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Do Firms Benefit from Having a Fortune 500 Customer/Supplier?Evidence from the Loan Market

Hsiangping TsaiCollege of Management

Yuan Ze University135 Yuan-Tung Road, Chung-Li, Taiwan

Phone: +886-3-463-8800 ext. 2672Fax: +886-3-463-3824

E-mail: [email protected]

Chuan-Ying YangCollege of Management


Phone: +886-3-463-8800 ext. 2672Fax: +886-3-463-3824


Haoyi WangCollege of Management


Phone: +886-3-463-8800 ext. 3624Fax: +886-3-463-3824


JEL classification: G15; G20; G21

Keywords: Fortune500, Supply Chain relationship, Terms of Loan

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Do Firms Benefit from Having a Fortune 500 Customer/Supplier? Evidence from the Loan Market

ABSTRACT

This study examines whether a firm’s customer-supplier relationship affects its loan

terms. Large and well-performed firms, such as Fortune 500, are prudent in selecting their

supply chain partners. From bank lenders’ view point, such relationship can convey information

regarding the borrower’s quality. Thus, we infer that a firm with a Fortune 500 as main partner

in the supply chain is of good quality. Consistent with this idea, we show that borrowers with a

Fortune 500 main partner in the supply chain obtain loans with lower spread and larger loan

amount. We further examine whether firms benefit from the Fortune partners’ trade credit

extension during crisis periods. The results show that, during crisis period, borrowers

themselves as Fortune suppliers (customers) have significantly larger accounts receivable (lower

accounts payable), while borrowers with Fortune customers have significantly lower accounts

receivable, indicating that Fortune firms help their partners through the trade credit arrangements.

That is, during crisis periods, Fortune firms extend trade credit and speedup payments to their

partners.

Keywords: Fortune 500, Supply Chain, Syndicated Loans

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1. Introduction

Existing studies have shown that a firm’s customer-supplier relationship affects its

performance, corporate policies, and shareholder wealth. Some studies examine the effect of a

firm’s specific event on its suppliers, customers, and rivals, such as merger and acquisitions (Fee

and Thomas 2004; Shahrur 2005), and bankruptcy filings (Hertzel et al. 2008). Others analyze

how the customer-supplier relationship affects a firm’s capital structure choice (Kale and Shahrur,

2007), information disclosure decision (Ellis, Fee and Thomas 2012) and investor wealth (Pandit,

Wasley, and Zach 2011; Wang 2012). Results of these studies imply that a firm’s

customer-supplier relationship contains crucial information about its position, competitive

relations in the product market, and future prospects in terms of performance. We expect that

such relationship could also affect the firm’s financing activities, i.e., the interaction between a

firm and its bank lenders. In this study, we aim at investigating whether bank lenders rely on

such relationship to gather information about its borrowers.

Literature regarding the bank-firm relationship indicates bank’s role in generating

information is the key to mitigate the information asymmetry between the two parties and thus

both parties can benefit from maintaining good relationship with each other. To name a few,

with expertise in screening and interaction with the borrowers, banks generate valuable

information about the borrowers and hence bank loans serve a certification role to signal

borrower quality to outside investors (James 1987, Lummer and McCollell 1989, Slovin, Sushka

and Hudson 1990; Dahiya, Puri, and Saunders 2003) and banks benefit from higher likelihood of

obtaining future business from existing customers (Bharath et al. 2007). With good banking

relationship, benefits to borrowers includes increased funding availability for small firms with

close ties to banks (Petersen and Rajan 1994), better performance during crisis period for firms

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with high-quality banks (Fok, Chang, and Lee, 2004), and smaller spreads, larger amounts and

reduced requirements on collaterals for firms that repeatedly borrow from the same lender

(Bharath et al. 2011).

In this study, we propose that banks can rely on the customer-supplier relationship as a

source of information to identify a borrower’s quality. It is well known that large and

well-performed firms, such as Fortune 500, are prudent in selecting their supply chain partners.

If a firm is selected as a main supplier of a Fortune 500, such relationship can convey information

regarding the non-Fortune firm’s quality to its lender. Thus, we expect lenders may incorporate

the information into the decisions of loan terms by assuming that a firm’s main partner in the

supply chain is a Fortune 500 as good quality.

Consistent with this idea, we show that borrowers with a Fortune 500 main partner in the

supply chain obtain loans with lower spread and larger loan amount. We further examine

whether firms benefit from the Fortune partners’ trade credit extension during crisis periods.

The results show that, during crisis period, borrowers themselves as Fortune suppliers (customers)

have significantly larger accounts receivable (lower accounts payable), while borrowers with

Fortune customers have significantly lower accounts receivable, indicating that Fortune firms

would extend trade credit or speedup payments to help their suppliers/customers during crisis

periods.

The rest of this paper is organized as follows. Section 2 provide theoretical background and

develop the empirical hypothesis. Section 3 describes the data and my sample. In Section 4, we

present the empirical design and variable definitions in the model. Section 5 reports the

empirical results. The final section concludes.

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2. Related literature and hypotheses

2.1 The importance of supply chain

Previous studies have shown that the supply chain plays an important role to affect firms

along the chain in different financial dimensions. Some scholars use firm specific events to

study the impacts on firms along the supply chain. Fee and Thomas (2004) examines the effects

of horizontal merge and acquisitions on rivals, customers and suppliers to identify the source of

gains to horizontal M&As. Their evidence supports the improvements in efficiency of

production and purchasing power, but does not support for collusions with rivals. In support of

efficiency considerations for takeovers, Shahrur (2005) demonstrates the positive wealth effect of

horizontal takeovers is associated with significant positive abnormal returns to rivals, suppliers

and customers. On the other hand, Hertael et al. (2008) examine the impact of a firm’s financial

distress along the supply chain. The effects on suppliers/customers depend on the industry

structure, i.e., whether there is a contagion effect on the rivals in the bankruptcy firm’s industry.

If the rivals experience contagion effects, suppliers suffer more from the contagion, but

customers are less affected.

Kale and Shahrur (2007) investigate the impact of supply chain relationship on a firm’s

capital structure decision. They propose that firms would reduce leverage (i.e. to keep low

likelihood of liquidation) to convince their suppliers/customers to develop relationship-specific

investments. They provide evidence that a firm’s leverage is more likely to be low when R&D

intensities of suppliers/customers are high, when strategic alliances and joint ventures along the

supply chain are more common, and when the industry of supplier/customer is more

concentrated.

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Some other scholars study the interrelationship between a firm’s information disclosure and

reactions of investor along the supply chain. Ellis, Fee and Thomas (2012) show that firms avid

disclosing proprietary information about customers when they concern that such action may turn

out to be aiding rivals. Pandit, Wasley and Zach (2011) documents information externalities

along the supply chain by showing that a firm’s stock price reacts to the earnings announcements

of its major customers.

Existing study shows that the interaction among firms along the supply chain contains

crucial information about a firm’s business relation, position in the product market, and its future

prospects in terms of performance. Such crucial information should also affect a firm’s

financing activities. However, we know little about this field. Only a few studies try to link a

firm’s supply chain relation to its financing activities. For example, Files and Gurun (2014)

document evidence that a firm’s loan terms are negatively affected by the financial restatement

announced by peer firms and firms along the supply chain.

2.2 Bank’s role in generating information

In the process of loan making, banks play a key role in generating borrower information to

mitigate the information asymmetry problem and thus both parties can benefit from maintaining

good relationship with each other.

It is well known that bank loans serve a certification role to signal borrower quality to

outside investors (James 1987; Lummer and McCollell 1989; Slovin, Sushka, and Hudson 1990).

