The information role of audit opinions in debt contracting

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The information role of audit opinions in debt contracting Peter F. Chen, Shaohua He, Zhiming Ma, Derrald Stice

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S0165-4101(15)00035-X http://dx.doi.org/10.1016/j.jacceco.2015.04.002 JAE1062

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Journal of Accounting and Economics

Received date: 12 December 2012 Revised date: 20 April 2015 Accepted date: 23 April 2015 Cite this article as: Peter F. Chen, Shaohua He, Zhiming Ma, Derrald Stice, The information role of audit opinions in debt contracting, Journal of Accounting and Economics, http://dx.doi.org/10.1016/j.jacceco.2015.04.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

The information role of audit opinions in debt contracting

Peter F. Chen School of Business & Management Hong Kong University of Science & Technology [email protected]

Shaohua He Department of Accounting & Finance Lancaster University [email protected]

Zhiming Ma Guanghua School of Management Peking University [email protected] Derrald Stice* School of Business & Management Hong Kong University of Science & Technology [email protected]

Draft: April, 2015

JEL Classification: G01, M4, M49 Keywords: Debt Contracting, Audit Opinions, Going Concern Opinions, Explanatory Language We thank Valerie Li, Michael Minnis, Tomomi Takada, Anne Wyatt, Hansang Yi, Jerold Zimmerman (the editor), and especially Mark DeFond (the referee) for helpful comments and suggestions. We appreciate comments and suggestions from participants at the 2012 Brigham Young University Symposium; the 2012 Japanese Accounting Review Conference in Kyoto, Japan; the 2013 European Accounting Association Conference in Paris, France; the 2013 Korean Accounting Association Conference in Gyeongju, South Korea; and the 2013 AAA Annual Meeting, as well as from workshop participants at Bond University, INSEAD, University of Queensland, and Singapore Management University. This paper was previously circulated as “Qualified audit opinions and debt contracting.” *Address for correspondence: Department of Accounting, School of Business and Management, Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong. Phone: 852-2358-7556. Email: [email protected]

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ABSTRACT

This study examines the relevance of modified audit opinions (MAO) in private debt contracting. We use the auditor’s explanatory language to partition MAOs into Inconsistency opinions, resulting from an accounting change or a restatement; and Inadequacy opinions, arising from a material uncertainty or a going concern (GC) opinion. Using the loan contracts of firms with MAOs; we find that, compared with loans issued in the year after a clean opinion, loans issued in the year after an MAO are associated with higher interest spreads (17 basis points on average), fewer financial covenants, more general covenants, smaller loan sizes, and a higher likelihood of requiring collateral. We find that the effect on loan spreads (as well as on other non-price terms) varies by the type of MAO, ranging from no effect for an accounting change to an average increase of 107 basis points for a GC opinion. Additional analyses of GC opinions find that auditors communicate incremental information to lenders about clients’ credit risk. Overall, our empirical results suggest that lenders incorporate the information contained in MAOs into debt contracting.

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1. Introduction The auditor’s report is the primary channel through which auditors communicate with financial statement users information discovered during the auditing process. Financial analysts and regulators have criticized the existing auditor’s reporting model as having little communicative value, and additional disclosures have been proposed to enhance the value of the audit report (Church et al., 2008; PCAOB, 2013).1 Under the Securities and Exchange Commission (SEC) requirement and Generally Accepted Auditing Standards (GAAS), the auditor’s reporting options are to issue either an unqualified opinion or an unqualified opinion with explanatory language, commonly called a modified audit opinion (MAO). 2 This inclusion of discretionary explanatory language, such as citing a lack of accounting consistency, emphasizing a material uncertainty, or expressing a going concern (GC) opinion, is the only difference between a standard clean opinion and an MAO.3 In fact, an MAO is the sole way for auditors to communicate audit-specific information to outsiders. 4 There is still debate, however, about the extent to which the auditor’s report communicates relevant information to financial statement users (Church et al., 2008; DeFond and Zhang, 2014). In this study, we empirically investigate the relevance of MAOs in private debt contracting.

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The Public Company Accounting Oversight Board (PCAOB) has proposed a number of standards to include additional disclosures in the audit report. The expanded disclosures include “critical audit matters”, auditor independence, and auditors’ responsibilities relating to other information contained in financial reports (PCAOB, 2013). 2

Per Rule 2-02 of Regulation S-X, the SEC will not accept financial statements with an audit opinion that is not unqualified. Throughout the paper we use the term “modified” to denote unqualified opinions with explanatory language. Under SAS 58 (effective for reports issued after January 1, 1989) certain opinions previously classified as “qualified” (such as changes from one GAAP method to another and material uncertainty) are now classified as unqualified with explanatory language. Consistent with prior research conducted during our sample period (e.g., Butler et al., 2004), our MAOs are almost exclusively unqualified with explanatory language. 3

The auditor’s discretion is based on the professional standard indicating that “Certain circumstances, while not affecting the auditor’s unqualified opinion, may require that the auditor add an explanatory paragraph (or other explanatory language) to the audit report (PCAOB, 2003, AU Section 508.11). 4

One exception is the auditor change 8-K. Prior research, however, finds that, in addition to being infrequent, these 8-Ks likely underreport disagreements between auditors and clients (Smith and Nichols, 1982; DeFond and Jiambalvo, 1993).

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A large portion of the prior research that studied modified opinions focuses on the market reaction to the auditor’s report and provides evidence that equity investors react negatively to the issuance of a nonstandard opinion, especially to a GC opinion (e.g. Loudder et al., 1992; Fleak and Wilson, 1994; Menon and Williams, 2010). We investigate the relevance of auditor reporting from the perspective of debtholders for several reasons. First, accounting information is often a direct input in debt contracting; for example, debt covenants may rely on accounting numbers to monitor firm performance. If an MAO indicates low financial reporting quality, lenders will change the relative use of accountingbased covenants and general covenants in loans issued after MAOs. Second, debt contracts provide multi-dimensional information about borrowers; loan contracts allow us to investigate the effects of audit opinions on both price and non-price costs of debt, including loan covenants, loan size, loan maturity, and collateral requirements. Finally, debtholders, whose payoffs are concave in relation to firm value, demand a timely shift of control rights in order to discipline managers in the case of poor firm performance (Dewatripont and Tirole, 1994; Ball et al., 2008). This implies that debtholders should react to information in MAOs for both pricing and monitoring purposes, because MAOs are likely to communicate negative news about clients’ financial reporting quality and credit worthiness. Thus, examining lenders’ responses to MAOs can enhance our understanding of the usefulness of auditor reporting. Lenders are likely to find the audit report relevant and useful in two ways. First, through the audit opinion and associated explanatory language, the auditor communicates information about the quality of financial reports for debt contracting purposes. The relevance of MAOs in debt contracting depends on the severity of the auditor’s concern as expressed in the explanatory language. Second, MAOs communicate private information about clients’ credit risk to lenders that is not available elsewhere. Therefore, we expect the relevance of MAOs to lenders varies with the risks conveyed in the MAO.

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To analyze the relevance of MAOs in debt contracting, we first categorize audit opinions into clean and modified opinions. If an MAO indicates low financial reporting quality and potential credit risk, we predict that loans issued to firms after an MAO will be associated with a higher cost of debt, reflected across different contract terms. To examine the incremental relevance of explanatory language, we classify MAOs into two general types, which we further partition into four groups by their cause: Inconsistency opinions are caused by an Accounting Change or a Restatement; and Inadequacy opinions are caused by Material Uncertainty or a GC Opinion.5 MAOs issued because of an accounting Inconsistency alert financial statement users about the incomparability of data contained in the financial statements. MAOs issued for Inadequacy reasons express auditors’ more serious concerns about the quality of the financial statements because of unrecognized losses or risk. Material uncertainty concerns are related to the resolution of future economically relevant unknowns (e.g., contingent liabilities, litigation risk, and business uncertainty), and GC opinions indicate that a key assumption of the accounting model is violated (i.e., that the firm will continue as a going concern for at least one year). The severity of the auditor’s concern is greater in Inadequacy opinions than in Inconsistency opinions. Among the four types of MAOs, GC opinions require the most auditor judgment and, therefore, are likely to convey more of auditors’ private information than other types of MAOs.6 We find that loans initiated after MAOs have higher loan spreads (on average, 17 basis points higher) compared to loans initiated after clean opinions, controlling for other determinants of the interest rate. In addition, the effects of MAOs on loan spreads vary

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Our partitions of MAOs are guided by the explanatory language options under Auditing Standard AU Section 508 (PCAOB, 2003) and by the partitions used in Butler et al. (2004). 6

According to AU Section 508 (PCAOB, 2003), the auditor may add an explanatory paragraph (or other explanatory language) in certain circumstances, including: lack of consistency caused by a change of accounting principles or misstatements, substantial doubt about the entity’s ability to continue as a going concern, and emphasizing a matter regarding the adequacy of the financial statements to reflect significant subsequent transactions or events.

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significantly depending on the type of explanatory language. The loan spread effect increases from 0 basis points after an Accounting Change to 25, 49, and 107 basis points, on average, after Restatement, Material Uncertainty, and GC Opinions, respectively. These results support our prediction that interest rates increase after an MAO and that the effect varies with the type of MAO. We also find that lenders decrease the use of financial covenants and increase the use of general covenants in loans initiated after an MAO. Specifically, lenders decrease the use of financial covenants by 3.8% and increase the use of general covenants (such as restrictions on dividend payouts or on the uses of borrowed funds) by 4.2%, on average, after an MAO. Again, the relevance of an MAO depends on the type of modification. We find that Inconsistency MAOs have a smaller, but still significant, effect on the use of financial and general covenants than do Inadequacy MAOs. Within Inadequacy MAOs, Material Uncertainty opinions are associated with an average increase of 18.2% in the use of general covenants but have no effect on the use of financial covenants. In contrast, GC opinions are associated with an average decrease of 9.1% in the number of included financial covenants and an average increase of 12.3% in the number of included general covenants. The results from the debt covenant tests suggest that MAOs convey relevant information to lenders and that the use of the financial reports in debt contracting varies with the type of concern expressed by the auditor in the MAO. We provide further evidence on the relevance of MAOs on other loan terms including loan size, the likelihood of requiring collateral, and loan maturity. Consistent with our predictions, we find that lenders reduce loan sizes and increase the likelihood of requiring collateral following an MAO. The results on loan maturity show a significant decrease in loan maturities after a GC opinion, which is consistent with GC opinions communicating the auditor’s information about the client’s credit risk to lenders. The overall decrease in loan

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term favorability and the variation of the effects of MAOs suggest that MAOs communicate information useful to lenders in negotiating debt contracts. Following our main empirical analysis, we perform two separate analyses on our sample of observations with GC opinions. First, following Menon and Williams (2010), we classify GC opinions as being related to firm performance, financing concerns, or other issues and investigate the effects of each type of GC opinion on the loan contract terms previously tested. We find that each type of GC opinion has a unique effect on loan spread and other loan terms; this finding reinforces the usefulness of information provided in the auditor’s report. Second, we test whether GC opinions communicate private information about a client’s credit risk that is not available elsewhere. We do so by matching firms with GC opinions to similar firms based on the determinants of receiving a GC opinion using variables from prior studies (DeFond et al., 2002), but that received clean opinions in their audit reports. If GC opinions simply capture the probability of financial distress or bankruptcy, as predicted by publically observable information, they should have no incremental effect on loan contract terms in the matched-sample specifications. We find, however, that GC opinions continue to have a significant effect on all of the loans terms examined except financial covenants. Overall, these results are consistent with a GC opinion communicating the auditor’ private information regarding the client’s credit risk to lenders. It stands to reason that managers of firms preparing to secure financing are likely to be in contact with potential lenders preceding a loan issuance, but we are unable to directly control for information that managers privately provide to lenders. To alleviate the concern that our empirical results regarding the association between MAOs and loan terms may pick up unobservable public and private communication channels, we include a new indicator variable, Before_MAO, that takes a value of 1 if a loan is issued in the 12 months preceding

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an MAO, and zero otherwise. If the private information conveyed by the auditor is actually preempted elsewhere (by managers communicating to lenders or other public information) before an MAO, then we would expect a significant coefficient on this new variable. In tests of each of the contract terms we investigate, however, Before_MAO is insignificant, which provides further support that our results capture the effects of audit opinions. Our study makes several contributions to the existing literature. First, our study investigates the usefulness of the audit report to lenders in debt contracting by incorporating the auditor’s explanatory language. The empirical results provide evidence that MAOs are informative to lenders in negotiating loan agreements, but more importantly, that the different types of MAOs communicate different concerns to lenders about financial reporting quality and credit risk. While prior studies show that the voluntary use of auditing or the employment of Big N auditors is associated with a lower cost of debt (Fortin and Pittman, 2007; Lennox and Pittman, 2011; Minnis, 2011), we provide evidence on the incremental value of audit reporting to lenders. For regulators, our empirical results may be useful in informing the current debate on regulatory initiatives to include disclosures of “critical audit matters” in the audit report (PCAOB, 2013; DeFond and Zhang, 2014). Second, our study is related to the literature on the role of financial reporting quality in debt contracting. Bharath et al. (2008) provide evidence that the quality of a borrower’s accounting information can affect their access to the private versus public debt market and the included loan terms. Costello and Wittenberg-Moerman (2011) document that lenders alter debt contracts when a borrower reports an internal control weakness. These studies, however, do not address the specific role of the audit report within financial reporting. Our study provides empirical evidence that audit opinions are incrementally informative to lenders in the private debt market.

