Capital Structure of Banks and their Borrowers: an Empirical Analysis

Valeriia Dzhamalova*

Abstract The paper performs empirical analysis of capital structure of banks and their borrowers for a sample of financial and non-financial companies around the world. We find a positive effect of lenders’ leverage on the leverage of their borrowers. A one standard deviation increase in lenders’ leverage corresponds to 6.2% increase in the leverage of their borrowers. This effect is smaller for the borrowers’ with low level of financial distress. We find that borrowers’ leverage is not important in determining their lenders’ capital structure; but lenders’ characteristics, such as a proportion of loans on the balance sheet, has positive and significant effect on the capital structure. JEL classification: Keywords: Capital structure, Capital regulation, Bank, Borrowers

* Department of Economics, Lund University, Box 7082 S-22007, Lund, Sweden; E-mail: Valeriia.Dzhamalova@nek.lu.se.

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1. Introduction This paper provides empirical evidence on the relationship between the capital structure of lenders

and their borrowers. Given that banks provide debt for companies and debt capacity of banks

depends on indebtedness and solvency of their borrowers, the capital structure of banks and

borrowers may be related. Most of the researches on capital structure ignore interaction among

different economic agents. Recent empirical studies incorporate the characteristics of peer firms in

capital structure models (Leary and Roberts (2014)), but empirical research lacks the evidence on

the relationship between the capital structure decisions of lenders and their borrowers. Theoretical

research relates banks’ and borrowers’ capital structure by modelling the essential functions of

banks. Diamond and Rajan (2000) model optimal capital structure, using the interaction between

depositors, equity (debt) holders and borrowers of a bank. They argue banks’ capital structure

determines the nature of banks’ customers because different customers rely to a different extent on

liquidity and credit. Gornall and Strebulaev (2015) develop a model of capital structure decisions

by modeling the interaction between a bank’s debt decisions and the debt decisions of that bank’s

borrowers.

Our study is the first performing an empirical analysis of capital structure decisions of banks and

their borrowers. The paper provides new evidence on capital structure determinants of financial

and non-financial companies. We also contribute to the discussion on the effect of banks’ capital

regulation on the real economy. By limiting banks’ leverage, regulators place a stricter limit on the

relative level of debt that banks can use to finance their assets. If the leverage of borrowers

decreases together with the leverage of banks, capital regulation may lead to less indebtedness and

vulnerability of the economy.

To relate borrowers and lenders we use syndicated loan contracts from DealScan. Using a total

amount of a contract and allocation of each lender within a syndicate, we determine to which extent

borrowers and lenders are related. The repayment schedule of a contract allows us to track the

changes in borrower-lender relationship over time. The advantage of using syndicated loan

contracts is that they relate a borrower and multiple lenders, which most realistically models the

real world relationship. We obtain the borrower specific information from Compustat and

lender-specific information from Capital IQ. Our sample of borrowers consists of around 1000

borrowers and 1200 lenders around the world (North America, Asia, Europe). The sample covers

the period of 20 years, 1995-2014. We estimate two models: 1) a linear fixed effect regression of

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borrower’s leverage on the weighted average of its lenders’ leverage; 2) a linear fixed effect

regression of lender’s leverage on the weighted average of its borrowers’ leverage. Controlling for

size, profitability, tangibility, growth and risk we find that leverage of borrowers is positively

related to the average leverage of their lenders. A one standard deviation increase in the average of

lenders’ leverage is associated with 6.2% increase in their borrower’s leverage. This effect differs

depending on the level of borrowers’ distress: a one standard deviation increase in the average of

lenders’ leverage is associated with only 3.2 % increase in the leverage of borrowers with small

probability of bankruptcy. As borrowers with low probability of bankruptcy have lower leverage,

they receive less tax benefits from debt and affected by lenders’ leverage to a lesser extent.

We do not find any significant effect of borrowers’ leverage on the leverage of their lenders. The

coefficient on lenders leverage has expected sign in some of the specifications, but the coefficient

is economically small. One of the explanations for statistical insignificance is regression

attenuation due to the measurement error in regressor. In particular, our weighted average of

borrowers’ leverage captures only part of lender-borrowers relationship, while true relationship is

not observable. Imprecise measurement of weighted average of borrowers’ leverage can lead to

underestimation of absolute value of its coefficient.

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2. Capital Structure of Financial and Non-Financial Companies: Related Literature

2.1. Capital Structure of Non-Financial Companies Two important capital structure theories are tradeoff theory (Kraus and Litzenberger (1973)) and

pecking order theory (Myers (1984)). According to the trade-off theory, debt financing provides

tax advantage comparing to equity financing, but at the same time high level of debt increases the

probability of bankruptcy. The tradeoff between tax-savings from debt and financial cost of

bankruptcy determines the capital structure of a company. The pecking order theory suggests that

companies would prefer internal funds (retained earnings or initial equity) for financing their

investments. If a company lacks internal financing, it would prefer to issue debt first and equity

only at the last resort.

Empirical tests of pecking order and trade-off theories provide the evidence on important

determinants of leverage. For example, Hovakimian et al (2001) analyze the optimal choice of debt

to equity ratio for a large sample of the U.S companies and find that past profits and stock prices

play important role in the companies’ decision to issue debt or equity. Jandik and Makhija (2001)

examine firm-specific determinants of leverage for a sample of pooled time-series cross-sectional

data for the single industry (electric and gas utilities) for period 1975-1994. They conclude that

bankruptcy costs, growth, non-debt tax shields, collateral profitability, size and risk are important

determinants of leverage; even with the risk having a positive sign, contrary to both pecking order

and trade-off theories. Fama and French (2002) conclude that pecking-order and trade-off theories

share the same predictions regarding the effect of investments, size, nondebt tax shield and share

opposite predictions regarding the effect of profitability on leverage.

Several studies extend the models of capital structure by macroeconomic and industry-level

variables. Korajcsyk and Levy (2003) model the capital structure as a function of macroeconomic

conditions and company-specific variables for the samples of constrained and unconstrained

firms. They find leverage is counter-cyclical for the relatively unconstrained sample, but

pro-cyclical for the relatively constrained sample. MacKay and Phillips (2005) investigate the

effect of industry on companies’ capital structure and find that industry’s effect accounts for

around 13% of variation in capital structure, but capital structure also depends on firm’s position

within its industry. Leary and Roberts (2014) further investigate the effect of industry on capital

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structure. They show that companies’ financing decisions are responses to the financing decisions

and characteristics of the peer firms within the industry.

2.2. Capital Structure of Financial Companies Traditionally, banks provide loans to the customers with a shortage of funds by borrowing from the

customers with excessive funds. In other words, banks fulfill the role of intermediary between the

companies and investors by granting loans and receiving deposits. The intermediary role allows

banks to finance their activity with high level of debt and low level of equity. High proportion of

deposits in banks’ liabilities allows leverage (total liabilities to total assets) of banks to be very

high. Figure 1illustrates that the leverage ratio of American banks during 1934-20141 is 87%-95%

with proportion of deposits between 65%-93%.The leverage of the European banks is also high, for

example the ratio of total liabilities to total assets in Germany2 for period 1979-2014 is 94%-96% .

On the one hand, the core of the banking activities (attract deposits and grant loans) explains the

high level of banks’ leverage. On the other hand, financing by deposits is risky because depositors

are subject to a collective action problem, so called bank runs, when a large number of customers

withdraw their deposits from banks at the same time. Why do banks lever up despite the high

riskiness of leverage? Existing research on banks’ capital structure searches for explanation for

high banks’ leverage, but results are still inconclusive.

Determinants of capital structure from pecking order or trade-off theories explain some variation in

banks’ capital structure (see for example Gropp and Heider (2010)), but both theories ignore

important characteristics of banking industries (deposits, deposits insurance and government

guarantees). Some studies argue that government guaranties and deposit insurance have a positive

effect on bank leverage (see for example Juks (2010)). But Gropp and Heider (2010) find that

mispriced deposit insurance and capital regulation has a second-order importance in determining

the capital structure. They find that only the bank fixed-effects are important determinants of

banks’ capital structure and the leverage converges to bank specific, time-invariant targets. In other

words, Gropp and Heider (2010) do not find any effect of regulation and deposit insurance on

banks’ capital structure and more theoretical and empirical evidence will shed a light on capital

structure puzzle.

1 Similar to Gornall and Strebulaev (2015), we estimate historical averages using the data of Federal Deposit Insurance Corporation: https://www2.fdic.gov/hsob/ 2 We estimate the averages for the German banks using the data from German Central Bank’s web-page: http://www.bundesbank.de/Navigation/EN/Statistics/Banks_and_other_financial_institutions/Banks/banks.html

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Figure 1

After the recent financial crisis, theoretical and empirical research on banks’ capital structure and

regulation is growing (see Thakor (2014) for the review of existing research). In the next section I

review the articles which model capital structure by connecting banks and non-financial

companies. These articles provide a theoretical background for our study.

3. Theoretical Background Diamond and Rajan (2000) explain the optimal capital structure of banks, by modelling the

interactions between banks’ liquidity-creation and credit-creation functions. The authors model

optimal capital structure using the interaction between depositors, equity (debt) holders and

borrowers of a bank. They show that trade-offs between liquidity-creation, credit-creation and

bank stability determines the optimal capital structure. Diamond and Rajan (2000) argue that

banks’ capital structure also determines the nature of banks’ customers because different customers

rely to a different extent on liquidity and credit. The model of Diamond and Rajan (2000) derives

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

1934

1938

1942

1946

1950

1954

1958

1962

1966

1970

1974

1978

1982

1986

1990

1994

1998

2002

2006

2010

2014

Total Liabilities/Total Assets

Total Liabilities minusDeposits/Total Assets

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the implications of “a tough capital structure on intermediary’s behavior towards borrowers” and

effect of minimum capital requirements on the banks’, its lenders and its borrowers. Sundaresan

and Wang (2014) analytically solve for the liability structure of banks by connecting banks and

non-financial companies.

