consistent with the tradeoff theory of capital structure.
5
2.4. Macroeconomic influence
The importance of macroeconomic risk is widely recognized. It is well known that
when a firm’s operating cash flow depends on economic conditions it should adjust its
leverage in accordance with the economy’s business circle phase. Hackbarth, Miao, and
Morellec (2006) develop an approach to analyze the impact of macroeconomic factors on the
level of credit risk and dynamic capital structure choice. Some of their findings have yet to be
shown in an empirical study.
Previously, exogenous factors were rarely included in empirical studies of capital
structure choice. With the exception of Marsh (1982) who includes a forecast of aggregate
debt and equity issue as a measure of “market conditions” in estimating issue choice. Bayless
and Chaplinsky (1991) include a measure of equity market performance and the change in T-
bill interest rate in estimating issue choice.
Recently, researchers have examined the effect of macro-factors on capital structure.
Korajczyk and Levy (2003) examine domestic non-financial corporate profit growth, two-
year equity market returns, and the spread between three month commercial paper and T-
bills. Huang and Ritter (2004) find that real GDP growth increases the likelihood of debt
issuance. However, its' relation to the likelihood of equity issuance is not clear. Drobetz and
Wanzering (2006) test of macroeconomic factors on the pace of capital structure changes on
the sample of 91 Swiss firms find that in good conditions (term spread is higher, economic
prospects are good) the adjustment speed is higher. Haas and Peeters (2006) also find that
“higher GDP growth increases the adjustment speed [to target leverage] in Estonia, Lithuania
and Bulgaria.” The most recent study by Tang and Cook (2009) includes term spread, default
spread, GDP growth, and dividend yield and looks precisely at the determinants of the
adjustment speed of the capital structure towards its target.
6
3. OBJECTIVES, RESEARCH QUESTIONS AND HYPOTHESIS
This research breaks into two main parts. The first part is dedicated to capturing the
effect of different variables on leverage, selecting variables to be used in the second part, and
estimating the target leverage equation. The second part is where I answer main questions of
this paper. How different capital structure decisions (right and wrong) were affected by the
macroeconomic factors. Consequently, were the right decisions simple luck or were they
based on a good macroeconomic forecast. Previous work on the determinants of the capital
structure choices and financing decisions start by analyzing drivers behind a single
issue/repurchase choice, i.e. debt issue versus equity issue or debt retirement versus equity
repurchase (see for example, Hovakimian et al., 2001). Who look at the influence of firm-
specific and market return variables on single financing decisions. Korajczyk, Levy (2003)
extend the research by adding macroeconomic variables into the model. A year later
Hovakimian et al. look at determinants of dual debt-equity issues. Gaud et al. (2007) research
the drivers of different financing decisions on the sample of European firms, but ignore the
mixed issue/repurchase choices. The primary concern of this paper is exactly mixed
issue/repurchase decisions. One of which is equity issue and debt reduction versus debt issue
and equity repurchase. This transaction type has potentially the most dramatic effect on the
leverage ratio and theoretically should reflect managers concerns about the optimal capital
structure better than single issue/repurchase decisions. When the economy is in a good shape
costs of financial distress are lower and the adjustment speed towards an optimal leverage can
be done faster (Cook and Tang, 2009). However, during the recession distress costs
skyrocket, there are fewer resources available, default spread is higher and the adjustment is
more difficult to make. Add to that the dependency of the cash flow on the economic circle
and the increased relevance on the capital structure during a recession is unquestionable.
Furthermore, when the inevitability of the upcoming recession is getting clearer, it is better be
7
prepared before, than deal later with a whole lot of self-caused problems. Thus, adjustments
made to the capital structure prior to the burst of the bubble should have, theoretically, been
influenced by the macroeconomic forecast to the greater extent than those, made while the
worse is over and there are no signs of a storm on the horizon. It is obvious that managers
who decided to repurchase stocks, while its price was at its peak made worse decision than
those, who used the moment to raise cash through equity issue and used it to prepare the firm
for the recession by reducing its debt burden. The main hypothesis is that those who “won”
made a better job with an economic forecast than those who “lost”, and it was not a matter of
luck or a simple coincidence. Similar to debt-equity choice analysis I test how
macroeconomic factors affect the debt maturity choice. This is also important, because the
optimal capital structure is dependent not only on the D/E ratio, but also on the ratio between
short and long term debt and the maturity of latter (Philosophov L.V., Philosophov V.L.,
2005). Moreover, higher portion of a short-term debt in the capital structure increase the
probability of bankruptcy, especially during a recession.
4. METHODOLOGY
I use quarterly firm-specific data from 2009 Standard and Poor’s Compustat database
and macroeconomic data are taken from web sites of the U.S. Treasury1 and Department of
Commerce2. I exclude financial firms (SIC between 6000 – 6999), because their capital
structures are likely to be very different from those of non-financial firms. Then I require a
firm to have 3 preceding quarters of data. This measure is aimed to filter out young firms,
since both firm-specific and macroeconomic factors have less predictive power concerning
their capital structure decisions. Furthermore I exclude small firms, those who have less than
$7 million in assets. The data sample covers 11 years from 1998 to 2008, including the effect
1 http://www.federalreserve.gov/Releases/H15/data.htm 2 http://bea.gov/
8
of both the dot com bubble and the beginning of the recent crisis. After trimming the data set
from the presence of significant outliers by excluding the top and the bottom 0.5% of the
firm-specific variables (Total Assets, Book Value, Current Liabilities, Total Debt, Long-
Term-Debt, Stock Return, Sales, Tax Expense, Market-to-Book, Return on Assets) the data
sample comprises 130,098 firm-quarter observations for 8,578 firms. I require SGA expenses
to be positive, since they are used as normalizing variables. Summary statistics are given in
the Table 1. Issue/repurchase events are defined using according data points from Compustat
database and are required to be bigger than 5% of Assets to be defined as an event.
Table 1 Summary Statistics Variable N Mean Std Dev Minimum Maximum
Leverage 129772 0.2598726 0.226126 0 1 SIZE 129772 4.062727 2.07038 -6.90776 9.540964 TANG 129772 0.286808 0.240334 0 0.993862 ROA 129772 0.00636 0.06024 -0.53029 0.156218 dRoA 129772 0.344982 0.475364 0 1 CASHr 129772 0.147873 0.191128 -0.07896 0.998043 dPEdil 129772 0.254292 0.435464 0 1 dPBdil 129772 0.187233 0.390100 0 1 MTB 129772 1.830983 1.311743 0.46702 12.27788 RET 129772 0.01946 0.293348 -0.71518 2.188438 RDr 129772 0.003786 0.013544 0 0.526215 RDD 129772 0.151866 0.358893 0 1 SE 129772 0.077605 0.068998 0 1.588178 Risk 129772 0.133424 2.819712 4.89E-05 249.4379 Ind_Lev 129772 0.260361 0.04001 0.210559 0.368098 Tspread 129772 1.962124 1.435883 -0.18667 4.28 DefaultS2 129772 1.89576 0.576866 1.196667 5.006667 Div_Yield 129772 0.004133 0.000942 0.002689 0.007564
4.1 Target leverage estimation
The target leverage is the debt ratio that firms would choose in the absence of
informational asymmetries between managers and shareholders, transaction costs, or other
adjustment costs. Even though existing theories explaining firms’ financing decisions
(pecking order, trade off, market timing) do not unanimously support the idea that firms
operate around target leverage, there is evidence that target leverage do exist (Hovakimian et
9
al. (2001), Hovakimian et al. (2004)3, Graham and Harvey (2001)4. Thus, I include it into
financing choice regressions. Korajczyk et al. (2003) assume that firm’s actual leverage
equals its target leverage plus measurement error that is orthogonal to the explanatory
variables. Cook and Tang (2009) argue that there are methodological problems when using
linear models for fractional data, thus, use quasi-maximum-likelihood (QMLE) estimation
model to compute the fitted value of target leverage equation. They specify the target
leverage as a function of prior period macro variables and firm-specific variables. Gaud et al.