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Lastly, our empirical results on the usefulness of GC opinions in debt contracting contribute to the vast auditing literature on GC opinions. Prior studies report evidence that GC opinions are useful in predicting subsequent bankruptcies and are associated with negative stock market reactions (Hopwood et al., 1989; Raghunandan and Rama, 1995; Chen and Church, 1996; Menon and Williams, 2010; Kaplan and Williams, 2013), but these studies examine the relevance of GC opinions independent of other MAOs. Our empirical results on the effect of GC opinions on various loan terms, relative to the effect of other MAOs, shed light on the unique value of the auditor’s information that is not available in the financial statements or through other channels accessible to sophisticated lenders (Mutchler, 1985; Menon and Schwartz, 1987). In the next section we develop our hypotheses. We describe the sample selection procedures and variables used in this study in Section 3. Section 4 presents the main empirical results, and Section 5 presents the results of additional analyses. A summary and conclusions are provided in Section 6.

2. Background and Hypothesis Development As capital providers, lenders are interested in ensuring the timely repayment of the loan and interest that are claims on the borrower’s future cash flow and assets. When contemplating a potential loan recipient, banks and other lenders analyze the risk of default, estimate the market value and liquidation values of assets, and evaluate the management’s character and ability (Tirole, 2007). If lenders decide to initiate a loan after the credit analysis, they negotiate the price and non-price terms of the debt contract, which compensate them for risk and allow them to monitor the borrower’s performance over the life of the loan. Audited financial statements provide important information to lenders for evaluating the borrower’s credit worthiness and default risk (e.g., Beaver, 1966; Altman, 1968; Ohlson,

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1980), and auditors play an important role in this process. 7 The audit report is the primary means by which the auditor communicates to users their degree of assurance that the financial statements reflect the firm’s economic activities. To arrive at an opinion, the auditor amasses a large body of evidence and offers insights into the estimates and assumptions underlying the financial reports prepared by managers. The auditor uses explanatory language in the audit report to communicate material concerns discovered during the auditing process that may be relevant to users. These include Inconsistency concerns, arising from an accounting change or restatement, and Inadequacy concerns, relating to material uncertainty or an outright GC opinion. The severity of the auditor’s concern is reflected in the content of explanatory language included in the report, which is useful to lenders when negotiating debt agreements. Since Inconsistency opinions refer to accounting changes or restatements that are usually disclosed in the notes of financial reports, they may not be incrementally informative to lenders. Auditors, however, have some discretion in whether to include these items, rather than being required to include them in the audit report as a form of mechanical compliance with accounting standards. 8 In addition, Inconsistency opinions may turn a lender’s attention to the issue of incomparable information in financial statements when contemplating a debt contract. 9 Prior work (Czerney et al., 2013) provides evidence that explanatory language mentioning accounting changes and restatements is associated with a higher probability of 7

For example, prior studies find that the voluntary use of auditing or the employment of Big N auditors is associated with a lower cost of debt (Fortin and Pittman, 2007; Lennox and Pittman, 2011; Minnis, 2011). 8

Only changes in accounting principles that materially affect the comparability of the financial statements presented are included in the explanatory paragraph. Otherwise, even if they are disclosed as notes to the financial statements, they are not included in the auditor’s report (PCAOB, 1972, AU Section 420.02 and 420.05; PCAOB, 2008, AS No. 6: Section 2). 9

This is consistent with the notion that auditors are responsible for assuring a level of financial reporting quality that is more than a mechanical compliance with accounting standards. ASN No.14 requires that auditors evaluate the qualitative aspects of a company’s accounting practices, including potential biases in management’s judgment. Fair representation, in accordance with GAAP, requires the use of professional judgment in making estimates and assumptions that reflect the firm’s underlying economic activities.

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subsequent misstatements. Graham et al. (2008) find that earnings restatements are associated with increases in price and non-price costs of debt, which is consistent with financial restatements leading to higher information uncertainty.

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These studies suggest that

Inconsistency opinions may still affect the direct or indirect cost of debt. Another reason that audit opinions may be relevant in debt contracting is through auditors’ communication of private information about a borrower’s credit risk and financial health. For lenders, Inadequacy opinions (especially GC opinions) are particularly relevant because they convey an auditor’s judgment about potential losses or default risks that might not be reflected elsewhere in the financial report. Therefore, the effect of MAOs related to Inadequacy opinions should be greater than the effect of Inconsistency opinions. With respect to GC opinions, prior studies find that these opinions are associated with a higher probability of bankruptcy (Mutchler, 1985; Menon and Schwartz, 1987; Hopwood et al., 1989; Raghunandan and Rama, 1995; Mutchler et al., 1997). These findings are consistent with the effectiveness of SAS 59 which provides guidance to auditors in evaluating a client’s ability to continue as a going concern. We predict that the effect of GC opinions on price and non-price loan terms will be greater than the effects of the other MAO opinions.11 If MAOs communicate relevant information about the quality of financial reports or about a client’s credit risk, then lenders should respond with higher interest rates to reflect borrowers’ greater credit risk and the additional costs of monitoring using alternative

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The sample of restatements in Graham et al. (2008) is based on restatements announced in filed financial reports. These restatements are not always mentioned in the explanatory language in the audit report. All of the restatements in our sample are restatements mentioned in the explanatory paragraph of the audit report. 11

Given the potential economic consequences of MAOs, auditors are likely to experience pressure from clients to issue a clean opinion. If auditors’ issuance of MAOs is conservative, it should bias against our finding an effect of MAOs on loan terms as lenders rationally adjust for the conservative bias of MAOs. This is consistent with the use of modified opinions (in particular GC opinions) as a measure of audit quality in prior studies (see DeFond and Zhang (2014) for a review of the literature).

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mechanisms. Furthermore, we predict that the effect on interest rates varies with the severity of issues identified in the MAO. This leads to our first empirical hypothesis, stated as follows:

H1:

Compared with loans issued in the year after a clean opinion, loans in the year after an MAO have higher loan spreads. In addition, the effect on spread is greater for GC opinions than for other MAO types.

The monitoring of a borrower’s behavior through debt covenants to mitigate agency conflicts between shareholders and debtholders is an important part of debt contracting (Jensen and Meckling, 1976; Smith and Warner, 1979). In the case of covenant violation, control rights can be transferred quickly to lenders. Accounting information and financial ratios are widely used to monitor borrowers’ performance in debt covenants. A pre-condition for using financial covenants is the assurance that the accounting information reflects the borrower’s actual performance. If accounting numbers are of questionable quality, lenders may use general covenants that do not rely on accounting information. General covenants often specify events that would require the borrower to pay down the balance of their loan, whether dividends may be paid, or the allowed uses of borrowed funds. If lenders view an MAO as decreasing the value of including financial covenants, then they may compensate by increasing the number of general covenants. Alternatively, if financial and general covenants are independent in purpose, the optimal number (and type) of included general covenants may already be included and no change will be observed. The implications of an MAO for debt covenants vary by the type of modified opinion. If Inconsistency opinions are indicators of a high likelihood of subsequent misstatements, then financial covenants become less effective as a monitoring tool (Czerney et al., 2013). Further, the effects of Inadequacy opinions on covenant choices should be stronger than the effects of Inconsistency opinions, because Inadequacy MAOs convey an auditor’s greater

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concern regarding potential losses or default risk not reflected in financial statements. In particular, GC opinions may indicate that a basic assumption of the accounting model (i.e. that the firm will persist as a going concern) is violated, undermining the usefulness of the financial statements to lenders as an effective monitoring tool. To protect their investments, lenders will identify alternative measures for liquidation values of assets. We expect the effect on the use of debt covenants in loan contracts to be strongest for GC opinions. Stated in the alternative form, our second hypothesis is:

H2:

Compared with loans issued in the year after a clean opinion, loans issued in the year after an MAO are associated with a decrease in the number of financial covenants and/or an increase in the number of general covenants. In addition, the effect of GC opinions is stronger than those of other MAO types.

To assess the total effect on the contract design of an MAO, it is important to consider the many different contract provisions from which lenders can choose (Gigler et al., 2009). Up to this point, we have considered only the use of spread and covenants in contract design. In reality, lenders also consider other loan terms to protect themselves. We consider the effects of an MAO on three additional contracting terms: loan size, the requirement of collateral, and the maturity of the loan. If lenders view an MAO as a disclosure event that communicates a negative signal about the borrower’s financial reporting quality and credit risk, we predict that lenders will be more likely to reduce loan size, require collateral, and shorten the loan maturity after an MAO. As before, the relevant effects should vary for different types of MAOs. Therefore, we expect that Inadequacy MAOs have a greater effect on these loan terms than Inconsistency MAOs. Economic theory on credit rationing (see, e.g., Jaffee and Russell, 1976; Stiglitz and Weiss, 1981) proposes that loan size is not a linear function of the signaled default risk, because credit risk may increase if the loan size can only finance riskier small projects. As a result, rather than reducing the loan size, lenders may simply deny the loan application after

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an MAO, especially after a GC opinion. Similar to loan size, Diamond’s (1991) theory suggests that debt maturity is a non-monotonic function of a firm’s default risk, with low risk firms obtaining both short- and long-term debt, while high risk firms are forced to borrow at maturities shorter than they desire because of the moral hazard of asset substitution. Finally, we expect GC opinions to have a stronger effect than the other three types of MAOs on the likelihood of a loan requiring collateral. Stated in the alternative form, we predict that:

H3:

Compared with loans issued in the year after a clean opinion, loans issued in the year after an MAO are smaller in size, more likely to require collateral, and shorter in maturity. In addition, the effect of GC opinions is larger than those of other MAO types.

3. Research Design and Sample Selection 3.1 Research Design To examine the relevance of audit opinions in debt contracting, we select those firms with at least one MAO in our sample period so that we can isolate the effects attributable to MAOs. We perform empirical tests of the effect of an MAO on the contract terms of loans issued in the year after an MAO by estimating the following model: Loan Term = α + β1 MAO + β2 After_MAO + ∑ βi (Controli)

(1)

where Loan Term is a variable representing the specific contracting terms of the loan agreement that we investigate including: interest spread, number of financial covenants, number of general covenants, loan size, whether or not a loan requiring collateral, and maturity length of a loan. MAO is an indicator variable equal to one if the loan is initiated in the year after the borrower receives an MAO, and zero otherwise. To examine whether there is any lingering effect on contract terms for loans initiated beyond the first year after an MAO, we include After_MAO to capture any continuing effect of an MAO after a borrower receives a clean opinion. After_MAO is an indicator variable equal to one if a firm currently has a 13

clean opinion but had an MAO in at least one of the previous three years, and zero otherwise. We expect MAO to have a positive (β1 ) effect on loan spread, use of general covenants, and likelihood of requiring collateral, but a negative (β1) effect on the use of financial covenants, loan size, and maturity length. To investigate the differential effects of different types of MAOs on debt contracts, we replace the generic MAO with the types of MAOs. We first examine the relevance of MAOs for loan spreads after controlling for the other known determinants of interest rates. We control for firm size, because small firms have greater information asymmetry and higher default risk (Forth and Pittman 2004; Bharath et al. 2007). We control for loan size, because larger loans are priced at lower interest rates (Booth, 1992; Beatty et al., 2002). We include a number of controls related to financial distress found in the prior literature: Z-score, market-to-book, leverage, cash flow volatility, tangibility, and credit and term spreads (Graham et al., 2008). We include a measure of abnormal accruals which have been shown to affect interest rates (Bharath et al., 2008), as well as, a control for revolvers, because these loans have a lower loan spread than term loans (Zhang, 2008). Institutional loans, relative to bank loans, have a higher loan spread because of the higher information symmetry with the borrower. We control for the existence of performance pricing provisions, because they reduce adverse selection and moral hazard costs for lenders (Asquith et al., 2005). We also control for the number of lenders in the loan. A larger number of participants in the loan syndicate indicates a higher quality borrower and less information asymmetry between syndicate participants. Last, we control for other contracting devices available to lenders: loan maturity, collateral, number of financial covenants, loan purpose, and year fixed effects.12 To mitigate the influence of outliers, we winsorize all continuous variables at the top and bottom 1% of their respective distributions. We also adjust standard errors using two-way firm and year clusters in all regressions. 12

All variables are defined in Appendix A.