Another paper which relates capital structure decisions of banks their borrowers is Gornall and

Strebulaev (2015). They argue that tax benefits from debt originate only at the banks’ level and

banks’ and companies’ leverages act as strategic substitutes and strategic complements. Strategic

complementarity effect arises because banks pass tax benefits from debt to their borrowers.

Strategic substitution effect arises because banks pass distress costs to their borrowers.

We reckon that leverages of borrowers and lenders may indeed be related. Banks earn their

margins on the difference between the interest expenses from deposits and interest incomes from

loans. The more deposits banks have on their balance sheet, the higher the leverage. Higher

leverage implies excess liquidity and allows borrowers receiving more debt because banks need to

invest their cash. Thus, high leverage of banks allows their borrowers to receive more debt and

increase the leverage. The aim of our paper is to test empirically if the leverages of borrowers and

lenders are related. Using the information on the debt contracts, we identify the relationship

between lenders and borrowers. As banks have multiple borrowers as well as borrowers have

multiple lenders, we compute the weighted average of leverages and construct the “average

borrower” for each bank and “average lender” for each borrower. Use of “average borrowers” and

“average lenders” allows us to remove any potential effect from relationship lending. Relationship

lending in our context is a lending with long history of cooperation between a borrower and a

lender. Relationship lending can result in superior contract conditions for a particular borrower (for

example lower interest rate) because of personal relationship between the bank’s and borrower’s

managers. Rather than analyzing the effect of one particular lender to one particular borrower, our

approach disentangles the average effect of lenders’ leverage on borrowers’ leverage (and vice

versa). Next section describes the computation of weighted averages of lenders and weighted

averages of borrowers.

4. How do we relate Borrowers and Lenders To identify the relationship between borrowers and lenders we use debt contracts, in particular

syndicated loan contract. Use of syndicated loan contracts perfectly suits for connecting borrowers

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and lenders due to the following reasons: 1) total amount of debt contract allows determining the

extent to which a borrower depends on a lender; 2) repayment schedule of debt contract allows to

track changes in borrower-lender relationship over time; 3) syndicated loan contracts allow relating

a borrower to multiple lenders and a lender to multiple borrowers, which most realistically models

the borrowers-lenders’ relationship.

We get the information on syndicated loans contracts from DealScan, the database on 270 000 loan

facilities issued by financial institutions in 161 countries over a 30-year period. Most of the loan

facilities in the DealScan originate from the USA, but data for European and Asia-Pacific countries

is also available. Data allows us to study joint capital structure decisions between borrowers and

lenders across different countries and over time.

4.1. Hypotheses tested in the study According to Gornall and Strebulaev (2015), the effect of lenders’ leverage on companies’

leverage is non-linear, depending on the level of lenders’ leverage. For moderately high levels of

lenders’ leverage, borrowers receive more tax benefits and borrow more from their lenders

(strategic complementarity effect). For very high levels of lenders’ leverage, companies stop

borrowing from their lenders because the borrowing costs in the case of bankruptcy are too high

(strategic substitution effect). For very low levels of lenders’ leverage (but high enough to transfer

tax benefits), the probability of bankruptcy and hence borrowing costs are low and companies

borrow more from a lender.

Leaving the analysis of non-linearity for further stages of research, we start our analysis from

identifying if linear relationship between the lenders’ and borrowers’ leverage exists. In other

words, we assume that the trade-off theory holds, tax benefits are important for the company’s

financial decisions, tax benefits originate only at the bank’s level and bank transfer tax benefits to

borrowers by issuing loans3. The first hypothesis we test in our study is the following:

3 According to Gornall and Strebulaev (2015), the debt benefits originate only at the bank level because of

fundamental asymmetry between final users of financing (“downstream” borrowers) and the intermediaries which

pass financing along (“upstream” borrowers).

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Hypothesis 1: The relationship between a borrower’s and its lenders leverages is positive because

debt benefits originate at the lenders’ level. The higher the leverage of lenders, the more tax

benefits lenders transfer to borrowers and the higher the leverage of borrowers.

Using the second hypothesis, we test if the borrowers’ leverage has any effect on lender’s leverage.

Hypothesis 2: The relationship between lender’s and its borrowers’ leverages is negative.

The seniority of bank’s debt explains this negative relationship. Seniority implies that in the case of

bankruptcy, corporate borrowers paid their debt to the bank before paying to other creditors. If the

company’s leverage decreases, the leverage of its bank increases correspondingly because the

larger fraction of company’s debt becomes senior. For a bank, decrease in leverage of its borrowers

means that portfolio of the bank loans became less risky, but due to the seniority of bank debt and

diversification of loan portfolio, bank prefers to have high level of leverage to be able to earn the

margins on its activities.

To test the hypothesis described in two previous paragraphs, we compute the weighted average of

borrowers’ leverage for each bank and weighted average of lenders’ leverage for each borrower.

Next section describes the methodology we use to calculate the weighted averages.

4.2. Borrower-Lenders Relationship To test relationship between lenders’ and borrowers’ capital structures, one needs to know how

much a lender lent to a company at specific point of time. Usually, the information on banks’ loans

is confidential, but DealScan database provides the information on the syndicated loan transaction

of large corporate and middle market commercial loans filed with the Securities and Exchange

Commission or obtained through other reliable public sources.

We consider each loan facility in DealScan as a debt contract because the loan facility provides

necessary attributes of a debt contracts: total amount, maturity, repayment schedule, name of a

borrower, lenders and the amount each lender allocates within a particular syndicated loan

(allocation). We only consider the facilities if all mentioned attributes are available. DealScan does

not provide company-specific data for lenders and borrowers. To compute leverage ratios for

borrowers and lenders, we link DealScan with Compustat North America, and S&P Capital IQ

databases.

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To relate a borrower to lenders, we use the amount of outstanding debt: total amount of a loan

minus repayment installment4. Repayment of a loan can begin at the year of the loan issue or with

a time lag. We construct a matrix D of outstanding debt of company k at time t. Each element in the

debt matrix looks as follows:

𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = � 0

𝐿𝐿𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝑡𝑡 < 𝑡𝑡𝑝𝑝

𝐿𝐿𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 − 𝑝𝑝𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝑡𝑡 ≥ 𝑡𝑡𝑝𝑝 ,

(1)

where 𝑡𝑡𝑝𝑝 is the start date of the loan’s repayment, 𝐿𝐿𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is the amount of loan i borrower k

received from lender j at time t; 𝑝𝑝𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is the payment installment repaid at a period t. We use the

debt matrix D to construct the weight which relates a borrower and lenders. We compute the weight

of lender j in company’s k debt at time t as follows:

𝑤𝑤𝑖𝑖𝑖𝑖𝑖𝑖 =

∑ 𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 ∗ 𝑠𝑠𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖∑ ∑ 𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 ∗ 𝑠𝑠𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖

,

(2)

where 𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖 is the amount of outstanding debt of company k, 𝑠𝑠𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is lender’s j allocation of debt

to a borrower k within a syndicate i at time t. 𝑠𝑠𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is based on the “BankAllocation” variable from

DealScan, we compute 𝑠𝑠𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 as follows:

𝑠𝑠𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 =

"BankAllocation"100

(3)

Using the weight computed in formula (2), we compute the weighted average of lenders’ leverages

for each borrower k at time t as follows:

𝒀𝒀𝒌𝒌𝒌𝒌∗ = ∑ 𝒘𝒘𝒋𝒋𝒌𝒌𝒌𝒌𝑱𝑱𝒋𝒋=𝟏𝟏 𝒀𝒀𝒋𝒋𝒌𝒌 ,

(4)

where 𝑌𝑌𝑖𝑖𝑖𝑖 is the leverage of lender j at time t. Section 6.2 provides the details on how we define the

leverage of lender j. To test the Hypothesis 1 (lenders’ leverage has a positive effect on the leverage

of their borrowers) we estimate the following equation:

𝒁𝒁𝒌𝒌𝒌𝒌 = 𝜷𝜷𝟎𝟎 + 𝜷𝜷𝟏𝟏𝒀𝒀𝒌𝒌𝒌𝒌−𝟏𝟏∗ + 𝜷𝜷𝟐𝟐𝑿𝑿𝒌𝒌𝒌𝒌−𝟏𝟏𝑩𝑩 , (5)

4 We distinguish between different repayments frequencies available at DealScan: quarterly, semi-annualy, annualy, monthly, daily, weekly, bi-annualy, tri-annualy or by final bullet payment.

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where 𝑍𝑍𝑖𝑖𝑖𝑖 is the leverage of borrower k at time t computed as the ratio of total debt to total assets,

𝒀𝒀𝒌𝒌𝒌𝒌−𝟏𝟏∗ as described above and 𝑿𝑿𝒌𝒌𝒌𝒌−𝟏𝟏𝑩𝑩 is the matrix of control variables on the borrower’s level.

Section 5 describes how we define 𝒁𝒁𝒌𝒌𝒌𝒌 and what we include in 𝑿𝑿𝒌𝒌𝒌𝒌−𝟏𝟏𝑩𝑩 .

4.3. Lender-Borrowers Relationship Similar to the previous section, we use debt contracts to identify the relationship between a lender

and its borrowers. But in this section we interpret the debt matrix D as the matrix of outstanding

loans issued by lender j to borrower k. We weight the borrowers leverage 𝑍𝑍𝑖𝑖𝑖𝑖 by the amount of

loan each lender allocated to a borrower relative to the total amount of loans issued by a lender to

all borrowers at time t. We compute the weighted borrowers’ leverage 𝑍𝑍𝑖𝑖𝑖𝑖∗ as follows:

𝑍𝑍𝑖𝑖𝑖𝑖∗ = ∑ 𝑤𝑤𝑖𝑖𝑖𝑖𝑖𝑖𝐾𝐾𝑖𝑖=1 𝑍𝑍𝑖𝑖𝑖𝑖, (6)

where 𝑤𝑤𝑖𝑖𝑖𝑖𝑖𝑖 and 𝑍𝑍𝑖𝑖𝑖𝑖 as described in the previous section. To illustrate the difference in

computation of Ykt∗ and Zjt∗ , let us consider how the elements of matrix D look after the

multiplication with lenders’ allocation (Dikt ∗ sijkt ). For simplicity, we consider only three lenders

j=1, 2, 3 and two borrowers k=1,2 . We assume each borrower has only one loan facility at time t.