(2007), consistent with Hovakimian et al. (2001) use Tobit regression to determine the target
leverage ratio. Following them, I use the Tobit regression model with double censoring at 0
and 1, since the leverage ratio is naturally bounded between zero and one. The following
equation describes the model:
��� ��,�� �� ���,� � ���,� � �� � ��,� (1)
where
��� ��� The Target Leverage for the firm i in the year t
�� ���,� Macroeconomic explanatory variables
��,� Firm-specific explanatory variables
�� Vector of time dummy variables
��,� Stochastic error term
This will also allow me to select significant determinants of leverage for the
financing decisions Logit regressions. In particular, tested variables are as follows:
3 They found evidence that firms tend to operate in line with the static trade-off theory, offsetting previous earnings-driven decisions towards the target capital structure. And that the target D/E ratio has different importance for different financing decisions.
4 Roundabout 80% of questioned managers in their sample admit having a “strict” target or a set range for the D/E ratio.
10
4.1.1 Firm-specific target leverage variables
SIZE - as a proxy of size I am using the natural logarithm of sales. This measure was
previously used by Booth et al. (2001) and Gaud et al. (2007). However, many other
researchers prefer using the natural log of total assets as a proxy for size. In this particular
case log of total assets would create bias, because it would be highly correlated with the next
coefficient. TANG - to hold for the effect of collateral I use the ratio of Tangible Assets /
Total Assets, where tangible assets are defined as Net Property Plant and Equipment,
following Gaud et al. (2007). ROA is defined as EBITDA / Total Assets and it serves as a
measurement of profitability and to some extent is a proxy for firm’s internal financing
capacity (Miguel & Pindado, 2001, Gaud et al. 2007). CASH – is intended to hold for the
effect of accumulated financial slack. Cash ratio is defined as Cash and equivalents / Total
Assets (Gaud et al., 2007). MTB - Market-to-book ratio is used as a common measure of
growth opportunities. (Booth et al., 2001, Gaud et al., 2007). It is defined as a quotient of the
sum of total assets and the market value of equity minus book value of equity, divided by
total assets. [(Total Assets + Price * Shares Out. – Book Value of Equity) / Total Assets]
RET – is defined as the ratio of the quarterly change in market value of equity to the market
value of equity in the previous quarter. This variable is aimed to control for the stock price
effects. ATA - is the ratio of Depreciation and Amortization in Total Assets, used as an
explanatory variable of non-debt tax shield. Risk – according to the trade-off theory higher
cost of financial distress should lead to lower target leverage. Higher earnings volatility
increases the probability of bankruptcy, thus, increasing the distress cost. So to control for the
effect of risk I use the standard deviation of the annual difference of EBIT / Total Assets
(Delcoure, 2007) over the preceding 5 years. (σ(∆(EBIT / Total Assets))). Cook and Tang
(2009) control for firm uniqueness by introducing three variables. RD, which is R&D
Expense normalized (divided) by Book Assets. RDD, dummy variable, equals 1 if a firm
11
reports R&D expense and 0 otherwise. SE, which equals Sales Expenses divided by Total
Sales. Firms with high R&D and high sales expense are more likely to have unique assets and
consequently higher costs of financial distress (Hovakimian et al., 2004). For that reason such
firms might want to protect themselves with lower leverage ratios.
4.1.2 Macroeconomic target variables
Term spread – defined as a difference between 20 year Gov bond and three-month-T-
bill. Although some researchers use 3 month T-bill rate as a macroeconomic variable (Drobez
and Wanzenried, 2006), some argue (Estrella, Hardouvelis, 1991) that the slope of the yield
curve has more predictive power. Cook and Tang, (2009) lag this variable by one year,
because it has been known as a strong predictor of a good economy (Estrella, Mishkin, 1998).
Default spread – Following Cook and Tang (2009) and Korajczyk and Levy (2003), and
Fama and French (1989). Default Spread is defined as the difference between the average
yield of Baa rated and Aaa rated corporate bond. Each rated by Moody’s and with maturity of
20-25 years. Fama and French (1989) show that this factor is higher during recessions and
lower during expansions. In addition to that I test another variation of the Default spread,
defined as the difference between Baa rated corporate bond and 20 year T-bill. This
interpretation should be less biased towards the political influence on the highest rating.
Because it is the fact that many Aaa rated bonds didn’t actually deserve this rating, what was
proven right during the 2008-2009. GDP growth – Annual percent change in GDP in constant
2000 prices. Div Yield – Consistent with Drobez and Wanzenried, 2006 I take the total
dividend paid on value-weighted NYSE/AMEX/NASDAQ portfolio over a year t-1 divided
by the current value of the portfolio (time t). As Drobez and Wanzenried, 2006 indicate
because dividends tend to be sticky, high dividend yield means portfolio value is low and
thus it is a downturn.
12
4.2 Debt-Equity choice regressions
In the second stage, to determine the drivers of the particular financing choice, I use
the Logit regression model:
����,� � 1� � ������� ,!"��� ,!#$%&'()* ,!$+, ,!
-.������� ,!"��� ,!#$%&'()* ,!$+, ,!� �� � /�,� (2)
����,� � 1� The probability of a firm i, operating in time t, choosing one financing
option rather than another
��� ��,�0 ����,� The deviation from the target leverage
�� ���,� Macroeconomic explanatory variables
��,� Firm-specific explanatory variables
�� Vector of time dummy variables
/�,� Stochastic error term
Following Korajczyk, Levy (2003) I define a firm as issuing (repurchasing) equity
(debt) when net equity (debt) issued (repurchased) for cash in the particular quarter divided
by the book value of assets in the previous quarter exceed 5%.
Apart from some variables discussed earlier I include specific potential determinants
of the financing choice. Hovakimian et al. (2001) argue that managers are involved into the
calculation of accounting numbers and are affected by them. For example, managers may be
evaluated partly based on accounting numbers. Thus, accounting figures may play significant
role in debt-equity decisions. Because, if a firm has low stock price relative to its earnings
(P/E) or book value (P/B), issuance of equity will further decrease those ratios. To account
for the effect mentioned above, I, following Hovakimian et al. (2001), include two dummy
variables. dPEdil is a dummy variable equals 1 when after-tax cost of debt exceeds firms’ E/P
ratio, 0 otherwise. [E/P > rd(1 - Tc), E/P = Net Profit / Market Value of Equity] This variable
13
reflects if the equity issue will dilute firms’ earnings per share more than the debt issue. The
second dummy variable dPBdil equals 1 when MTB < 1, and zero otherwise. It, similarly to
the previous variable shows if the equity issue will dilute firms’ book value per share. dRoA
equals 1 when ROA < 0, and zero otherwise, controlling for losses, since assets are required
to be positive. AdjRET is a spread between firm year return and country return (market).
TLevDif – is the difference between the target leverage, estimated in the first part and the
actual leverage at time t. IndLev – is the mean leverage in the industry. The industry is
defined as the first for numbers in the SIC code. ObsOPsize is the ratio of absolute value of
net amount of the transaction to TA at the beginning of the year5.
5Though, it is excluded for regressions related to External versus Internal financing choice, due to possible bias.
14
5. RESULTS AND DISCUSSION
5.1. Target leverage estimation
In order to estimate the target leverage a company would chose in absence of
stockholders influence I use Tobit regression model. I regress Leverage ratio, a company has
at the quarter t, on a set of firm-specific and macroeconomic variables. Hovakimian et al.
(2004) use one-year-lagged firm-specific variables to determine firms target leverage, but I
have found that there is virtually no difference in coefficients between regressions with
lagged and regressions with actual values. Nevertheless, the overall fit of the model
drastically reduces when lagged figures are used. (See Appendices: Table , Table ). ATA
variable, the ratio of depreciation and amortization in total assets, has been excluded from the
regression because it is not significant, not even at 10% level. Gaud et al. (2007) studying
Europe got the same result for ATA variable across almost all countries. Results for the
Tobit leverage estimator are presented in the Table 2.
