Accounting quality and the choice of borrowing base restrictions in debt contracts

Abstract

This paper examines the effect of accounting quality on the inclusion of a specific debt contract feature, the borrowing base restriction, which limits the borrower’s access to the credit line by the amount of its working capital assets. The quality of the working capital, in turn, becomes relevant to the choice of borrowing base restrictions. I argue that private monitoring of working capital decreases the effect of publicly available accounting information in debt contracts. I find that borrowers with low accounting quality, measured as low accrual quality, are more likely to access borrowing base lines of credit, as they face high adverse selection costs in non-borrowing base lines of credit. Accordingly, I show that the effect of accounting quality on the cost of debt is diminished in borrowing base lines of credit as compared to non-borrowing base lines of credit. Further results show that the diminishing effect of accounting quality in borrowing base lines is mainly due to the discretionary portion of accrual quality, rather than the innate portion. Moreover, based on the narrative length of borrowing base restrictions specifically written on eligible accounts receivables in loan contracts, I construct a borrowing base restrictiveness measure and find that the effect of accounting quality on the cost of debt is decreasing with the restrictiveness, supporting the substitution effect between contractual monitoring mechanisms and borrower accounting quality.

Keywords:

• debt contracts
• accounting quality

Acknowledgements

This paper is based on my doctoral dissertation at The University of Texas at Dallas. I am grateful for helpful comments and suggestions from my dissertation committee members Daniel Cohen (chair), Suresh Radhakrishnan, Gil Sadka and Jieying Zhang. I am also grateful to John Abernathy, Ashiq Ali, William Cready, Umit Gurun, Duanping Hong, Sharon Katz, Mehmet Kuzu, Ningzhong Li, Stanimir Markov, Karen McCarron (discussant), Paige Patrick (discussant), Velina Popova, Hong Qu, Matias Sokolowski, Alfred Wagenhofer, and seminar participants at Bentley University, Clayton State University, Kennesaw State University, Mercer University, UT Dallas, 2014 AAA Accounting Doctoral Consortium, 2015 Miami Rookie Camp, 2016 AAA Southeast Region Meeting and 2016 AAA Annual Meeting. All errors are my own.

Disclosure statement

No potential conflict of interest was reported by the author.

Supplemental data

Supplemental data for this article can be accessed at https://doi.org/10.1080/00014788.2019.1683440.

ORCID

Sunay Mutlu http://orcid.org/0000-0001-5011-272X

Notes

1 For example, Frankel et al. (2011, p. 2) state that ‘lenders request timely financial reports and aging schedules from the borrower to assess the quality of borrowing base assets.’

2 To put this number into perspective, in 2017, US firms raised capital through new equity issues of $144 billion, and through bond issues of $1808 billion. (https://www.federalreserve.gov/data/corpsecure/corpsecure20181231.htm). Moreover, based on Dealscan, total capital made available by secured credit lines other than borrowing base revolvers was $128 billion in 2017.

3 Along with accounting quality (Bharath et al. 2008, Costello and Wittenberg-Moerman 2011), other financial reporting attributes such as voluntary accounting changes (Beatty et al. 2002), conservatism (Beatty et al. 2008, Nikolaev 2010), standard setting environment (Demerjian 2011) have been analyzed as to how they affect contractual features in debt contracts.

4 I define restrictiveness as the amount of restrictions written on the debt contract. These restrictions can be evaluated as the lender’s commitment to perform monitoring activity. If a contractual restriction states receivables from certain customers are not accepted in the borrowing base, then the lender has to monitor the origin of receivables in order to assess the borrower’s report on the borrowing base assets. I avoid using ‘strictness’, because in the context of debt contracts, ‘strictness’ might refer to the probability that a certain contractual term will be violated, as in Demerjian and Owens (2016).

5 Bharath et al. (2008), and Costello and Wittenberg-Moerman (2011) are important examples of this early literature examining the effect of accounting quality on the choice of monitoring mechanisms.

6 Armstrong et al. (2010) provide a nonexhaustive list of factors, such as differential access to information, costs of renegotiation, length of the lending relationship and monitoring capacity. Disentangling all potential explanations for the diminished importance of accounting quality in certain debt markets is beyond the scope of this study.

7 The negative association between accounting quality and cost of debt is also documented by Francis et al. (2005) and Graham et al. (2008). The survey evidence by Donelson et al. (2017) also confirms this association.

8 There is a large body of accounting literature on debt covenants (Dichev and Skinner 2002, Li 2010, Demerjian 2011, Christensen and Nikolaev 2012). See Taylor (2013) for a review of this literature. Also, other debt contractual features such as performance pricing grids (Asquith et al. 2005), maturity and collateral (Bharath et al. 2008), syndicate structure (Ball et al. 2008) and cross-acceleration provisions (Beatty et al. 2012) have been studied in accounting literature.