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To test H2 we include a similar set of control variables in testing the effects of MAOs on the use of financial and general covenants. In these empirical models, we include loan interest rate as a control variable, because agency theory on debt covenants predicts a negative relation between loan spread and the use of covenants (Jensen and Meckling, 1976; Smith and Warner, 1979). We select other control variables similar to those in prior studies on the determinants of covenants in debt contracting (Beatty et al., 2002; Sufi, 2007; Graham et al., 2008; Costello and Wittenberg-Moerman, 2011). In testing H3, we follow a similar procedure to include control variables when we examine the effects of MAOs on loan size, likelihood of requiring collateral, and loan maturity. 3.2 Data Sources and Sample Selection We obtain data on MAOs in audit reports from COMPUSTAT (variable name AUOP) for the period from 1992 to 2009.13 We match our MAO sample with public firms in the Dealscan database, which contains contractual terms such as interest rate, size, collateral requirement, and covenants, of loans issued in the United States. 14 To be included in our sample, we require borrowers to obtain a loan during the window just after an MAO (in either the year of MAO or within the three years following the first clean opinion after an MAO) and outside of the MAO window (either before the MAO or more than three years after the first clean opinion following an MAO). This requirement is to ensure that our results are not attributable to differences between MAO and non-MAO borrowers. After eliminating

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Audit Analytics provides GC opinions after 2000, and we use these data to check our sample of MAOs from COMPUSTAT. We find three cases in which Audit Analytics classified an observation as a going concern but we did not find a GC statement in the audit report. We also identify 11 cases in which Audit Analytics classified an observation as “non-going concern,” but we observed a GC statement in the audit report. Including or excluding these observations does not affect our results. 14

Dealscan is provided by the Loan Pricing Corporation (LPC). Sufi (2007) reports that approximately 90% of the 500 largest nonfinancial firms in COMPUSTAT obtained a loan through private channels during his sample period of 1994 to 2002 and that the market for these loans reached $1 trillion during this period. The value of private loans grew to $1.7 trillion in 2007 (Kim et al., 2011).

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observations with missing data needed in our analyses, our final sample includes 8,473 loans issued to 5,377 borrowers during the 1992-2009 period.15 To classify MAOs based on the stated reasons for modification, we read the explanatory paragraph section of the audit report for each MAO identified above from the borrower’s 10-K on EDGAR or LexisNexis. Following Butler et al. (2004), we classify the reason for each MAO into one of two broad categories: Inconsistency and Inadequacy. 16 Inconsistency MAOs refer to a lack of consistency according to GAAP accounting principles (AU Section 508), and we break down Inconsistency into Accounting Change and Restatement depending on whether the auditor mentions accounting changes or restatements in the explanatory paragraph. 17 Likewise, we decompose Inadequacy MAOs into two types: Material Uncertainty and GC Opinion. We classify an MAO as Material Uncertainty if the auditor mentions a business uncertainty, litigation risk, or contingent liability in the audit report, and we classify an MAO as GC Opinion if the auditor mentions going concern, bankruptcy, financing difficulty, or distress. Table 1 reports the annual distribution of loans and annual frequency of each type of modification over our sample period. As shown in the table, the number of MAOs increases from 3 in 1992 to 345 in 2004 and decreases afterward. A notable jump in the number of 15

Syndicated loans often bundle multiple facilities into one transaction. These different facilities have different contract terms but are syndicated as a single deal. Consistent with other work using private debt contracts, we conduct our tests at the individual facility level. 16

For those opinions that contain multiple reasons for modification, we classify the modified opinion based on the most severe concern expressed by the auditor in the client’s financial report. Our results are robust to allowing overlap across MAO observations and to deleting observations modified for more than one reason. In our sample: 31 firm-year observations are classified as GC Opinion when they overlap with a material uncertainty, 4 firm-year observations are classified as GC Opinion when they overlap with an accounting change, 5 firm-year observations are classified as Material Uncertainty when they overlap with an accounting change, and 135 firm-year observations are classified as Restatement when they overlap with an accounting change. 17

There are two major differences between our partition of MAOs and that of Butler et al. (2004). First, we read explanatory paragraphs in the audit reports of all loan observations identified on COMPUSTAT as having an MAO, rather than searching audit opinions by keyword as in their study. Second, for MAOs with multiple reasons for modification in the explanatory language, we classify the observation as having the most severe MAO type, rather than allowing multiple reasons for an MAO as in their study.

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MAOs takes place starting in 2002 when the Sarbanes-Oxley Act (SOX) was implemented. This is consistent with SOX increasing the pressure on auditors to issue MAOs. Similar to findings in Butler et al. (2004), most MAOs are related to Inconsistency (1,881 observations), with less than 10% related to Inadequacy (175 observations). Our sample of MAOs contains a higher proportion of Inconsistency modifications than does Butler et al.’s sample, probably because firms in our sample are relatively large and are able to obtain financing. Most Inconsistency MAOs are Accounting Change (1,680 observations), with about 10% being Restatement MAOs (201 observations). Among Inadequacy modifications, there are about three times as many GC Opinion MAOs (131 observations) as Material Uncertainty modifications (44 observations). In addition, the number of GC opinions varies over time with some concentration in the years around the implementation of SOX in 2002. The percentage of GC opinions in our sample (6.4%) is less than those reported in prior studies (Butler et al., 2004; DeFond at al., 2002); this is likely a result of our requirement that firms must obtain debt financing in the post-MAO period to be included in our sample. If a firm is denied a loan or files for bankruptcy because of a GC opinion, then the economic consequence of a GC opinion on debt contracting will not be captured by our empirical analysis. The implication is that our empirical analysis has a bias of underestimating the cost of MAOs, especially for GC opinions. 3.3 Descriptive Statistics Table 2 Panel A reports the descriptive statistics of the MAO and non-MAO loans in our sample. The MAO loans in our sample have a mean spread above LIBOR of 222.15 basis points. This is higher than the non-MAO loan average of 200.56 basis points and the 199.6 average spread of all loans included in the DealScan database (p-value < 0.001). The loans issued after an MAO (clean opinion) include an average of 2.34 (2.63) financial covenants and 5.67 (5.22) general covenants. The average loan size after an MAO (clean opinion) is

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$379.12M ($258.72M). The average loan maturity after an MAO (clean opinion) is 48.12 months (47.58 months). More than half of the loans include a performance pricing provision, require collateral, and are a revolver, regardless of whether a firm has an MAO or not. Of these 5,270 non-MAO observations, 1,884 fall into the after-MAO category. In other words, these 1,884 debt contracts are signed within three years after an MAO, even though the opinion for the year of the debt issuance is not an MAO. Table 2 Panel B reports the descriptive statistics of firm characteristics for our sample of loan observations. The mean natural log of firm size for loans issued after an MAO (clean opinion) is 6.93 (6.17). The means of profitability, leverage and tangibility are 0.12 (0.13), 0.26 (0.25), and 0.34 (0.32), respectively, for loans issued after an MAO (clean opinion). Table 2 Panel C shows the correlation matrix of variables in our sample. Many of the contracting terms are significantly correlated. As expected, spread is positively correlated with the number of debt covenants and with the requirement of collateral, and it is negatively correlated with loan size, profitability, firm size, and Z-score. These univariate results are consistent with lenders having many different loan terms to negotiate (Melnik and Plaut, 1986), not just interest spreads. As expected, MAO is positively correlated with interest and general covenants, but negatively correlated with financial covenants. These correlations are consistent with the predicted relations between MAOs and loan terms. However, to make inferences about the effects of MAOs on debt contracts, we perform empirical tests by estimating multiple regression models.

4. Empirical Results 4.1 Audit Opinions and Loan Spreads Table 3 presents the effects of modified opinions on loan spreads. We regress loan spread on MAO, After_MAO, and a set of control variables. Our first hypothesis predicts that

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if lenders view the auditor’s MAO as a negative indicator of financial reporting quality and/or of the borrower’s credit risk, then loans issued after an MAO will have a higher interest rate than loans issued after a clean audit opinion. In addition, if an MAO has a lingering effect, even after the MAO has been removed, then debt issued during the after-MAO period will have a higher spread as well. In Column 1, the coefficient on MAO is positive and statistically significant; loans initiated after an MAO have a spread over LIBOR 17.31 basis points higher than loans issued during a non-MAO period. This represents an increase in the interest spread of 8.6%. 18 In Column 2, we report a significant incremental effect on loan spreads for Inadequacy modifications, 92.14 basis points, but no effect for Inconsistency modifications. Column 3 reports that firms with GC Opinion, Material Uncertainty, and Restatement MAOs experience significant increases in their loan spreads of 107.13, 49.37, and 25.27 basis points, respectively. The result that MAOs issued because of an accounting change have no effect on the borrower’s interest spread is consistent with these modifications being less of a concern to lenders and resulting in no interest spread effect for borrowers. In contrast, the significant coefficients of the other three types of MAOs suggest that they raise the cost of loans to the borrower, increasing with the severity of the MAO. The incremental effect on spread of GC Opinion is significantly larger than any of the other three MAOs, and it represents a 53.4% increase relative to the non-MAO period. 19 These results support H1. Many of the included control variables are statistically significant. Spreads are negatively associated with whether or not the loan is a revolver, loan size, loan maturity, inclusion of a performance pricing provision, firm size, market-to-book, borrower

18

Throughout the paper, economic magnitudes are calculated by comparing the coefficient of the variable of interest to the mean of that variable when there is a clean audit opinion, as reported in Table 2. For example, the overall increase in the interest spreads for firms with an MAO is 17.31 / 200.56 = 8.6%. 19

The tests of the difference between the coefficient on GC Opinion and those of each of the other opinion types are statistically significant. The p-values of these coefficient tests are 0.029 (Material Uncertainty), <0.01 (Restatement), and <0.01 (Accounting Change).