In terms of benefits to borrowers, Petersen and Rajan (1994) documents small firms that buildup

close ties with a lender benefit from increased funding availability. Bharath et al. (2011) offers

evidence that firms may acquire better loan terms by repeatedly borrowing from the same lender,

including larger loans and reduced requirements on collaterals. For less transparent borrowers,

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borrowing from a relationship lender who is familiar with the borrower further helps reduce their

loan spread. In terms of benefits to banks, Bharath et al. (2007) provides evidence that the

probability of relationship banks to obtain future business from corporate customers is

significantly higher than that of non-relationship banks.

Evidence regarding lenders’ effort in gathering information to help monitor the borrowers is

also well-documented. For example, if the borrower is information problematic, lead arranges

would hold a larger loan share, arrange a more concentrated syndicate, and invite participant

lenders who are more familiar with the borrower (Dennis and Mullineaux 2000; Lee and

Mullineaux 2004; Sufi 2007). In addition, when borrowers are in financial distress, lenders

would suffer significant losses (Dahiya, Saunders and Srinivasan 2003).

2.3 Bank-firm relationship and access to credit during crisis period

One strand of literature focuses on exploring the effects of financial crisis or bank distress

on the bank-firm relationship. Slovin, Sushka, and Polonchek (1993) are the first to document

evidence that the failure of a bank harms its borrowers. Their evidence indicates the insolvency

of Continential Illinois Bank had negative impact on its borrower stock prices. Fok, Change,

and Lee (2004) examine the impact of bank relationship on firm performance for Taiwanese firms

during 1997 Asian financial crisis. They show that borrowing firms performed better around

Asian financial crisis if they were with high-quality banks or foreign banks. Looking at U.S.

firms during the Russian crisis period, Chava and Purnanadam (2011) offers evidence that

bank-dependent firms suffered larger declines in value, capital expenditure, and performance than

firms that were less-dependent on banks.

Above findings suggest that a firm’s funding source is restricted when its lender experiences

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unanticipated shocks and thus the performance will be negatively affected. Some scholars

suggest that firms with limited access to credit market may use trade credit from suppliers as a

source of funds. For example, Petersen and Rajan (1997) suggest that suppliers have advantage

in gathering information about buyers and thus are able to offer trade credit to firms that are

constrained from bank financing. Cull, Xu, and Zhu (2009) show that China firms with limited

access to formal bank credit tended to use trade credit from profitable suppliers as a substitute for

loans. Love, Preve, and Sarria-Allende (2007) examine effects of show that firms that are more

vulnerable to financial crisis reduce the provision of trade credit to customers. In addition,

Giannetti, Burkart and Ellingsen (2011) suggest that the extension of trade credit conveys

favorable information to uninformed lenders, which become more willing to lend.

Based on existing studies, our analysis includes trade credit and assumes that Fortune 500

firms are less vulnerable to financial crisis and are more likely to extend trade credit.

2.4 Hypotheses

This study aims at linking the two strands of literature to explore the effect of

supplier-customer relationship on a firm’s loan characteristics. Specifically, for each borrower,

we classify its main business partners along the supply chain into Fortune 500 firms or others.

Since the Fortune 500 tend to have stringent requirements in selecting their corporate partners,

we believe that a firm has a Fortune 500 firm in its supply chain help improve its operations,

performance, and thus its overall quality. We expect that bank lenders may take such firms as of

good quality (i.e, lower likelihood of default) and will be more willing to offer better loan terms

to them.

Hypothesis 1: A firm with a Fortune 500 supplier/customer is more likely to obtain better loan

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terms.

Also, we consider the supplier-customer relationship to have different effect on firm’s

funding source during crisis period. The benefit of being a partner of a Fortune 500 could be

that a firm would receive better trade credit terms from its Fortune partner during the crisis

periods. Thus, we create dummy variables for crisis periods to examine the effect of the

supplier-customer relationship on a firm’s trade credit in different economic cycles.

Hypothesis 2: A firm with a Fortune 500 supplier/customer is more likely to receive better trade

credit terms from its Fortune partner during crisis periods.

3. Data and sample

The relationship between suppliers and customers is collected from Compustat Segment

data. Compustat Segment data reports a firm’s main corporate customers. For each firm, we

identify a unique supplier-customer relationship. A firm’s major customer is the corporate

customer that accounts for the highest proportion of its sales revenue. A firm’s major supplier is

the supplying firm that accounts for the highest proportion of its cost of goods sold. In other

words, each firm has a most important customer/supplier. Then, we obtain the Fortune 500 rank

from Compustat and match it to our supplier-customer sample. Based on the Fortune 500

information, we are able to identify 3 possible combinations. First, both the supplier and

customer are not Fortune 500. Second, either the supplier or the customer is a Fortune 500.

Third, both the supplier and the customer are Fortune 500.

Loan data comes from Dealscan loan database. We use Dealscan-Compustat link from

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Chava and Roberts (2008) to connect the supplier-customer sample with the loan data. For each

loan, we rely on the information during 3 years prior to the deal active date to identify whether

the borrowing firm has a Fortune 500 supplier/customer or not. Lastly, we obtain borrower’s

financial information from Compustat database. The final sample contains 1,512 loan deals for

the supplier firms and 2,411 loan deals for the customer firms from 1984 to 2010.

Table 1 exhibits the descriptive statistics of our sample. Panel A in Table 1 provides the

information about the number of supplier firms and customer firms in syndicated loan market by

year. It shows from 1996 to 2007 the syndicated loan market boomed. Both for supplier and

customer, there are more than 4% of firms joining in syndicated loan market every year during

this period. Panel A in Table 1 also displays the distribution of the quantity of syndicated loan

deals. There are 1,512 loan deals made to suppliers and 2,411 loan deals made to customers

during the sample period. Then, it is consistent with the left-hand side of Panel A that the

syndicated loan market is prosperous from 1996 to 2007. According to the number of firms and

number of deals, the peak of syndicated loan is from 2000 to 2004, which include over 7% of

firms and deals. The number of loan deals is obviously much larger than other time period.

From Panel B of Table 1, we can observe how many supplier firms and customer firms in

the sample and how many of them had ever been included in the Fortune 500 list. There are 742

supplier firms and 722 customer firms in our sample. It shows that 36% of the customers had

ever been included in Fortune 500, but only 5% of suppliers had ever been included in Fortune

500. Fortune 500 lists contain more companies which are customers in supply chain such as

Wal-Mart, so there is a quite asymmetrical distribution on supplier and customer in the sample.

[Insert Table 1 here]

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4. Methodology

The first issue of this paper is whether the firms with Fortune 500 supplier/customer can get

better loan terms when they obtain funds from loan market. Secondly, we also investigate trade

credit of firms during different economic situations. For this reason, we develop the following

models to test our hypothesis. Please refer to Table 2 for detail information about variable

definitions.

Termit ∝ β1Fortune_partnerit β2Fortune_borrowerit β3Fortune_bothit β4Crisisit

∑ βjnj 5 Xit,j εit (1)

TradeCreditit ∝ β1Fortune_partnerit β2Fortune_borrowerit β3Fortune_bothit β4Crisisit

∑ βj7j 5 *FortuneDummyit,j ∑ βk

nk 8 Xit,k εit (2)

4.1 The terms of loans

Term is the terms of loans made to borrowers. In this study, we examine loan amount, loan

spread and the maturity. Generally speaking, the high quality firms are more likely to acquire

larger amount of loans from banks. We use deal amount (DealAmt) to measure the loan amount

and the unit of variable is millions of dollar. On the other hand, firms with good quality are

more likely to obtain loans with lower spread, and vice versa. We apply all-in spread drawn

(AllInDrawn) to measure the loan spread, which is the total costs borrowers pay for their loan and

contains coupon spread plus annual fee. Maturity (Maturity) represents the extension of loans

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in number of months.

