Skip to main content

CDS trading and nonrelationship lending dynamics


We investigate how credit default swaps (CDSs) affect lenders’ incentives to initiate new lending relationships. We predict that CDSs reduce adverse selection that nonrelationship lead arrangers face when competing for loans. Consistently, we find that a loan is more likely to be syndicated by a nonrelationship lead arranger following CDS trading initiation on a borrower’s debt. We also show that borrowers that obtain loans from nonrelationship lead arrangers in the post-CDS trading initiation period are more opaque, in line with the effect of CDSs being more pronounced for borrowers for which adverse selection costs are higher. Further analyses show that, relative to relationship lead arrangers, nonrelationship lead arrangers have lower monitoring incentives in the post-period, as reflected by less restrictive covenants and performance pricing provisions they impose and by the reduced loan shares they retain. Moreover, we find that borrowers of nonrelationship lead arrangers following CDS trading initiation have higher growth opportunities and more volatile operations, consistent with such borrowers benefiting more from weaker restrictions on their activities imposed by lenders. Lastly, lower monitoring incentives of CDS-protected nonrelationship lead arrangers also decrease the propensity of inexperienced participants to join their syndicates. Overall, our findings suggest that CDS trading significantly changes nonrelationship lending dynamics.

This is a preview of subscription content, access via your institution.


  1. 1.

    In a concurrent paper, Shan et al. (2016) also document the higher likelihood of loans syndicated by nonrelationship lead arrangers following CDS initiation. They attribute this finding to borrowers switching to new lead arrangers because their current borrower-lead-arranger relationship is compromised by CDSs (i.e. borrowers switch away from relationship lenders because they are concerned about these lenders’ weaker monitoring incentives). In contrast, we suggest that CDSs decrease adverse selection between incumbent and nonrelationship lenders, thus increasing the latter’s willingness to compete for new borrowers and consequently the likelihood of winning a loan deal. Our findings with respect to nonrelationship lead arrangers’ monitoring incentives further undermine Shan et al.’s (2016) motivation, as we show that relationship lead arrangers actually do not decrease their monitoring following CDS initiation, while nonrelationship lead arrangers monitor substantially less intensively than relationship lead arrangers.

  2. 2.

    Hedging channel is important, even if CDS spreads reflect private information, because this information may not fully reveal relationship lenders’ knowledge. Although CDS spreads provide information about the overall credit riskiness of the borrower, nonrelationship lenders cannot pinpoint the drivers of this riskiness. This should allow relationship lenders to draw more accurate inferences about the borrower and loan quality. Further, frictions associated with CDS trading are likely to prevent CDS spreads from being fully revealing (Blanco et al. 2005; Batta et al. 2016).

  3. 3.

    Corporate loan securitizations differ from the securitization of other loan types, such as mortgages, where originating banks indeed sell off the entire mortgage to the securitization SPV.

  4. 4.

    Consider hypothetically that a lead arranger can sell its entire share to a CLO or on the secondary market. In this case, the lead arranger relinquishes its control rights to the loan buyer and essentially forgoes the lending relationship with the borrower. Therefore, if a nonrelationship lead arranger wants to establish a new lending relationship with a borrower, it is unlikely to sell its entire share. In contrast, when a lead arranger hedges its credit exposure via CDSs, it retains control rights and its lending relationship because hedging does not require the lender to sever a loan contract with the borrower, further highlighting the benefits of the CDS setting for exploring our research question.

  5. 5.

    High riskiness of these borrowers is explained by the demand of CLOs and institutional secondary market buyers for loans with very high interest spreads (e.g., Bushman et al. 2010; Benmelech et al. 2012; Lim et al. 2014; Bozanic et al. 2018; Loumioti and Vasvari 2019; Addoum and Murfin 2020).

  6. 6.

    We acknowledge that, with respect to our second channel of the revelation of private information in the CDS spread, loan fair values as estimated and reported by CLOs and secondary loan prices may also reflect private information. However, as we discuss above, loan fair values and secondary prices are not available for the largest segment of the loan market—investment grade and lower risk non-investment grade borrowers. CDS trading also provides us with a more powerful setting to explore the reduction in adverse selection and its effect on nonrelationship lending, because it works through both the information revelation and the hedging channel, with the latter being largely unavailable for lead arrangers via securitization and secondary market sale channels.