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profitability, and Z-score. Spreads are positively associated with whether or not the loan is institutional, whether or not the borrower provides collateral, leverage, cash flow volatility, abnormal accruals, and credit spread. After_MAO is positive but not statistically significant across all specifications, providing no evidence that lenders continue to charge higher interest rates to borrowers who recently received MAOs. Generally, control variables are significant in the predicted direction; one notable exception is the coefficient on maturity, which is negative and statistically significant. It is opposite to the predicted relation between loan maturity and interest rate for a given borrower. However, the negative coefficient is consistent with theory work which predicts that loans with long maturity are issued to firms with the lowest credit risk, and that high credit risk firms can only borrow at short or medium maturity terms (Diamond, 1991). This is because the increase in interest spread in long maturities induces high risk borrowers to take on riskier projects despite their desire to secure loans with long maturity. As a result, we may observe a less positive or even negative coefficient on loan maturity in a pooled sample of loans to borrowers with differential credit risk. 4.2 Audit Opinions and Use of Financial and General Covenants Table 4 presents the effects of MAOs on the use of financial and general covenants. Hypothesis 2 states that lenders will be less willing to rely on financial covenants in debt contracts after the quality of the financial statements is brought into question by a modified opinion from an auditor. The first column in Table 4 provides evidence that the number of financial covenants included in a debt contract is lower in the MAO and after-MAO periods. The coefficient on MAO is -0.10 and statistically significant, representing a decrease in the use of financial covenants by 3.8%. Column 2 shows a decrease in the use of financial covenants after MAOs related to both Inadequacy (-0.17) and Inconsistency (-0.10). Column 3 provides the results for each type of MAO and shows that decreases in the use of financial

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covenants are driven by GC Opinion and Accounting Change. In contrast, the coefficients on Restatement and Material Uncertainty are not statistically significant. The coefficient on After_MAO is negative and significant across all three specifications, which indicates that lenders are reluctant to include financial covenants for up to three years after a clean opinion is restored to the borrower. This suggests a lingering effect of MAOs on the use of financial covenants. H2 also predicts that lenders will increase the use of general covenants in loans after an MAO. Column 4 of Table 4 provides evidence consistent with this hypothesis. The coefficient on MAO is 0.22 and statistically significant, and it indicates an average increase in the use of general covenants of 4.2%. This finding provides evidence consistent with our prediction that when lenders cannot rely on accounting numbers in debt contracts, they increase their use of the non-accounting debt covenants. In contrast to the use of financial covenants, the coefficient on After_MAO is insignificant. Column 5 shows that the use of general covenants increases after both Inadequacy and Inconsistency modifications, and Column 6 provides evidence that the use of general covenants increases after all MAOs except those related to restatements. The largest effect (0.95) relates to material uncertainty MAOs, those modifications in which the auditor mentions business uncertainty, litigation risk, or contingent liability issues. This result provides evidence that one way that lenders respond to a potential loss/liability conveyed by an MAO is through the increased use of general covenants. As in tests of H1, many of the control variables are as predicted and statistically significant. Overall, our empirical results regarding the effects of MAOs on the use of financial and general covenants are consistent with H2. 4.3 Audit Opinions and Use of Additional Loan Terms Hypothesis 3 predicts that lenders will include more stringent non-accounting loan terms after an MAO. We investigate three non-accounting mechanisms that lenders can

21

include in debt contracts: loan size, requirement of collateral, and loan maturity length, and we report the results in Table 5. Columns 1, 2, and 3 of Table 5 provide evidence that lenders decrease the loan sizes offered to borrowers with all types of MAOs except those related to material uncertainties. In addition, After_MAO is significant across each loan size specification. These results are consistent with lenders reacting to an MAO by reducing loan sizes. GC opinion has the largest effect on loan size based on the coefficient magnitude even though the difference between the coefficient of GC Opinion and each of the other three is not significant. This suggests that the loan-size component of the loan contract is insensitive to the differential signals of MAOs on the borrower’s default risk or financial reporting quality. This finding is consistent with those of the credit rationing literature (see e.g., Jaffee and Russell, 1976; Stiglitz and Weiss, 1981), which indicate that lenders negotiate loan size reacting to signals of default risk in a non-linear fashion. In addition, our empirical results are biased against finding a differential effect of GC opinions from those of other MAOs, as loan applications that were denied because of GC opinions are not represented. We also find that the likelihood of requiring collateral increases significantly after Inadequacy MAOs. Column 6 indicates that the probability of requiring collateral increases after GC opinions and material uncertainty MAOs and that this increased probability lingers for up to three years after a clean opinion is issued. The probability of requiring collateral increases 15.9% and 14.4% when a firm receives an MAO related to a GC opinion and material uncertainty, respectively; these two coefficients are not statistically different from each other. It is not surprising that lenders are eager to require collateral when firms receive an Inadequacy modification, because other monitoring mechanisms, such as covenants, are unlikely to be effective when the firm’s ability to continue as a going concern is in question or when there is serious uncertainty regarding a borrower’s prospects.

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Our last test of H3 investigates the loan maturity choices that lenders make at the contract initiation; we predict that lenders will shorten loan maturity after an MAO. Columns 7 and 8 indicate there is no effect on loan maturity after an MAO, even when MAOs are broken down in to Inadequacy and Inconsistency modifications. Column 9, however, indicates a decrease in maturity length for firms with GC opinions but an increase in maturity length for those with material uncertainty modifications. Although it is not surprising that lenders prefer to decrease loan lengths after GC opinions, it is less clear why an MAO related to a material uncertainty would lead to an increase in loan maturity. This is consistent, however, with borrowers negotiating other contract terms with lenders to secure longer term financing to manage their liquidity risk in the face of future cash flow uncertainty (e.g., ongoing litigation). This explanation is consistent with the unpredicted positive coefficient on leverage, indicating that borrowers with higher liquidity risk negotiate for longer maturities. We also observe that the coefficient on firm size is significant in the opposite direction than that predicted. The negative coefficient on firm size is consistent with Guedes and Opler (1996) who find that large firms borrow at both the short end and long end of the maturity spectrum.

5. Additional Analyses 5.1 The Effect of Different Types of GC Opinions on Loan Terms A large body of auditing literature examines the information role of GC opinions in the equity market. This is understandable because the consequence of GC opinions is severe and GC opinions reveal information about a client’s financial health to the public. In the main tests of our empirical analyses, we study the information content of GC opinions in relation to other MAOs to provide evidence of both the broad value and the differential value of audit

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opinions in the context of debt contracting. We provide further evidence on the communicative value of auditors’ stated causes underlying GC opinions. Menon and Williams (2010) use a large sample of GC audit reports and document significantly negative stock returns when these GC opinions are disclosed. This negative reaction is more severe when the audit report specifically mentions a problem with obtaining financing. We manually collect and categorize all the GC opinions contained in our sample, following Menon and Williams (2010), and create three new variables: 1) GC_Performance if a GC opinion mentions poor financial performance or operating problems (e.g., loss of a major customer/supplier) 2) GC_Financing if a GC opinion mentions financing problems and 3) GC_Other if a GC opinion mentions other issues (e.g., litigation risk or regulatory issues).20 We investigate the effects of the different types of GC opinions on each of the price and non-price terms we have previously tested and report the results in Table 6. Column 1 of Table 6 reports the separate effects of the three types of GC opinions on loan spreads. The coefficients of GC_Performance and GC_Financing are positive and significant with the coefficient of GC_Financing being larger in magnitude (p-value = 0.029 for the coefficient difference test). This is consistent with Menon and Williams (2010) who report that the most negative stock price reactions are caused by disclosure of GC opinions that mention financing problems. Column 2 provides evidence that the decrease in the use of financial covenants after a GC opinion reported in Table 4 is driven by GC_Performance. This result is intuitive; when financial performance is low, we would expect financial covenants to be the least useful to lenders. Column 3 reports a positive coefficient on GC_Financing, indicating that borrowers who receive GC opinions that mention problems arranging financing have more general covenants. A firm facing financing difficulties is 20

Menon and Williams (2010) separate their sample of GC opinions into four categories. We combine their poor financial performance and operating problem variables because we have only one operating problem observation that does not also mention poor financial performance.

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likely to be asked by the lender to restrict dividend payments, commit to not take on additional debt, pay off loans when capital assets are sold, and agree to other common provisions found in general covenants. Column 4 of Table 6 provides evidence that loan sizes decrease when a firm is given a GC opinion related to performance and financing. Column 5 provides the breakdown of the effect of GC opinions on the likelihood of requiring collateral. The coefficient on GC_Financing is positive and significant. It is not surprising that lenders would want collateral provided when a firm is given a GC opinion that mentions difficulties in securing financing. Finally, Column 6 provides evidence that the reduction in loan maturities for borrowers with GC opinions is driven by GC_Other; no other GC opinion coefficient is significant. Overall, Table 6 provides evidence that different causes of GC opinions lead to different loan contract changes and highlights the communicative value of the different reasons for GC opinions. These results suggest that auditors’ reasons for GC opinions in the explanatory paragraph are informative to lenders in designing efficient debt contracts, supplementing limited empirical evidence on the value of what auditors say beyond the simple binary decision to issue a GC opinion. 5.2 Information Leakage Before Modified Audit Opinions Are Issued A key inference drawn in this study is that auditors communicate unique information to lenders through the audit opinion and additional explanatory language. A limitation of our tests, however, is that we cannot perfectly identify whether it is the unique value of MAOs or information accessible to lenders through private channels that drives our results. Such information might include private communication from top managers related to major company developments, such as an update about ongoing litigation. To mitigate concerns that our results are capturing publically available information, we employ a variety of control variables in each of our tests. Managers of firms preparing to

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secure financing are likely to be in contact with potential lenders preceding a loan issuance, however, and we are unable to directly control for information that managers privately provide to lenders. To address the concern that our results are driven by unobserved information transfers from managers to lenders or by other publically available information leading up to an MAO, we construct a new indicator variable, Before_MAO, which takes a value of 1 if a loan is issued in the 12 months preceding an MAO. If the private information conveyed by the auditor is preempted by managers communicating to lenders before an MAO or by other publically available information, then we would expect a significant coefficient on this new variable in tests of the contract terms we have previously examined. We add this new variable to our contract term specifications and present the results in Table 7. Consistent with the audit opinion, and not with private information leaked from other channels providing value to lenders, we find that Before_MAO is insignificant in tests of each of the six contract terms we investigate. This result alleviates the concern that our results are driven by unobserved information leakage instead of by the auditor’s report. 5.3 The Information Role of GC Opinions with Matched Samples Unlike other MAOs, a GC opinion requires that the auditor make a judgment on the client’s ability to continue as a going concern, considering all the evidence accumulated on the audit. As a result, GC opinions contain auditors’ private information about client’s financial health that is useful to lenders. We further provide evidence that GC opinions communicate to lenders the auditor’s private information about the client’s default risk. We use propensity score matching to test the uniqueness of the auditor’s GC opinion by matching each firm in our sample that received a GC opinion with a clean-opinion firm that was predicted to receive a GC opinion but did not. Following prior research, we limit our sample to financially distressed firms when modeling the likelihood of receiving a GC opinion in the first stage (DeFond et al., 2002). We also require that the matching firm must obtain a loan

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after the pseudo “GC opinion.” Following the prior literature (Zmijewski, 1984; DeFond et al., 2002; Graham et al., 2008), we model the probability of receiving a GC opinion using two different specifications and present the results in Appendix B. 21 We present descriptive statistics of our sample firms before and after matching in Panel A of Table 8. Although most variables are statistically different between GC and nonGC firms before propensity score matching, most are indistinguishably different after using both the baseline and extended models. These descriptive statistics provide additional support indicating that the matching process is effective. We present our results after propensity score matching in Panels B (baseline model) and C (extended model) of Table 8. Because the pseudo “GC opinions” are predicted using public information contained in the financial reports, if there is no private information, there should be no difference in contract terms between the sample and matched firms. The coefficients on GC Opinion in Table 8, however, remain significant in both panels in the predicted direction for five of the six contract terms we examine, with the exception being the financial covenant specification. The coefficients on GC Opinion suggest that lenders demand a higher interest rate, increase the use of general covenants, decrease loan sizes and loan maturities, and increase the likelihood of requiring collateral to reflect auditors’ private information about borrowers. The coefficients of GC Opinion in the financial covenant tests reported in Column 2 of Panels B and C are not significant, indicating that perhaps GC opinions have no incremental effect on the use of financial covenants beyond the information available elsewhere in the public domain. In untabulated results we create samples using simple exact matching (instead of propensity score matching) on firm size and the probability of bankruptcy, and the results are not qualitatively different from those in Table 8. We also limit 21

We include firm-level control variables in the main regression for the basic PSM sample, and we include additional control variables from the prior literature in the extended PSM sample.