Table 2 Determinants of target leverage Parameter Estimate Parameter Estimate
SIZE 0.008126*** RDD -0.039149***
TANG 0.123396*** SE -0.466985***
ROA -0.873225*** GDP Growth 0.817312***
CASHr -0.573947*** Term Spread -0.002095***
MTB -0.001553*** Default Spread 0.010011***
RET -0.007627*** Dividend Yield -14.13696***
Risk 0.000562* _Sigma 0.21963***
RDr 1.059279*** Log Likelihood -7901
***, **, * indicates significance at 1%, 5% and 10% level accordingly.
SIZE variable enters regression with a positive sign, which supports the hypothesis
that larger companies have higher debt ratios. That is because they have more stable cash
flows, which reduces costs of financial distress. Moreover, bigger companies have a higher
chance of exhausting the debt tax shield. This is consistent with a trade-off theory and prior
studies i.e. Gaud et al., (2007), Hovakimian et al. (2004).
15
TANG also enters regression with a positive sign, in line with a hypothesis that
tangible assets acts as collateral. Potentially, in case of default, tangible assets have higher
residual values than other assets. The more there tangible assets the higher is the firm’s debt
capacity and debt ratios. Because debt holders have a right to request the selling of assets
they will most likely prefer tangible assets as collateral.
ROA and CASHr both have a negative sign in the regression. On the one hand, it is
not consistent with a trade-off theory, because higher operating margins suggests more stable
cash flows, increasing the chances to fully exhaust tax shield and decreases costs of financial
distress. Cash, under the trade-off theory, is viewed as a negative debt, which together with
higher operation margins should increase debt ratios. On the other hand, signs on those
variables are consistent with pecking-order theory. Higher profitability and financial slack
imply bigger capacity of internal financing, which is the number one choice under the
pecking-order hypothesis. Thus, negative signs on CASHr and ROA support the pecking-
order hypothesis. Moreover, Graham and Campbell, 2002 in their survey found that ~58%
from 392 questioned CFOs find insufficient internal funds as a main factor to issue more
debt.
The negative sign on Market to Book ratio suggests that higher MTB value
diminishes the residual value of assets, acting as collateral, thus increasing the costs of
financial distress. The higher is MTB ratio the lower the leverage should be. This is in line
with the trade-off theory.
Another market-performance variable is RET. It has the same, negative, sign as a
Market to Book ratio. The possible explanation lies in the Market Timing theory, which
implies that managers try to time markets, issuing equity when they think their stock is
overvalued (Baker and Wurgler, 2002). This is consistent with the pecking-order theory,
which implies that the variation in the level of asymmetry of information between managers
16
and stakeholders lead to negative underinvestment cots. Thus, if managers actively try to time
the market the stock price should negatively impact leverage ratios.
Risk variable enter the regression with a positive sign, which is counterintuitive.
According to trade-off theory higher earnings volatility increases the possibility of a default
and consequently increases distress costs. This in turn should reduce leverage ratios.
Compared to other variables in the model significance of the Risk variable is low, it is
significant only at 10% level. This variable is excluded from the financing choice regressions.
RDr, RDD and SE variables proxy for the uniqueness of company’s assets. RDD is a
dummy variable equals one if a company reports R&D expenses and zero otherwise. RDr and
SE are ratios of R&D and SG&A expenses accordingly in total assets. RDD and SE
variables both enter the regression with a negative sign, which is consistent with Hovakimian
et al., (2004), who finds that firms reporting R&D expense and having high SG&A expenses
are more likely to have unique assets, which are harder to sell in case of default. This
increases the cost of distress and in turn according to the trade-off model reduces the target
leverage ratios. On the other hand RDr enter the regression with the negative sign. This is
counterintuitive according to the trade-off model, because the higher is R&D expense the
higher is the distress cost. On the other hand high R&D costs mean that a firm is more likely
to have unstable cash flows and requires external financing to fund its costly projects. The
second reason outweighs the potential downside of debt financing.
5.1.1 Macroeconomic factors
There are four macroeconomic factors included in the Tobit target leverage estimation
model. In the current section I estimate the target leverage a company would have in perfect
world in absence of any information asymmetries and stakeholders influence. For that reason
macroeconomic variables in target leverage regressions are taken at the time t. More detailed
discussion concerning lagging macro variables follows in the next section.
17
Term Spread is calculated as a difference between long and short term government
bond yields. It enters the regression with a negative sign, and taking into account that high
Term Spread is known to be strong predictor of a good economy (Estrella & Mishkin, 1998) I
can conclude that it is another consequence of market timing. When prospects of economy
are good, stock prices are generally raising and manage are more likely to issue equity. On
the other hand when prospects of economy become gloomy it could be reasonable to issue
debt, if needed, at a still lower rate than is possible during a recession. But those assumptions
are yet to be tested in Logit financing choice regressions.
Default Spread is the proxy for the business cycle; it is higher during recessions and
lower during expansions. Default Spread is calculated as Baa Moody’s rated corporate long-
term bond’s yield minus government twenty year bond’s yield. This measure of Default
Spread proved to be more significant than the difference between Baa and Aaa rated bond’s
yields. Moreover, the argument for using it is that Aaa rated corporate bonds in most cases
were overrated and hardly represented the most financially stable companies. It enters
regression with a positive sign, which can be explained with the Market Timing theory.
During recessions the stock value is low and managers are reluctant to issue equity, because
they think that their stock is undervalued. The opposite is true for expansions.
Dividend Yield enter regression with a negative sign. It is another indicator of the
state of economy. Dividend Yield is calculated as dividends paid on S&P 500 portfolio over
the time t-1 divided by its current value at time t. Due to the fact that dividends tend to be
sticky, higher Dividend Yield variable indicates a recession and decreasing portfolio value.
The sign on this variable is consistent with the Market Timing model.
GDP Growth enters regression with a positive sign. Growing economy creates
conditions for more stable cash flows and lower distress costs, greater growth opportunities
and higher investment needs. The sign on this variable is consistent with the Trade-off theory.
18
5.1.2 Lag vs. Lead
Since the main purpose of this study is to capture the effect of macroeconomic
variables on capital structure decisions, special attention is paid to them. Previous studies in
this area mainly used lagged macroeconomic variables to describe the target leverage. The
lag varied from 3 quarters (Korajczyk & Levy, 2003) to one year (Cook & Tang, 2009).
Because in this paper I use quarterly data, I tested how well macro variables, lagged one, two
and three quarters fit into the model. Assumption behind lagging macro variables is that
macroeconomic data is not reported immediately and that could create a lagging effect on
financing decisions. On the other hand, macroeconomic statistics doesn’t predict the future,
instead it simply describes the current situation in the economy and probably sets a short term
trend. One cannot simply take a historic dataset and predict the future. There are so many
different factors affecting the real economy that we can assume at least a semi-strong
efficiency. This leads to an idea that it could be more beneficial to use leading macro
variables, not lagged. The assumption behind this idea is that leading t+3 macroeconomic
factors would be a perfect prognosis at time t. Budgeting decisions, by their nature, should be
made taking in consideration economic forecasts, because their effect lasts for years.
Moreover, Cook & Tang (2009) show that the rebalancing speed reduces during recessions
and increases during booming economic conditions. That is why I test both three quarters
lagged and three quarters leading macroeconomic variables. Results are given in Appendices.
Table 4 Macro Variable 3 Quarters Lagged" presents results for models with lagged macro
variables. What is notable about those regressions in that significance of Default Spread
diminishes when lagging it farther back. The return variable becomes insignificant is macro
variables are lagged more than one quarter back. The situation with leading variables is
different. Table 3 Macro Variable 3 Quarters Forward" presents results for models with
leading macro variables. Here all variables remain significant at least at 5% level no matter
19
whether it is t+1 or t+3 variables. Though, the significance of Default Spread increases form
5% level at t+1 to 1% level at t+2 and t+3. Significance of Term Spread decreases when leading
farther in the future. This is happening because Term Spread, according to Estrella &
Mishkin, 1998 already has some predictive power. Overall fit of models including leading
variables is higher than including lagged variables. And the overall fit of leading variable
models increases when going farther in the future. This proves the assumption that
macroeconomic forecast has a significant effect on target leverage ratio. And that such
forecast is more likely to be made for a medium-term period than a short-term one.