The leftmost column of Table 1lists the indices for borrowers and at the top row lists the indices for

lenders. Each cell in the table shows the outstanding debt of each borrower to each lender if we

consider the borrowers (columns); and the amount of loan issued by each lender to each borrower if

we consider the lenders (rows). To compute Ykt∗ we sum the weights for each lender wkj, i.e. we

sum the columns of the lenders-borrowers matrix and to compute Zjt∗ we sum the rows of the

lenders-borrowers matrix.

To test the second hypothesis of this study (the relationship between lender’s and its borrowers’

leverages is negative), we estimate the following model:

𝒀𝒀𝒋𝒋𝒌𝒌 = 𝜶𝜶𝟎𝟎 + 𝜶𝜶𝟏𝟏𝒁𝒁𝒌𝒌𝒌𝒌−1∗ + 𝜶𝜶𝟐𝟐𝑿𝑿𝒋𝒋𝒌𝒌−1𝐿𝐿 (7)

Section 5 describes how we define 𝒀𝒀𝒋𝒋𝒌𝒌 and what we include in 𝑿𝑿𝒋𝒋𝒌𝒌−𝟏𝟏𝑳𝑳 .

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Table 1 Computation of weighted average of lenders’ and borrowers’ leverage (an illustration)

Lenders

Borrow

ers

j1 j2 j3 Weighted Lender’s

Leverage

k1 𝒘𝒘𝟏𝟏𝟏𝟏 = 𝑫𝑫𝟏𝟏𝟏𝟏𝒔𝒔𝟏𝟏𝟏𝟏∑ 𝑫𝑫𝒌𝒌𝒋𝒋𝒔𝒔𝒌𝒌𝒋𝒋𝟑𝟑𝒋𝒋=𝟏𝟏

𝑫𝑫𝟏𝟏𝟐𝟐 = 𝟎𝟎 𝒘𝒘𝟏𝟏𝟑𝟑 =𝑫𝑫𝟏𝟏𝟑𝟑𝒔𝒔𝟏𝟏𝟑𝟑

∑ 𝑫𝑫𝒌𝒌𝒋𝒋𝒔𝒔𝒌𝒌𝒋𝒋𝟑𝟑𝒋𝒋=𝟏𝟏

𝒀𝒀𝟏𝟏∗ = �𝒘𝒘𝒌𝒌𝒋𝒋

𝟑𝟑

𝒋𝒋=𝟏𝟏

𝒀𝒀𝒋𝒋

k2 𝒘𝒘𝟐𝟐𝟏𝟏 = 𝑫𝑫𝟐𝟐𝟏𝟏𝒔𝒔𝟐𝟐𝟏𝟏∑ 𝑫𝑫𝒌𝒌𝒋𝒋𝒔𝒔𝒌𝒌𝒋𝒋𝟑𝟑𝒋𝒋=𝟏𝟏

𝑫𝑫𝟐𝟐𝟐𝟐 = 𝟎𝟎 𝑫𝑫𝟐𝟐𝟑𝟑 = 𝟎𝟎 𝒀𝒀𝟐𝟐∗ = �𝒘𝒘𝒌𝒌𝒋𝒋

𝟑𝟑

𝒋𝒋=𝟏𝟏

𝒀𝒀𝒋𝒋

Weighted

Borrower’s

Leverage

𝒁𝒁𝟏𝟏∗ = �𝒘𝒘𝒌𝒌𝒋𝒋

𝟑𝟑

𝒋𝒋=𝟏𝟏

𝒁𝒁𝒌𝒌

𝒁𝒁𝟏𝟏∗ = �𝒘𝒘𝒌𝒌𝒋𝒋

𝟐𝟐

𝒋𝒋=𝟏𝟏

𝒁𝒁𝒌𝒌

5. Econometric models To test for borrower-lenders relationship we construct a panel of borrower-year observations and

estimate fixed effect panel data regressions of the borrower’s leverage on the weighted average of

lenders’ leverage and a number of control variables. The model we estimate looks as follows:

𝑍𝑍𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽𝑖𝑖 + 𝛽𝛽1𝑌𝑌𝑖𝑖𝑖𝑖−1∗ + 𝛽𝛽2𝑿𝑿𝒌𝒌𝒌𝒌−𝟏𝟏𝑩𝑩 + 𝑢𝑢𝑖𝑖𝑖𝑖𝐵𝐵 (8), where 𝑍𝑍𝑖𝑖𝑖𝑖 is the leverage of a borrower k at time t, 𝛽𝛽0 is constant, 𝛽𝛽𝑖𝑖 is borrower’s fixed effect,

𝑌𝑌𝑖𝑖𝑖𝑖−1∗ is weighted average of lenders’ leverages as described in section 4.2 and 𝑿𝑿𝒌𝒌𝒌𝒌−𝟏𝟏𝑩𝑩 is a matrix

of borrower-specific control variables which we describe in the next paragraph, 𝑢𝑢𝑖𝑖𝑖𝑖𝐵𝐵 is a

borrower-specific error term. We use first lags of independent variables to address the potential

endogeneity problem arising from simultaneity bias. To understand what we mean by simultaneity

12

bias, consider company’s leverage as a dependent variable and company’s profitability as a

regressor. If we observe both the dependent variable and the regressor at the same time t, we cannot

distinguish if a company has low leverage because of high profitability or the company is more

profitable because of low leverage. By lagging the regressors we measure the effect of their

realized values on dependent variable at t-1.

We define the dependent variable 𝑍𝑍𝑖𝑖𝑖𝑖 in two different ways:

- book leverage = book value of debt (long term debt plus debt in current liabilities) divided

by total assets;

- market leverage = book value of debt divided by market value of assets (market value of

equity plus book value of debt).

In our definition of leverage we follow numerous literature on companies’ capital structure (see for

example Korajczyk and Levy (2003) ). We use three different measures of lenders’ leverage: debt –

to-book assets, total liabilities- to- assets and deposits- to- assets.

The control variables in matrix 𝑿𝑿𝒌𝒌𝒌𝒌−𝟏𝟏𝑩𝑩 are the borrower-specific determinants of capital structure

according previous studies (Fama and French (2002), Jandik and Makhija (2001), Korajczyk and

Levy (2003), Leary and Roberts (2014)). We summarize control variables, their definitions and

expected signs in Table 2.

To test for lender-borrowers relationship we construct a panel of lender-year observations and

estimate fixed effect panel data regressions of the lender’s leverage on the weighted average of its

borrowers’ leverage and a number of control variables. We estimate the following model:

𝒀𝒀𝒋𝒋𝒌𝒌 = 𝜶𝜶𝟎𝟎 + 𝜶𝜶𝒋𝒋 + 𝜶𝜶𝟏𝟏𝒁𝒁𝒌𝒌𝒌𝒌−1∗ + 𝜶𝜶𝟐𝟐𝑿𝑿𝒋𝒋𝒌𝒌−1𝐿𝐿 + 𝒖𝒖𝒋𝒋𝒌𝒌𝐿𝐿 (9)

Where 𝒀𝒀𝒋𝒋𝒌𝒌 is leverage of a lender j at time t , 𝛼𝛼0 is constant, 𝛼𝛼𝑖𝑖 is lender’s fixed effect, 𝑍𝑍𝑖𝑖𝑖𝑖−1∗ is

weighted average of borrowers’ leverages as described in previous section, 𝑋𝑋𝑖𝑖𝑖𝑖−1𝐿𝐿 is a matrix of

lender-specific control variables described in the next paragraph and 𝑢𝑢𝑖𝑖𝑖𝑖𝐿𝐿 is a lender-specific error

term. Similar to borrower-lenders case we use the first lag of dependent variables to address the

endogeneity problem arising from simultaneity bias. We use three different measures of 𝒀𝒀𝒋𝒋𝒌𝒌: debt

to assets, total liabilities to assets and deposits to assets.

Some control variables and their expected signs such as profitability, size, risk, investment

opportunities are the same as in borrower-lenders regression described in Table 2. However, the

13

definition of collateral for the lenders’ case is slightly different. Due to the essence of their activity,

lenders (which are mostly banks in our case) do not usually own a lot of buildings, land or

machinery, but they can use securities and cash as collateral for the short-term borrowings. We

define collateral available for the banks in the following way:

Bank collateral= (mortgage backed securities + investment securities + net property plant and

equipment + cash) /total assets.

As a measure of risk, we use the ratio of non-performing loans to total assets. We expect to find a

negative relationship between the non-performing loans and banks’ leverage. Creditors can

consider high ratio of non-performing loans as signal of banks distress and will be reluctant to lend

to such bank. Finally, we control for the total amount of loans on banks’ balance sheet. By

controlling for a total amount of loans, we control for a demand for credit from the bank’s side. If a

bank has large amount of loans, it needs to receive more debt to be able to finance the loans. We

expect to have a positive relationship between the amount of loans and banks’ leverage.

14

Table 2 Control variables for borrower-lenders regression: proxies, expected sign and rationale for

predictions

Determinant of

Capital Structure

Proxies used in our study Expected

sign

Rationale for expected sign

Profitability Opertaing income before

deprectiation, tax and

interest expenses

+/- More profitable firms have more book

leverage (trade-off model); controlling

for investment opportunities firms with

more profitable assets have less market

leverage (pecking order model)

Investment

opportunities

Market Value of

Company/Book Value of

Company

-/+ Controlling for profitability, firms with

larger investments have lower book

and market leverage(trade-off

model)/given the profitability firms

with more investments have more book

leverage (pecking order model)

Collateral Net Property Plant and

Equipment/Total Assets

+ More collateral allows firms issue

more debt and increase leverage.