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our sample to the period after which Audit Analytics data become available (2000), with no change in inferences. Overall, our additional analyses with matched samples show that GC opinions are incrementally informative to lenders, beyond the two mechanical models that predict GC opinions and bankruptcy using publicly available information. 5.4 Differential Reporting of MAOs by Different Types of Audit Firms Prior studies find that large auditors have greater reputation assets than small auditors, and therefore, they have greater incentives to provide a high-quality audit. Large auditors are used as a proxy for audit quality (Pittman and Fortin, 2004). We investigate whether an MAO from a large audit firm has a greater impact on the contractual terms of subsequent debt contracts than an MAO from a small audit firm. Our results (untabulated) suggest no significant difference on any of the debt contract terms that we examine. Conditional on an MAO being issued, we do not find any significant difference in the effects on contract terms between large and small auditors. This does not necessarily contradict the conclusions drawn in prior studies that Big N audit firms have higher audit quality, because the propensity to issue an MAO may differ across large and small auditors or there may be endogeneity in the match between auditor size and client quality. For our sample, 93.5% of the total observations are audited by Big N auditors. The propensity to issue an MAO is 38.1% for Big N auditors and 33.6% for non-Big N auditors. This result, while providing neither necessary nor sufficient evidence, is consistent with Big N auditors yielding less to client pressure and providing higher-quality audits. Non-Big N auditors in our sample, however, were more likely to issue a GC opinion than Big N auditors, 3.9% compared to 2.3%. This is consistent with DeFond et al. (2011), who document that firms with non-Big N auditors are more likely to perform poorly and receive GC opinions. We also use auditors’ market share in an industry as a proxy for industry expertise to examine whether there are differences in the information content of opinions issued by

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industry specialists compared to non-industry specialists; we find no systematic differences. We also find no difference in the lenders’ reactions by to high- and low-quality audits using audit fees as a proxy for audit quality. Overall, we find no differences in the relevance to lenders of opinions issued by different types of auditors in our sample. 5.5 The Differential Effect of Internal Control Weaknesses and Audit Opinions We investigate an additional source of information in financial statements. Beginning in 2004, managers are required to report on the quality of the firm’s internal controls, and auditors are required to include this as part of the audit. Prior studies find that disclosures of internal control weakness under section 302 or section 404 of SOX affect subsequent debt contracting (Costello and Wittenberg-Moerman, 2011; Kim et al., 2011). We investigate the effect of disclosing an internal control weakness on our main results. In untabulated results, we find that even after controlling for the disclosure of an internal control weakness, our variables of interest remain statistically significant, indicating that MAOs inform lenders about the usefulness of accounting in debt contracting incremental to the disclosure of a weakness in internal controls.

6. Summary and Conclusions In this study, we examine the effect of audit opinions on private debt contracting by incorporating the auditor’s explanatory language. We partition modified audit opinions into Inconsistency, caused by an Accounting Change or Restatement, and Inadequacy, arising from Material Uncertainty or a GC Opinion. We analyze the differential information conveyed by each type of MAO to lenders related to borrowers’ financial reporting quality and default risk. We predict that, compared with loans issued in the year after a clean opinion, loans issued in the year after an MAO are associated with higher loan spreads and less

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favorable non-price loan terms. Moreover, we predict that the effect of MAOs on loan terms varies by the severity of the MAO, with GC opinions having the largest effect. Using the loan contracts of firms with at least one modified opinion, we find empirical results that support our predictions. Specifically, we find that, compared with loans issued in the year after a clean opinion, loans issued in the year after a modified opinion are associated with higher interest spreads, fewer financial covenants, more general covenants, smaller loan sizes, and a higher likelihood of requiring collateral. We find that the effect on loan spreads (as well as other non-price terms) varies by the type of modified opinion, ranging from no effect for an accounting change to an average increase of 107 basis points for a GC opinion. Additional analyses of GC opinions using propensity score matching on the probability of receiving a GC opinion show that auditor opinions are still associated with less favorable loan terms after controlling for these effects. Given the various other potential channels through which lenders may communicate with borrowers, we do not claim causality between auditor reporting and debt contracting. Our empirical results, however, suggest that auditor reporting is relevant and valuable to lenders in debt contracting, with the relevance increasing with the severity of concerns communicated in the MAO. An independent audit is an essential part of the financial reporting process. Our empirical analyses contribute knowledge about the value-adding function of audit reporting. Our results suggest that the explanatory language in audit reports communicates relevant and useful information to lenders. As market demand for more disclosures in audit reports increases, regulators are contemplating whether to include a discussion of “critical audit matters” to enhance the audit report’s value (PCAOB, 2013). An implication of our study is that such disclosures have the potential to enhance the relevance of auditor reporting. While our study speaks to the potential benefits of additional disclosure by auditors, we do not investigate the associated costs. The empirical results we document are not obvious, given

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that private lenders have access to private information unavailable to most market participants, and they suggest that auditors play a unique information role in debt contracting.

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Appendix A: Variable Definitions Auditor Opinion Variables MAO After_MAO

An indicator variable equal to 1 if the firm receives an opinion from the auditor that is not unqualified, and zero otherwise. An indicator variable equal to 1 if the firm does not have an MAO this year but had an MAO equal to 1 in at least one of the previous three years, and zero otherwise.

Before_MAO

An indicator variable equal to 1 if the firm does not have an MAO this year but has an MAO equal to 1 in the next year, and zero otherwise.

Inadequacy 1

An indicator variable equal to 1 if the auditor signals a going concern (GC Opinion = 1) or an uncertainty problem (Material Uncertainty = 1) in the auditor report, and zero otherwise.

Inconsistency

An indicator variable equal to 1 if the auditor signals a restatement (Restatement = 1) or accounting change (Accounting Change = 1) problem in the auditor report, and zero otherwise.

GC Opinion2

An indicator variable equal to 1 if the auditor questions the ability of a firm to continue as a going concern in the auditor report, and zero otherwise.

Material Uncertainty

An indicator variable equal to 1 if the auditor mentions a business uncertainty, litigation risk, or contingent liability problem in the auditor report, and zero otherwise.

Restatement

An indicator variable equal to 1 if the auditor mentions that the firm’s financial statements have been restated in the auditor report, and zero otherwise.

Accounting Change

An indicator variable equal to 1 if the auditor mentions that there has been an accounting method change or reliance on another auditor in the auditor report, and zero otherwise.

GC_Performance3

An indicator variable equal to 1 if a GC opinion issued by an auditor mentions poor financial performance or operating problems (e.g., loss of a major customer/supplier), and zero otherwise.

GC_Financing

An indicator variable equal to 1 if a GC opinion issued by an auditor mentions financing problems, and zero otherwise. An indicator variable equal to 1 if a GC opinion issued by an auditor mentions other issues (e.g., litigation risk or regulatory issues), and zero otherwise.

GC_Other

Borrower-Specific Variables Size Market-to-book Leverage Profitability Cash flow volatility

The natural log of total assets, estimated in the year prior to entering into a loan contract. Market value of equity plus the book value of debt over total assets in the year prior to entering into a loan contract. Long-term debt divided by total assets, estimated in the year prior to entering into a loan contract. EBIDTA divided by total assets, estimated in the year prior to entering into a loan contract. Standard deviation of quarterly cash flows from operations over previous four fiscal years, scaled by total assets.

34

Tangibility Z-score

Net PPE divided by total assets, estimated in the year prior to entering into a loan contract. Probability of bankruptcy score (Zmijewski 1984). We exclude the Market-to-book component, because we include Market-to-book in our tests as a separate control variable.

Abnormal_Accruals

Absolute abnormal accruals calculated as the residual of a crosssectional version of the Butler et al. (2004) model for each (two-digit SIC) industry and year.

Credit spread

The difference between the BAA corporate bond yield and the AAA corporate bond yield. The difference between the 10-year Treasury yield and the 2-year Treasury yield.

Term spread

Loan-Specific Variables Financial Covenants

The number of financial covenants included in the loan agreement.

General Covenants

The number of general covenants included in the loan agreement. (Examples of general covenants include: the percentage of net proceeds from an asset sale that must be used to pay down an outstanding loan balance, whether or not a borrower is allowed to pay dividends, and the percentage of excess cash flow a borrower is allowed to use towards dividends.)

Institutional Investor

An indicator variable equal to 1 if the loan’s type is term loan B, C, or D (institutional term loans), and zero otherwise. The interest rate is the All-in-Drawn-Spread measure reported by DealScan, and it is equal to the number of basis points over LIBOR. Amount borrowed in millions of dollars.

Interest Rate Loan Size Maturity Number of Lenders PP Indicator Revolver Collateral Loan Purpose Effect

The number of months between the facility’s issue date and the loan maturity date. Number of participants in the loan syndicate. An indicator variable equal to 1 if the loan contract includes a performance pricing provision, and zero otherwise. An indicator variable equal to 1 if the loan is a revolver, and zero otherwise. An indicator variable equal to 1 if the loan is backed by collateral, and zero otherwise. A series of indicator variables for the purposes of loan facilities in DealScan, including: corporate purposes, debt repayment, working capital, CP backup, takeover, and acquisition line.

1. Inadequacy and Inconsistency together comprise all MAO observations and do not overlap in measurement. For those observations that overlap in the raw data, we choose the more serious category according to the following ranking: GC Opinion, Material Uncertainty, Restatement, Accounting Change. Results are robust to allowing overlap. 2. GC Opinion and Material Uncertainty together comprise all Inadequacy observations. Restatement and Accounting Change comprise all Inconsistency observations. These variables do not overlap in measurement. For those observations that overlap in the raw data, we choose the more serious category according the ranking above. Results are robust to allowing overlap. 3. GC_Performance, GC_Financing, and GC_Other together comprise all GC Opinion observations. GC observations are not limited to one sub-classification.

35

Appendix B: Determinants of Going Concern Opinions Dependent Variable = GC Opinion Predicted Sign Baseline PSM Extended PSM Firm Size

-

Market-to-book

-

Total Liability

+

Profitability

-

Cash flow volatility

+

Tangibility

-

Z-score

-

Abnormal_Accruals

+

Age

-

Big

+

Δ Total Liability

+

Cash

-

Operating cash flow

-

-0.11*** (-2.85) -1.18*** (-7.17) 2.81*** (11.29) -1.48** (-2.27) 1.91 (1.10) 0.09 (0.34) -0.16*** (-3.62) 1.37*** (2.59)

Intercept

Observations Pseudo R2

-1.59*** (-4.96)

-0.12*** (-2.99) -1.40*** (-7.18) 2.66*** (10.13) -1.65** (-2.31) 1.38 (0.97) -0.04 (-0.14) -0.18*** (-3.82) 1.30** (2.40) 0.08 (0.91) 0.17 (0.83) 0.76*** (3.07) -0.71 (-1.05) 0.44 (0.71) -1.43*** (-3.79)

1,750 0.307

1,750 0.312

This panel presents the probit regression results of modeling the probability of receiving a GC opinion to generate a score for matching. Following prior research, we limit our sample to financially distressed firms when modeling the likelihood of receiving a GC opinion in the first stage. Distressed firms are defined as those firms with negative earnings or operating cash flows in the current fiscal year (Reynolds and Francis, 2000; DeFond et al., 2002). We benchmark at loss firms since going concern audit opinions are extensively given to loss firms (122 of 131 firms received a GC are loss firms in our sample). We include firm-level control variables used in our main regressions in the baseline PSM. Following DeFond et al. (2002) we include Total Liability, defined as total liability scaled by total assets, because GC opinion firms have more short term debt than long term debt. We include additional firm-level control variables in the extended PSM. Age is the natural log of firm year. Big is a dummy variable which is equal to one if the firm is a big N client, and zero otherwise. Δ Total Liability is the change of Total Liability from year t-1 to year t. Cash is the cash holding divided by total assets and Operating cash flow is the cash flow from operating divided by total assets.

36

Facilities 63 139 205 293 459 551 558 602 582 658 678 702 711 679 573 530 320 170 8,473

Firms 42 92 129 191 299 327 322 330 353 412 450 448 481 448 370 320 226 137 5,377

MAO 3 22 54 81 94 42 28 23 39 48 101 318 345 218 129 229 179 103 2,056

Inadequacy 2 1 2 9 5 4 2 10 21 16 23 23 11 10 16 7 7 6 175

Inconsistency 1 21 52 72 89 38 26 13 18 32 78 295 334 208 113 222 172 97 1,881

GC Opinion 0 0 1 3 3 3 2 7 17 15 22 15 9 7 13 5 4 5 131

37

Table 1 presents the annual distribution of observations. See Appendix A for the variable definitions.

Year 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Total

Table 1: Sample Distribution Material Uncertainty 2 1 1 6 2 1 0 3 4 1 1 8 2 3 3 2 3 1 44

Restatement 0 0 1 2 4 3 2 1 2 5 39 50 43 28 13 7 1 1 201

Accounting Change 1 21 51 70 85 35 24 13 17 30 73 256 284 165 85 209 165 96 1,680

Table 2: Descriptive Statistics Panel A: Loan Characteristics MAO=1(N=3,203) Mean

MAO=0(N=5,270)

Median

Std. dev.