5.2 Financing Choice Regressions
There are eight financing transaction I look into. Three pure financing transactions:
debt issue, equity issue and issue of both, three pure payout transactions: debt retirement,
share repurchase and both at the same time.
Table 3 Descriptive statistics for debt-equity choice
Transaction Type /
Variable
Debt Issue
Equity Issue
Debt and Equity Issue
Debt Reduction
Share Repurchase
Debt Reduction and Equity Repurchase
Debt Issue and Equity Repurchase
Equity Issue and
Debt Reduction
No Transaction
SIZE 3.819 3.815 4.606 3.679 4.793 4.366 5.582 4.564 3.348
TANG 0.387 0.199 0.355 0.333 0.212 0.304 0.336 0.290 0.278
ROA -0.020 -0.006 -0.006 -0.014 0.015 0.003 0.011 -0.001 -0.014
CASHr 0.061 0.283 0.081 0.090 0.232 0.117 0.056 0.131 0.164
MTB 1.470 2.471 1.844 1.400 2.183 1.447 1.700 1.961 1.734
RET -0.019 0.065 0.056 -0.007 -0.002 -0.022 -0.005 0.071 -0.030
RDr 0.002 0.009 0.003 0.002 0.006 0.001 0.001 0.004 0.003
SE 0.060 0.090 0.062 0.074 0.083 0.075 0.059 0.075 0.083
TLevDif 0.066 -0.027 0.007 0.065 -0.048 -0.016 0.006 0.006 0.005
RDD 7% 30% 12% 9% 24% 8% 10% 17% 10%
dPEdil 67% 79% 73% 69% 90% 81% 91% 72% 74%
dPBdil 26% 7% 11% 32% 12% 31% 15% 10% 23%
dRoA 48% 34% 33% 45% 17% 26% 14% 28% 41%
T. Spread 1.887 2.057 1.964 2.107 1.966 1.956 1.823 2.065 1.647
Default Spread
1.977 1.828 1.880 1.987 1.952 2.074 1.933 1.849 1.852
Div. Yield 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004
GDP Growth
0.006 0.006 0.006 0.006 0.005 0.005 0.005 0.006 0.008
20
And two mixed financing-payout transaction types: debt issue and equity repurchase, equity
issue and debt retirement. I do not include dividend payout because it is not the main purpose
of this study.
Table 3 presents descriptive statistics for financing choices mentioned above. Bigger
companies tend to be those who are issuing debt and simultaneously repurchasing equity. On
average, companies with dual transactions, such as issuing both debt and equity or
repurchasing debt and issuing equity, tend to be much bigger than companies only debt or
equity and companies with no transactions.
Debt issuers have much higher tangible assets ratio than those issuing equity. This is
perfectly in line with the trade-off theory. Moreover, those who decide to retire debt have
much more tangible assets than who repurchase equity. This supports the assumption that
bigger companies and companies with more tangible assets ratio have more debt capacity,
due to more stable cash flows and, thus, lower distress costs.
Companies repurchasing equity tend to be more profitable than companies with no
transactions or issuing debt or equity. Pure debt issuers tend to be the less profitable. 48% of
debt issuers have losses versus around 33% for equity issuers and double issuers. There is a
high percentage of troubled companies among equity issuers too, but few of them have MTB
less than 1. Firms that reduce debt have the lowest MTB, relatively low RET and the second
highest proportion of unprofitable firms – 45%. This statistics is similar to those reported by
Gaud et al. (2007) for companies in European Union. Those, who repurchase equity, tend to
be more profitable than those, who retire debt. Moreover, those who issue or repurchase
N 2699 17119 2328 29676 7570 2260 6839 34719 26562
This table represents the mean values of variables used in debt-equity choice Logit regressions. The data are from Compustat database and the sample contains all firms operated during the period from 1998 to 2008. SIZE is the natural logarithm of sales. TANG is the ration of Property Plant & Equipment in total assets.ROA is NI/Assets. CASHr is the ratio of cash and equivalents in total assets. MTB is (TA + (Shares*Price) – (TA –TL)) / TA. RET is company’s stock return adjusted for stock splits. RDr is the ratio of R&D expenses in total assets. SE is the ration of SG&A expenses I total assets. TLevDif is the distance of the actual leverage from the target leverage ratio calculated above. RDD is a dummy variable equals one of a firm reports R&D expense and zero otherwise. dROA is a dummy variable equals one if ROA < 1 and zero otherwise. pPEdil is a dummy variable equals one if equity issue will dilute EPS more than debt issue and zero otherwise. dPBdil is a dummy variable equals one if MTB < 1 and zero otherwise.
21
equity tend to have more financial slack, than those who issue or repurchase debt or do
nothing.
Overall, companies issuing or repurchasing equity tend to have higher MTB ratios
than companies issuing or repurchasing debt. Stock returns of equity issuers are significantly
higher than of debt issuers, who tend to have negative stock returns. Companies involved in
all observed payout transactions tend to have low or negative stock return. That strongly
supports the Market Timing theory.
Companies repurchasing equity are more likely to be concerned with EPS dilution.
Companies repurchasing equity and issuing debt or just repurchasing equity have a 90%
chance to have equity dilution dummy equals 1.
Equity issuers tend to be under levered and debt issuers tend to be over levered, which
doesn’t support the hypothesis of rebalancing capital structure towards its target. However,
with payout transactions the situation is the opposite. Over levered firms tend to retire debt,
whether under levered firms tend to repurchase equity. Companies repurchasing both debt
and equity tend to be under levered, whether those with mixed transactions and no financing
activity at all have their leverage close to its target.
Companies reporting R&D expenses are more likely to issue equity instead of debt
and have higher R&D expense than those issuing debt. Notably, many of them decide to
repurchase their stock. Same is true for SG&A expense. Though, the mean SG&A expense is
almost even among companies who made different financing decisions. Companies tend to
issue equity and repurchase debt in better economic conditions and do nothing in worse
economic situation. This alone is intuitive. Further investigation into this relationship is
conducted in the next section.
22
5.2.1 Pure issue
ObtSize variable, which holds for the transaction size, is excluded from regressions
with passive strategy. Otherwise it would create serious bias.
Table 4 Pure issue choice regression results Ind. Lev. TLevDif ROA CASHr MTB AdjRET
Debt issue vs. Equity issue
-1.528** -2.775*** 6.77*** 7.683*** 0.434*** 0.056 0.636 0.118 0.774 0.278 0.038 0.091
Debt issue vs. Debt and Equity issue
-0.412 -1.472*** 7.755*** 1.162*** 0.251*** 0.526***
0.558 0.111 0.728 0.252 0.034 0.085
Equity issue vs. Debt and Equity issue
1.998*** 2.328*** -0.067*** -7.501*** -0.153*** 0.294***
0.341 0.076 0.416 0.119 0.013 0.053
Debt issue vs. No transaction
-0.04 -1.285*** 3.912*** 5.445*** 0.049* -0.452*** 0.562 0.09 0.613 0.246 0.028 0.068
Equity issue vs. No transaction
2.934*** 1.553*** -4.317*** -1.437*** -0.163*** -0. 641*** 0.285 0.057 0.256 0.056 0.008 0.039
RDr RDD SE dPEdil dPBdil ObtSize Debt issue vs. Equity issue
-10.827*** 0.778*** 6.713*** 0.501*** -0.824*** -3.081*** 2.574 0.118 0.5 0.069 0.072 0.159
Debt issue vs. Debt and Equity issue
-6.815** 0.5*** 2.404*** 0.548*** -0.645*** 0.363** * 3.338 0.118 0.452 0.063 0.061 0.091
Equity issue vs. Debt and Equity issue
0.124 -0.128** -4.613*** -0.233*** 0.064 3.944***
1.661 0.05 0.237 0.039 0.048 0.104
Debt issue vs. No transaction
-11.606*** 0.139 5.583*** 0.226*** -0.035 3.333 0.128 0.416 0.058 0.056
Equity issue vs. No transaction
0.084 -0.698*** -0.354** -0.243*** 1.002*** 1.033 0.044 0.165 0.029 0.036
Term Spread Default Spread Div. Yield GDP Growth Log Likelihood Debt issue vs. Equity issue
0.051*** -0.346*** 188.8*** -7.929 5653
0.019 0.054 27.913 5.332
Debt issue vs. Debt and Equity issue
0.053*** -0.108** 116*** -9.128* 1241
0.017 0.044 24.993 4.726
Equity issue vs. Debt and Equity issue
0.003 0.139*** -101.5*** -18.005*** 16421
0.009 0.025 15.044 2.943
Debt issue vs. No transaction
-0.134*** -0.271*** -103.6*** 35.035*** 1859
0.019 0.053 28.049 5.692
Equity issue vs. No transaction
-0.218*** 0.087*** -356.3*** 45.851*** 11797
0.008 0.027 13.788 2.623
***, **. * indicates significance at 1%, 5% and 10% respectively. Standard Errors are given in italic.
23
Industry leverage and the distance from the target leverage in all issue choice
regressions have signs suggesting that target leverage matters, and firms actually try to adjust
to it, issuing more equity if a firm is over levered or issuing more debt if it is under levered.