Size The natural logarithm of

sales

+ Expected costs of financial distress are

likely to be lower for large (arguably

older and more stable) companies

(Weiss (1990) ) and hence larger firms

can issue more debt.

Risk Volatility of earnings

computed as a standard

deviation of earning for

the past five years

- Higher volatility of earnings can signal

unstable environment and debt

providers can be reluctant to issue debt.

15

6. Data and Results To relate borrowers to lenders, we use DealScan, a database which provides historical information

on terms and conditions of syndicated loans in the global commercial market. DealScan provides

the information on amount, maturity, payment schedule and participants of each loan, but it lacks

the data on financial statements of the companies. To include in the analysis financial statements’

information, we link DealScan with Compustat North America and S&P Capital IQ. Most of the

information for the borrowers we download from Compustat North America using the matching

provided by Chava and Roberts (2008). We do hand-matching of lenders with S&P Capital IQ

because this database allows finding the information easily even if the company is renamed or

merged. We match lenders by their name, country and state (for the United States), SIC code and

parent’s company name. The sample period is from 1995 to 2014 because most of the information

in DealScan is available for this period. To construct weighted average of borrowers’ and lenders’

leverage we use the data on 3195 lenders and 2478 borrowers. The sample with non-missing data

for all variables consists of around 1000 borrowers5 with the average of 4.5 observations per

borrower and around 1200 lenders with an average of 6.4 observations per lender.

6.1. Sample of Borrowers Sample of borrowers consists of non-financial companies, identified as borrowers in syndicated

loans. Left panel of Figure 2 illustrates that majority of our sample constitute North American

companies (66.41 % of the sample); Asian and European companies comprise 20.96% and 9.45%

respectively. Around 66% of North American companies are the companies from the USA;

majority of Asian companies are from Taiwan (5.68 %) and Hong Kong (5.12%). European

companies are mostly from the countries - members of European Union. Appendix 2 lists the

frequencies of observations for different countries in our sample. Our study is the first one which

analyzes companies from different regions in one sample. Data from different regions allows to

investigate the differences in capital structure in general rather than differences in capital structure

within a particular region or a country. To account for heterogeneity of companies from different

5 The number of borrowers and lenders differs if the dependent variable is book or market leverage.

16

countries, we control for time-invariant firm-specific characteristics using the fixed effect panel

regression.

Figure 2. Distribution of the borrowers and lenders over the geographical regions

The industries’ distribution of borrowers in our sample is diverse. The sample includes 58

industries as measured by standard industry classification (SIC) with two-digits codes. Figure 3

presents the histogram of industries distribution of borrowers. As the histogram illustrates, none of

the industries dominates the sample considerably: the percentage of most frequently observed

industry (SIC 48 “Communications”) is around 11 %. The second most frequent industry is SIC

36 (“Electronic and other Electrical Equipment and Components, except Computer Equipment”) –

7.2 % and the third most frequent industry is SIC 73 (“Business services”) – 5.6%. As similar

factors affect the financial policies of the companies within an industry endogeneity, we exclude

financial companies from the sample of borrowers to avoid potential endogeneity.

Table 3 presents descriptive statistics for the sample of borrowers. To mitigate the influence of

extreme observations, we Winsorize all variables at the 1st and 99th percentiles. The upper part of

Table 3 shows the descriptive statistics for dependent variables and borrower-specific control

variables; the lower part of the table shows the statistics for lenders-specific regressors. Similar to

previous studies (see for example Jandik and Makhija (2001), Frank and Goyal (2009)),

non-financial companies in our sample finance with debt 40 % of book value and 45.8% of

market value of assets. Average profitability (EBITDA-to-Assets), market-to-book, tangibility and

Figure 2

Borrowers

20.96%

9.453% 66.41%

Africa Asia Europe Latin America Middle East North America

Lenders

32.31%

24.85%

41.28%

Africa Asia Europe Latin America Middle East North America

17

size are similar to the previous studies. These similarities indicate that our sample is an unbiased

selection from a population. Similar to Jandik and Makhija (2001), we measure risk as standard

deviation of the percentage change in companies operating income for the past five years,

including the year of interest. Some authors (Frank and Goyal (2009)) measure risk as a variance of

stock returns, but we prefer to use the standard deviation in operating income because more data is

available for the later measure. The lower part of

Table 3 presents the descriptive statistics for weighted averages of lenders’ leverages (Section 4.2

explains computation of weighting).In contrast to borrowers, of lenders have large proportion of

liabilities on their balance sheets: lenders finance 70 % of their assets with liabilities (deposits and

non-deposit liabilities). On average, the proportion of deposits to total assets is 46.5 % , while the

proportion of debt to total assets (lenders’ leverage) is only around 19%. Appendix 2 presents the

correlation matrix for all the variables.

Figure 3 Histogram of industries’ distribution of borrower.

02

46

810

perc

ent

10 12 13 14 15 16 17 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 44 45 47 48 49 50 51 52 53 54 55 56 57 58 59 70 72 73 75 78 79 80 82 83 87 90 99

Industries (SIC2)

18

Table 3 Descriptive statistics for the sample of borrowers The table presents number of observations, means, standard deviations (Std.Dev.), minimums (Min) and maximums (Max) for the borrower-lenders regression. Sample of borrowers consists of non-financial companies, identified as borrowers in the syndicated loans; sample of lenders consists of financial companies identifies as lenders in DealScan. Size is measured as natural logarithm of sales. All sales are converted to U.S. dollars by the exchange rate as of the end of the corresponding year. Appendix 1 provides the definition of all variables. All variables are Winsorized at the 1st and 99th percentiles. The upper part of this table shows dependent variables and borrower-specific control variables; the lower part of the table shows lenders-specific regressors. Mean Std.Dev. Mmin Max Borrower-Specific Factors

Book Leverage 0.390 0.243 0.000 1.177

Market Leverage 0.458 0.315 0.000 1.000

EBITDA-to-Assets 0.107 0.088 -0.207 0.381

Size 6.671 1.910 -0.356 11.050

Market-to-Book 1.213 1.091 0.001 7.813

Tangibility 0.339 0.238 0.000 0.894

Risk 1.678 4.692 0.039 37.055

Lender-Specific Factors

Lenders Leverage 0.187 0.119 0.000 0.493

Lenders Liabilities-to- Assets 0.694 0.217 0.001 0.954

Lenders Deposits-to-Assets 0.465 0.163 0.001 0.784

Observations 4383

19

6.2. Sample of Lenders Sample of lenders consists of the financial companies identified as lenders in syndicated loans by

DealScan. We cover the period from 1995-2014 because DealScan provides most of the

information for this period. Our unbalanced panel of lender-time observations includes around

1200 lenders (depending on which dependent variable we use) with the average of 6.4 observations

per lender. Majority of lenders are banks: Commercial Banks (SIC 602) constitute 61 % of the

sample, Foreign Banking and Branches and Agencies of Foreign Banks (SIC 608) constitute 17%

and Business Credit Institutions constitute around 6%. The rest of the sample is distributed among

23 different financial industries. Right panel of Figure 2 illustartes geographical distribution of

lenders in our sample, majority of the sample are North American companies (41.28%) and Asian

companies (32.31%). Most of the North American lenders are the U.S. lenders; most of Asian

lenders are from Japan (8.17%) and Taiwan (7%). European countries are mostly the members of

European Union: Germany (7.23%), France (5.35%), Italy (3.36%). Appendix 2 lists the

frequencies of observations for different countries in our sample.Table 4 presents summary

statistics for lender-borrowers regression. To mitigate the influence of extreme observations, we

Winsorize all variables at the 1st and 99th percentiles.

The sample consists of around 1200 financial companies identified as lenders in a syndicated loan

contracts. Similar to the 80 years’ average for Amercian banks (Figure 1), total liabilities to assets

in our sample has very high average (0.937) and low standard deviation (0.038).

Deposits-to-Assets and Non-Deposit Liabilities have the averages of 0.568 and 0.274

correspondingly. These averages illustrate that 56.8% of banks’ assets are funded from deposits

and this financing structure is similar to the values presented at Gropp and Heider (2010). The

ratios of deposits and non-deposists’ liabilities are more volatile (with a standard deviation almost

6 times higher) than total liabilities-to-assets. Figure 4shows that most of the variation in the banks’

leverages comes from the changes in structure between deposits and non-deposits financing.

Banks’ total liabilities-to-assets are stable over time (average varies only between the values of

0.9-0.925). The difference between the market and book leverages is striking because market value

of assets (measured as market capitalization plus total debt) is on average lower than book value of

20

assets. Consider for example one of the lenders in our sample - Allied Irish Banks, p.l.c. (ISE:AIB).

Its market capitalization by the end of 2009 was only 1059.3 million Euro, in contrast to total assets

of 174314 million Euro and total debt of 74431 million Euro. Due to the lower market value of

assets, the ratio of debt to total assets and deposits to total assets higher than market leverage, but

total libilities to market value of assets measure as 1- (total equity/market value of assets) is lower.

Weighted averages of the borrowers’ leverage (0.346) has a typical value for a leverage of

non-financial companies (see for example Jandik and Makhija (2001)). Similarities of summary

statistics of our sample to the statistics from previous studies suggest that our sample represents the

unbiased selection from the population. Correlation matrix of all the variables is at the Appendix 3.