  7. 7.

    Amiram et al. (2017) are interested in exploring the overall effect of CDSs on information asymmetry between the lead arranger and syndicate participants and therefore do not distinguish empirically between adverse selection and moral hazard. However, Sufi (2007) suggests that moral hazard is the key problem associated with information asymmetry within the syndicate and emphasizes that moral hazard, and not adverse selection, forces the lead arranger to retain a larger share of the loan and form a more concentrated syndicate. This suggests that Amiram et al. (2017) findings may be attributed primarily to the moral hazard problems within the syndicate.

  8. 8.

    One may argue that hedging is likely to be more expensive for nonrelationship lead arrangers, because CDS sellers should price their weaker monitoring incentives, as moral hazard problems increase the costs of insurance protection (Arrow 1963; Pauly 1968; Zeckhauser 1970; Shavell 1979; Arnott and Stiglitz 1991). However, the positive relation between the cost of insurance and the moral hazard hinges on two primary assumptions: 1) insurers can identify protection buyers with moral hazard problems, and 2) insurers bear higher expenses, due to the moral hazard problem of the insured. These assumptions are unlikely to hold in the CDS setting. First, because of the very high volume of speculative trades in the CDS market (e.g., Brunnermeier 2009; Oehmke and Zawadowski 2016), CDS sellers are mostly unable to determine whether the buyer’s intent is hedging or speculative trading. Thus, if CDS sellers cannot separate hedgers from speculators, they cannot require higher CDS spreads from nonrelationship lenders with a greater moral hazard problem. Second, CDS sellers do not necessarily bear the costs of credit events because they mostly maintain matched positions (D’Errico et al. 2018). Specifically, when CDS sellers write a CDS contract, they typically resell it to other market participants by employing opposite trades (i.e., back-to-back trades), which minimizes their credit risk. Thus, even if CDS sellers can recognize weaker monitoring incentives of nonrelationship lead arrangers, they may not bear the related costs and thus do not incorporate them into the CDS spreads. Consistently, in untabulated analyses, we find that borrowers that issue loans with nonrelationship lead arrangers do not have higher CDS spreads, relative to borrowers of relationship lead arrangers.

  9. 9.

    The vast majority of the CDS initiations are in years 2001–2007, ranging from 23.72% in 2001 to 4.39% in 2006. Around 58% of the CDS firm observations are post-CDS initiation.

  10. 10.

    We acknowledge that defining No Relationship Lead Arranger based on the CDS initiation date for CDS firms and based on loan issuance date for non-CDS firms may undermine our findings. In untabulated analyses, for each nonrelationship lead arranger of CDS firms, we exclude from the sample all loans the lead arranger syndicated after the first loan it issued to the borrower following CDS initiation. Consequently, our definition of nonrelationship loans in these analyses is identical for CDS and non-CDS firms. Our findings continue to hold. In additional robustness tests, we restrict research sample to the three-year, five-year and seven-year windows around the CDS initiation and exclude subsequent loans after the nonrelationship lead arranger’s first loan in both the pre- and post-CDS initiation periods. Our findings are robust.

  11. 11.

    This variable takes the value of 0 for all loans to non-CDS firms.

  12. 12.

    Our findings are unchanged when we control for the weighted-average loan characteristics of all loans in the package, where weights are based on loan (facility) size (untabulated).

  13. 13.

    We do not control for the number of covenants and performance pricing provisions, as these characteristics are typically determined during the loan negotiation and therefore cannot affect lenders’ choice of whether to start a new lending relationship. In untabulated analyses, we find that our results are robust to the inclusion of these variables.

  14. 14.

    The number of observations in these analyses is smaller than that in the full sample because a logit estimation drops observations if the binary dependent variable takes the same value within the same firm or year, while an OLS estimation drops observations if there is only one observation for each firm or year-month (i.e., within each fixed effect group).

  15. 15.

    Note that we measure economic significance based on the OLS specification for all tests.

  16. 16.