4.2 Fortune 500 as a mark of high quality firms

Based on publicly available revenue data, Fortune Magazine publishes a list for the most

500 successful U.S. corporations as Fortune 500 in each year. Being included as Fortune 500 is

considered as a mark of prestige, i.e., high quality and low default probability from a banker’s

view point. Thus, most bankers are willing to offer better loan terms to such firms. In this

study, we hypothesize that a firm may benefit from having a high quality firm in its supply chain

when it raises bank loans. The possible benefits could be the availability or cost of funds.

Therefore, we rely on Fortune 500 firms to measure the quality of a firm’s partners in its supply

chain.

In order to examine the above benefits, we set three dummy variables into the model. If

both the supplier and the customer are not Fortune 500 firms, it will be the basic group of the

sample. Fortune_partner is one if a borrower is not a Fortune 500, but its partner

(supplier/customer) is a Fortune 500; zero otherwise. Fortune_borrower is one if the borrower

itself is a Fortune 500, but its partner (supplier/customer) is not a Fortune 500; zero otherwise.

Fortune_both is one if both the borrower and its partner (supplier/customer) are in the Fortune

500; zero otherwise.

We are curious about whether banks would base on the supply chain relationship to

determine the terms of loans. So, we use DealActiveDate in DealScan loan database from

Thomson Reuters LPC for further analysis. Based on the DealActiveDate, we search whether

borrowers have Fortune 500 suppliers/customers in the previous three years. We assume that

banks would depend on past supply chain relationship to determine the current loan deals. If a

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firm with at least a Fortune 500 supplier/customer in the previous three years, we identify it as a

firm with Fortune 500 business partner.

4.3 Crisis period

Previous research has shown that banks may reduce lending and raise the loan interest rates

during crisis period, thus we also consider the crisis effect on the loan terms. We indentify three

substantial crisis periods as bad time, including Asian crisis (1997/07/02~1998/12/31), dot-com

crisis (2000/03/10~2001/12/31) and 2008 Financial crisis (2008/09/15~2009/06/30),

respectively.1 Based on these crisis periods, we set a dummy variable - Crisis to distinguish the

good time and the bad time. Crisis equals to one if a loan was arranged in crisis period,

otherwise Crisis equals to zero.

4.4 The use of trade credit during crisis periods

The fact that banks tend to reduce lending in crisis periods may have relatively smaller

effect on high quality firms, such as Fortune 500. But, such reduction in lending could have

significantly negative effect on other firms. Therefore, we ask whether the supplier-customer

relationship helps to alleviate such negative effect by allowing firms to rely on trade credit during

an economic downturn, i.e. financing based on the supplier-customer relationship. For suppliers,

we use TC_AR to measure the trade credit effect. TC_AR is account receivables divided by total

assets. For customers, we use TC_AP to measure the trade credit effect. TC_AP is account

payables divided by total assets. To supplier borrowers, increased accounts receivables indicate

extending trade credit to customers, while reduced accounts receivables imply collecting

1 The time periods of these three substantial crises are identified by some news on internet. The news provides the complete process of those crises. The beginning date of crisis is the day that crisis exploded. Then, based on news, I can realize how long the crisis it sustained. The end day of crisis is the fiscal year end date that one or one and half year later the crisis.

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receivables more quickly. To customer borrowers, reduced accounts payables indicates

speeding up payments, while increased accounts payables imply delaying payments. With good

supplier-customer relationship, we expect Fortune 500 firms would extend trade credit or

speedup payments to help their suppliers/customers during crisis periods.

4.5 Other control variables

Size. We use a firm’s total assets (TA) to measure its firm size. In comparison to small firms,

large firms have more resources, more stable performance, and are less likely to default. Thus,

they are more likely to obtain better loan terms.

Investment. PP&E (PPE) measure the fixed assets of firms for their business operations. In the

process of loan offering, a firm with high PP&E indicates the lender could take it as a source of

repayment. We use PP&E scaled by total assets to mitigate the size effect on PP&E. Tobin’s q

(TBQ) is the book value of debt plus the market value of equity divided by total assets. A higher

Tobin’s q indicates a firm has better investment opportunities.

Leverage. Cash (CASH) measure the firm’s ability to repay. As for banks, the cash held by

firms directly affects whether borrower will repay their debt on schedule. The firms with more

cash holdings have higher probability to pay back their loans. We also scale the cash of firms

by their total assets in order to eliminate the size effect as PP&E case. Leverage (LVG) is total

debt divided by total assets. Firms with high leverage are more likely to default than firms with

low leverage. Thus, other things being equal, high leverage firms are less likely to obtain better

loan terms from banks.

Profitability. We use nature log of sales (LNS) and earnings before interest, tax, depreciation

and amortization (EBITDA) to evaluate a firm’s business performance and profitability. EBITDA

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is scaled by total assets. A firm with higher profitability is less likely to default and thus can

obtain better loan terms.

Credit quality. We obtain S&P credit rating from Compustat to represent a firm’s credit quality.

To transfer the S&P credit rating into rating scores (Rating_score), we assign the value of 1 to

AAA rated firms, 2 to AA+, etc. A higher value of Rating_score indicates lower credit quality.

5. Empirical Results

5.1 Firm and loan characteristics and by borrower type

Table 3 presents firm characteristics by borrower type. We can observe a pattern that, in

comparison to supplier firms, a larger proportion of customers in our sample are Fortune 500.

From the subsample that suppliers who borrow in the loan market, we can observe 34.4% of their

customers are Fortune 500. From the subsample that customers as borrowers, about 48% of

themselves are Fortune 500. A further comparison on the firm size (total assets, TA) suggests

that firm size of customers (Median=339.5) is much larger than the firm size of supplier

(Median=5,033). Both confirm that the sample is somewhat asymmetric as identified in Table 1

Panel B, i.e. more customers are Fortune 500 in our sample. Also, the customer borrowers have

significant better credit rating (mean=11.78, median=10) than the supplier borrowers

(mean=18.51, median 23). During the sample periods, about 21%~25% of bank loans are

offered during crisis period, while more than 75% of loans are offered during normal time.


In comparison to supplier borrowers, customer borrowers have better profitability in terms

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of sales (LNS), EBITDA (EBITDA), and tax payment (TAX). But, supplier borrowers have

higher sales growth (SG). Similarly, supplier borrowers have much larger Tobin’s Q

(Median=2.590) than customer borrowers (Median=1.806). Focusing on Dividend policy,

consistent with the business growth, supplier borrowers have lower dividend payout than

customer borrowers, probably due to the need to support for the higher growth. Based on the

maturity structure of long-term debt, supplier borrowers seem to have higher long-term debt ratio

(LTD1, LTD3 and LTD5) than those customer borrowers.

Table 4 presents loan and trade credit characteristics by borrower type, classified by supplier

and customer. In general, the characteristics of our customer sample seem to be more diffuse.

In comparison to customer loans, on average, loans to suppliers seem to have relatively smaller

amount, higher spread, and shorter maturity. Also, suppliers tend to have larger size of accounts

receivables and accounts payables, indicating that suppliers rely more on the trade credit. We

further classify loans into loans made in crisis versus normal periods. Loans obtained during

crisis periods tend to have shorter maturity. For the suppliers, average level of accounts

payables tends to be larger during crisis periods, possibly due to the management to postpone

payments.


5.2 Does having a Fortune 500 supplier/customer help obtain better loan terms?

Table 5 presents results for the effect of having a Fortune 500 along the supply chain on a

borrower’s loan terms. We study the suppliers and customers as borrowers separately and report

the results in Panels A and B of Table 4, respectively.


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In Panel A Table 5, we examine loan terms with the subsample that suppliers are borrowers.