Though, industry leverage has been found insignificant for dual issue regression and debt
issue versus no transaction regression. Difference from the target leverage is highly
significant in all issue choice regressions. These findings are in line with what Gaud et al.
(2007) found across the EU.
The operating performance variables both tell the same story. ROA and CASH enter
‘debt vs. equity’ regression with positive sign, which is I line with findings of Gaud et al.
(2007), but contradicts findings of Hovakimian et al. (2004), who do not include Cash and
report a negative effect of operating performance on the probability of the issuance of debt
instead of equity. Nonetheless, even excluding Cash from my regressions I find the same sign
on operating performance across all issue choice regressions. Notably, those variables in the
target leverage regression have an opposite sign. It is consistent with findings of Korajczyk et
al. (2003). This effect has at least two explanations. Firstly, for highly profitable firms debt
acts as a disciplinary device. And issuing more debt firms limit their future financial slack,
since it is a source of conflict between managers and shareholders. Moreover, it is consistent
with a short run pecking order model where internal funds are preferred over external
financing. The negative sign on CASH variable in ‘equity vs. no transaction’ regression
supports that idea. Secondly, this effect is consistent with the long run trade-off theory, where
highly profitable firms, accessing public markets tend to issue debt.
Market performance variables AdjRET and MTB show different signs compared to
what Gaud et al. (2007) found in EU. Though, my results are consistent across different
regressions and do not change if different measures of return and/or market-to-book ratio are
introduced. Observed results suggest that high market performance increases the likelihood of
24
debt issuance and poor market performance increases the likelihood of equity issuance. This
doesn’t support the agency theory. On the other hand it supports the long run trade-off theory,
that profitable firms, and you can expect such firms to have their shares doing well, are more
likely to issue debt instead of more equity. AdjRET itself shows support for both market
timing and pecking order theory. It enters ‘debt issue vs. debt and equity issue’ regression
with a positive sign and ‘equity issue vs. no transaction’ with a negative sign, which clearly
supports the pecking order theory. On the other hand, it enters ‘equity vs. debt and equity
issue’ regression with a positive sign, which supports the idea of market timing. When there
is a choice between mixed issue and a pure equity issue high share price increases the
likelihood of going with a pure equity issue.
The EPS dilution variable dPEdil is significant across all issue regressions and has
signs consistent with those found in previous studies. In all cases the sign on dilution dummy
suggests that managers try to avoid it and supports the idea that EPS one of primary concerns
for CFOs. This found support in the survey by Graham and Harvey (2001) when
approximately 70% of surveyed CFOs admitted that EPS dilution is the key factor in capital
budgeting decisions.
The issue size variable suggests that firms tend to stick with one financing instrument
in case of big financing needs. This is opposite to what Gaud et al., 2007 found in EU. This
can be explained with differences in accessibility of borrowed capital between EU and US
and lower borrowing costs in the latter. However, choosing from debt issue and equity issue
bigger financing requirements favor equity issue. This result is consistent with findings in
previous studies including those done in EU.
Three proxies for the uniqueness of assets complement one another. On the one hand,
a firm reporting R&D expenses have costly projects and require external financing, thus is
likely to issue debt or both debt and equity if possible. On the other hand the higher are R&D
25
expenses the more unique assets the firm has, which increases the cost of financial distress.
Therefore, increases the likelihood of equity issuance instead debt issuance. Firms with high
SG&A expenses are likely to have seasonality in sales revenues and require debt financing,
due to frequent financing needs such firms prefer debt financing over equity financing.
Signs on the macroeconomic variables support the idea that the future state of
economy is among of key factors affecting capital structure decisions. Sings on Default
spread suggest that when the economy is expected to decline e.g. increasing default spread
companies are more likely to issue equity than issue debt. The effect is consistent even in
‘equity vs. debt and equity’ regression. The same is true for the GDP growth. If the economy
is going to shrink or expand at a slower pace, companies are more likely to issue equity to
prepare for the downturn. Term Spread has the same effect on debt-equity choice. While big
Term Spread is a sign of a recovering economy and prospects of growth, low Term Spread
suggests worsening of the economy. Companies tend to issue more debt if they anticipate
economy to improve in the near future, and tend to issue equity is the opposite is true.
Nevertheless, coefficients on ‘debt vs. no transaction’ and ‘equity vs. no transaction’ are not
consistent and contradict one another. This can be explained by the nature of the choice.
More likely the choice to do nothing has significantly different determinants than choice to
issue capital. This creates certain bias when comparing the effect of macro factors, due to the
fact that in different cases the ‘no transaction’ choice was determined by many different
factors and is not directly linked to macroeconomic conditions. Overall, macroeconomic
factors are found to be highly significant in all issue choice regressions.
26
5.2.2 Pure repurchase
Table 5 Pure repurchase choice regression results Ind. Lev. TLevDif ROA CASHr MTB AdjRET
Debt retirement vs. Equity repurchase
-4.398*** -4.691*** 12.857*** 4.971*** 0.335*** -0.61***
0.449 0.113 0.551 0.106 0.017 0.073
Equity repurchase vs. No transaction
4.09*** 2.084*** -
12.546*** -1.406*** -0.062*** -0.061
0.409 0.091 0.445 0.077 0.012 0.06
Debt retirement vs. No transaction
-0.0269 -1.4703*** -
0.6788*** 2.6421*** 0.1528*** -0.5335***
0.2458 0.0443 0.2615 0.0664 0.0113 0.0337
RDr RDD SE dPEdil dPBdil ObtSize
Debt retirement vs. Equity repurchase
-5.85*** 0.656*** 1.776*** 1.849*** -0.671*** -3.032***
2.072 0.069 0.267 0.059 0.048 0.205
Equity repurchase vs. No transaction
2.652 -0.618*** -0.697*** -0.657*** 0.461***
1.786 0.064 0.231 0.048 0.046
Debt retirement vs. No transaction
-2.5849** -0.0984** 0.8744*** 0.4485*** -0.3578***
1.2806 0.0496 0.1456 0.0253 0.0239
Term Spread Default Spread Div. Yield GDP Growth
Log Likelihood
Debt retirement vs. Equity repurchase
-0.042*** 0.105*** -1.554 2.161 11418
0.012 0.034 19.264 3.596
Equity repurchase vs. No transaction
-0.166*** -0.113*** -209.1*** 40.706*** 5210
0.012 0.035 19.401 3.764
Debt retirement vs.
No transaction -0.2229*** -0.3362*** -249.6*** 37.8048***
8371 0.00759 0.0248 12.1556 2.1677
***, **. * indicates significance at 1%, 5% and 10% respectively. Standard Errors are given in italic.
Repurchase regressions support evidence from issue choice regressions. Even though,
Dividend Yield and GDP growth are not significant for ‘debt retirement vs. equity
repurchase’ regressions, other variables compliment the results on issues in showing a move
toward debt financing during downturns and equity financing during an upturn in the
economy.
The transaction size positively affects the likelihood of equity repurchase, which
means equity repurchases tend to be bigger than debt reductions.