    With respect to controls, the negative and significant coefficient on Leverage suggests that higher leverage deters nonrelationship lead arrangers. The positive and significant coefficient on Tangibility implies that higher asset tangibility attracts nonrelationship lead arrangers. When borrowers issue longer maturity loans and those with guarantors, we find that these loans are more likely to be arranged by nonrelationship lenders.

  17. 17.

    We measure reporting conservatism following Khan and Watts (2009) and accrual quality based on the modified Jones model, as adjusted by Hribar and Nichols (2007).

  18. 18.

    To classify covenants as capital covenants, we follow Christensen and Nikolaev (2012). For loans with zero covenants, the Performance covenants variable takes the value of zero. In untabulated robustness tests, we find consistent results when we restrict our sample to loan packages with available covenant data.

  19. 19.

    Some of these contracts may simultaneously have a provision that lowers the interest rate if a borrower improves its performance (i.e., an interest rate decreasing provision). However, the effect of Interest Rate Increasing PP on a lender’s control rights does not depend on whether this other provision is included.

  20. 20.

    If a loan package has more than one lead arranger (6.5% of sample packages), all lead arrangers are accounted for in estimating model (3). Our results are unchanged when we exclude these deals from the analyses.

  21. 21.

    We exclude #Covenants (PP) from the model when we examine performance covenants (interest increasing provisions), as it is highly correlated with the dependent variable.

  22. 22.

    In addition, while nonrelationship lead arrangers retain higher loan share in the pre-CDS initiation period, as reflected by the positive and significant coefficients on No Relationship Lead Arranger, the sum of the coefficients on No Relationship Lead Arranger and POST × No Relationship Lead Arranger indicates that the loan share nonrelationship lead arrangers retain in the post-period resembles that of relationship lead arrangers. This evidence further highlights the weakening of their monitoring incentives.

  23. 23.

    Consistently, Asquith et al. (2005) show that the interest rate at loan origination decreases with the inclusion of the interest-rate-increasing but not with the interest-rate-decreasing performance pricing provision.

  24. 24.

    Because Amount is in natural logarithm, we take the exponential of the coefficient on POST × No Relationship Lead Arranger (i.e., exp.(−0.084)-1) to calculate the economic significance of −8.1%, which translates into $93 million. The amounts represent 9.5% of our sample mean, given the average facility amount of $983 million in our sample.


  1. Acharya, V., & Johnson, T. (2007). Insider trading in credit derivatives. Journal of Financial Economics, 84(1), 110–141.

    Google Scholar 

  2. Addoum, J., & Murfin, J. (2020). Equity price discovery with informed private debt. The Review of Financial Studies, 33(8), 3766–3803.

    Google Scholar 

  3. Amiram, D., Beaver, W., Landsman, W., & Zhao, J. (2017). The effects of CDS trading initiation on the structure of syndicated loans. Journal of Financial Economics, 126(2), 364–382.

    Google Scholar 

  4. Arnott, R., & Stiglitz, J. (1991). Moral hazard and nonmarket institutions: Dysfunctional crowding out of peer monitoring? The American Economic Review, 81, 179–190.

    Google Scholar 

  5. Arrow, K. (1963). Uncertainty and the welfare economics of medical care. The American Economic Review, 53(5), 941–973.

    Google Scholar 

  6. Ashcraft, A., & Santos, J. (2009). Has the CDS market lowered the cost of corporate debt? Journal of Monetary Economics, 56(4), 514–523.

    Google Scholar 

  7. Asquith, P., Beatty, A., & Weber, J. (2005). Performance pricing in bank debt contracts. Journal of Accounting and Economics, 40(1), 101–128.

    Google Scholar 

  8. Ball, R., Bushman, R., & Vasvari, F. (2008). The debt contracting value of accounting information and loan syndicate structure. Journal of Accounting Research, 46(2), 247–287.

    Google Scholar 

  9. Batta, G., Qiu, J., & Yu, F. (2016). Credit derivatives and analyst behavior. The Accounting Review, 91(5), 1315–1343.

    Google Scholar 

  10. Benmelech, E., Dlugosz, J., & Ivashina, V. (2012). Securitization without adverse selection: The case of CLOs. Journal of Financial Economics, 106(1), 91–113.