Models (1)-(3) are the results for loan amount (DEALAMT), spread (AllInDrawn), and maturity

(Maturity), respectively. When a non-Fortune supplier borrower has a Fortune partner, i.e., a

Fortune customer, the loan spread is significantly lower (at 10% level), but no effects for loan

amount and maturity. When both the supplier borrower and its customer are Fortune 500, the

loan spread is significantly lower (at 1% level) and loan maturity is significantly shorter (at 1%

level). The results imply that banks may incorporate the supply chain relationship to identify

whether a firm is of good quality or not and thus building a good supplier-customer relationship

with Fortune Company helps to reduce loan spread. Also, being a Fortune firm is a mark of

high quality. Therefore, when both firms along the supply chain are Fortune firms, the

reduction in spread is significantly larger.

As for the borrower characteristics, most of them are consistent with our expectation. For

example, large firms tend to obtain loans with larger amount, lower spread, and longer maturity.

Firms with more PP&E are more likely to obtain long-term loans. Firms with better

performance (EBITDA), higher growth opportunity (Tobins q, TBQ), and better credit quality

(low RATING_SCORE) tend to obtain loans with lower spread. Loans offered during crisis

periods (CRISIS) tend to have shorter maturity.

In Panel B Table 5, we report the results for the subsample that customers are borrowers.

Models (1)-(3) are the results for loan amount (DEALAMT), spread (AllInDrawn), and maturity

(Maturity), respectively. When a non-Fortune customer has a Fortune partner, i.e., a Fortune

supplier, the loan amount is significantly larger (1% level), and the loan spread is significantly

lower (5% level). When a customer borrower itself is a Fortune firm but its supplier is not, the

loan amount is significantly larger (1% level), and the loan spread is significantly lower (5%

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level). However, when both the borrower and it supplier are Fortune firm, the loan spread

become significantly higher (1%), although the loan amount is larger (10% level). If we add up

the coefficient for both Forutne_partner and Fortune_both, the net effect of having a Fortune

supplier on a customer borrower’s spread is still lower than those without a Fortune supplier.

The effects of other borrower characteristics are generally consistent with intuition.

In general, our results suggest that a Fortune partner may help a firm to obtain better loan

terms. The implication to a non-Fortune borrower is that maintaining strong relationship with

high quality firms, such as Fortune 500, does help to improve the lender’s view on its quality.

5.3 Can a firm obtain better trade credit terms from its Fortune partner in crisis periods?

In Models (4)-(5), Panel A Table 5, we present results for the effect of a supplier borrower

having a Fortune 500 customer on its trade credit, TC_AR, measure by accounts receivables over

its total assets. We find that the TC_AR of a supplier who have a Fortune customer, are

significantly larger than those without a Fortune customer. However, during crisis period, such

firm’s TC_AR is significantly lower (1% level). The results indicate that with a Fortune

customer, the suppliers extend trade credit to its Fortune supplier in normal time, but can collect

receivables more quickly during hard time. Also, when a supplier itself is a Fortune firm, its

TC_AR is not different from those who are not Fortune firms. However, during crisis periods, it

has significantly lager TC_AR. The results imply that a Fortune supplier tends to extend trade

credit to its non-Fortune customers during crisis periods.

In Models (4)-(5), Panel B Table 5, we study the customer borrower to see the effect of

having a Fortune 500 supplier on its trade credit, TC_AP, measured by accounts payables over its

total assets. We find when the customer borrower itself is a Fortune 500, it tends to have

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significantly larger TC_AP during normal time, but have significantly smaller TC_AP during

crisis periods. The results are consistent with our results in Panel A Table 5 in that Fortune

firms speedup their payments to supplier during crisis period, although their payment periods

seems to be longer in normal time.

Generally, our results are consistent with our expectation that, with strong supplier-customer

relationship, Fortune firms would extend trade credit or speedup payments to help their

suppliers/customers during crisis periods.

5.4 Further tests

The core concept of our study relies on using Fortune 500 to identify whether the largest

supplier/customer is of high quality. This design has some drawbacks. For example, a firm

may have other important Fortune suppliers/customers, but our analysis excludes them. A firm

may have a high quality supplier/customer which is not included in the Fortune 500 list. Thus,

we also develop the following criteria to identify the effect of high quality suppliers/customers.

5.4.1 Customer-supplier relation based on Fortune proportion

In this subsection, we measure the customer-supplier relation with the size of the business.

We use the proportion of sales to Fortune customers for our supplier borrowers and the

proportion of purchase on the cost of goods sold from Fortune suppliers for our customer

borrowers.

Panel A Table 6 presents results for our supplier borrowers. We identify whether the

borrower itself is a Fortune supplier (Fortune_supplier) and the proportion of its sales to Fortune

firms (Fortune_CR). Models (1)-(3) reports results for loan terms. As expected, loan spread is

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significantly lower when the borrower itself is a Fortune firm and when the borrower has larger

proportion of sales goes to Fortune firms. In addition, we also find that a Fortune supplier tends

to obtain loans with shorter maturity. In Models (4)-(5), Panel A Table 6, we also conduct this

analysis for the trade credit terms, but do not obtain significant Fortune firm effects during crisis

periods.


Models (1)-(3), Panel B Table 6 report Fortune firm effects on loan terms for our customer

borrowers. Loan amount is significantly larger when the borrower itself is a Fortune firm and

when the borrower has larger proportion of purchase from a Fortune firm. The loan spreads is

also significantly lower when the borrower itself is a Fortune firm. However, in Models (4)-(5),

Panel B Table 6, the analysis on trade credit terms does not have significant Fortune firm effects

during crisis periods.

Based on the size of the customer-supplier relation, our evidence support that non-Fortune

firm could benefit from its Fortune partner to obtain better loan terms, such as larger loan

amounts and lower loan spread.

5.4.2 Classifying the quality of firms base on sales revenue

Since many high quality firms may not be included in the Fortune 500 list, the way we

separate firms into Fortune versus non-Fortune may categorize a non-Fortune high quality firm

into the low quality group. Thus, in this subsection, we follow the Fortune Magazine’s logic in

creating the Fortune 500 list to use a firm’s sales revenue to re-classify firms into high versus low

quality. We mark a firm as high quality if its sales revenue is greater than the median level;

otherwise, it is marked as low quality.

19


Table 7 displays the results of our new criterion for high quality firms. Good_partner

indicates the borrower itself is a low quality firm with a high quality partner, Good_borrower

indicates the borrower itself is a high quality firm with a low quality partner, and Good_both

indicates both the borrower and its partner are high quality firms. With this new classification,

we do observe evidence consistent with our expectation, but the results become less significant.

Such less significant results may indicate that firms benefit from their Fortune partner not only

because the Fortune partner has high sales revenue, but also because the Fortune partner offers

certification to reveal the firm’s quality.

6. Conclusions

A firm’s supply chain relation may contain information about its business relation, position

in the product market, and its future prospects in terms of performance. Although existing

literature has identified evidence that such relation may affect a firm’s corporate policies and

investor wealth, we know little about its impact on a firm’s financing activities.

In this study, we propose that banks can rely on such relation to identify a borrower’s quality

and then incorporate it into the decision on the borrower’s terms of loans. Based on the idea

that high quality firms have stringent requirements in selecting their corporate partners, we

believe that a high quality partner in a firm’s supply chain helps improve its operations,

performance, and its overall quality. Since the Fortune 500 firms are well-known firms and

considered as a mark of prestige and high quality, we use them to measure the quality of a firm in

the supply chain. Hence, we examine whether a Fortune partner in a firm’s supply chain helps

20

certify the quality of this firm to its lenders by examining the loan terms. In addition, we also

examine whether Fortune firms help their supply chain partners to go through tough times with

trade credit arrangements.

In support of our expectation, we show that a Fortune partner may help a firm to obtain

loans with lower spread and larger amount. We also find evidence that Fortune firms would

extend trade credit or speedup payments to help their suppliers/customers during crisis periods.

The implication to a non-Fortune borrower is that maintaining strong relationship with high

quality firms, such as Fortune 500, does help to improve the lender’s view on its quality.