Table 4 Summary statistics for lender-borrowers regression

The table presents number of observations, means, standard deviations (Std.Dev.), minimums (Min) and maximums (Max) for the lender-borrowers regression. All variables are Winsorized at the 1% level. The sample includes financial companies identified as lenders by DealScan and non-financial companies identified as borrowers by DealScan. The sample consists of American, Asian and European companies for the period 1995-2014. Appendix 1contains definitions of all the variables. The upper part of the table shows dependent variables and lender-specific control variables; the lower part of the table shows borrowers-specific regressors. Mean Std.Dev. Min Max

Lender-Specific Factors Debt-to-Book Assets 0.274 0.198 0.000 0.878

Total Liabilities-to-Assets 0.937 0.038 0.356 0.986

Deposits-to-Assets 0.568 0.213 0.000 0.916

Total Liabilities-to-Market Value 0.766 0.170 0.003 0.968

Debt-to-Market Value 0.677 0.239 0.000 0.997

Deposits-to-Market Value 2.430 2.404 0.000 21.514

Collateral 0.263 0.114 0.009 0.691

Loans-to-Assets 0.566 0.163 0.027 0.897

EBITDA-to-Assets 0.108 0.175 -0.741 0.455

Size 8.165 1.845 2.429 14.185

Non-Performing Loans 0.010 0.018 0.000 0.108

Market-to-Book 0.241 0.177 0.000 0.925

Borrower-Specific Factors

Borrowers Book Leverage 0.346 0.177 0.000 0.757

Borrowers Market Leverage 0.476 0.343 0.000 1.177

N 8325

21

Figure 4. Structure of Lenders’ Liabilities

6.3. Effect of the Lenders’ Leverage on the Leverage of their Borrowers Previous research on banks’ leverage use total liabilities to assets as main dependent variable (see

for example Gropp and Heider (2010)). We use lenders’ leverage (total debt to total assets) as our

main regressor because it suits best for testing the hypotheses of our study due to the following two

reasons. Firstly, lenders’ leverage defined as total debt to total assets is consistent with the

definition of borrowers’ leverage (total debt to total assets). Secondly, if lenders’ and borrowers’

leverages are related through tax benefits of debt, the use of total liabilities is not appropriate as

they also include non-interest bearing liabilities and deposits. Non-interest bearing liabilities do not

provide tax-benefits; and deposits are different from debt of non-financial companies because they

can run and they are insured by the government. We argue that banks do not choose deposits solely

because of tax benefits, but rather deposits reflect the traditional activities of the banks. If the tax

benefits indeed originate only at the banks’ level (Gornall and Strebulaev (2015)), the banks

transmit the tax benefits from their debt to the borrowers debt, rather than deposits or non-deposits

liabilities.

.25

.26

.27

.28

.29

Non

-Dep

osit

Liab

ilitie

s

1995 1997 1999 2001 2003 2005 2007 2009 2011 2013year

.58

.6.6

2.6

4.6

6 D

epos

its-to

-Ass

ets

1995 1997 1999 2001 2003 2005 2007 2009 2011 2013year

.9.9

05.9

1.9

15.9

2.9

25Li

abili

ties-

to-A

sset

s

1995 1997 1999 2001 2003 2005 2007 2009 2011 2013year

22

Panel A of Table 5 presents results of estimation of equation (8) with the dependent variable equal

to total debt to total assets. To simplify interpretation, the coefficients in Table 5 are standardized

by the standard deviation of a corresponding variable. Columns (1)-(5) present estimation results

for different specifications. The specification in the first column has only one regressor (lenders’

book leverage) and the fifth column presents the model with all control variables as described in

Table 2. The coefficient on lenders’ book leverage is positive and significant in all the

specifications. Keeping the effect of size, tangibility, market-to-book, profitability and risk fixed,

one standard deviation increase in lenders’ book leverage corresponds to 6.2 % increase in

borrowers’ leverage. This result accepts the first hypothesis of our study about the positive effect of

lenders’ leverage on the leverage of their borrowers. Among the control variables, tangibility has

the largest effect on leverage of non-financial companies. We interpret tangibility (ratio of net

property plant and equipment to book assets) as collateralizable assets, which increase the ability of

a company to issue debt. One standard deviation increase in borrowers’ collateral, ceteris paribus,

corresponds to 16.8% increase of their leverage. Our results of positive effect of tanginbility are

similar to Frank and Goyal (2009) and Leary and Roberts (2014). Similar to their studies, we find

negative and significant effect of profitability on leverage. Kayhan and Titman (2007) explain

negative relation of leverage and profitability by firms passively accumulating profits. The effect

of size and growth (market-to-book) is ambiguous.

Panel B of the Table 4 presents results of estimation of equation (8) with the dependent variable

equal to total debt to market value of assets. Market value of assets is the sum of market

capitalization book value of debt. In contrast to Panel A, the coefficient on lenders leverage

becomes statistically and economically insignificant. Insignificant coefficient of lenders’ leverage

in regression with market leverage of borrowers is not surprising. Theoretical model of (Gornall

and Strebulaev (2015)) describes the leverage measured by debt to total assets, but theoretical

predictions about market leverage is missing.

23

Table 5

The sample of borrowers consists of nonfinancial companies identified as borrowers in syndicated loans by DealScan. Lenders are financial companies, mostly banks.. All independent variables except of Risk are lagged by one year. Risk is a standard deviatiof a percentage change in the operating income for the past five years and already reflects the information on the past activities of a company. Estimated coefficients are scaled by the corresponding variable’s standard deviation. In Panel A, the dependent variable is debt scaled by total assets and in Panel B, dependent variable is total debt scaled by market value of assets. Appendix 1 provides detailed definitions of variables. Standard errors, robust to heteroscedasticity and within-borrower dependence, are in parentheses. Statistical significance at the 1% and 5% and 10% is denoted by * ,** and *** respectively. The table shows results of estimating the following equation, with Ykt−1∗ as lenders’ average debt to assets and Zkt as borrowers debt to book assets (Panel A) and borrowers debt to market value of assets (Panel B):

𝑍𝑍𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽𝑖𝑖 + 𝛽𝛽1𝑌𝑌𝑖𝑖𝑖𝑖−1∗ + 𝛽𝛽2𝑋𝑋𝑖𝑖𝑖𝑖−1𝐵𝐵 + 𝑢𝑢𝑖𝑖𝑖𝑖𝐵𝐵

Panel A: Book Leverage (1) (2) (3) (4) (5) Lenders Leverage 0.069** 0.068* 0.064* 0.062* 0.062* (0.068) (0.070) (0.070) (0.073) (0.068) Size -0.025 0.001 -0.004 0.029 (0.008) (0.008) (0.008) (0.008) Tangibility 0.198*** 0.188*** 0.168** (0.057) (0.060) (0.069) Market-to-Book -0.018 0.017 (0.005) (0.005) EBITDA-to-Assets -0.115*** (0.070) Risk 0.003 (0.001) Observations 4402 4332 4320 4140 3558 R2 0.003 0.003 0.018 0.016 0.033 Adjusted R2 0.003 0.003 0.017 0.015 0.031

Panel B: Market Leverage (1) (2) (3) (4) (5) Lenders Leverage 0.016 0.003 0.003 -0.003 0.024 (0.099) (0.097) (0.097) (0.083) (0.081) Size 0.121* 0.140* 0.076 0.215*** (0.012) (0.012) (0.010) (0.011) Tangibility 0.183*** 0.169*** 0.177*** (0.089) (0.081) (0.086) Market-to-Book -0.249*** -0.198*** (0.012) (0.019) EBITDA-to-Assets -0.127*** (0.115) Risk -0.020 (0.002) Observations 4443 4346 4334 4297 3715 R2 0.000 0.004 0.014 0.086 0.123 Adjusted R2 -0.000 0.004 0.014 0.085 0.121 Constant Yes Yes Yes Yes Yes Borrower Fixed Effect Yes Yes Yes Yes Yes

24

Table 6 illustrates the results of estimation of equation (8) with two other measures of lenders’

leverage: total liabilities to assets and deposits to assets. As expected the coefficients on both

measures of leverage are statistically and economically insignificant. This implies that lenders do

not transfer tax-benefits to borrowers through leverage as measured by liabilities to assets and

deposits to assets. The explanation for this insignificance is that total liabilities also include

non-interest bearing liabilities, which do not provide tax benefits. We also doubt if deposits can

provide tax benefits of debt because deposits reflect traditional banking operations, rather than

banks chose deposits solely because of tax benefits of debt.

Table 6 The sample of borrowers consist of nonfinancial companies identified as borrowers in syndicated loans by DealScan. Lenders are financial companies, mostly banks. All independent variables except of the Risk are lagged by one year. As Risk is a standard deviation of percentage change in the operating income for the past five years and already reflects the information on the past activities of a company. Estimated coefficients are scaled by the corresponding variable’s standard deviation. In columns (1)-(2), the dependent variable is debt scaled by total assets and in columns (3)-(4), the dependent variable is total debt scaled of market value of assets. Appendix 1 provides detailed definitions of variables. Standard errors, robust to heteroscedasticity and within-borrower dependence, are in parentheses. Statistical significance at the 1% and 5% and 10% is denoted by * ,** and *** respectively. The table shows results of estimating the following equation, with Ykt−1∗ as lenders’ average liabilities to assets or deposits to assets:

𝑍𝑍𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽𝑖𝑖 + 𝛽𝛽1𝑌𝑌𝑖𝑖𝑖𝑖−1∗ + 𝛽𝛽2𝑋𝑋𝑖𝑖𝑖𝑖−1𝐵𝐵 + 𝑢𝑢𝑖𝑖𝑖𝑖𝐵𝐵 (1) (2) (3) (4) Book

Leverage Book Leverage Market Leverage Market

Leverage Lenders Liabilities-to- Assets 0.010 -0.014 (0.035) (0.033) Lenders Deposits-to-Assets 0.026 -0.036 (0.045) (0.049) Size 0.038 0.058 0.217*** 0.243*** (0.008) (0.009) (0.011) (0.011) Tangibility 0.160** 0.173** 0.176*** 0.186*** (0.068) (0.068) (0.083) (0.084) Market-to-Book -0.004 0.001 -0.206*** -0.204*** (0.007) (0.007) (0.017) (0.017) EBITDA-to-Assets -0.098*** -0.110*** -0.118*** -0.128*** (0.075) (0.077) (0.108) (0.109) Risk -0.001 0.011 -0.025 -0.021 (0.001) (0.001) (0.002) (0.002) Observations 3679 3505 3838 3662 R2 0.025 0.029 0.123 0.126 Adjusted R2 0.023 0.028 0.121 0.125 Constant Yes Yes Yes Yes Borrower Fixed Effect Yes Yes Yes Yes To test if banks pass to their borrowers cost of distress as described in the first paragraph of Section

4.1, we distinguish between the groups of distressed banks and distressed borrowers. We present

25

the results of the analysis of distress costs on borrowers’ leverage in the next section.