Median

Std. dev.

222.15

200.00

162.74

200.56

200.00

128.32

Number of Financial Covenants

2.34

2.00

1.40

2.63

3.00

1.49

Number of General Covenants

5.67

5.00

2.77

5.22

5.00

2.86

Number of Lenders

9.21

6.00

9.74

8.62

6.00

9.46

Loan Size (in millions)

379.12

150.00

964.23

258.72

100.00

753.47

Maturity (in months)

48.12

56.00

22.41

47.58

50.00

24.28

PP Indicator

0.64

1.00

0.48

0.68

1.00

0.47

Collateral

0.72

1.00

0.45

0.73

1.00

0.44

Institutional Investor

0.12

0.00

0.32

0.10

0.00

0.30

Revolver

0.61

1.00

0.49

0.62

1.00

0.49

Interest Rate

Mean

Panel B: Firm Characteristics

Mean Firm Size (in millions) Firm Size (nlog) Market-to-book Leverage Profitability Cash flow volatility Tangibility Z-score Abnormal_Accruals Credit spread Term spread

MAO=1(N=2,056) Median Std. dev.

3562.07 6.93 1.58 0.26 0.12 0.03 0.34 1.64 0.08 1.00 1.24

931.19 6.84 1.36 0.24 0.12 0.02 0.28 1.65 0.06 0.90 1.33

6833.59 1.63 0.78 0.22 0.09 0.03 0.24 1.33 0.08 0.46 0.91

Mean

MAO=0(N=3,321) Median Std. dev.

1796.54 6.17 1.77 0.25 0.13 0.03 0.32 1.92 0.09 0.86 0.77

473.05 6.16 1.47 0.22 0.13 0.02 0.26 1.92 0.06 0.81 0.44

4305.90 1.64 1.00 0.21 0.10 0.03 0.23 1.34 0.09 0.29 0.84

Table 2 presents the descriptive statistics for the total sample. Panel A provides loan characteristics at the facility level, and Panel B provides firm characteristics. Firms may obtain more than one facility in any given year. See Appendix A for the variable definitions.

38

Interest Rate Number of Financial Covenants Number of General Covenants Number of Lenders Loan Size (in millions) Maturity (in months) PP Indicator Collateral Firm Size Market-to-book Leverage Profitability Cash flow volatility Tangibility Z-score Abnormal_Accruals Credit spread Term spread MAO

0.15 0.26 -0.25 -0.16 -0.02 -0.36 0.50 -0.30 -0.19 0.15 -0.30 0.12 -0.03 -0.29 0.14 0.22 0.19 0.07

1

0.39 -0.06 -0.15 0.11 0.15 0.23 -0.22 -0.05 0.08 0.02 -0.04 -0.09 -0.02 0.01 0.02 0.01 -0.10

2

0.11 0.00 0.32 0.15 0.36 0.11 -0.09 0.24 0.04 -0.13 -0.04 -0.09 -0.06 0.08 -0.01 0.08

3

0.29 0.17 0.18 -0.19 0.52 0.01 0.16 0.08 -0.19 0.07 -0.01 -0.11 -0.07 -0.04 0.03

4

0.02 0.06 -0.18 0.40 0.02 0.03 0.04 -0.12 0.06 -0.03 -0.04 0.00 -0.05 0.07

5

0.13 0.15 0.08 -0.01 0.22 0.11 -0.16 0.04 -0.03 -0.07 -0.14 -0.16 0.01

6

-0.18 0.17 0.04 -0.02 0.20 -0.13 0.02 0.17 -0.08 -0.01 -0.09 -0.03

7

-0.37 -0.15 0.16 -0.19 0.09 -0.05 -0.19 0.11 0.04 0.04 -0.02

8

-0.06 0.18 0.05 -0.35 0.15 -0.03 -0.19 0.11 0.04 0.22

9

-0.10 0.41 0.06 -0.10 0.12 0.11 -0.07 -0.12 -0.10

10

0.01 -0.20 0.21 -0.35 -0.07 -0.06 0.01 0.02

11

-0.05 0.13 0.48 -0.13 -0.06 -0.03 -0.06

12

-0.21 0.16 0.16 0.00 0.05 -0.02

13

-0.21 -0.09 -0.01 0.01 0.04

14

-0.11 -0.03 -0.04 -0.11

15

39

16

0.01 0.00 -0.03

Table 2 Panel C presents the Pearson correlation matrix. All variables are defined in Appendix A. Correlations in bold are significant at the 5% level or less.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Panel C: Correlation Matrix

0.47 0.19

17

0.26

18

Table 3: Effect of Modified Audit Opinions on Loan Spreads Loan Spread Predicted Sign

(1)

MAO

+

17.31*** (3.24)

Inadequacy

+

Inconsistency

+

GC Opinion

+

Material Uncertainty

+

Restatement

+

Accounting Change

+

After_MAO

+/?

Institutional Investor

+

Revolver

-

Loan Size

-

Financial Covenants

-

Maturity

+

Number of Lenders

-

PP Indicator

-

Collateral

+

Firm Size

-

Market-to-book

-

Leverage

+

Profitability

-

Cash flow volatility

+

Tangibility

-

Z-score

-

(2)

(3)

92.14*** (7.77) 5.95 (1.52)

2.03 (0.43) 47.15*** (6.97) -27.07*** (-6.89) -12.62*** (-5.90) 1.09 (0.76) -0.25*** (-2.74) -0.31 (-1.42) -48.57*** (-7.57) 78.88*** (13.49) -8.84*** (-3.26) -7.97*** (-4.17) 66.96*** (8.08) -146.32*** (-5.95) 218.32*** (3.04) 7.51 (0.66) -8.34*** (-5.60)

40

0.68 (0.15) 47.32*** (6.83) -27.21*** (-6.87) -12.84*** (-5.94) 1.20 (0.84) -0.22** (-2.49) -0.36 (-1.54) -45.41*** (-7.78) 76.87*** (13.69) -8.39*** (-3.14) -7.17*** (-4.11) 76.16*** (8.65) -144.17*** (-6.12) 190.13*** (2.58) 6.88 (0.60) -6.23*** (-4.98)

107.13*** (7.67) 49.37** (2.56) 25.27*** (3.21) 3.64 (0.88) 0.90 (0.20) 46.79*** (6.72) -27.23*** (-6.86) -12.74*** (-5.79) 1.27 (0.88) -0.21** (-2.21) -0.37 (-1.52) -45.23*** (-7.80) 77.08*** (13.98) -8.28*** (-3.09) -7.01*** (-3.93) 76.41*** (8.70) -142.65*** (-6.19) 183.18** (2.44) 7.06 (0.61) -5.93*** (-4.59)

Abnormal_Accruals

+

Credit spread

+

Term spread

+

Intercept Loan Purpose FE Year FE Observations Adj. R2

46.78** (2.38) 29.49* (1.69) 3.71 (0.60) 490.08*** (12.92)

34.08* (1.70) 31.20* (1.73) 3.50 (0.56) 474.72*** (12.44)

34.07* (1.70) 31.11* (1.74) 3.78 (0.62) 471.73*** (12.29)

Included Included 8,473 0.515

Included Included 8,473 0.526

Included Included 8,473 0.528

Table 3 presents the results from the estimation of the following model: Interest Rate = α+ β1MAO + β2After_MAO + βi CONTROLS + ε We regress the interest rate on MAO, After_MAO, and loan- and firm-specific control variables in Column 1. We regress the interest rate on Adequacy and Consistency, After_MAO, loan- and firm-specific control variables in Column 2. In Column 3 we include GC Opinion, Material Uncertainty, Restatement and Accounting Change. All variables are defined in Appendix A. Firm-specific financial variables are winsorized at the 0.01 level. Regressions include loan purpose and year fixed effects and standard errors are heteroskedasticity robust and clustered at both the firm and year level. Z-statistics are reported in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

41

Table 4: Effect of Modified Audit Opinions on Debt Covenants Financial Covenants Predicted Sign

MAO

-

Inadequacy

-

Inconsistency

-

GC Opinion

-

Material Uncertainty

-

Restatement

-

Accounting Change

-

After_MAO

-/?

Institutional Investor

+

Revolver

?

Loan Size

-

Maturity

+

Number of Lenders

?

PP Indicator

+

Collateral

+

Interest rate

-

Firm Size

-

Market-to-book

-

Leverage

+

Profitability

?

Cash flow volatility

?

Tangibility

?

(1)

(2)

General Covenants (3)

-0.10** (-2.04)

Predicted Sign +

-0.17** (-1.99) -0.10* (-1.74)

-0.16*** (-3.25) 0.45*** (5.40) -0.00 (-0.08) -0.04 (-1.62) 0.00*** (3.30) 0.01*** (2.66) 0.40*** (6.75) 0.35*** (5.31) 0.00 (0.89) -0.18*** (-7.01) -0.10*** (-4.02) 0.34*** (2.71) 1.50*** (5.31) -4.84*** (-4.83) -0.54***

(-5.08)

(-5.06)

(-5.07)

+

-0.04 (-0.47) 0.88*** (6.87) -0.44*** (-6.52) 0.36*** (6.85) 0.01*** (6.12) -0.00 (-0.11) 1.03*** (11.14) 1.93*** (15.49) 0.00*** (10.72) 0.03 (0.73) -0.15*** (-2.84) 0.92*** (4.04) 2.86*** (4.60) -4.66*** (-2.86) -1.03***

-0.04 (-0.57) 0.89*** (6.88) -0.45*** (-6.63) 0.35*** (6.87) 0.01*** (6.26) -0.00 (-0.21) 1.04*** (11.30) 1.93*** (15.32) 0.00*** (10.82) 0.03 (0.78) -0.14*** (-2.81) 0.99*** (4.23) 2.85*** (4.67) -4.82*** (-2.86) -1.04***

0.64** (2.18) 0.95*** (4.12) 0.17 (0.88) 0.15* (1.77) -0.04 (-0.56) 0.89*** (6.87) -0.45*** (-6.60) 0.35*** (6.87) 0.01*** (6.15) -0.00 (-0.19) 1.04*** (11.14) 1.93*** (15.38) 0.00*** (11.26) 0.03 (0.77) -0.14*** (-2.76) 0.99*** (4.22) 2.85*** (4.69) -4.79*** (-2.85) -1.04***

(-6.54)

(-6.57)

(-6.55)

+ + + +/? + ? ? + ? + + + + ? ? ?

(6)

0.72*** (3.21) 0.15* (1.75)

+

-0.16*** (-3.24) 0.45*** (5.39) -0.00 (-0.10) -0.04 (-1.64) 0.00*** (3.31) 0.01*** (2.63) 0.40*** (6.74) 0.35*** (5.30) 0.00 (0.80) -0.18*** (-7.01) -0.10*** (-3.98) 0.35*** (2.84) 1.50*** (5.32) -4.86*** (-4.85) -0.54***

(5)

0.22*** (3.01)

+

-0.24** (-2.56) 0.04 (0.17) -0.09 (-0.99) -0.09* (-1.71) -0.16*** (-3.23) 0.45*** (5.43) -0.00 (-0.08) -0.04 (-1.63) 0.00*** (3.32) 0.01*** (2.69) 0.40*** (6.73) 0.35*** (5.16) 0.00 (0.91) -0.18*** (-7.02) -0.10*** (-4.04) 0.34*** (2.71) 1.50*** (5.30) -4.81*** (-4.78) -0.54***

42

(4)

Z-score

-

Abnormal_Accruals

-

Credit spread

?

Term spread

?

Intercept

Loan Purpose FE Year FE Observations Adj. R2

-0.02 (-1.29) -0.45*** (-3.11) 0.03 (0.40) 0.11* (1.70) 0.72 (1.45)

-0.03 (-1.37) -0.44*** (-3.12) 0.03 (0.37) 0.11* (1.71) 0.72 (1.45)

-0.03 (-1.47) -0.44*** (-3.15) 0.03 (0.39) 0.11* (1.67) 0.73 (1.47)

Included Included 8,473 0.336

Included Included 8,473 0.337

Included Included 8,473 0.337

? ?