21

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24

Table 1. The sample distribution by year, Fortune500 Company.

Panel A: The distribution of firms in syndicated loan market

Supplier Customer Supplier CustomerYear # of firms % # of firms % # of deals % # of deals %

1984 0 0.00 1 0.05 0 0.00 1 0.04

1985 0 0.00 2 0.10 0 0.00 2 0.08

1986 0 0.00 3 0.15 0 0.00 5 0.21

1987 7 0.53 13 0.66 9 0.60 14 0.58

1988 13 0.99 32 1.62 16 1.06 40 1.66

1989 18 1.37 31 1.57 27 1.79 43 1.78

1990 21 1.60 28 1.42 25 1.65 35 1.45

1991 19 1.45 25 1.27 25 1.65 33 1.37

1992 19 1.45 27 1.37 20 1.32 35 1.45

1993 34 2.60 43 2.18 38 2.51 53 2.20

1994 45 3.44 67 3.40 53 3.51 80 3.32

1995 44 3.36 68 3.45 55 3.64 85 3.53

1996 58 4.43 92 4.67 69 4.56 114 4.73

1997 75 5.73 86 4.36 96 6.35 111 4.60

1998 71 5.42 82 4.16 87 5.75 98 4.06

1999 64 4.89 88 4.46 69 4.56 104 4.31

2000 95 7.25 124 6.29 116 7.67 174 7.22

2001 115 8.78 151 7.66 128 8.47 190 7.88

2002 106 8.09 167 8.47 115 7.61 194 8.05

2003 84 6.41 168 8.52 93 6.15 209 8.67

2004 99 7.56 183 9.28 110 7.28 213 8.83

2005 75 5.73 143 7.26 83 5.49 172 7.13

2006 82 6.26 109 5.53 93 6.15 132 5.47

2007 64 4.89 108 5.48 74 4.89 132 5.47

2008 52 3.97 59 2.99 58 3.84 68 2.82

2009 25 1.91 36 1.83 25 1.65 36 1.49

2010 25 1.91 35 1.78 28 1.85 38 1.58

Total 1,310 100.00 1,971 100.00 1,512 100.00 2,411 100.00

25

Table 1. The sample distribution by year, Fortune500 Company.(Conti.)

Panel B: The distribution of Fortune 500 Company

Supplier Customer

Fortune 500 Obs. % Obs. %

0 706 95% 465 64%

1 36 5% 257 36%

Total 742 100% 722 100%

26

Table 2. Variable definitions Category Variable Name Definition

TA TA is total assets of firms as reported the current fiscal year prior to the syndicated loan deal active date.

Fortune dummy

Fortune_partner Fortune_partner is a dummy variable that equals to one if firm isn’t Fortune500 Company itself but has Fortune500 partner in supply chain.

Fortune_borrower Fortune_borrower is a dummy variable that equals to one if firm is Fortune500 Company itself but doesn’t have Fortune500 partner in supply chain.

Fortune_both Fortune_both is a dummy variable that equals to one if firm is Fortune500 Company itself and has Fortune500 partner in supply chain.

Timing dummy

CRISIS CRISIS is a dummy variable that take the value of one if the syndicated loan deal take place in crisis period.

Investment

CAPEX CAPEX is capital expenditure of firms divided by total assets of firms in the fiscal year prior the loan deal active date.

PPE PPE is property, plant and equipment of firms divided by total assets of firms in the fiscal year prior the loan deal active date.

CLT CLT is collateral of firms, which equals inventories plus PP&E divided by total assets of firms in the fiscal year prior the loan deal active date.

Credit quality RATING_SCORE RATING_SCORE measures the credit quality of firms. The original rating AAA will equal 1, AA+ equals 2 and so on.

Leverage

LVG LVG is total debts of firms divided by total assets of firms in the fiscal year prior the loan deal active date.

LTD LTD is long-term debt of firms divided by total assets of firms in the fiscal year prior the loan deal active date.

INTEX INTEX is interest expense of firms divided by total assets of firms in the fiscal year prior the loan deal active date.

CASH CASH is cash of firms divided by total assets of firms in the fiscal year prior the loan deal active date.

Maturity structure

LTD1 LTD1 is long-term debt due in one year of firms divided by total liabilities of firms in the fiscal year prior the loan deal active date.

LTD3 LTD3 is long-term debt due in three years of firms divided by total liabilities of firms in the fiscal year prior the loan deal active date.

LTD5 LTD5 is long-term debt due in five years of firms divided by total liabilities of firms in the fiscal year prior the loan deal active date.

Profitability

LNS LNS is the natural log of one plus sales of firms as reported the current fiscal year prior to the syndicated loan deal active date.

SG SG is annually sales growth of firms as reported the current fiscal year prior to the syndicated loan deal active date.

EBITDA EBITDA is earnings before interest, tax, depreciation and amortization of firms divided by total assets of firms in the fiscal year prior the loan deal active date.

TAX TAX is income taxes of firms divided by Pre-tax income of firms in the fiscal year prior the loan deal active date.

Dividend policy

DYc DYc is dividend yield based on calendar year, which is dividend per share of firms divided by share price of firms as reported the current fiscal year prior to the syndicated loan deal active date.

DYf DYf is dividend yield based on fiscal year, which is dividend per share of firms divided by share price of firms as reported the current fiscal year prior to the syndicated loan deal active date.

DPO DPO is dividend payout, which is dividend of firms divided by the EBITDA of firms in the fiscal year prior the loan deal active date.

Performance TBQ TBQ is Tobin’s q, which is total assets minus share equity plus market value of firms then divided by total assets of firms in the fiscal year prior the loan deal active date.

27

Table 3. Firm characteristics by borrower type

Suppliers Customers

Variable Name N Mean Median N Mean Median

TA 1,511 2,214 340 2,402 36,669 5,033

Fortune Dummy

Fortune_partner 1,512 0.344 0.000 2,411 0.007 0.000

Fortune_borrower 1,512 0.025 0.000 2,411 0.481 0.000

Fortune_both 1,512 0.060 0.000 2,411 0.015 0.000

Crisis Dummy

CRISIS 1,512 0.255 0.000 2,411 0.215 0.000

Investment

CAPEX 1,493 0.074 0.043 2,354 0.060 0.046

PPE 1,463 0.305 0.213 2,392 0.329 0.271

CLT 1,440 0.424 0.421 2,355 0.462 0.463

TBQ 1,444 2.590 1.479 2,226 1.806 1.449

Credit rating

RATING_SCORE 1,512 18.510 23.000 2,411 11.780 10.000

Leverage

LVG 1,508 0.666 0.550 2,401 0.664 0.644

LTD 1,508 0.246 0.197 2,401 0.247 0.229

INTEX 1,379 -0.142 0.088 2,316 0.147 0.139

CASH 1,497 0.103 0.047 2,352 0.062 0.033

Maturity structure of debt

LTD1 1,463 0.045 0.010 2,294 0.035 0.014

LTD3 1,217 0.046 0.008 1,967 0.039 0.018

LTD5 1,191 0.055 0.003 1,928 0.049 0.017

Profitability

LNS 1,510 5.467 5.554 2,402 8.319 8.434

SG 1,504 0.137 0.119 2,396 0.122 0.077

EBITDA 1,458 0.003 0.109 2,388 0.124 0.119

TAX 1,510 0.207 0.272 2,402 0.371 0.335

Dividend policy

DYc 1,444 0.011 0.000 2,237 0.021 0.007

DYf 1,446 0.011 0.000 2,237 0.022 0.008

DPO 1,452 0.078 0.000 2,380 0.075 0.063

Variable are defined as: TA is total assets of firms as reported the current fiscal year prior to the syndicated loan deal active date.

