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Default clauses in debt contracts

Review of Accounting Studies volume 20, pages 1596–1637 (2015)Cite this article

An Erratum to this article was published on 10 September 2015

Abstract

We examine the determinants of events of default clauses in syndicated loan and bond contracts, provisions that allow lenders to request the repayment of principal and to terminate lending commitments. We document significant variation in the use of default clauses and their restrictiveness within the same type of lending contract but also across loans and bonds. We find that default clauses in public bond contracts are less restrictive than those in syndicated loan contracts. We also document that two ex ante proxies for bankruptcy costs, the level of intangible assets and capitalized research and development expenditures at the time of debt contracting, are associated with less restrictive default clauses, especially in bond contracts. We conclude that bondholders attempt to mitigate the occurrence of inefficient defaults. Given their inability to coordinate with each other and their ownership of subordinated claims, bondholders incur higher default costs than bank lenders.

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Fig. 1
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Notes

  1. Although these clauses seem to be standard, their grace periods vary significantly. For instance, the grace period for missed interest payments ranges from 0 to 90 days, depending on the type of lending contract.

  2. We use the term cross-default to describe both cross-default and cross-acceleration clauses. Wight et al. (2009) point out differences between these two provisions. Cross-default allows the credit agreement to be accelerated whenever a default or an event of default occurs on another instrument, whether or not the debt under that instrument has been or may be accelerated. Cross-acceleration allows the credit agreement to be accelerated only when the other debt has been accelerated.

  3. To the extent that a company pays retired employees a defined pension benefit, the company is required under ERISA to pay into a trust an amount sufficient to cover its future benefit obligations. ERISA events are situations in which a firm fails to meet its pension funding obligations.

  4. Consistent with this argument, Gilson et al. (1990) document that lenders prefer to avoid in-court corporate reorganizations if the borrower has a large fraction of intangible assets.

  5. Ayotte and Morrison (2009) document that senior bank creditors’ exercise substantial control over bankrupt firms by adding strict terms to additional financing they provide in bankruptcy through debtor-in-possession loans. These terms include liens on all the firm’s assets, restrictions on the use of cash while operating in bankruptcy, the imposition of specific budgets, requirements of detailed reports on cash receipts and expenditures, and so on. Dahiya et al. (2003) find that a majority of the firms that file for Chapter 11 obtain debtor-in-possession financing from senior banks that provided debt before the filing.

  6. Our paper complements Beatty et al. (2012) by providing unique insights on the determinants of the full set of default clauses and their characteristics (grace periods and financial threshold triggers) and by explaining differences between bond and loan contracts. We find that our bond sample results are not fully driven by the cross-acceleration provision.

  7. For instance, bank lending agreements rely on financial covenants to monitor the performance of a borrower, while bond contracts require mainly event-driven covenants (e.g., asset sales, M&As or additional borrowing restrictions).

  8. In a traditional Chapter 11 case, the debtor files a bankruptcy petition with the court. Under US bankruptcy codes, the debtor then has the exclusive right to propose a plan of reorganization within 120 days following the filing date. With a prepackaged filing, the bankruptcy petition and the plan of reorganization are filed concurrently.

  9. In addition, unsecured creditors suffer further losses because the strict priority of claims rule is violated in reorganization. These claimants often receive lower recovery rates because they accept compensation for even more junior creditors and equityholders to induce them to accept a reorganization plan sooner. See Weiss (1990) for empirical evidence.

  10. The models of Bergman and Callen (1991) and Rajan (1992) provide support for this argument. They show that an increase in the number of lenders lowers the probability that a single lender is pivotal in renegotiation. In particular, small lenders have an incentive to free ride, thereby increasing the inefficiencies in liquidity defaults.

  11. Gilson et al. (1990) study the restructuring of 169 financially distressed US companies. They find that companies are more likely to be successfully restructured when the number of lenders is small and the share of bank debt is high. More recently, Ivashina et al. (2013) find that distressed borrowers with a more concentrated set of creditors go through Chapter 11 restructuring faster and have a lower likelihood of liquidation.

  12. Banks often obtain super-seniority in bankruptcy proceedings by providing debtor-in-possession loans to the firm. These loans are typically short-term revolving lines of credit that enable a financially distressed firm to restructure its financial and operational base.

  13. In liquidations, lenders bear additional costs such as the costs associated with investigating the borrower's true financial resources, filing claims with the borrower or its liquidator, hiring legal advisors, following up through an insolvency process, communicating and negotiating with the borrower, loss of tax credits that the firm would have received had it not gone bankrupt, etc. All these costs are paid before debtholders’ claims are covered, further contributing to the lower recovery rate of junior bondholders.

  14. We note that in return for less restrictive default clauses, bondholders could obtain other contractual terms that are more favorable, such as higher yields. An analysis of the tradeoff between the restrictiveness of default clauses and offering bond yields is beyond the scope of our paper.

  15. We start with the year 1996 because before 1995, electronic filings are not available on a large scale in EDGAR, the SEC’s electronic filing system.

  16. FISD identifies 82 types of SEC forms from which it collects bond-specific data.

  17. In the case of syndicated loans, the clause on missed principal payments has relevance when a loan is amortizing (i.e., payable in installments) or the loan agreement provides for mandatory partial prepayments.