27
5.2.3 Mixed transactions
I exclude dilution dummies from the next set of regressions, due to their mixed nature,
dilution dummies would most likely create bias.
Table 6 Mixed transaction regression results Ind. Lev. TLevDif ROA CASHr MTB
Equity Issue and Debt retirement vs. Debt issue and
Equity repurchase
-0.669* 0.148* 4.192*** -4.03*** 0.073***
0.364 0.08 0.631 0.186 0.018
Equity Issue and Debt retirement vs. No transaction
0.68*** 0.061 -5.123*** 1.882*** -0.11***
0.233 0.046 0.246 0.058 0.009
Debt issue and Equity repurchase vs. No transaction
2.267*** 0.131* -8.941*** 6.73*** -0.104*** 0.404 0.076 0.605 0.195 0.018
AdjRET RDr RDD SE Equity Issue and Debt
retirement vs. Debt issue and Equity repurchase
-1.284*** -8.796** -0.018 -2.376***
0.07 3.683 0.071 0.292
Equity Issue and Debt retirement vs. No transaction
-1.034*** 0.221 -0.539*** 1.354*** 0.035 1.06 0.041 0.146
Debt issue and Equity repurchase vs. No transaction
-0.243*** 10.551*** -0.54*** 3.515*** 0.061 3.727 0.085 0.291
Term Spread
Default Spread
Div. Yield
GDP Growth
Log Likelihood
Equity Issue and Debt retirement vs. Debt issue and
Equity repurchase -0.099*** 0.368*** -130.1*** 4.134 3080
0.011 0.027 15.794 3.134
Equity Issue and Debt retirement vs. No transaction
-0.222*** -0.104*** -365.6*** 46.306*** 9828 0.007 0.023 11.464 2.116
Debt issue and Equity repurchase vs. No transaction
-0.114*** -0.321*** -228.5*** 42.059*** 5496 0.013 0.037 19.49 3.956
***, **. * indicates significance at 1%, 5% and 10% respectively. Standard Errors are given in italic.
Mixed choice regressions are the most difficult to interpret, though they shed the most
light on drivers of financing choice. Signs on the target leverage variable and industry
leverage variable are predicted and support the idea of rebalancing towards the target capital
structure.
28
In the ‘Equity Issue and Debt retirement vs. Debt issue and Equity repurchase’
positive sign on ROA suggests the idea that profitable firms are more likely to decide to issue
equity and repurchase debt. Though, it is different from what I saw in issue choice
regressions, where higher profitability increased the chance of issuing debt, as a constraining
factor for managers. This may be due to the fact that high profitability is likely to cause a
higher stock price and thus making share repurchase less attractive. The sign on CASH
variable support the pecking order theory, more slack the less likely equity is being issued.
More importantly, signs on CASH and adjRET suggest that shareholders have a strong
influence on management team and use share repurchase mechanism as a payout tool,
negotiating share repurchases while the stock price is high and the company has some free
funds at hand. On the other hand, the positive sign on MTB indicates the presence of market
timing strategy. Managers reluctant to issue more equity while they think the company is
undervalued, and vice versa. RDD variable is not significant. However, RDr is significant
and suggests that the higher are R&D expenses the more is the probability to issue debt and
repurchase equity. This is not surprising, because in previous regressions I found a strong
support to the idea that high R&D expenses lead to low debt ratios and increase the likelihood
of equity issue. Thus, such companies, naturally, do not have enough debt to consider its
retirement. Even though, GDP growth is insignificant, other macro variables suggest that
macroeconomic conditions are strongly significant for a mixed issue/repurchase choice and
when approaching a downturn companies are more likely to make a “right” decision to Issue
Equity and retire Debt than a “wrong” decision to issue Debt and repurchase equity. This
answers the main question of this paper: were "good" decisions based on a solid
macroeconomic forecast or just a pure luck.
29
5.2.4 Debt maturity choice
In this part I introduce Logit regression aimed to identify the determinants of debt
maturity choice. The issue event is identified if the book value of the long/short term debt in
the quarter t exceeds its lagged value by more than 5%. Since ‘debt issues versus no
transaction’ regressions were introduced earlier, in this part I estimate only the long maturity
debt choice over short maturity debt choice regression. Because debt financing is tend to be
used more during recessions I include macro variables at time t, not lagging or leading them.
This is done due to the fact that each downturn is unique and lasts different amount of
months, thus leading macro variables in this case would create severe bias. Lagging on the
other hand is not quite appropriate in this case, because we compare short term strategy with
the long term strategy and lagged variables would obviously be biased towards long term
one.
Table 7 Debt maturity choice regression results
TLevDif SIZE TANG ROA CASHr
LTD vs. STD -0.878*** -0.026*** -0.341*** 0.972*** 2.923***
0.051 0.006 0.047 0.299 0.106
MTB AdjRET RDr RDD SE
LTD vs. STD 0.043*** 0.464*** -2.366 0.369*** 0.164
0.012 0.042 1.484 0.054 0.197
Term Spread Default Spread Div. Yield. GDP Growth Likelihood Ratio
LTD vs. STD 0.052*** -0.448*** -153.5 -48.699***
2893 0.008 0.024 13.2 2.436
***, **. * indicates significance at 1%, 5% and 10% respectively. Standard Errors are given in italic.
The TLevDif – the distance from leverage target – suggests that the more over levered
a firm is the less likely it is to issue more long term debt and more likely to issue short term
debt. SIZE and TANG variables support the notion that bigger firms have easier access to
borrowed funds, and those with high tangible assets ratio can probably negotiate lower rates,
due to lower distress costs. Thus, both variables enter the regression with a negative sign.
30
Consistent with previous results from issue choice regressions, market performance and
operating performance variables suggest that the pore profitable firm is the more likely it to
chose debt financing, in this case long term debt. Higher amount of slack also increases LTD
issue. Firstly, financial slack is likely to be distributed among stakeholders and, secondly, it is
viable substitute for a short term debt.
Despite R&D expenses increase the probability of issuing long term debt, the higher
research and development expenses are the higher is the chance to use short term debt
instead. From the previous regressions it was clear that high R&D costs lead to high
probability of equity issue and low probability of debt issue. While the latter probability still
exists it is more likely to be short term debt instead. Higher sales and administrative expense
demand external financing and is often linked to seasonality of cash flows, thus due to the
frequency of financing needs long term debt is more likely to be issued.
Macroeconomic factors complement one another and suggest that in worsening
economy long term debt is preferable to short term debt. This supports findings of
Philosophov, Philosophov (2002) who captured the strong positive relation between the
amount of short term debt and the probability of bankruptcy and found that historically
approximately a year prior to bankruptcy the amount of short term debt has dramatically risen
among observed firms.
6. CONCLUSION AND RECOMENDATIONS
In this paper I investigate the debt-equity choice using the quarterly data sample of
more than 8000 US firms from 1998:1 to 2008:4. I test the pecking order, trade-off and
market timing theory and focus on the importance of macroeconomic factors for capital
structure decisions. I find that neither of mentioned theories fully describes debt-equity
choices. On the other hand I find a strong support that macroeconomic factors are among key
determinants of the capital structure choice. They are not only strongly significant in most
regressions, but have a strong predictive power about the nearest future. This effect is best
shown by the mixed issue/repurchase regressions. Where it is clearly seen that, for example,
equity issue in order to retire some debt is more likely been made prior to a downturn than
decision to issue more debt and repurchase equity. Most firm-specific issue determinants are
consistent with previous researches. I find further support for the market timing theory and
highlight how shareholders affect corporate decisions and how financing and investment
decisions interact. Shareholders have a strong influence on management team and use share
repurchase mechanism as a payout tool, negotiating share repurchases while the stock price is
high and the company has some free funds at hand. And finally I estimate the maturity of
debt choice regression to find which factors affect the maturity of the debt and how. My
findings support the importance of the balance between short and long term debt.
Furthermore, they support the idea that macroeconomic timing is crucial to such choice. Even
though, debt financing is more popular during downturns, it is long term debt that is being
issued. Short term debt is more likely to be issued in upturns.
