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.

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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.

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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.

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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.

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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