  18. Although this clause is not common in bond contracts, it can have significant consequences. For example, in 1984, Texaco faced an $11 billion judgment for allegedly interfering with Pennzoil’s acquisition of Getty Oil. Because the lawsuit could contribute to a breach in debt agreements, Texaco was forced to file for bankruptcy to gain the benefit of the bankruptcy code’s automatic stay and thereby gain the time it needed to appeal.

  19. A fundamental change may involve a merger, an acquisition, a sale-and-leaseback transaction, or the delisting of the company’s stock (see “Appendix 1” for more details). Also, another clause that triggers default is material misrepresentations. We do not report this clause because it is mentioned in all loan agreements and is implicitly required in all bond agreements according to the Securities Exchange Act of 1934.

  20. See Beatty et al. (2012) for a detailed investigation of the determinants of cross-acceleration clauses in bond contracts.

  21. A pending litigation clause differs from a court order clause. Although both relate to litigation, the former can trigger an event of default if a lawsuit is brought against a borrower, while the latter constitutes an event of default only if the borrower cannot pay an amount set by a court judgment.

  22. Both measures are imperfect proxies for the restrictiveness of default clauses due to their potentially arbitrary computation. However, they have several advantages. First, they provide an aggregate measure of the overall restrictiveness of default clauses. Second, they are potentially more objective than if one were to focus on the presence of an individual clause or a subset of clauses. The use of individual clauses, as opposed to an aggregate measure, requires a subjective assessment of their relative importance. Third, these indices are transparent and thus easy to replicate. Similar indices have been constructed to assess the restrictiveness of covenant packages (e.g., Bradley and Roberts 2004; Moody’s 2010).

  23. In this approach, we assume that default clauses without a grace period or a threshold amount are comparable to the most lenient case of clauses that have one of these features. We also calculate two alternative indices by assigning either 1.5 or 2 to clauses without features, assuming that these clauses are comparable to clauses with the medium or toughest strictness in the group of clauses that have these features. All results are robust to these alternative indices.

  24. Our results are robust to dropping these bond/loan variables from the regressions.

  25. In the context of bond contracts, De Franco et al. (2014) document that the restrictiveness of covenant packages is very sticky over time. This is partly driven by bond underwriters.

  26. In untabulated tests, we try two alternative specifications that use the full sample of bonds and loans. First, we include all loans in Table 4 and replace Credit rating with O-score. Second, we include all loans in Table 4, assign the lowest rating to unrated firms and include a dummy variable for unrated firms. In both cases, the results are very similar to those in Table 4.

  27. There are two other types of bankruptcy costs that are borne by creditors: (1) the direct administrative expenses paid in fees to various third parties involved in the bankruptcy proceedings and (2) the loss of tax credits that the firm would have received had it not gone bankrupt. We cannot measure the expectations about these costs.

  28. As reported intangible assets are less likely to be firm specific (since by definition they are acquired by the firm), we exclude them from the calculation of Intangible capital. The results are qualitatively similar when we include them into the measurement of Intangible capital.

  29. We also compare the effects of expected bankruptcy costs in subsamples constructed based on the number of existing loan lenders, assuming that the inter-creditor coordination problems are more serious when there are more existing loan lenders that need to negotiate. We find similar results.

  30. In untabulated tests, we run two alternative specifications that use the full sample of bond and loans. First, we include all loans by replacing Credit rating with O-score. Second, we include all loans by assigning the lowest rating to unrated firms and include a dummy variable for unrated firms in the regression. In both cases, the results are very similar to those reported in Table 7.

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Acknowledgments

We thank an anonymous referee, Anne Beatty (discussant), Hans Christensen (discussant), Brandon Julio, Laurence van Lent, Stan Markov, Stephen Penman (editor), K. Ramesh, participants at the 2012 FARS Meetings, 2013 AAA Annual Meetings and seminar participants at Bocconi University, Erasmus University, ESSEC, HEC Paris, London Business School, Rice University, the University of New South Wales, and the University of Texas at Dallas for valuable comments and suggestions. We gratefully acknowledge the financial support of the AXA Research Fund and the London Business School RAMD Fund. We also thank Giulia Pizzini, Surabhi Rajagopal, and Sundipika Wahal for excellent research assistance.

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Authors and Affiliations

  1. University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080, USA

    Ningzhong Li

  2. HEC Paris, 1 Rue de La Liberation, Jouy-en-Josas, 78350, France

    Yun Lou

  3. London Business School, Regent’s Park, London, NW1 4SA, UK

    Florin P. Vasvari

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  1. Ningzhong Li
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  2. Yun Lou
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  3. Florin P. Vasvari
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Correspondence to Florin P. Vasvari.

Appendices

Appendix 1: Descriptions of major default clauses

See Table 9.

Table 9 This appendix provides general descriptions and specific examples (extracted from sample contracts) for all default clauses summarized in Table 1
Full size table

Appendix 2: Variable definitions

See Table 10.

Table 10 This appendix provides the definitions of all variables used in the analyses
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Li, N., Lou, Y. & Vasvari, F.P. Default clauses in debt contracts. Rev Account Stud 20, 1596–1637 (2015). https://doi.org/10.1007/s11142-015-9337-8

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Keywords

  • Events of default
  • Default clauses
  • Loan contracts
  • Bond contracts
  • Cross-default

JEL Classification

  • G21
  • G33
  • M41