    Google Scholar 

  11. Berger, A., & Udell, G. (1995). Relationship lending and lines of credit in small firm finance. Journal of Business, 68(3), 351–381.

    Google Scholar 

  12. Berlin, M., & Mester, L. (1992). Debt covenants and renegotiation. Journal of Financial Intermediation, 2(2), 95–133.

    Google Scholar 

  13. Beyer, A., Cohen, D., Lys, T., & Walther, B. (2010). The financial reporting environment: Review of the recent literature. Journal of Accounting and Economics, 50(2–3), 296–343.

    Google Scholar 

  14. Bharath, S., Dahiya, S., Saunders, A., & Srinivasan, A. (2011). Lending relationships and loan contract terms. The Review of Financial Studies, 24(4), 1141–1203.

    Google Scholar 

  15. Blanco, R., Brennan, S., & Marsh, I. (2005). An empirical analysis of the dynamic relation between investment grade bonds and credit default swaps. The Journal of Finance, 60(5), 2255–2281.

    Google Scholar 

  16. Bolton, P., & Oehmke, M. (2011). Credit default swaps and the empty creditor problem. Review of Financial Studies, 24(8), 2617–2655.

    Google Scholar 

  17. Boot, A. (2000). Relationship banking: What do we know? Journal of Financial Intermediation, 9(1), 7–25.

    Google Scholar 

  18. Bozanic, Z., Loumioti, M., & Vasvari, F. (2018). Corporate loan securitization and the standardization of financial covenants. Journal of Accounting Research, 56(1), 45–83.

    Google Scholar 

  19. Bradley, M., & Roberts, M. (2015). The structure and pricing of corporate debt covenants. The Quarterly Journal of Finance, 5(2), 1550001.

    Google Scholar 

  20. Brunnermeier, M. (2009). Deciphering the liquidity and credit crunch 2007-2008. Journal of Economic Perspectives, 23(1), 77–100.

    Google Scholar 

  21. Bushman, R., Smith, A., & Wittenberg-Moerman, R. (2010). Price discovery and dissemination of private information by loan syndicate participants. Journal of Accounting Research, 48, 921–972.

    Google Scholar 

  22. Bushman, R., Williams, C., & Wittenberg-Moerman, R. (2017). The informational role of the media in private lending. Journal of Accounting Research, 55(1), 115–152.

    Google Scholar 

  23. Chakraborty, I., Chava, S., & Ganduri, R. (2015). Credit default swaps and moral hazard in bank lending. Working paper, Georgia Institute of Technology.

  24. Christensen, H., & Nikolaev, V. (2012). Capital versus performance covenants in debt contracts. Journal of Accounting Research, 50(1), 75–116.

    Google Scholar 

  25. Christensen, H., Nikolaev, V., & Wittenberg-Moerman, R. (2016). Accounting information in financial contracting: The incomplete contract theory perspective. Journal of Accounting Research, 54(2), 397–435.

    Google Scholar 

  26. D’Errico, M., Battiston, S., Peltonen, T., & Scheicher, M. (2018). How does risk flow in the credit default swap market? Journal of Financial Stability, 35, 53–74.

    Google Scholar 

  27. Drucker, S., & Puri, M. (2005). On the benefits of concurrent lending and underwriting. The Journal of Finance, 60(6), 2763–2799.

    Google Scholar 

  28. Ertan, A. (2017). Real earnings management through syndicated lending. Working Paper.

  29. Fang, L., Ivashina, V., & Lerner, J. (2013). Combining banking with private equity investing. The Review of Financial Studies, 26(9), 2139–2173.

    Google Scholar 

  30. Financial Times. (2005). Banks warned about insider trading in credit derivatives, April 25.

  31. Godlewski, C., Sanditov, B., & Burger-Helmchen, T. (2012). Bank lending networks, experience, reputation, and borrowing costs: Empirical evidence from the French syndicated lending market. Journal of Business Finance & Accounting, 39(1–2), 113–140.

    Google Scholar 

  32. Güntay, L., & Hackbarth, D. (2010). Corporate bond credit spreads and forecast dispersion. Journal of Banking & Finance, 34(10), 2328–2345.