28

Fortune_partner equals to one if firm isn’t Fortune500 Company itself but has Fortune500 partner in supply chain. Fortune_borrower equals to one if firm is Fortune500 Company itself but doesn’t have Fortune500 partner in supply chain. Fortune_both equals to one if firm is Fortune500 Company itself and has Fortune500 partner in supply chain. CRISIS equals one if the syndicated loan deal take place in crisis period. CAPEX is capital expenditure of firms divided by total assets of firms in the fiscal year prior the loan deal active date. PPE is property, plant and equipment of firms divided by total assets of firms in the fiscal year prior the loan deal active date. CLT is collateral of firms, which equals inventories plus PP&E divided by total assets of firms in the fiscal year prior the loan deal active date. RATING_SCORE measures the credit quality of firms. The original rating AAA will equal 1, AA+ equals 2 and so on. LVG is total debts of firms divided by total assets of firms in the fiscal year prior the loan deal active date. LTD is long-term debt of firms divided by total assets of firms in the fiscal year prior the loan deal active date. INTEX is interest expense of firms divided by total assets of firms in the fiscal year prior the loan deal active date. CASH is cash of firms divided by total assets of firms in the fiscal year prior the loan deal active date. LTD1 is long-term debt due in one year of firms divided by total liabilities of firms in the fiscal year prior the loan deal active date. LTD3 is long-term debt due in three years of firms divided by total liabilities of firms in the fiscal year prior the loan deal active date. LTD5 is long-term debt due in five years of firms divided by total liabilities of firms in the fiscal year prior the loan deal active date. LNS is the natural log of one plus sales of firms as reported the current fiscal year prior to the syndicated loan deal active date. SG is annually sales growth of firms as reported the current fiscal year prior to the syndicated loan deal active date. EBITDA is earnings before interest, tax, depreciation and amortization of firms divided by total assets of firms in the fiscal year prior the loan deal active date. TAX is income taxes of firms divided by Pre-tax income of firms in the fiscal year prior the loan deal active date. DYc is dividend yield based on calendar year, which is dividend per share of firms divided by share price of firms as reported the current fiscal year prior to the syndicated loan deal active date. DYf is dividend yield based on fiscal year, which is dividend per share of firms divided by share price of firms as reported the current fiscal year prior to the syndicated loan deal active date. DPO is dividend payout, which is dividend of firms divided by the EBITDA of firms in the fiscal year prior the loan deal active date. TBQ is Tobin’s q, which is total assets minus share equity plus market value of firms then divided by total assets of firms in the fiscal year prior the loan deal active date.

29

Table 3. Loan and trade credit by borrower type

Supplier Customer

N Min Mean Median Max N Min Mean Median Max

All Loans

DEALAMT 1512 11.62 18.27 18.42 22.65 2411 12.47 19.72 19.81 24.04

AllInDrawn 1383 0.88 209.64 187.50 980.00 2237 7.00 119.83 65.00 1505.00

Maturity 1440 1.00 44.85 36.00 361.00 2320 1.00 52.05 36.00 362.00

TC_AR 1498 0.00 0.17 0.15 0.84 2379 0.00 0.15 0.12 0.98

TC_AP 1509 0.00 0.12 0.07 14.39 2379 0.00 0.11 0.07 0.89

Crisis Periods

DEALAMT 386 13.53 18.02 18.15 22.60 518 14.51 19.74 19.81 24.04

AllInDrawn 341 15.00 218.47 200.00 900.00 481 7.00 116.94 75.00 1200.00

Maturity 355 2.00 40.98 36.00 144.00 487 1.00 34.29 24.00 120.00

TC_AR 383 0.00 0.17 0.15 0.70 508 0.00 0.15 0.11 0.89

TC_AP 386 0.00 0.14 0.07 14.39 509 0.00 0.11 0.07 0.89

Normal Periods

DEALAMT 1126 11.62 18.35 18.42 22.65 1893 12.47 19.71 19.81 23.90

AllInDrawn 1042 0.88 206.75 175.00 980.00 1756 8.50 120.62 62.50 1505.00

Maturity 1085 1.00 46.11 46.00 361.00 1833 1.00 56.76 43.00 362.00

TC_AR 1115 0.00 0.17 0.15 0.84 1871 0.00 0.15 0.12 0.98

TC_AP 1123 0.00 0.11 0.07 3.19 1870 0.00 0.11 0.08 0.89 Variable are defined as: DEALAMT is the loan amount, which is in millions of dollar. AllInDrawn is loan spread, which equals coupon spread plus annual fee. It counts by numbers of basis points. Maturity represents the extension of loans in number of months. TC_AR is trade credit for supplier, which equals account receivables divided by total assets. TC_AP is trade credit for customer, which equals account payables divided by total assets.

30

Table 5. Regressions of term of loan for borrowers Panel A. Supplier Borrowers, Fortune Dummy (1) (2) (3) (4) (5) Dependent Variable DEALAMT AllInDrawn Maturity TC_AR TC_ARVariable Parameter Parameter Parameter Parameter ParameterIntercept 16.256 *** 255.357 *** 32.405 *** 0.385 *** 0.382 ***

(52.707) (7.807) (5.727) (14.744) (14.642) Fortune_partner -0.017 -14.391 * -0.828 0.020 *** 0.030 ***

(-0.203) (-1.645) (-0.547) (2.891) (3.761) Fortune_borrower 0.033 -15.980 -3.388 0.015 0.000

(0.140) (-0.635) (-0.779) (0.753) (-0.018) Fortune_both 0.005 -72.178 *** -8.967 *** 0.012 0.017

(0.029) (-3.845) (-2.761) (0.793) (1.056) LNTA 0.444 *** -13.854 *** 0.960 ** -0.014 *** -0.014 ***

(17.636) (-5.189) (2.078) (-6.733) (-6.757) PPE 0.147 -6.516 8.304 *** -0.222 *** -0.222 ***

(0.918) (-0.383) (2.823) (-16.342) (-16.351) CASH 0.121 15.866 6.310 -0.307 *** -0.310 ***

(0.408) (0.504) (1.158) (-12.201) (-12.351) LVG 0.189 *** 7.252 0.878 -0.007 * -0.006 *

(4.415) (1.593) (1.115) (-1.826) (-1.758) EBITDA -0.041 -21.520 *** -0.480 0.014 *** 0.014 ***

(-0.831) (-4.097) (-0.528) (3.411) (3.435) TBQ 0.019 -4.411 *** 0.222 0.001 0.001

(1.346) (-2.960) (0.861) (0.880) (0.901) RATING_SCORE -0.039 *** 2.676 *** 0.104 -0.002 *** -0.002 ***

(-4.194) (2.722) (0.611) (-2.528) (-2.495) CRISIS -0.086 5.152 -7.714 *** 0.008 0.020 **

(-0.962) (0.544) (-4.708) (0.994) (2.058) F01_CRISIS -0.040 ***

(-2.483) F10_CRISIS 0.118 **

(2.210) F11_CRISIS -0.024

(-0.635)

NOBs 1,188 1,188 1,188 1,188 1,188Adjusted R2 0.433 0.150 0.034 0.264 0.270

31

Table 5. Regressions of term of loan for borrowers (Conti.) Panel B. Customer Borrowers, Fortune Dummy (1) (2) (3) (4) (5) Dependent Variable DEALAMT AllInDrawn Maturity TC_AP TC_AP Variable Parameter Parameter Parameter Parameter Parameter Intercept 15.766 *** 196.831 *** -222.601 * 0.084 *** 0.081 ***

(70.776) (9.209) (-1.917) (3.580) (3.430) Fortune_partner 1.060 *** -70.830 ** -12.932 0.024 0.037

(3.148) (-2.192) (-0.074) (0.679) (0.931) Fortune_borrower 0.190 *** -12.048 ** 46.575 0.024 *** 0.028 ***