Nevertheless, there is still a lot of space in this area for a future research. The
predictive strength of different macroeconomic factors and the degree of manager’s
awareness of it should be further investigated. New proxies for the market conditions and
GDP growth should be found, due to several issues with the used variables. Div. Yield, being
32
a reflection of the state of the market doesn’t necessarily reflect state of economy and,
moreover, the effect of the beginning of the recession is not immediately reflected by the
market (in some cases the effect may not occur for as long as year and a half). GDP Growth
is a very strong macro factor, which has a significant effect on any kind of economic activity,
which creates bias in regressions, including passive tactics, because growing GDP favor any
activity and shrinking GDP favor passive strategies. For the future research I recommend
looking for other proxies of the state of current economy and predictors of the short-term
future economic conditions.
REFERENCES
Baker, M., Wurgler, J., (2002). “Market Timing and Capital Structure” The Journal of Finance, Vol. 57, No. 1 (Feb., 2002), pp. 1-32
Bayless, M., Chaplinsky, S., (1991) “Expectations of security type and information content of debt and equity offers”. Journal of Financial Intermediation 1, 195-214.
Booth, L., Aivazian, V., Demirgüç-Kunt, A., & Maksimovic, V. (2001). “Capital structure in developing countries”. Journal of Finance, 56, 87−130.
Cook, D., Tang, T., (2009). “Macroeconomic conditions and capital structure adjustment speed”. Journal of Corporate Finance, Article In Press.
Donaldson, G., (1961). “Corporate debt capacity: A study of corporate debt policy and the determination of corporate debt capacity”. Harvard Business School, Division of Research.
Drobetz, W., Wanzenried, G., 2006. “What determines the speed of adjustment to the target capital structure?” Applied Financial Economics, 1466-4305, Volume 16, Issue 13, Pages 941 – 958
Estrella, A., Hardouvelis, G., (1991). “The term structure as a predictor for real economic activity”. Journal of Finance 46, 555–576.
Estrella, A., Mishkin, F., (1998). “The predictive content of the interest rate term spread for future economic growth”. Federal Reserve Bank of Richmond Economic Quarterly
Ibrahimo, M. V., Barros, C.P., (2006). “Relevance or Irrelevance of Capital Structure?” Economic Modelling 26, 473-479.
Fama, E., French, K., (2002). “Testing tradeoff and pecking order predictions about dividends and debt”, Review of Financial Studies 15, 1-33.
Gaud, P., Hoesli, M., Bender, A., (2007). “Debt-Equity choice in Europe” International Review of Financial Analyst 16, 201-222
Graham, J., R., (2000), “How big are the tax benefits of debt?” Journal of Finance 55, 1901-1941.
Graham, J., R., Harvey C., R., (2002). “How do CFOs make capital budgeting and capital structure decisions?” The Journal of Applied Corporate Finance Vol. 15, No. 1, 2002
De Haas, R., Peeters, M., (2006). “The dynamic adjustment towards target capital structures of firms in transition economies”. Economics of Transition, Vol. 14, No. 1, pp. 133-169, European Bank for Reconstruction and Development.
Harbarth, D., Miao, J., Morellec, E., (2006). “Capital Structure, credit risk, and macroeconomic conditions”. Journal of Financial Economics 82, 519-550.
Heaton, J.B., (2002). “Managerial optimism and corporate finance”. Financial Management 31, 33-45.
Hovakimian, A., (2004). “The role of target leverage in security issues and repurchases”. Journal of Business 77, forthcoming.
Hovakimian, A., Orler, T., Titman, S., (2001) “ The Debt-Equity Choice” The Journal of Financial and Quantitative Analysis, Vol. 36, No. 1 (Mar., 2001), pp. 1- 24
Hovakimian, A., Hovakimian, G., Tehranian, H., (2004). “Determinants of targetcapital structure: The case of dual debt and equity issuers”. Journal of Financial Economics 71, forthcoming.
Huang, R., Ritter, J., (2004). “Testing the market timing theory of capital structure”. University of Florida.
Kayhan, A., Titman, S., (2007). “Firms’ histories and their capital structure”. Journal of Financial Economics Volume 83, Issue 1, Pages 1-32
Korajczyk, R., Levy, A., (2003). “Capital structure choice: macroeconomic conditions and financial constraints”. Journal of Financial Economics, 68, 75–109.
34
Marsh, P., (1982). “The choice between equity and debt: an empirical study”. Journal of Finance 37, 121-144
Miguel, A., & Pindado, J. (2001). “Determinants of capital structure: new evidence from Spanish panel data”. Journal of Corporate Finance, 7, 77−99.
Milken, M., (2009) “Why Capital Structure Matters” The Wall Street Journal, Vol. CCLIII NO. 92
Modigliani, F., Miller, M. H., (1958). “The cost of capital, corporate finance and the theory of
investment.” American Economic Review 53, 433-492.
Myers, S., C., (1984). “The capital structure puzzle”. Journal of Finance 39, 575-592.
Myers, S., C., Majluf, N., (1984). “Corporate financing and investing decisions when firms have information that investors do not have”. Journal of Financial Economics 13, 187-222.
Papke, L., Woolridge, J., (1996). “Econometric methods for fractional response variables with an application to 401(k) plan participation rates”. Journal of Applied Econometrics 11, 619–632
Philosophov, L., V., Philosophov, V., L., (2005). “Optimization of a firm’s capital structure: A quantitative approach based on a probabilistic prognosis of risk and time of bankruptcy”. International Review of Financial Analysis 14, 191-209
Seifert, B., Gonenc, H., (2008). “The international evidence on the pecking order hypothesis”. Journal of Multinational Financial Management 18, 244-260.
Shyam-Sunder, Lakshimi, and Myers S.,, (1999). “Testing static tradeoff against pecking order models of capital structure”. Journal of Financial Economics 51, 219-244.