    Google Scholar 

  33. Greene, W. (2004). The behaviour of the maximum likelihood estimator of limited dependent variable models in the presence of fixed effects. The Econometrics Journal, 7(1), 98–119.

    Google Scholar 

  34. Greenbaum, S., & Thakor, A. (1995). Contemporary financial lntermediation. Dryden Press.

  35. Hoshi, T., Kashyap, A., & Scharfstein, D. (1990). The role of banks in reducing the costs of financial distress in Japan. Journal of Financial Economics, 27(1), 67–88.

    Google Scholar 

  36. Hribar, P., & Nichols, C. (2007). The use of unsigned earnings quality measures in tests of earnings management. Journal of Accounting Research, 45(5), 1017–1053.

    Google Scholar 

  37. Hu, H., & Black, B. (2008). Debt, equity and hybrid decoupling: Governance and systemic risk implications. European Financial Management, 14(4), 663–709.

    Google Scholar 

  38. Ivashina, V. (2009). Asymmetric information effects on loan spreads. Journal of Financial Economics, 92(2), 300–319.

    Google Scholar 

  39. Ivashina, V., & Kovner, A. (2011). The private equity advantage: Leveraged buyout firms and relationship banking. The Review of Financial Studies, 24(7), 2462–2498.

    Google Scholar 

  40. Khan, U., Li, X., Williams, C., & Wittenberg-Moerman, R. (2019). The effect of information opacity and accounting irregularities on personal lending relationships: Evidence from lender and manager co-migration. The Accounting Review, 94(4), 303–344.

    Google Scholar 

  41. Khan, M., & Watts, R. (2009). Estimation and empirical properties of a firm-year measure of accounting conservatism. Journal of Accounting and Economics, 48(2–3), 132–150.

    Google Scholar 

  42. Kim, J., Shroff, P., Vyas, D., & Wittenberg-Moerman, R. (2018). Credit default swaps and managers’ voluntary disclosure. Journal of Accounting Research, 56, 953–988.

    Google Scholar 

  43. Lee, S., & Mullineaux, D. (2004). Monitoring, financial distress, and the structure of commercial lending syndicates. Financial Management, 33(3), 107–130.

    Google Scholar 

  44. Liberti, J., & Petersen, M. (2018). Information: Hard and soft. Review of Corporate Finance Studies, 8(1), 1–41.

    Google Scholar 

  45. Lim, J., Minton, B., & Weisbach, M. (2014). Syndicated loan spreads and the composition of the syndicate. Journal of Financial Economics, 111(1), 45–69.

    Google Scholar 

  46. Loumioti, M., & Vasvari, F. (2019). Consequences of CLO portfolio constraints. Working Paper.

  47. LSTA. (2007). The handbook of loan syndications and trading. New York: McGraw-Hill.

    Google Scholar 

  48. Maddala, G. (1987). Limited dependent variable models using panel data. Journal of Human Resources, 22(3), 307–338.

    Google Scholar 

  49. Mansi, S., Maxwell, W., & Miller, D. (2011). Analyst forecast characteristics and the cost of debt. Review of Accounting Studies, 16(1), 116–142.

    Google Scholar 

  50. Martin, X., & Roychowdhury, S. (2015). Do financial market developments influence accounting practices? Credit default swaps and borrowers reporting conservatism. Journal of Accounting and Economics, 59(1), 80–104.

    Google Scholar 

  51. Oehmke, M., & Zawadowski, A. (2016). The anatomy of the CDS market. The Review of Financial Studies, 30(1), 80–119.

    Google Scholar 

  52. Parlour, C., & Winton, A. (2013). Laying off credit risk: Loan sales versus credit default swaps. Journal of Financial Economics, 107(1), 25–45.

    Google Scholar 

  53. Pauly, M. (1968). The economics of moral hazard: Comment. The American Economic Review, 58(3), 531–537.

    Google Scholar 

  54. Petersen, M., & Rajan, R. (1994). The benefits of lending relationships: Evidence from small business data. The Journal of Finance, 49(1), 3–37.

    Google Scholar 

  55. Petersen, M., & Rajan, R. (1995). The effect of credit market competition on lending relationships. The Quarterly Journal of Economics, 110(2), 407–443.