(3.202) (-2.115) (1.505) (3.754) (4.113) Fortune_both 0.323 * 56.021 *** 22.152 0.074 *** 0.079 ***

(1.791) (3.239) (0.236) (3.912) (3.799) LNTA 0.444 *** -19.996 *** 12.807 0.001 0.001

(24.118) (-11.328) (1.335) (0.450) (0.486) PPE -0.116 17.655 -29.172 -0.131 *** -0.132 ***

(-1.033) (1.644) (-0.500) (-11.070) (-11.105) CASH -0.258 85.660 *** 330.513 * -0.025 -0.029

(-0.730) (2.524) (1.792) (-0.662) (-0.762) LVG 0.585 *** 131.972 *** 30.289 0.072 *** 0.072 ***

(5.216) (12.258) (0.518) (6.049) (6.043) EBITDA 1.281 *** -274.940 *** 289.620 * -0.008 -0.008

(4.294) (-9.604) (1.862) (-0.247) (-0.248) TBQ 0.018 -5.749 *** -5.171 -0.007 *** -0.007 ***

(0.723) (-2.457) (-0.407) (-2.662) (-2.556) RATING_SCORE -0.026 *** 3.725 *** 7.210 *** 0.001 *** 0.001 ***

(-5.163) (7.775) (2.770) (2.408) (2.433) CRISIS -0.049 6.473 -17.379 0.003 0.015

(-0.829) (1.141) (-0.564) (0.459) (1.591) F01_CRISIS -0.062

(-0.698) F10_CRISIS -0.021 *

(-1.674) F11_CRISIS -0.025

(-0.496)

NOBs 1,932 1,932 1,932 1,932 1,932Adjusted R2 0.486 0.351 0.002 0.102 0.102

Variable are defined as: DEALAMT is the loan amount, which is in millions of dollar. AllInDrawn is loan spread, which equals coupon spread

plus annual fee. It counts by numbers of basis points. Maturity represents the extension of loans in number of months. TC_AP is trade credit for

customer, which equals account payables divided by total assets. TA is total assets of firms as reported the current fiscal year prior to the

syndicated loan deal active date. Fortune_partner equals to one if firm isn’t Fortune500 Company itself but has Fortune500 partner in supply chain.

Fortune_borrower equals to one if firm is Fortune500 Company itself but doesn’t have Fortune500 partner in supply chain. Fortune_both equals to

one if firm is Fortune500 Company itself and has Fortune500 partner in supply chain. CRISIS equals one if the syndicated loan deal take place in

crisis period. PPE is property, plant and equipment of firms divided by total assets of firms in the fiscal year prior the loan deal active date. CLT is

collateral of firms, which equals inventories plus PP&E divided by total assets of firms in the fiscal year prior the loan deal active date.

RATING_SCORE measures the credit quality of firms. The original rating AAA will equal 1, AA+ equals 2 and so on. LVG is total debts of firms

divided by total assets of firms in the fiscal year prior the loan deal active date. CASH is cash of firms divided by total assets of firms in the fiscal

year prior the loan deal active date. EBITDA is earnings before interest, tax, depreciation and amortization of firms divided by total assets of firms

in the fiscal year prior the loan deal active date. TBQ is Tobin’s q, which is total assets minus share equity plus market value of firms then divided

by total assets of firms in the fiscal year prior the loan deal active date. F01_CRISIS equals Fortune_partner times CRISIS. F10_CRISIS equals

Fortune_borrower times CRISIS. F11_CRISIS equals Fortune_both times CRISIS. The numbers in parentheses are t-value.

Note: ***, **, * Significant at the 1, 5, and 10 percent levels, respectively, for a two‐tailed test.

32

Table 6. Regressions of term of loan for borrowers based on Fortune proportion Panel A. Supplier Borrowers, Fortune Proportion (1) (2) (3) (4) (5) Dependent Variable DEALAMT AllInDrawn Maturity TC_AR TC_ARVariable Parameter Parameter Parameter Parameter ParameterIntercept 16.272 *** 226.827 *** 42.340 *** 0.398 *** 0.397 ***

(63.018) (8.467) (8.892) (17.637) (17.590) Fortune_supplier 0.070 -35.656 *** -7.643 *** -0.009 -0.008

(0.567) (-2.765) (-3.335) (-0.825) (-0.658)Fortune_CR -0.121 -106.721 *** -1.573 -0.036 -0.021

(-0.424) (-3.601) (-0.299) (-1.431) (-0.727)LNTA 0.439 *** -12.900 *** 0.289 -0.014 *** -0.014 ***

(20.074) (-5.684) (0.717) (-7.471) (-7.473)PPE 0.138 2.245 4.605 * -0.234 *** -0.234 ***

(1.010) (0.158) (1.829) (-19.596) (-19.614)CASH 0.272 43.440 3.837 -0.317 *** -0.318 ***

(0.996) (1.532) (0.761) (-13.294) (-13.314)LVG 0.120 *** 6.207 * 0.365 -0.002 -0.002

(3.901) (1.940) (0.641) (-0.598) (-0.600)EBITDA -0.046 -26.882 *** -0.202 0.015 *** 0.016 ***

(-0.958) (-5.350) (-0.226) (3.631) (3.672) TBQ 0.016 -5.453 *** 0.278 0.001 0.001

(1.142) (-3.778) (1.082) (0.998) (1.044) RATING_SCORE -0.038 *** 3.788 *** -0.122 -0.002 *** -0.002 ***

(-4.881) (4.653) (-0.842) (-2.679) (-2.704)CRISIS -0.036 8.110 -8.311 *** 0.005 0.010

(-0.474) (1.037) (-5.976) (0.736) (1.222) FS_CRISIS -0.010

(-0.429)FCR_CRISIS -0.059

(-1.040)

NOBs 1,542 1,542 1,542 1,542 1,542Adjusted R2 0.448 0.184 0.031 0.265 0.265

33

Table 6. Regressions of term of loan for borrowers based on Fortune proportion (Conti.) Panel B. Customer Borrowers, Fortune Proportion (1) (2) (3) (4) (5) Dependent Variable DEALAMT AllInDrawn Maturity TC_AP TC_APVariable Parameter Parameter Parameter Parameter ParameterIntercept 15.629 *** 197.409 *** -188.222 * 0.084 *** 0.083 ***

(77.115) (8.838) (-1.877) (3.985) (3.932) Fortune_customer 0.153 *** -10.241 * 37.679 0.025 *** 0.027 ***

(2.875) (-1.741) (1.427) (4.415) (4.534) Fortune_SR 2.140 *** 86.556 -70.429 0.138 ** 0.126 *

(3.389) (1.244) (-0.225) (2.089) (1.827) LNTA 0.458 *** -18.023 *** 10.400 0.001 0.001

(27.501) (-9.814) (1.261) (0.582) (0.567) PPE -0.162 -11.345 -14.993 -0.132 *** -0.133 ***

(-1.602) (-1.017) (-0.299) (-12.509) (-12.560)CASH 0.028 100.503 *** 255.644 * -0.013 -0.015

(0.092) (2.956) (1.675) (-0.412) (-0.462)LVG 0.682 *** 92.197 *** 47.769 0.064 *** 0.065 ***

(7.078) (8.684) (1.002) (6.413) (6.420) EBITDA 0.862 *** 40.760 ** 135.189 0.056 *** 0.056 ***

(4.782) (2.052) (1.516) (2.952) (2.966) TBQ 0.025 -26.320 *** 2.857 -0.011 *** -0.011 ***

(1.270) (-12.320) (0.298) (-5.319) (-5.248)RATING_SCORE -0.024 *** 4.888 *** 5.866 *** 0.001 *** 0.001 ***

(-5.383) (9.778) (2.613) (2.871) (2.842) CRISIS -0.053 15.261 *** -17.012 0.002 0.009

(-0.987) (2.559) (-0.635) (0.372) (1.074) FC_CRISIS -0.014

(-1.216)FSR_CRISIS 0.128

(0.527)