APPENDICES
Leverage Tobit Regression Lagged Firm-Specific Variables T-3
Table 1
Model Fit Summary
Endogenous Variable Leverage
Number of Observations 129772
Log Likelihood -12835
Maximum Absolute Gradient 0.10259
Number of Iterations 77
AIC 25703
Schwarz Criterion 25870
Parameter Estimate Standard Error t Value Pr > |t|
Intercept 0.117783 0.006807 17.3 <.0001
SIZE1 0.005969 0.000356 16.78 <.0001
TANG1 0.082357 0.003101 26.56 <.0001
ROA1 -0.624065 0.01221 -51.11 <.0001
CASHr1 -0.442985 0.00423 -104.73 <.0001
MTB1 -0.000731 0.000168 -4.34 <.0001
RET1 -0.008804 0.002289 -3.85 0.0001
RDr1 1.09125 0.068129 16.02 <.0001
RDD1 -0.022442 0.002627 -8.54 <.0001
SE1 -0.421426 0.01102 -38.24 <.0001
Risk 0.000663 0.000229 2.89 0.0039
Ind_Lev 0.745976 0.016901 44.14 <.0001
Tspread -0.004289 0.000494 -8.67 <.0001
DefaultS2 0.022249 0.001498 14.85 <.0001
Div_Yield -15.349006 0.775655 -19.79 <.0001
GDPGrowth 0.97407 0.146394 6.65 <.0001
_Sigma 0.231833 0.000505 459.35 <.0001
36
Leverage Tobit Regression Firm-Specific Variables T
Table 2 Model Fit Summary
Endogenous Variable Leverage
Number of Observations 129772
Log Likelihood -4567
Maximum Absolute Gradient 0.00951
Number of Iterations 78
AIC 9168
Schwarz Criterion 9334
Parameter Estimate Standard Error t Value Pr > |t|
Intercept 0.18192 0.00649 28.03 <.0001
SIZE 0.00801 0.000337 23.75 <.0001
TANG 0.105719 0.002935 36.02 <.0001
ROA -0.876926 0.0116 -75.6 <.0001
CASHr -0.574429 0.004102 -140.04 <.0001
MTB -0.00155 0.000159 -9.77 <.0001
RET -0.006182 0.002221 -2.78 0.0054
RDr 1.053338 0.065485 16.09 <.0001
RDD -0.027006 0.002535 -10.65 <.0001
SE -0.471997 0.010433 -45.24 <.0001
Risk 0.000445 0.000217 2.05 0.0403
Ind_Lev 0.565066 0.016229 34.82 <.0001
Tspread -0.001673 0.000463 -3.62 0.0003
DefaultS2 0.011116 0.001462 7.61 <.0001
Div_Yield -13.348558 0.7511 -17.77 <.0001
GDPGrowth 0.79239 0.138822 5.71 <.0001
_Sigma 0.218493 0.000473 461.61 <.0001
37
Table 3 Macro Variable 3 Quarters Forward
Parameter Estimate Estimate Estimate Estimate
RET -0.0049** -0.0047** -0.0044** -0.0048**
Tspread 0.0014
Tspread_1 -0.0011 -0.002***
Tspread_2 -0.0024 -0.0015***
Tspread_3 0.0008 -0.001**
DefaultS2 -0.0004
DefaultS2_1 0.0112*** 0.016**
DefaultS2_2 -0.0048 0.0155***
DefaultS2_3 0.0123*** 0.0149***
Div_Yield -4.6511***
Div_Yield_1 -0.2007 -13.723***
Div_Yield_2 -4.3197 -15.1993***
Div_Yield_3 -6.9549*** -15.8354***
GDPGrowth 0.5056***
GDPGrowth_1 0.5988*** 0.9607***
GDPGrowth_2 0.5553*** 0.6046***
GDPGrowth_3 0.5471*** 0.4223***
Log Likelihood -4388 -4873 -4681 -4417
Table 4 Macro Variable 3 Quarters Lagged
Parameter Estimate Estimate Estimate Estimate
RET -0.0024** -0.0062*** -0.0017 -0.0004
Tspread -0.0009
Tspread1 0.0019 -0.0023***
Tspread2 -0.0003 -0.0028***
Tspread3 -0.004*** -0.0031***
DefaultS2 0.0028
DefaultS21 0.0045 0.0041**
DefaultS22 0.017*** 0.0033*
DefaultS23 -0.0072 0.0022
Div_Yield -0.6832
Div_Yield1 -7.0021*** -15.1589***
Div_Yield2 -4.183 -13.8354***
Div_Yield3 0.6754 -11.9706***
GDPGrowth 0.9669***
GDPGrowth1 0.9478*** 0.8247***
GDPGrowth2 0.8617*** 0.9444***
GDPGrowth3 0.7418*** 1.0054***
Log Likelihood -4810 -5050 -5008 -4923
38
Correlation Matrix
Table 5 Leverage SIZE TANG ROA dRoA CASHr dPEdil dPBdil MTB
Leverage 1 0.111 0.285 -0.108 0.134 -0.409 -0.014 0.239 -0.115
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
SIZE 0.111 1 0.082 0.341 -0.356 -0.294 -0.337 -0.159 0.018
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
TANG 0.285 0.082 1 0.052 -0.021 -0.347 -0.097 0.054 -0.073
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
ROA -0.108 0.341 0.052 1 -0.599 -0.117 -0.407 -0.149 0.012
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
dRoA 0.134 -0.356 -0.021 -0.599 1 0.11 0.504 0.224 -0.047
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
CASHr -0.409 -0.294 -0.347 -0.117 0.11 1 0.116 -0.14 0.172
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
dPEdil -0.014 -0.337 -0.097 -0.407 0.504 0.116 1 0.055 0.074
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
dPBdil 0.239 -0.159 0.054 -0.149 0.224 -0.14 0.055 1 -0.392
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
MTB -0.115 0.018 -0.073 0.012 -0.047 0.172 0.074 -0.392 1
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
RET -0.051 0.003 0.003 0.126 -0.118 0.037 -0.029 -0.165 0.129
<.0001 0.2355 0.3329 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
RDr -0.134 -0.135 -0.194 -0.17 0.115 0.323 0.098 -0.088 0.096
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
RDD -0.187 -0.066 -0.238 -0.039 0.032 0.313 0.058 -0.139 0.082
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
SE -0.185 -0.233 -0.34 -0.296 0.167 0.197 0.199 -0.008 0.124
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.005 <.0001
Ind. Lev. 0.175 0.014 0.226 -0.028 0.041 -0.117 0.002 0.068 -0.024
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.5241 <.0001 <.0001
Term Spread -0.015 -0.002 0.011 -0.021 0.027 0.019 -0.019 0.062 -0.044
<.0001 0.3837 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
Default Spread 0.03 0.005 0.02 -0.076 0.072 -0.022 0.009 0.158 -0.065
<.0001 0.0669 <.0001 <.0001 <.0001 <.0001 0.0016 <.0001 <.0001
Div. Yield -0.096 0.105 -0.049 0.041 -0.043 0.08 -0.027 -0.102 0.004
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.1584
GDP Growth 0.019 -0.034 0.009 0.035 -0.037 -0.017 -0.01 -0.049 0.027
<.0001 <.0001 0.0021 <.0001 <.0001 <.0001 0.0002 <.0001 <.0001
39
RET RDr RDD SE Ind. Lev. Time Spread Default Spread Div. Yield GDP Growth
Leverage -0.051 -0.134 -0.187 -0.185 0.175 -0.015 0.03 -0.096 0.019
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
SIZE 0.003 -0.135 -0.066 -0.233 0.014 -0.002 0.005 0.105 -0.034
0.2355 <.0001 <.0001 <.0001 <.0001 0.3837 0.0669 <.0001 <.0001
TANG 0.003 -0.194 -0.238 -0.34 0.226 0.011 0.02 -0.049 0.009
0.3329 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0021
ROA 0.126 -0.17 -0.039 -0.296 -0.028 -0.021 -0.076 0.041 0.035
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
dRoA -0.118 0.115 0.032 0.167 0.041 0.027 0.072 -0.043 -0.037
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
CASHr 0.037 0.323 0.313 0.197 -0.117 0.019 -0.022 0.08 -0.017
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
dPEdil -0.029 0.098 0.058 0.199 0.002 -0.019 0.009 -0.027 -0.01
<.0001 <.0001 <.0001 <.0001 0.5241 <.0001 0.0016 <.0001 0.0002
dPBdil -0.165 -0.088 -0.139 -0.008 0.068 0.062 0.158 -0.102 -0.049
<.0001 <.0001 <.0001 0.005 <.0001 <.0001 <.0001 <.0001 <.0001
MTB 0.129 0.096 0.082 0.124 -0.024 -0.044 -0.065 0.004 0.027
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.1584 <.0001
RET 1 -0.021 -0.02 -0.013 0 0.042 -0.213 -0.09 0.122
<.0001 <.0001 <.0001 0.9398 <.0001 <.0001 <.0001 <.0001
RDr -0.021 1 0.66 0.244 -0.152 -0.045 -0.063 0.225 -0.05
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
RDD -0.02 0.66 1 0.107 -0.243 -0.068 -0.102 0.338 -0.073
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
SE -0.013 0.244 0.107 1 -0.052 0.005 0.005 -0.025 0.006
<.0001 <.0001 <.0001 <.0001 0.0652 0.0556 <.0001 0.0217
Ind. Lev. 0 -0.152 -0.243 -0.052 1 -0.02 -0.015 -0.113 0.046
0.9398 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
Term spread 0.042 -0.045 -0.068 0.005 -0.02 1 0.27 0.142 -0.055
<.0001 <.0001 <.0001 0.0652 <.0001 <.0001 <.0001 <.0001
Default Spread -0.213 -0.063 -0.102 0.005 -0.015 0.27 1 0.199 -0.599
<.0001 <.0001 <.0001 0.0556 <.0001 <.0001 <.0001 <.0001
Div. Yield -0.09 0.225 0.338 -0.025 -0.113 0.142 0.199 1 -0.303
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
GDP Growth 0.122 -0.05 -0.073 0.006 0.046 -0.055 -0.599 -0.303 1
<.0001 <.0001 <.0001 0.0217 <.0001 <.0001 <.0001 <.0001