-0.01 (-0.22) -1.34*** (-3.83) 0.21** (2.33) 0.20 (1.56) -8.41*** (-7.54)

0.00 (0.10) -1.42*** (-4.09) 0.23** (2.48) 0.20 (1.58) -8.44*** (-7.63)

0.00 (0.05) -1.42*** (-4.11) 0.23** (2.51) 0.20 (1.55) -8.43*** (-7.61)

Included Included 8,473 0.466

Included Included 8,473 0.467

Included Included 8,473 0.467

Table 4 presents the results from the estimation of the following model: Number of Covenants (Financial or General) = α+ β1MAO + β2After_MAO + βi CONTROLS + ε We regress the number of financial covenants (Column 1) and general covenants (Column 4) on MAO, After_MAO, and loan- and firm-specific control variables. We test Inadequacy and Inconsistency, After_MAO, and loan- and firmspecific control variables in Columns 2 and 5. In Columns 3 and 6, we test the effects of GC Opinion, Material Uncertainty, Restatement, and Accounting Change. All variables are defined in Appendix A. Regressions include loan purpose and year fixed effects and standard errors are heteroskedasticity robust and clustered at both the firm and year level. Z-statistics are reported in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

43

-

-

-

-

-

-

-/?

+

?

Inadequacy

Inconsistency

GC Opinion

Material Uncertainty

Restatement

Accounting Change

After_MAO

Institutional Investor

Revolver

?

+

?

Financial Covenants

Maturity

Number of Lenders

Loan Size

-

MAO

Predicted Sign

0.01 (1.43) 0.00*** (4.35) -0.00

-0.02* (-1.67) 0.07*** (3.09) 0.09*** (9.06)

-0.05*** (-3.62)

(1)

0.01 (1.42) 0.00*** (4.33) -0.00

-0.02* (-1.67) 0.07*** (3.08) 0.09*** (9.03)

-0.05*** (-2.87) -0.05*** (-3.67)

(2)

Loan Size

0.01 (1.39) 0.00*** (4.02) -0.00

-0.06*** (-2.75) -0.04 (-1.04) -0.04*** (-2.76) -0.05*** (-3.59) -0.02* (-1.68) 0.07*** (3.06) 0.09*** (8.94)

(3)

?

+

?

-

?

+

+/?

+

+

+

+

+

+

+

44

Predicted Sign

0.14* (1.82) 1.17*** (5.46) 0.07 (0.99) -0.15*** (-3.63) 0.13*** (4.53) 0.00*** (3.06) 0.01**

0.07 (1.01)

(4)

0.13* (1.74) 1.17*** (5.34) 0.07 (0.95) -0.15*** (-3.64) 0.13*** (4.39) 0.00*** (3.00) 0.01*

0.96*** (3.81) 0.01 (0.13)

(5)

Collateral

0.98*** (2.83) 0.95** (2.12) 0.09 (0.83) 0.00 (0.00) 0.13* (1.76) 1.17*** (5.31) 0.07 (0.95) -0.15*** (-3.61) 0.13*** (4.36) 0.00*** (3.00) 0.01*

(6)

+

?

?

?

+

-/?

-

-

-

-

-

-

-

Predicted Sign

Table 5: Effect of MAOs on Loan Size, Likelihood of Requiring Collateral, and Loan Maturity

0.01***

-0.00 (-0.07) 0.56*** (13.83) 0.18*** (4.65) 0.06*** (5.06) 0.04*** (4.89)

0.00 (0.12)

(7)

0.01***

0.00 (0.00) 0.56*** (13.70) 0.19*** (4.68) 0.06*** (4.99) 0.04*** (4.82)

-0.12 (-1.47) 0.02 (0.83)

(8)

Log(Maturity)

0.01***

-0.20** (-1.99) 0.12* (1.66) -0.04 (-1.11) 0.03 (1.13) -0.00 (-0.02) 0.56*** (13.60) 0.19*** (4.69) 0.06*** (4.93) 0.04*** (4.92)

(9)

+

-

PP Indicator

Collateral

Interest rate

-

+

+

+

-

+

?

?

Leverage

Profitability

Cash flow volatility

Tangibility

Z-score

Abnormal_Accruals

Credit spread

Term spread

Loan Purpose FE Year FE Observations Adj. R2/Pseudo R2

Intercept

+

Market-to-book

Firm Size

?

0.04*** (4.18) -0.05** (-2.18) 0.25*** (3.01) 1.40*** (8.89) 0.04*** (4.18) -0.05** (-2.18) 0.25*** (3.01) -0.01 (-0.48) 0.00 (0.11) -0.10** (-2.17) Included Included 8,473 0.238

Included Included 8,473 0.238

(-0.19) 0.03*** (3.36) 0.06*** (6.12) -0.00 (-0.19)

0.04*** (4.20) -0.05** (-2.22) 0.25*** (3.01) 1.40*** (8.83) 0.04*** (4.20) -0.05** (-2.22) 0.25*** (3.01) -0.01 (-0.47) 0.00 (0.11) -0.10** (-2.20)

(-0.22) 0.03*** (3.38) 0.06*** (6.14) -0.00 (-0.26)

Included Included 8,473 0.238

0.04*** (4.16) -0.05** (-2.18) 0.25*** (3.02) 1.40*** (8.86) 0.04*** (4.16) -0.05** (-2.18) 0.25*** (3.02) -0.01 (-0.47) 0.00 (0.10) -0.10** (-2.17)

(-0.19) 0.03*** (3.35) 0.06*** (6.01) -0.00 (-0.15)

?

?

+

-

-

+

-

+

-

-

-

45

Included Included 8,473 0.339

-0.35*** (-10.08) -0.13*** (-4.44) 1.57*** (8.19) -1.65*** (-3.62) 4.04*** (2.65) -0.20 (-1.42) -0.13*** (-3.56) 0.89*** (6.14) 0.23 (1.21) 0.00 (0.06) 6.08*** (8.71)

(2.04) -0.28*** (-4.28)

Included Included 8,473 0.345

-0.34*** (-10.06) -0.13*** (-4.29) 1.65*** (8.41) -1.59*** (-3.47) 3.87** (2.55) -0.20 (-1.43) -0.12*** (-3.22) 0.77*** (5.67) 0.24 (1.24) 0.01 (0.12) 5.92*** (8.34)

(1.91) -0.26*** (-3.96)

Included Included 8,473 0.345

-0.34*** (-10.03) -0.13*** (-4.22) 1.65*** (8.47) -1.58*** (-3.44) 3.86** (2.54) -0.20 (-1.42) -0.12*** (-3.21) 0.77*** (5.94) 0.24 (1.24) 0.01 (0.12) 5.92*** (8.27)

(1.90) -0.26*** (-3.97)

?

?

-

+

+

-

+

-

-

+

-

+

+

Included Included 8,473 0.294

(2.82) 0.25*** (7.37) 0.16*** (6.21) -0.00** (-2.10) -0.06*** (-4.22) -0.03*** (-2.66) 0.27*** (6.61) 0.50*** (3.88) -1.84*** (-5.15) -0.01 (-0.31) 0.01 (0.65) -0.11 (-1.02) -0.12*** (-2.76) -0.01 (-0.37) 3.09*** (15.43) Included Included 8,473 0.295

(2.85) 0.24*** (7.11) 0.16*** (6.22) -0.00* (-1.72) -0.06*** (-4.22) -0.03*** (-2.77) 0.25*** (5.60) 0.51*** (3.95) -1.81*** (-5.08) -0.01 (-0.29) 0.00 (0.41) -0.09 (-0.83) -0.12*** (-2.78) -0.01 (-0.35) 3.10*** (15.46) Included Included 8,473 0.297

(2.98) 0.24*** (7.03) 0.15*** (5.97) -0.00 (-1.56) -0.06*** (-4.18) -0.03*** (-2.85) 0.25*** (5.57) 0.50*** (4.01) -1.77*** (-5.03) -0.01 (-0.32) 0.00 (0.26) -0.10 (-0.87) -0.12*** (-2.83) -0.01 (-0.45) 3.11*** (15.74)

46

All variables are defined in Appendix A. Firm-specific financial variables are winsorized at the 0.01 level. Regressions include loan purpose and year fixed effects, and standard errors are heteroskedasticity robust and clustered at both the firm and year level. Z-statistics are reported in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

In Column 1 we estimate the effect on loan size of an MAO where Loan Size is equal to the amount borrowed scaled by the borrower’s assets. In Column 4 we estimate the probability that the lender requires a borrower to provide collateral. The dependent variable is equal to one if the loan includes collateral, and zero otherwise. Column 7 presents results of our estimate of the effect on loan maturity of an MAO. Maturity is the duration of the loan contract in months. Columns 2, 3, 5, 6, 8, and 9 examine the effects of different subtypes of MAOs.

Log (Maturity) = α + β1 MAO + β2 After_MAO + βi CONTROLS + ε

P (Collateral =1) = α + β1MAO + β2 After_MAO + βi CONTROLS + ε

Loan Size (scaled by total assets) = α + β1MAO + β2 After_MAO + βi CONTROLS + ε

In Table 5 we present results for the following specifications:

Table 6: Effect of Different Types of Going Concern Opinions

Dependent Variable =

GC_Performance GC_Financing GC_Other Material Uncertainty Restatement Accounting Change After_MAO Institutional Investor Revolver Loan Size Financial Covenants Maturity Number of Lenders PP Indicator Collateral

(1) Loan Spread 47.15*** (4.10) 93.10*** (5.93) 55.11 (0.58) 44.23** (2.33) 23.20*** (3.17) 1.91 (0.49) -0.41 (-0.09) 46.89*** (6.87) -27.02*** (-6.93) -12.63*** (-5.93) 1.23 (0.85) -0.22** (-2.54) -0.36 (-1.45) -45.52*** (-7.49) 77.24*** (13.71)

Interest rate Firm Size Market-to-book Leverage Profitability Cash flow volatility Tangibility Z-score

-8.21*** (-3.10) -6.96*** (-3.77) 77.44*** (8.64) -143.51*** (-6.01) 195.22*** (2.67) 6.64 (0.60) -6.41***

(2) Financial Covenants

(3) General Covenants

-0.29* (-1.92) -0.03 (-0.21) -0.28 (-0.55) 0.05 (0.22) -0.09 (-0.98) -0.09* (-1.66) -0.16*** (-3.24) 0.45*** (5.19) -0.00 (-0.09) -0.04 (-1.64)

-0.28 (-0.64) 1.09*** (4.04) -0.27 (-0.36) 0.92*** (3.94) 0.15 (0.76) 0.13 (1.56) -0.06 (-0.81) 0.89*** (6.97) -0.45*** (-6.51) 0.35*** (6.84)

0.00*** (3.23) 0.01*** (2.76) 0.40*** (6.73) 0.35*** (5.10) 0.00 (0.82) -0.18*** (-6.80) -0.10*** (-4.06) 0.34*** (2.66) 1.49*** (5.28) -4.80*** (-4.77) -0.53*** (-5.11) -0.03

0.01*** (6.07) -0.00 (-0.14) 1.04*** (10.86) 1.92*** (14.95) 0.00*** (10.76) 0.03 (0.80) -0.14*** (-2.75) 0.99*** (4.18) 2.81*** (4.59) -4.58*** (-2.75) -1.03*** (-6.39) -0.00

47

(4)

(5)

(6)

Loan Size

Collateral

Log(Maturity)

-0.05** (-2.18) -0.02** (-2.00) -0.00 (-0.13) -0.04 (-0.99) -0.04*** (-2.73) -0.04*** (-3.55) -0.02 (-1.60) 0.07*** (3.06) 0.09*** (8.98)

0.19 (0.57) 0.90** (2.57) -0.38 (-0.85) 0.93** (2.07) 0.07 (0.65) -0.01 (-0.23) 0.11 (1.63) 1.17*** (5.31) 0.06 (0.89) -0.15*** (-3.63) 0.13*** (4.38) 0.00*** (2.99) 0.01** (1.97) -0.26*** (-3.97)

-0.16 (-0.94) -0.02 (-0.09) -0.40*** (-3.92) 0.15** (1.98) -0.04 (-0.87) 0.04 (1.38) 0.00 (0.14) 0.56*** (13.28) 0.18*** (4.66) 0.06*** (4.97) 0.04*** (4.92)