NOBs 2,239 2,239 2,239 2,239 2,239Adjusted R2 0.505 0.285 0.002 0.002 0.101

Variable are defined as: DEALAMT is the loan amount, which is in millions of dollar. AllInDrawn is loan spread, which equals coupon spread



syndicated loan deal active date. Fortune_customer equals to one if customer firm is Fortune500 Company. Fortune_SR is the proportion of

COGS from Fortune suppliers. CRISIS equals one if the syndicated loan deal take place in crisis period. PPE is property, plant and equipment of

firms divided by total assets of firms in the fiscal year prior the loan deal active date. CLT is collateral of firms, which equals inventories plus

PP&E divided by total assets of firms in the fiscal year prior the loan deal active date. RATING_SCORE measures the credit quality of firms. The

original rating AAA will equal 1, AA+ equals 2 and so on. LVG is total debts of firms divided by total assets of firms in the fiscal year prior the

loan deal active date. CASH is cash of firms divided by total assets of firms in the fiscal year prior the loan deal active date. EBITDA is earnings

before interest, tax, depreciation and amortization of firms divided by total assets of firms in the fiscal year prior the loan deal active date. TBQ is

Tobin’s q, which is total assets minus share equity plus market value of firms then divided by total assets of firms in the fiscal year prior the loan

deal active date. FC_CRISIS equals Fortune_customer times CRISIS. FSR_CRISIS equals Fortune_SR times CRISIS. The numbers in

parentheses are t-value.


34

Table 7. Regressions of term of loan for borrowers based on firm revenue Panel A. Supplier Borrowers, Firm revenue (1) (2) (3) (4) (5) Dependent Variable DEALAMT AllInDrawn Maturity TC_AR TC_AR Variable Parameter Parameter Parameter Parameter Parameter Intercept 16.005 *** 243.878 *** 36.366 *** 0.332 *** 0.332 ***

(44.580) (6.458) (5.482) (10.925) (10.859) Good_partner 0.092 -10.036 -0.926 -0.037 *** -0.038 ***

(0.613) (-0.635) (-0.333) (-2.903) (-2.598) Good_borrower -0.156 -6.378 -2.141 0.031 ** 0.041 ***

(-0.874) (-0.340) (-0.649) (2.041) (2.373) Good_both -0.033 -30.435 *** -6.170 *** 0.038 *** 0.035 ***

(-0.282) (-2.460) (-2.839) (3.787) (3.131) LNTA 0.458 *** -14.979 *** 0.607 -0.015 *** -0.015 ***

(15.964) (-4.966) (1.146) (-6.199) (-6.120) PPE -0.073 -2.037 5.514 -0.196 *** -0.197 ***

(-0.367) (-0.098) (1.508) (-11.713) (-11.738) CASH 0.204 10.597 12.545 ** -0.256 *** -0.256 ***

(0.598) (0.296) (1.994) (-8.890) (-8.884) LVG 0.518 *** 3.792 3.085 * 0.019 *** 0.019 ***

(5.551) (0.386) (1.790) (2.443) (2.438) EBITDA 0.159 -29.915 6.273 * 0.083 *** 0.083 ***

(0.846) (-1.513) (1.806) (5.229) (5.196) TBQ -0.002 -3.684 ** -0.302 -0.001 -0.001

(-0.096) (-2.138) (-0.998) (-0.394) (-0.379) RATING_SCORE -0.032 *** 3.974 *** 0.139 -0.001 -0.001

(-3.400) (3.953) (0.786) (-1.181) (-1.147) CRISIS -0.156 9.424 -6.729 *** 0.007 0.003

(-1.592) (0.912) (-3.707) (0.862) (0.145) G01_CRISIS 0.005

(0.165) G10_CRISIS -0.038

(-1.139) G11_CRISIS 0.012

(0.568)

NOBs 937 937 937 937 937Adjusted R2 0.449 0.151 0.032 0.315 0.315

35

Table 7. Regressions of term of loan for borrowers based on firm revenue (Conti.) Panel B. Customer Borrowers, Firm revenue (1) (2) (3) (4) (5) Dependent Variable DEALAMT AllInDrawn Maturity TC_AP TC_APVariable Parameter Parameter Parameter Parameter ParameterIntercept 15.089 *** 178.183*** -231.393* 0.072 *** 0.071***

(62.877) (7.490) (-1.835) (2.820) (2.746)Good_partner 0.139 5.216 1.285 0.009 0.009

(1.512) (0.572) (0.027) (0.926) (0.829)Good_borrower 0.455 *** 13.711 -6.582 -0.009 -0.008

(5.044) (1.535) (-0.139) (-0.886) (-0.761)Good_both 0.507 *** 9.278 10.567 0.014 * 0.017**

(6.889) (1.272) (0.273) (1.816) (1.982)LNTA 0.490 *** -20.528*** 16.767* 0.003 * 0.003*

(27.186) (-11.482) (1.769) (1.782) (1.756)PPE 0.108 20.702* -21.390 -0.124 *** -0.124***

(0.942) (1.826) (-0.356) (-10.114) (-10.134)CASH -0.264 79.894** 338.834* -0.018 -0.020

(-0.755) (2.305) (1.844) (-0.477) (-0.528)LVG 0.628 *** 142.077*** 40.555 0.074 *** 0.074***

(5.660) (12.924) (0.696) (6.255) (6.266)EBITDA 0.831 *** -285.972*** 286.380* -0.018 -0.019

(2.753) (-9.554) (1.805) (-0.556) (-0.582)TBQ 0.018 -5.112** -5.981 -0.007 *** -0.007***

(0.754) (-2.135) (-0.471) (-2.662) (-2.652)RATING_SCORE -0.028 *** 4.054*** 6.116*** 0.001 0.001

(-5.835) (8.569) (2.439) (1.342) (1.354)CRISIS -0.045 6.566 -16.448 0.003 0.011

(-0.758) (1.128) (-0.533) (0.468) (0.724)G01_CRISIS -0.001

(-0.061)G10_CRISIS -0.001

(-0.030)G11_CRISIS -0.015

(-0.842)

NOBs 1,934 1,934 1,934 1,934 1,934Adjusted R2 0.500 0.350 0.001 0.095 0.094Variable are defined as: DEALAMT is the loan amount, which is in millions of dollar. AllInDrawn is loan spread, which equals coupon spread



syndicated loan deal active date. Good_partner equals to one if firm revenue of firm isn’t higher than median of firm revenue but its partner is.

Good_borrower equals to one if firm revenue of firm is higher than median of firm revenue but its partner isn’t. Good_both equals to one if firm

revenue of firm and its partner is higher than median of firm revenue. CRISIS equals one if the syndicated loan deal take place in crisis period.

PPE is property, plant and equipment of firms divided by total assets of firms in the fiscal year prior the loan deal active date. CLT is collateral of

firms, which equals inventories plus PP&E divided by total assets of firms in the fiscal year prior the loan deal active date. RATING_SCORE

measures the credit quality of firms. The original rating AAA will equal 1, AA+ equals 2 and so on. LVG is total debts of firms divided by total

assets of firms in the fiscal year prior the loan deal active date. CASH is cash of firms divided by total assets of firms in the fiscal year prior the

loan deal active date. EBITDA is earnings before interest, tax, depreciation and amortization of firms divided by total assets of firms in the fiscal

year prior the loan deal active date. TBQ is Tobin’s q, which is total assets minus share equity plus market value of firms then divided by total

assets of firms in the fiscal year prior the loan deal active date. G01_CRISIS equals Good_partner times CRISIS. G10_CRISIS equals

Good_borrower times CRISIS. G11_CRISIS equals Good_both times CRISIS. The numbers in parentheses are t-value.


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