6.4. Borrowers’ Leverage and Distress Costs To test if the relationship between the leverage of borrowers and its lenders differs for different

level of financial distress, we analyze the effect of lenders leverage for two different groups:

lenders with a low leverage (below the median value) and lenders with a high leverage (above the

median value). The median value of total liabilities to assets for the average lender in our sample is

equal to 0.742. We include a Distress Dummy (Lender) in equation (8). This dummy equals one if

lenders’ leverage is below the median and zero otherwise. We also add an interaction term between

Distress Dummy (Lender) and average banks leverage in (equation(8)) and estimate the following

equation:

𝑍𝑍𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽𝑖𝑖 + 𝛽𝛽1𝑌𝑌𝑖𝑖𝑖𝑖−1∗ + 𝛽𝛽2𝑋𝑋𝑖𝑖𝑖𝑖−1𝐵𝐵 + 𝐷𝐷𝑖𝑖𝑖𝑖 + 𝛽𝛽4𝐷𝐷𝑖𝑖𝑖𝑖𝑌𝑌𝑖𝑖𝑖𝑖−1∗ + 𝑢𝑢𝑖𝑖𝑖𝑖𝐵𝐵 (10)

where 𝑍𝑍𝑖𝑖𝑖𝑖 is equal to borrowers debt to book assets and 𝑌𝑌𝑖𝑖𝑖𝑖−1∗ is lenders’ average debt to book

assets and 𝐷𝐷𝑖𝑖𝑖𝑖 is Distress Dummy (Lender) or Distress Dummy (Borrower) as the next paragraph

describes.

We also test if the effect of lenders leverage on the leverage of a borrower differs for distressed or

non-distressed borrowers. To distinguish if the borrower is distressed or not, we use Altman’s

Z-score. According to bankruptcy prediction model of Altman (1968), the company has a low

probability of bankruptcy if the value of Altmans’ Z score is more than three. We create a Distress

Dummy (Borrower), which equals one if the value of Altman’s Z-score is greater than three and

zero otherwise. We add the Distress Dummy (Borrower) and interaction term of dummy with the

average banks leverage in our initial model (equation(8)) and estimate the equation (10).

Table 7 presents the results of estimation of equation (10) with Distress Dummy (Borrower) and

column (2) presents the results with Distress Dummy (Lender).

As column (1) of

Table 7 illustrates, the coefficient on dummy for borrowers’ distress is negative and significant,

which indicates that leverage in the group of borrowers with low probability of bankruptcy is

smaller than in the group with high probability of bankruptcy. Fitted values of borrowers’ leverage

(Figure 5) illustrate the lower level of leverage for distressed borrowers and higher level of

leverage for non-distressed borrowers. In other words, we find a positive effect of distress on

26

borrowers’ leverage. The positive effect of risk on leverage is similar to the results of previous

research, for example Byoun (2008), Jandik and Makhija (2001). A coefficient on Lenders’ Book

Leverage indicates a slope for borrowers with high probability of bankruptcy; a coefficient of

-0.032 on Lenders’ Book Leverage* Distress Dummy (Borrower) suggest that the slope of

Lenders’ Leverage is almost two times smaller for borrowers with low bankruptcy costs. In other

words, the relationship between the leverages of borrowers and their lenders is economically

smaller for a group with low bankruptcy costs. Given that borrowers with low bankruptcy costs

rely on leverage at a lesser extent, they rely on the tax benefits transmitted from lenders to a lesser

extent also.

According to Gornall and Strebulaev (2015), if the lender’s leverage is extremely high and the

lender will be pushed into distress by even a small loss, the borrower will avoid issuance of new

debt because the borrowing costs are too high. In other words, for extremely high values of lenders’

leverage, the leverage of lenders negatively affects the leverage of borrowers. We test this

relationship by dividing the lenders into two groups: with the average liabilities below and above

the median, but we do not find confirmation of these predictions for our sample. As column (2) of

Table 7 illustrates, both Distress Dummy (Lender) and interaction term Lenders’ Book Leverage*

Distress Dummy (Lender) have expected negative sign, but statistically insignificant. If we divide

the groups by 75% percentile instead of the median, results are not affected and still insignificant.

27

Table 7 The sample of borrowers consists of nonfinancial companies identified as borrowers in syndicated loans by DealScan. Lenders are financial companies, mostly banks. All independent variables except of Risk are lagged by one year. Risk is a standard deviation of a percentage change in the operating income for the past five years and already reflects the information on the past activities of a company. Estimated coefficients are scaled by the corresponding variable’s standard deviation. Appendix 1 provides detailed definition of variables. Standard errors, robust to heteroscedasticity and within-borrower dependence, are in parentheses. Statistical significance at the 1% and 5% and 10% is denoted by * ,** and *** respectively. This table shows the results of estimating the following equation, with Zkt as borrowers’ debt to assets ratio and Ykt−1∗ as lenders’ average debt to assets ratio; in column (1) Dkt and Dkt−1 are borrowers’ distress dummy and in column (2) Dkt and Dkt−1 are lenders’ distress dummies.

𝑍𝑍𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽𝑖𝑖 + 𝛽𝛽1𝑌𝑌𝑖𝑖𝑖𝑖−1∗ + 𝛽𝛽2𝑋𝑋𝑖𝑖𝑖𝑖−1𝐵𝐵 + 𝐷𝐷𝑖𝑖𝑖𝑖 + 𝛽𝛽4𝐷𝐷𝑖𝑖𝑖𝑖−1𝑌𝑌𝑖𝑖𝑖𝑖−1∗ + 𝑢𝑢𝑖𝑖𝑖𝑖𝐵𝐵 (11) (1) (2) Book Leverage Book Leverage Lenders’ Book Leverage 0.066* 0.047 (0.068) (0.073) Lenders’ Book Leverage* Distress Dummy (Borrower) -0.032** (0.037) Distress Dummy (Borrower) -0.199*** (0.009) Lenders’ Book Leverage* Distress Dummy (Lender) -0.003 (0.054) Distress Dummy (Lender) -0.018 (0.010) Size 0.056 0.012 (0.010) (0.009) Tangibility 0.171** 0.159** (0.078) (0.073) Market-to-Book 0.044** 0.017 (0.005) (0.005) EBITDA-to-Assets -0.075*** -0.136*** (0.073) (0.081) Risk 0.003 0.012 (0.001) (0.002) Fixed Effect Yes Yes Constant Yes Yes Observations 3230 3060 R2 0.100 0.041 Adjusted R2 0.097 0.039

28

Figure 5 This figure presents fitted values of borrowers leverage estimated by equation (10). Fitted values are for two groups of borrowers: distressed and undistressed borrowers. Unistressed borrowers have low probability of bankruptcy (with Altman’s Z score greater than three) and distressed borrowers are borrowers with Altman’s Z-score smaller than three.

6.5. Effect of the Borrowers’ Leverage on the Leverage of their Lenders To test if the borrowers’ leverage affects the capital structure of their lenders we estimate the

equation (9). We use three different measures of bank leverage: Debt-to-Book Assets,

Deposits-to-Assets and Liabilities-to-Assets.

We start discussion of the results with the dependent variable measured as Debt-to-Book Assets.

This measure of leverage is most closely related to the definition of debt of non-financial firms

(total debt to total assets). And if the trade-off theory holds, it should first of hold for the

non-deposit (Debt-to-Book Assets) liabilities of the banks. In other words tax benefits of debt

should be transmitted to borrowers through this part of total liabilities. To test the second

hypothesis of this study (borrowers’ leverage has negative effect on the leverage of their lenders),

we use banks total debt scaled by assets and weighted average of their borrowers debt scaled by

assets. Table 8 reports the results of the estimation of equation (9).

.25

.3.3

5.4

.45

1995 1997 1999 2001 2003 2005 2007 2009 2011 2013year

Undistressed Borrowers Distressed Borrowers

Fitted Values of Borrowers Leverage, Average over time

29

To simplify the interpretation, we scale all reported coefficients by standard deviation of

corresponding variable and present the results for dependent variable measured as debt to assets in

the first column and results for market leverage in the second column of Table 8. To compare the

results with previous studies, in the third and fourth columns of Table 8 we report the estimates of

comparable regressions for non-financial firms as reported in Leary and Roberts (2014)6. We

compare our results with this paper because it is most closely related to our study. Leary and

Roberts (2014) also model capital structure using the interaction among different economic agents,

namely they incorporate characteristics of peer firms in capital structure model.7

Second column of Table 8 illustrates that the coefficient on average borrowers’ book leverage has

negative sign which confirms the Hypothesis 2 of our study. However, the coefficient is

statistically and economically insignificant. One of the explanations for small and insignificant

coefficient is an error in measurement of our independent variable. As we explained in Section 4,

we relate borrowers to lenders using syndicated loans contracts. Use of syndicated loan contracts

allows us to capture only part of the relationship between borrowers and lenders, while the true

relationship between two groups is unobserved. Error in independent variables can bias the

regression slope to zero due to so called regression attenuation and this can explain the small and

insignificant coefficient on borrowers’ leverage. The specification with market leverages also

produces insignificant coefficients.

6 Table VIII (columns one and two) in Leary and Roberts (2014) 7 In contrast to our study, Leary and Roberts (2014) use instrumental variables estimator, industry fixed effect and time dummies.

