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Connected Lending in Bank Lines of Credit

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Abstract

We find that firms usually obtain larger credit lines if their executives have common past employers or past board memberships with lenders. The effect not only exists in the initial amount of credit lines but also the amendment amount during renegotiation. The effect is stronger during the financial crisis and persists after the financial crisis. Having a relationship with banks increases the lines of credit for borrowers with financial constraints or high idiosyncratic risks. More importantly, connected firms obtain larger increases in short-term funding during renegotiation when they have negative credit quality or earnings news. Overall, our findings suggest that asymmetric information is reduced through ties between borrowers and lenders, which significantly improves the short-term funding capacity in the dynamic contract of credit lines.

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Notes

  1. A number of papers have examined firms’ usage of credit lines and factors determining lines of credit (Sufi 2009; Yun 2009; Jiménez et al. 2009; Acharya et al. 2013; Acharya et al. 2014).

  2. Although the connection is established based on employment relationships, it is not clear whether the underlying mechanism is based on personal or professional ties between lenders and borrowers. Thus, we do not specifically refer the relationships as professional connections. We thank an anonymous referee for pointing this out.

  3. Chava and Roberts (2008) provide the links between company names in the DealScan and gvkeys in the Compustat.

  4. Roberts (2015) collects 114 random firms based on two considerations: power and cost. The cost refers to the work we need to perform in order to read the SEC 10-K filings and accurately identify each amount renegotiation. Therefore, We have to get a reasonable sample size before we can start hand collection.

  5. Mathers and Giacomini (2016) also mention several concerns regarding the accuracy of Capital IQ’s credit line data.

  6. It’s not guaranteed that the matched facility-years are the origination years of credit facilities because renegotiation may be recorded as a separate observation in DealScan. We follow the criteria of Roberts (2015) to identify origination and renegotiation. If the matched facility-years are the origination years, then we collect credit line data from the origination to the termination years; if the facility-years are not the origination years, we trace back to the origination years and collect the credit line data until termination years.

  7. SEC Electronic Data Gathering, Analysis, and Retrieval (EDGAR) filing system has various phase-in periods for test groups from 1993 till 1995.

  8. We compare our sample with companies in Sufi (2009), and there are no overlapping firms.

  9. Roberts (2015) focuses on the covenant, interest rate spread, and the probability of renegotiations. Ippolito et al. (2019) investigate renegotiations of interest rate spreads and commitment fees following a violation of covenant. We focus on the credit line’s amendment amounts.

  10. We have included two rating dummies (investment grade and speculative grade) in our regressions.

  11. The results are robust and available upon request.

  12. For the two ratios, total credit lines to cash adjusted assets and total credit lines to total assets, connections are positively significant at the 5% level in both the dummy and the logarithmic specifications. The coefficient on the logarithm of connections indicates that a firm would have about 1.6% higher in the ratio of total credit lines to total assets by doubling the number of connections.

  13. Adjusted R2 is lower if we use credit line ratios as the dependent variables.

  14. We don’t run regressions of Log(Lead) as the independent variable, since a syndicate loan usually has one lead bank and Log(Lead) does not give us more information than the measure of D(lead).

  15. Connections with lead banks also result in higher credit line ratios. The results are available upon request.

  16. We count a firm having a connection with lead banks as long as it has at least one connection with a lead bank in the origination year.

  17. We don’t report the regression results of control variables to save space, and the full regression results are available upon request.

  18. We can add more matching dimensions and the results are robust. For example, if we add tangibility as a matching variable, the coefficient of D(executive) for model (1) is 0.1413 with a t-value 2.95 which is significant at 1% level. However, adding more matching variables significantly reduces our matched sample size.

  19. We owe a debt of gratitude to Murfin (2012) for sharing the calculation code of covenant strictness.

  20. Results are available upon request.

  21. Connections have significant impacts on the two ratios for firms with higher idiosyncratic risk. The ratio of total credit lines to cash adjusted assets can increase by 4.1% if the companies have connections with banks.

  22. Besides Roberts (2015), Panyagometh and Roberts (2010) use changes of borrowers’ Altman’s Z scores after origination to proxy for bank private information. We think that M/B ratio and profitability ratio are better measures for firms’ credit quality related to revolving bank lines.

  23. The purpose of the matching is to reduce the impact of size on results and to control the magnitude of the changes of measures.