    Google Scholar 

  56. Qiu, J., & Yu, F. (2012). Endogenous liquidity in credit derivatives. Journal of Financial Economics, 103(3), 611–631.

    Google Scholar 

  57. Rajan, R. (1992). Insiders and outsiders: The choice between informed and arm’s length debt. The Journal of Finance, 47(4), 1367–1400.

    Google Scholar 

  58. Roberts, M., & Sufi, A. (2009a). Renegotiation of financial contracts: Evidence from private credit agreements. Journal of Financial Economics, 93(2), 159–184.

    Google Scholar 

  59. Roberts, M., & Sufi, A. (2009b). Control rights and capital structure: An empirical investigation. The Journal of Finance, 64(4), 1657–1695.

    Google Scholar 

  60. Roberts, M. (2015). The role of dynamic renegotiation and asymmetric information in financial contracting. Journal of Financial Economics, 116(1), 61–81.

    Google Scholar 

  61. Saretto, A., & Tookes, H. (2013). Corporate leverage, debt maturity, and credit supply: The role of credit default swaps. Review of Financial Studies, 26(5), 1190–1247.

    Google Scholar 

  62. Shan, S., Tang, D., & Yan, H. (2016). How does CDS trading affect bank lending relationships? Working paper, Shanghai University of Finance and Economics.

  63. Shan, S., Tang, D., & Yan, H. (2017). Regulation-induced financial innovation: The case of credit default swaps and bank capital. Working paper, Shanghai University of Finance and Economics.

  64. Shan, C., Tang, D., & Winton, A. (2019). Do banks still monitor when there is a market for credit protection? Journal of Accounting and Economics, 68(2–3), 101241.

    Google Scholar 

  65. Sharpe, S. (1990). Asymmetric information, bank lending, and implicit contracts: A stylized model of customer relationships. The Journal of Finance, 45(4), 1069–1087.

    Google Scholar 

  66. Shavell, S. (1979). On moral hazard and insurance. Foundations of Insurance Economics (pp. 280–301). New York: Springer.

  67. Standard&Poor’s. (2011). A guide to the loan market. Standard&Poor’s.

  68. Streitz, D. (2015). The impact of credit default swap trading on loan syndication. Review of Finance, 20(1), 265–286.

    Google Scholar 

  69. Subrahmanyam, M., Tang, D., & Wang, S. (2014). Does the tail wag the dog?: The effect of credit default swaps on credit risk. The Review of Financial Studies, 27(10), 2927–2960.

    Google Scholar 

  70. Sufi, A. (2007). Information asymmetry and financing arrangements: Evidence from syndicated loans. The Journal of Finance, 62(2), 629–668.

    Google Scholar 

  71. Taylor, A., & Sansone A. (2006). The handbook of loan syndications and trading. McGraw Hill Professional.

  72. Zeckhauser, R. (1970). Medical insurance: A case study of the tradeoff between risk spreading and appropriate incentives. Journal of Economic Theory, 2(1), 10–26.

    Google Scholar 

Download references


We are grateful to Lakshmanan Shivakumar (the editor), an anonymous reviewer, Eric Allen, Dan Amiram, Mei Cheng (discussant), Clive Lennox, Maria Loumioti, Maria Ogneva, K. R. Subramanyam, Aluna Wang (discussant) and the conference participants at the Tel Aviv Accounting conference, the JAAF Accounting conference, the Hawaii Accounting Research Conference, the LBS Trans-Atlantic Doctoral Conference and the AAA Western Region DSFI, and the seminar participants at Kent State University, Lancaster University, Temple University, University of Michigan, and University of Southern California for helpful comments.

Author information



Corresponding author

Correspondence to Regina Wittenberg-Moerman.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material


(PDF 173 kb)



Table 9 Variable definitions

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kang, J.K., Williams, C.D. & Wittenberg-Moerman, R. CDS trading and nonrelationship lending dynamics. Rev Account Stud 26, 258–292 (2021).

Download citation


  • Credit default swaps
  • CDS market
  • Non-relationship lending
  • Debt contracts
  • Adverse selection
  • Lender monitoring
  • Cross-selling

JEL classifications

  • G20
  • G21
  • G32
  • M40
  • M41