30

Table 8 The table presents results of fixed effect panel regression of lenders’ leverage on their borrowers average leverage. The sample includes financial companies identified as lenders and non-financial companies identified as borrowers in DealScan. The sample consists of American, Asian and European companies for the period 1995-2014. Appendix 1 presents complete definitions of the variables. All variables are Winsorized at the 1% level. Standard errors robust to heteroscedasticity and within-borrower dependence are in parentheses. Statistical significance at the 1% and 5% and 10% is denoted by * ,** and *** respectively. The dependent variables are at the top of the columns. All independent variables are lagged 1 year. All estimated coefficients (except constant) are scaled by the standard deviation of corresponding variable. Columns (3)-(4) present the estimates of comparable regressions for non-financial firms as reported in Table VIII (columns one and two) of Leary and Roberts (2014). Column (1)-(2) of this table presents the results of fixed effect panel regression of banks’ book leverage on their borrowers book leverages as described in the following equation:.

𝒀𝒀𝒋𝒋𝒌𝒌 = 𝜶𝜶𝟎𝟎 + 𝜶𝜶𝒋𝒋 + 𝜶𝜶𝟏𝟏𝒁𝒁𝒌𝒌𝒌𝒌−1∗ + 𝜶𝜶𝟐𝟐𝑿𝑿𝒋𝒋𝒌𝒌−1𝐿𝐿 + 𝒖𝒖𝒋𝒋𝒌𝒌𝐿𝐿 (1) (2) (3) (4) Debt-to-Market

Value of Assets Debt-to-Book

Assets Market

Leverage: Leary and Roberts

(2014)

Book Leverage: Leary and

Roberts (2014) Borrowers’ Market Leverage

0.004 (0.010)

Borrowers Book Leverage -0.011

(0.012)

Size 0.209*** 0.030 0.021** 0.021** (0.004) (0.002) (9.113) (10.435) Market-to-Book 0.043* 0.315*** -0.067** -0.018 (0.038) (0.030) (-42.570) (-11.257) Collateral -0.039* -0.064*** 0.034* 0.042** (0.049) (0.032) (11.281) (15.105) EBITDA-to-Assets -0.087*** -0.027*** -0.048** -0.037** (0.021) (0.009) (-28.700) (-20.769) Loans-to-Assets -0.063** -0.063** (0.045) (0.038) Non-performing Loans 0.009 0.006 (0.204) (0.097) Fixed Effect Yes Yes Yes Yes Constant Yes Yes Yes Yes Observations 4695 7780 80279 80279 R2 0.061 0.141 Adjusted R2 0.060 0.140 If we consider the coefficients on control variables, their signs are similar to the signs of

non-financial companies and theoretical predictions for both book and market leverages. For

31

example, size of the banks has positive effect on its leverage because larger companies face

relatively lower bankruptcy costs (see for example Weiss (1990)) and profitability has a negative

effect on leverage as predicted by pecking order theory and confirmed by several empirical studies

(Leary and Roberts (2014), Fama and French (2002)). The effect of market-to-book and collateral

are opposite to the results of Leary and Roberts (2014), but the effect of investment opportunities as

measured by market-to-book often produces conflicting results in empirical and theoretical studies

(see for example the discussion of Fama and French (2002) on leverage and investment

opportunities). Similar to Gropp and Heider (2010) we measure the collateral as a sum of mortgage

backed securities, investment securities, net property plant and equipment and cash. On the one

hand, banks can use these assets as collateral. On the other hand, if banks are lack of liquid assets

such as cash and investment securities, they might need more debt. This lack of liquidity can

explain the negative coefficient on collateral in our study. The coefficient on non-performing loans

is small and statistically insignificant because lenders in our sample have on average relatively low

ratio of non-performing loans. Finally, the negative coefficient on loans-to-assets suggests that

banks do not use debt for funding their loans, but rather they use other sources of funds such as

deposits.

To further investigate the effect of borrowers’ leverage on their lenders’ capital structure, we use

alternative measures of lenders’ leverage. Table 9 presents the results of estimation of equation

(9) with dependent variables measured as total liabilities and deposits to book value of assets

(columns (1)-(2)) and to market value of assets (columns (3)-(4)). Borrowers’ book or market

leverage is a weighted average of borrowers’ debt scaled by book or market value of assets. Similar

to the results of Table 8, the weighted average of borrowers’ leverage does not have any significant

effect on their lenders’ leverage. The only statistically significant coefficient is the coefficient on

the book leverage of borrowers, but the economically the coefficient is not significant. Given that

effect of other variables is fixed, one standard deviation change in book leverage of borrowers is

associated with only 2,4 % increase in the leverage of lenders measured as a ratio of total liabilities

to book values of assets. The positive sign of the coefficient contradicts to the Hypothesis 2 of our

study and predictions of Gornall and Strebulaev (2015). The positive coefficient on borrowers’

book leverage suggests that lender’s leverage is positively related to the leverage of its borrowers.

We explain the contradiction to the expected sign by the composition of the total liabilities. As total

liabilities of banks include non-interest bearing expenses also (such as accounts payable or

32

non-interest bearing deposits), relationship between borrowers and lenders due to the transfer of

tax benefits is not fully applicable here. As to the effect, of the control variables, their effect is quite

mixed first of all because we borrow the control variables from the literature on non-financial

companies. For example, the effect of the size on banks’ total liabilities and deposits is not

significant. As to the effect of variables specific to financial firms, the ratio of loans to assets has

positive and significant effect on the lenders’ leverage. The effect of loans is especially large for

the deposits to total assets: one standard deviation increase in the ratio of loans to assets is

associated with 23,2 % increase in the ratio of deposits to assets. This positive effect on deposits to

assets is consistent with a negative effect on debt to assets illustrated in Table 8. Different effect of

loans on deposits and debt suggests that if a lender in our sample needs to finance more loans, it

accepts more deposits, but decreases non-deposit liabilities (debt). Non-performing loans has a

small negative effect on both total liabilities and deposits, suggesting that depositors are more

cautious in borrowing to a bank if its ratio of non-performing loans is high. But as the average of

non-preforming loans (0.01) is small in our sample, the effect of non-performing loans on leverage

is also small.

33

Table 9

The sample includes financial companies identified as lenders and non-financial companies identified as borrowers in DealScan. The sample consists of American, Asian and European companies for the period 1995-2014. Appendix 1 presents complete definitions of the variables. All variables are Winsorized at the 1% level. Standard errors robust to heteroscedasticity and within-borrower dependence are in parentheses. Statistical significance at the 1% and 5% and 10% is denoted by * ,** and *** respectively. The dependent variables are at the top of the columns. All independent variables are lagged 1 year. All estimated coefficients (except constant) are scaled by the standard deviation of corresponding variable. The table presents the results of fixed effect panel regression of banks’ book leverage on their borrowers book leverages as described in the following equation:

𝒀𝒀𝒋𝒋𝒌𝒌 = 𝜶𝜶𝟎𝟎 + 𝜶𝜶𝒋𝒋 + 𝜶𝜶𝟏𝟏𝒁𝒁𝒌𝒌𝒌𝒌−1∗ + 𝜶𝜶𝟐𝟐𝑿𝑿𝒋𝒋𝒌𝒌−1𝐿𝐿 + 𝒖𝒖𝒋𝒋𝒌𝒌𝐿𝐿 (1) (2) (3) (4) Scaled by Book Assets Scaled by Market Value of Assets Total Liabilities Deposits Total Liabilities Deposits Borrowers Book Leverage 0.024** 0.007 (0.003) (0.011) Borrowers Market Leverage 0.001 0.013 (0.014) (0.160) Size -0.019 0.012 -0.034 -0.016 (0.001) (0.002) (0.004) (0.040) Market-to-Book 0.038** -0.264*** 0.229*** -0.214*** (0.004) (0.030) (0.051) (0.538) Collateral -0.008 0.108*** 0.022 0.036* (0.005) (0.025) (0.039) (0.402) EBITDA-to-Assets 0.001 0.007 0.026 -0.022 (0.002) (0.007) (0.021) (0.238) Loans-to-Assets 0.043** 0.232*** 0.046 0.049 (0.006) (0.030) (0.040) (0.435) Non-performing Loans -0.032*** -0.014* 0.017 -0.044*** (0.026) (0.095) (0.153) (1.755) Constant 0.930*** 0.403*** 0.741*** 2.815*** (0.007) (0.026) (0.037) (0.462) Observations 7824 7710 4723 4716 R2 0.013 0.177 0.070 0.072 Adjusted R2 0.013 0.176 0.068 0.071 Fixed Effect Yes Yes Yes Yes

34

7. Conclusions We estimate a linear fixed effect regression of borrower’s leverage on the weighted average of their

lenders’ leverage; and a linear fixed effect regression of lender’s leverage on the weighted average

of their borrowers’ leverage. The sample of borrowers with non-missing data for all variables

consists of around 1000 borrowers8 with the average of 4.5 observations per borrower; the sample

of lenders consists of around 1200 financial companies with an average of 6.4 observations per

lender. Majority of lenders (78%) are banks: Commercial Banks (SIC 602) and Foreign Banking

and Branches; borrowers are non-financial companies.

Keeping the effect of size, tangibility, market-to-book, profitability and risk fixed, one standard

deviation increase in lenders’ book leverage corresponds to 6.2 % increase in the borrowers’

leverage. The relationship between the leverage of borrowers and their lenders is economically

smaller for a group of borrowers with low bankruptcy costs. Among the control variables,

tangibility has the largest effect on leverage of non-financial companies. One standard deviation

increase in borrowers’ collateral, ceteris paribus, corresponds to 16.8% increase in their leverage.

We do not find a significant effect of borrowers’ leverage on their lenders’ leverage as measured by

debt to assets or deposits to assets. We find a significant effect of borrowers’ leverage on lenders’

total liabilities to total assets, but economically the effect is very small and opposite to the expected

sign. One standard deviation increase in book leverage of borrowers is associated with only 2,4 %

increase in the leverage of lenders, as measured by total liabilities to assets. One of the explanations

for statistical and economical insignificance is regression attenuation due to the measurement error

in regressor. In particular, our weighted average of borrowers’ leverage captures only part of

lender-borrowers relationship, while true relationship is not observable. Imprecise measurement of

weighted average of borrowers’ leverage can lead to underestimation of absolute value of its

coefficient.