  24. We run the robustness checks to control the financial ratios instead of the changes of the financial ratios and get similar results in terms of the signs and significance of the coefficients. We also add size as another control variable into models, and the coefficient is positively significant at the 1% level, while the signs and significance of the connection measures and the interaction terms do not change.

  25. Murfin (2012) summarizes the most popular financial covenants in the DealScan.

  26. The correlation table is available upon request. For example, the correlation between current ratio and quick ratio is 0.97, so we only include current ratio. The definitions of these financial ratios are listed in A.

  27. Without interaction terms, D(executive) and D(lead) are not significant in the regressions which indicates that percentage changes of total credit lines are generally similar for connected and unconnected facilities, as shown in Table 2. However, the timing of large and small percentage increases are different for connected and unconnected facilities.

  28. The results are available upon request.

  29. The results are available upon request.

  30. We use covenant violation data from Roberts’ website, which covers the period from 1996 to 2012.

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Acknowledgements

We thank Lamont Black, Pengjie Gao, Janet Gao, Todd Mitton, Wei Zhang and participants at the 2018 European Financial Management Association annual meeting and the Kent State University seminar for comments and suggestions.

Author information

Authors and Affiliations

  1. Department of Finance, Clark University, Worcester, MA, USA

    Shu Feng

  2. Department of Economics and Finance, Harmon College of Business and Professional Studies, University of Central Missouri, Warrensburg, MO, USA

    Chang Liu

  3. Department of Finance, Kent State University, Kent, OH, USA

    Xiaoling Pu

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  1. Shu Feng
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  2. Chang Liu
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Correspondence to Xiaoling Pu.

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Appendix: Variable definitions and constructions

Appendix: Variable definitions and constructions

Variable name

Variable definitions and constructions

Beta

using the past 3 years of monthly stock returns to run the Fama-French

 

/Carhart four factors model to obtain Beta

Cash

CHE

Cash adjusted assets

AT-CHE

Cash flow volatility

using past 3 years’ quarterly data to obtain the standard deviation of ratio (OANCFY/ATQ)

Deal in past 1-3 years

takes a value of one if the current borrower borrowed from one or more

indicator

members of the current syndicate in the most recent three years

Deal in past 4-6 years

takes a value of one if the current borrower borrowed from one or more

indicator

members of the current syndicate in the past 4 to 6 years

Deal in past 7 years or

takes a value of one if the current borrower borrowed from one or more

earlier years indicator

members of the current syndicate in the past 7 years or earlier

Firm age

the difference between the current year and the first year in which the

 

firm appeared in Compustat

Idiosyncratic risk

using the past 3 years of monthly stock returns to run the Fama-French

 

/Carhart four factors model to obtain the standard deviation of the residuals

Investment grade

takes a value of one if the credit rating is investment grade

Local bank indicator

local is defined as within 100 miles of the headquarters of the borrowers

M/B ratio

((AT-CHE)+PRCC_F*CSHO-CEQ)/(AT-CHE)

Net worth

(CEQ-CHE)/(AT-CHE)

Number of loans offered by

total number of non-overlapping loans offered by syndicate members

syndicate

during the prior year

Operating cash flow

OIBDP/AT

Profitability

OIBDP/(AT-CHE)

SA index

(-0.737*AT)+(0.043*AT*AT)-(0.040*age)

Size

Log(AT-CHE)

Speculative grade

takes a value of one if the credit rating belongs to speculative grade

Tangibility

PPENT/(AT-CHE)

Third-party past profess-

formed when two executives overlap through either a common past employer

-ional connections

or past board membership. Third party past connections

 

must predate the lending deal by more than 5 years and cannot involve

 

either the borrowing firm or lending institution in any way.

WW index

-0.091CF-0.062DIVPOS+ 0.021TLTD-0.044LNTA+ 0.102ISG-0.035SG

capital expenditure

CAPX/(AT-CHE)

debt to ebitda

(DLTT+DLC)/OIBDP

interest coverage

XINT/OIBDP

current ratio

ACT/LCT

net worth

(AT-LT-INTAN)/(AT-CHE)

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Feng, S., Liu, C. & Pu, X. Connected Lending in Bank Lines of Credit. J Financ Serv Res 61, 187–216 (2022). https://doi.org/10.1007/s10693-021-00354-z

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