9 Note that the term ‘low accounting quality’ is in relation to firms accessing non-borrowing base lines. I do not argue that low accounting quality increases the likelihood of accessing credit lines in general. In fact, Li and Radhakrishnan (2017) provide evidence that firms with low accounting quality face overall difficulties in accessing credit lines.

10 Based on a similar argument that private monitoring reduces the effect of publicly available information, Beatty et al. (2009) show that firms with low financial reporting quality are more likely to lease assets instead of purchasing them, as the lessor’s private monitoring on leased assets diminishes problems with the lessee’s financial reporting quality.

11 While eliminating non-secured credit lines factors out the confounding effect of the security decision in comparing borrowing base and non-borrowing base lines, it allows to analyse only a half of the credit lines available in Dealscan. Therefore, it reduces the generalizability of my results.

12 Accrual quality models are commonly used in the literature examining the effect of accounting quality on debt markets (Biddle and Hillary 2006, Bharath et al. 2008, Beatty et al. 2010, 2012, Jha 2013, Moscariello et al. 2014, Spiceland et al., 2016).

13 Dechow and Dichev (2002) use change in working capital accruals but McNichols (2002) notes this model might be noisy for total accruals. Although Ball and Shivakumar (2006) report their main findings with total accruals as the dependent variable, they mention their inferences hold also with working capital accruals.

14 A rich line of accounting literature supports the importance of conditional conservatism in debt markets. See for example, Ahmed et al. (2002), Watts (2003) and Zhang (2008).

15 Ball and Shivakumar (2006) also develop another version of model (A) where they define DCF as a dummy variable taking the value of one when change in operational cash flows is negative, zero otherwise. The results are qualitatively similar, when I repeat all the analyses with that alternative model.

16 Flannery and Wang (2011) include Altman’s (1968) Z score as a proxy for credit risk. Instead of the Z score, I include the borrowing firm’s credit rating. For firms without credit ratings, I follow Beatty et al. (2008) to estimate synthetic ratings. The main results are robust to the use of Altman’s Z score instead of credit ratings.

17 Specifically, I use ICODE50 developed by Hoberg and Phillips (2010, 2016) and provided in their website (http://hobergphillips.tuck.dartmouth.edu/industryclass.htm). Note, however, I use Fama-French 48 industry classifications for estimating Accrual Quality in model (A), because industry classification of Hoberg and Phillips (2010, 2016) start in year 1996, while model (A) requires earlier data due to the lagged variables and estimation window for the variance of residuals.

18 I follow the recent literature (e.g. Bharath et al. 2008, Graham et al. 2008, Flannery and Wang 2011) in controlling for the risk factors associated with the cost of debt. To the extent that Accrual Quality measure is associated with unobservable risk factors, the results should be interpreted with caution. Therefore, it is important to analyse other accounting quality proxies, as outlined in the robustness tests.

19 I use the log transformation of the SPREAD variable to mitigate effect of outliers, essentially because it is a percentage measure with positive values truncated at zero level.

20 One concern related to estimations with debt contractual terms is that these terms are simultaneously determined. In order to address this concern, I estimate models for Log(SPREAD), Log(Maturity), Log(Covenants) and PPIND simultaneously using seemingly unrelated regressions (SUR). Untabulated results yield similar inferences with Log(SPREAD) to those reported in Table 3, Panel B. See, for example, Costello and Wittenberg-Moerman (2011) for a similar application.

21 Untabulated OLS estimations without Inverse Mills Ratios yield similar but weaker coefficient estimates. The coefficient estimate of Accrual Quality is −0.21 (t-value = −0.59) in borrowing base lines, while it is −1.11 (t-value = −3.28) in non-borrowing base lines.

22 For a novel application of computational linguistics on debt contracts, see Bozanic et al. (2018).

23 I perform this analysis for cash-constrained and other firms separately, and find that the effect is relevant for both groups (untabulated).

24 I require each ICW firm to receive a secured revolver five years before and after the ICW, which yields a sample of 930 facilities, 358 of them being borrowing base lines. This leads to low statistical power in ICW analyses.

25 The sample period for the big auditor analysis is 2002–2017, as I use Audit Analytics database for identifying the auditors. Audit Analytics data is more complete after 2002 (DeFond et al. 2016).

26 I attempt to address the joint determination of debt contractual terms through a variety of approaches, including seemingly unrelated regressions (SUR), as mentioned in Section 4.2.2. One such approach is the use of firm fixed effects in estimations. Untabulated results with firm fixed effects show coefficient estimates consistent with H1 and H2, but these coefficients lack statistical significance.

27 In an attempt to address the fundamental imbalance in comparing firms accessing borrowing base and non-borrowing base lines, I employ the financial crisis of 2008 as a market-wide systemic shock and examine how access to borrowing base lines differs before and after the crisis. Untabulated results show that firms with better accrual quality became more likely to access borrowing base lines after the crisis. Also, the negative association between accrual quality and loan spreads weakened in non-borrowing base lines after the crisis.