8 The number of borrowers and lenders differs if the dependent variable is book or market leverage.

35

Appendix 1 Notation and Definition of Variables

𝑿𝑿𝒌𝒌𝒌𝒌𝑩𝑩 : control variables, borrower level

𝑿𝑿𝒋𝒋𝒌𝒌𝑳𝑳 : control variables, lender level

𝑠𝑠𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖: lender’s j allocation of a loan to borrower k at time t

𝑌𝑌𝑖𝑖𝑖𝑖: leverage of lender j at time t

𝑍𝑍𝑖𝑖𝑖𝑖: leverage of borrower k at time t

𝐿𝐿𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖: amount of loan i of borrower k received from lender j at time t

Borrower- Lender Regression Risk= standard deviation of the percentage change in companies’ operating income for the past five years, including the year of interest. Operating income is oibdp from Compustat and IQ_OPER_INC from Capital IQ. Book Leverage=Total Book Debt/Total Book Assets

Market Leverage= Total Book Debt / Market Value of Assets

Market Value = market capitalization plus + total book debt

Market Capitalization is defined as the monthly close price as of December of corresponding year

times the actual number of common shares outstanding, excluding dilution (conversion of

convertible preferred stock, convertible debentures, options and warrants).

EBITDA-to-Assets= Operating Income Before Depreciation/Total Assets

Size= ln(Total Revenue), total revenues are converted into US dollar by exchange rate as of the end

of a corresponding year

Market-to-Book=Market Value of Assets /Total Book Assets

Tangibility=Net Property Plant and Equipment/Total Book Assets

Lenders Leverage= Weighted Average of Lenders’ Total Debt/Total Book Assets; computation of

the weights is described in Section 4.2

Lenders Liabilities-to- Assets= Weighted Average of Lenders’ Total Liabilities/Total Book Assets;

computation of the weights is described in Section 4.2

Lenders Deposits-to-Assets =Weighted Average of Lenders’ Total Deposits/Total Book Assets;

computation of the weights is described in Section 4.2

36

Lender-Borrowers Regression Debt-to-Book Assets = Total Debt/Total Book Assets

Total Liabilities-to-Assets =1-(Bank Equity/Total Book Assets)

Deposits-to-Assets=Total Deposits/Total Book Assets

Market Value of Assets = Market Capitalization + Total Book Debt

Market Capitalization is defined as the monthly close price as of December of corresponding year times the

actual number of common shares outstanding, excluding dilution (conversion of convertible preferred

stock, convertible debentures, options and warrants).

Debt-to-Market Value = Total Debt/Market Value of Assets

Total Liabilities-to-Market Value= 1-(Bank Equity/ Market Value of Assets)

Deposits-to-Market Value =Total Deposits/ Market Value of Assets)

Collateral= (Mortgage Backed Securities + Investment Securities + Net Property Plant and

Equipmet+Cash) /Total Book Assets

Loans-to-Assets =Total Loans/Total Book Assets

Market-to-Book=Market Value of Assets /Total Book Assets

Non-Performing Loans= Total Non-Performing Loans/Total Book Assets

EBITDA-to-Assets = Operating Income Before Depreciation/Total Book Assets

Size =ln(Total Revenue), total revenues are converted into US dollar by exchange rate valid for a

corresponding year

Borrowers Book Leverage = Weighted Average of Borrowers’ Total Debt/Total Assets; computation of the

weights is described in Section 4.3

Borrowers Market Leverage = Weighted Average of Borrowers’ Total Debt/Market Value of Assets;

computation of the weights is described in Section 4.3

37

Appendix 2 Geographical Distribution of Lenders and Borrowers

Panel A: Lenders Panel B: Borrowers

Country Frequency Percent Country Frequency Percent Argentina 3 0.04 Argentina 6 0.09 Australia 169 2.03 Australia 64 0.99 Austria 165 1.98 Bahamas 17 0.26 Bahrain 24 0.29 Bahrain 7 0.11 Belgium 152 1.83 Belgium 2 0.03 Bermuda 2 0.02 Bermuda 18 0.28 Brazil 4 0.05 Bolivia 7 0.11 Canada 294 3.53 Brazil 1 0.02 China 253 3.04 British Virgin Islands 8 0.12 Czech Republic 6 0.07 Canada 73 1.13 Denmark 41 0.49 Cayman Islands 12 0.19 Finland 8 0.10 Chile 4 0.06 France 696 8.36 China 102 1.58 Germany 958 11.51 Colombia 5 0.08 Greece 32 0.38 El Salvador 1 0.02 Hong Kong 214 2.57 Finland 4 0.06 India 109 1.31 France 67 1.04 Indonesia 64 0.77 Germany 43 0.67 Ireland 67 0.80 Greece 5 0.08 Israel 43 0.52 Hong Kong 358 5.55 Italy 372 4.47 Hungary 7 0.11 Japan 742 8.91 India 74 1.15 Jordan 4 0.05 Indonesia 174 2.70 Kazakhstan 2 0.02 Ireland 22 0.34 Kuwait 15 0.18 Israel 21 0.33 Luxembourg 19 0.23 Italy 55 0.85 Malaysia 107 1.29 Japan 14 0.22 Mauritius 2 0.02 Korea (South) 50 0.78 Mexico 15 0.18 Liberia 23 0.36 Netherlands 281 3.38 Luxembourg 7 0.11 New Zealand 5 0.06 Macau 10 0.16 Nigeria 1 0.01 Malaysia 24 0.37 Norway 48 0.58 Mexico 30 0.47 Peru 3 0.04 Netherlands 59 0.92 Philippines 125 1.50 New Zealand 11 0.17 Poland 9 0.11 Norway 5 0.08 Portugal 56 0.67 Panama 8 0.12 Qatar 4 0.05 Peru 5 0.08 Russia 27 0.32 Philippines 44 0.68 Saudi Arabia 14 0.17 Portugal 10 0.16 Singapore 138 1.66 Romania 6 0.09 South Africa 8 0.10 Russia 47 0.73 South Korea 241 2.89 Saudi Arabia 3 0.05 Spain 236 2.83 Singapore 52 0.81 Sweden 59 0.71 Slovakia 2 0.03 Switzerland 95 1.14 South Africa 25 0.39 Taiwan 774 9.30 Spain 170 2.64 Thailand 54 0.65 Sweden 25 0.39 Turkey 19 0.23 Switzerland 1 0.02 Ukraine 3 0.04 Taiwan 304 4.72 United Arab Emirates 27 0.32 Thailand 48 0.74 United Kingdom 343 4.12 Turkey 3 0.05 United States 1173 14.09 USA 4163 64.58 Ukraine 2 0.03 United Arab Emirates 2 0.03 United Kingdom 128 1.99 Vietnam 8 0.12 Total 8325 100 Total 6446 100.00

38

Appendix 3 Correlation matrix for the variables in lender-borrowers relationship

Book Leverage

Market Leverage

EBITDA-to -Assets Size Market-to

-Book Tangibility Risk Lender Book Leverage

Lenders Liabilities-to Assets

Lenders Deposits-to- Assets

Lenders Market Leverage

Book Leverage 1.0000 Market Leverage 0.6005 1.0000

EBITDA-to-Assets 0.0074 -0.3273 1.0000 Size -0.0651 -0.0605 0.1202 1.0000

Market-to-Book 0.0184 -0.4872 0.3804 -0.0938 1.0000 Tangibility 0.1938 0.2151 0.0011 -0.1194 -0.0650 1.0000

Risk 0.0015 0.0448 -0.1644 -0.0407 -0.0279 0.0616 1.0000 Lender Book Leverage -0.0095 0.0487 -0.0624 0.0634 -0.0487 0.0414 0.0425 1.0000

Lenders Liabilities-to Assets -0.1746 -0.1022 -0.0393 -0.0274 -0.0275 0.0210 -0.0112 0.4925 1.0000 Lenders Deposits-to-Assets -0.2107 -0.1823 0.0133 -0.0452 0.0442 0.0059 -0.0481 -0.2996 0.4936 1.0000

Lenders Market Leverage -0.0559 0.0419 -0.1154 0.1165 -0.0067 0.0901 0.0902 0.4247 0.2464 0.0617 1.0000

39

Appendix 4 Correlation matrix for the variables for borrower-lenders relationship

Debt-to-Book Assets

Debt-to-Market Value

Deposits-to-Market

Liabilities-to-Assets

Deposits-to-Assets Collateral Loans-to-

Assets Market-to-Book

EBITDA-to-Assets

Size Non-Performing Loans

Borrowers Book Leverage

Borrowers Market Leverage

Debt-to-Book Assets 1.0000 Debt-to-Market Value 0.7338 1.0000

Deposits-to-Market -0.6499 -0.4248 1.0000 Liabilities-to-Assets 0.3518 0.5611 -0.0650 1.0000

Deposits-to-Assets -0.9055 -0.7141 0.6967 -0.2972 1.0000 Collateral -0.1194 -0.1880 0.0761 -0.1089 0.1214 1.0000

Loans-to-Assets -0.2222 -0.2314 0.2547 -0.2148 0.3926 -0.4606 1.0000 Market-to-Book 0.6302 0.1589 -0.5511 -0.0720 -0.5470 -0.1087 0.1667 1.0000

EBITDA-to-Assets -0.1445 -0.2680 -0.0723 -0.0995 0.1209 0.0381 -0.0276 0.0304 1.0000 Size 0.2338 0.3231 -0.3384 0.2776 -0.3249 -0.0599 -0.4193 -0.0344 0.2414 1.0000

Non-Performing Loans -0.0003 0.0144 0.0134 -0.0826 0.0236 -0.0121 0.1708 0.0178 -0.1983 -0.1130 1.0000 Borrowers Book

Leverage 0.0640 0.0245 -0.0315 0.0006 -0.0441 -0.0312 0.0263 0.0575 -0.0279 -0.0914 -0.0211 1.0000 Borrowers Market

Leverage -0.0238 -0.0170 0.0870 0.0086 0.0510 -0.0196 0.1099 -0.0159 -0.0771 -0.1713 0.0003 0.5584 1.0000

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