0.01 (1.38) 0.00*** (4.24) -0.00 (-0.21) 0.03*** (3.38) 0.06*** (6.02) -0.00 (-0.22)

0.04*** (4.16) -0.05** (-2.17) 0.25*** (2.99) 1.40*** (8.76) 0.10*** (3.71) -0.01

-0.34*** (-10.15) -0.13*** (-4.32) 1.63*** (8.57) -1.54*** (-3.37) 3.92*** (2.61) -0.22 (-1.52) -0.12***

0.01*** (2.91) 0.24*** (7.02) 0.15*** (5.95) -0.00 (-1.41) -0.06*** (-4.26) -0.03*** (-2.75) 0.25*** (5.52) 0.49*** (3.96) -1.79*** (-5.02) -0.01 (-0.31) 0.00

Abnormal_Accruals Credit spread Term spread Intercept Loan Purpose FE Year FE Observations Adj. R2/Pseudo R2

(-4.83) 33.96* (1.69) 31.42* (1.75) 3.60 (0.56) 472.37*** (12.82)

(-1.44) -0.42*** (-2.92) 0.03 (0.37) 0.11* (1.65) 0.73 (1.48)

(-0.11) -1.37*** (-3.94) 0.23** (2.46) 0.20 (1.48) -8.41*** (-7.46)

(-1.29) 0.20*** (2.77) -0.01 (-0.47) 0.00 (0.09) -0.11** (-2.18)

(-3.44) 0.82*** (6.70) 0.24 (1.25) 0.01 (0.21) 5.93*** (8.28)

(0.39) -0.09 (-0.79) -0.12*** (-2.81) -0.01 (-0.46) 3.11*** (15.67)

Included Included 8,473 0.527

Included Included 8,473 0.337

Included Included 8,473 0.468

Included Included 8,473 0.238

Included Included 8,473 0.349

Included Included 8,473 0.297

Table 6 presents the results from the estimation of the following model: Contract terms =α+ β1GC_Performance +β2GC_Financing +β3GC_Other +β4 Material Uncertainty +β5 Restatement +β6 Accounting Change +β7 After_MAO + βi CONTROLS + ε Our GC sample consists of 90, 80, and 6 observations for GC_Performance, GC_Financing, and GC_Other, respectively. Following Menon and Williams (2010), we allow GC opinions to appear in more than one category. We categorize 45 observations as both GC_Performance and GC_Financing. The names of the columns provide the dependent variables. All variables are defined in Appendix A. Firm-specific financial variables are winsorized at the 0.01 level. Regressions include loan purpose and year fixed effects, and standard errors are heteroskedasticity robust and clustered at both the firm and year level. Z-statistics are reported in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

48

Table 7: Effect of Information Leakage on MAOs Dependent Variable = GC Opinion Material Uncertainty

Restatement Accounting Change Before_MAO After_MAO Institutional Investor

Revolver Loan Size Financial Covenants

Maturity Number of Lenders PP Indicator Collateral

(1) Loan Spread 106.73*** (7.77) 49.02** (2.56) 24.85*** (3.46) 3.26 (0.83) -1.14 (-0.18) 0.87 (0.19) 46.77*** (6.67) -27.22*** (-6.86) -12.74*** (-5.79) 1.27 (0.88) -0.21** (-2.22) -0.37 (-1.36) -45.24*** (-7.82) 77.08*** (13.97)

Interest rate Firm Size Market-to-book Leverage Profitability Cash flow volatility Tangibility Z-score Abnormal_Accruals

-8.27*** (-3.09) -7.00*** (-3.95) 76.45*** (8.63) -142.84*** (-6.18) 183.00** (2.44) 7.09 (0.61) -5.93*** (-4.51) 34.08* (1.70)

(2) Financial Covenants

(3) General Covenants

-0.26*** (-2.67) 0.02 (0.08) -0.11 (-1.16) -0.12** (-2.01) -0.06 (-1.11) -0.16*** (-3.31) 0.45*** (5.41) -0.00 (-0.06) -0.04 (-1.64)

0.69** (2.33) 0.98*** (3.97) 0.21 (1.01) 0.19** (2.00) 0.12 (1.06) -0.04 (-0.52) 0.89*** (6.92) -0.45*** (-6.45) 0.35*** (6.84)

0.00*** (3.35) 0.01*** (2.64) 0.40*** (6.77) 0.35*** (5.16) 0.00 (0.90) -0.18*** (-6.95) -0.10*** (-4.00) 0.34*** (2.72) 1.49*** (5.15) -4.82*** (-4.78) -0.53*** (-5.08) -0.03 (-1.50) -0.44*** (-3.14)

0.01*** (6.13) -0.00 (-0.17) 1.04*** (11.09) 1.93*** (15.42) 0.00*** (11.35) 0.03 (0.73) -0.14*** (-2.81) 0.98*** (4.19) 2.88*** (4.74) -4.77*** (-2.86) -1.04*** (-6.56) 0.00 (0.07) -1.42*** (-4.08)

49

(4) Loan Size -0.07*** (-2.93) -0.04 (-1.14) -0.05*** (-3.01) -0.05*** (-3.55) -0.01 (-1.39) -0.02* (-1.68) 0.07*** (3.05) 0.09*** (9.00)

0.01 (1.36) 0.00*** (4.27) -0.00 (-0.19) 0.03*** (3.35) 0.06*** (5.98) -0.00 (-0.19)

0.04*** (4.17) -0.05** (-2.14) 0.25*** (2.96) 1.40*** (8.92) 0.10*** (3.68) -0.01 (-1.33) 0.19*** (2.76)

(5)

(6)

Collateral Log(Maturity) 0.96*** (2.75) 0.94** (2.10) 0.08 (0.61) -0.01 (-0.18) -0.04 (-0.57) 0.13* (1.75) 1.17*** (5.32) 0.07 (0.96) -0.15*** (-3.61) 0.13*** (4.36) 0.00*** (3.00) 0.01* (1.89) -0.26*** (-3.98)

-0.34*** (-9.95) -0.13*** (-4.19) 1.66*** (8.47) -1.59*** (-3.44) 3.86** (2.54) -0.20 (-1.43) -0.12*** (-3.22) 0.77*** (5.94)

-0.21** (-2.04) 0.12 (1.53) -0.05 (-1.16) 0.02 (0.72) -0.02 (-0.75) -0.00 (-0.05) 0.56*** (13.67) 0.19*** (4.70) 0.06*** (4.93) 0.04*** (4.93)

0.01*** (3.00) 0.24*** (7.04) 0.15*** (5.98) -0.00 (-1.54) -0.06*** (-4.14) -0.03*** (-2.84) 0.25*** (5.63) 0.50*** (4.01) -1.77*** (-5.01) -0.01 (-0.31) 0.00 (0.24) -0.09 (-0.86)

Credit spread Term spread Intercept Loan Purpose FE Year FE Observations Adj. R2/Pseudo R2

31.07* (1.73) 3.82 (0.63) 471.95*** (12.21)

0.03 (0.36) 0.11* (1.71) 0.74 (1.52)

0.23*** (2.59) 0.20 (1.51) -8.45*** (-7.68)

-0.01 (-0.50) 0.00 (0.14) -0.10** (-2.14)

0.24 (1.25) 0.01 (0.13) 5.93*** (8.29)

-0.12*** (-2.82) -0.01 (-0.41) 3.11*** (15.55)

Included Included 8,473 0.528

Included Included 8,473 0.337

Included Included 8,473 0.467

Included Included 8,473 0.238

Included Included 8,473 0.349

Included Included 8,473 0.297

Table 7 presents the results from the estimation of the following model: Contract terms =α+ β1 GC Opinion +β2 Material Uncertainty +β3 Restatement +β4 Accounting Change +β5 Before_MAO +β6 After_MAO + βi CONTROLS + ε The names of the columns provide the dependent variables. All variables are defined in Appendix A. Firm-specific financial variables are winsorized at the 0.01 level. Regressions include loan purpose and year fixed effects, and standard errors are heteroskedasticity robust and clustered at both the firm and year level. Z-statistics are reported in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

50

Table 8: Effect of GC Opinions on Loan Terms (Matched Samples) Panel A: Test of the Effectiveness of the Matching Process

Variable

GC GC Opinion=1 Opinion=0 (N=131) (N=5,246)

P value Baseline for the PSM mean (N=131) equivalent

P value P value Extended for the for the PSM mean mean (N=131) equivalent equivalent

Firm Size

5.864

6.473

<0.001

5.602

0.245

5.626

0.304

Market-to-book

1.258

1.710

<0.001

1.198

0.371

1.176

0.234

Total Liability

1.012

0.578

<0.001

0.893

0.007

0.853

<0.001

Profitability

0.024

0.134

<0.001

0.056

0.035

0.034

0.486

Cash flow volatility

0.048

0.031

<0.001

0.043

0.389

0.045

0.643

Tangibility

0.344

0.330

0.503

0.369

0.436

0.338

0.867

Z-score

0.157

1.854

<0.001

0.589

0.063

0.249

0.689

Abnormal_Accruals

0.146

0.084

<0.001

0.116

0.062

0.124

0.183

Panel B: Basic PSM Regression Result

Dependent Variable =

GC Opinion Institutional Investor

Revolver Loan Size Financial Covenants

Maturity Number of Lenders PP Indicator Collateral

(1) Loan Spread 91.18*** (5.13) 89.46*** (2.61) -72.22*** (-3.60) -18.40** (-2.04) 0.54 (0.07) -1.45*** (-3.30) -0.18 (-0.24) -32.27* (-1.76) 113.27** (2.57)

Interest rate Firm Size Market-to-book

-6.42 (-0.62) -5.20 (-0.31)

(2) Financial Covenants

(3) General Covenants

(4) Loan Size

-0.15 (-0.62) 0.31* (1.73) 0.04 (0.23) -0.02 (-0.21)

1.06*** (2.62) -0.17 (-0.44) 0.09 (0.35) 0.56*** (4.02)

-0.04* (-1.91) 0.08** (2.40) 0.09*** (3.79)

0.01*** (2.83) 0.01 (1.40) 0.07 (0.29) -0.23 (-0.48) 0.00 (0.07) -0.00 (-0.03) 0.12 (0.45)

0.03*** (3.12) 0.02 (1.32) 0.02 (0.05) 3.83*** (3.75) 0.00 (1.61) 0.24 (1.25) -0.46 (-1.32)

0

-0.00 (-0.18) 0.00*** (2.73) -0.00 (-0.42) 0.07** (2.16) -0.02 (-0.38) -0.00* (-1.80)

0.03 (0.93)

(5)

(6)

Collateral Log(Maturity) 0.94** (2.03)

0.01 (0.05) 0.11 (0.77) 0.02* (1.71) 0.02 (0.56) -0.75** (-2.33)

-0.44** (-2.01) -0.14 (-0.32)

-0.24** (-2.11) -0.08 (-0.67) -0.48*** (-5.51) 0.10** (2.44) 0.11*** (2.99)

-0.02*** (-4.03) 0.31*** (3.25) 0.09 (0.53) -0.00*** (-3.04) -0.04 (-0.85) 0.02 (0.14)

Leverage Profitability Cash flow volatility Tangibility Z-score Abnormal_Accruals Credit spread Term spread Intercept

Loan Purpose FE Year FE Observations Adj. R2/Pseudo R2

92.57*** (2.99) -123.22 (-1.15) -640.88** (-2.00) -13.32 (-0.31) -5.93 (-0.91) -69.56 (-0.86) -22.76 (-0.53) -8.96 (-0.21) 681.67*** (4.78)

0.17 (0.46) 1.57 (1.29) -1.26 (-0.38) 0.87* (1.86) -0.01 (-0.15) -0.89 (-0.89) -0.70** (-2.02) 0.95** (2.43) 0.22 (0.13)

2.15*** (3.48) 5.19*** (2.85) -1.15 (-0.18) -0.98 (-1.39) -0.13 (-1.03) -0.58 (-0.31) 0.43 (0.64) 0.16 (0.27) -16.60*** (-6.31)

0.01 (0.13) -0.04 (-0.27) 1.02** (2.08) 0.08 (1.21) -0.01 (-1.13) 0.07 (0.67) 0.29** (2.58) -0.03 (-0.78) -0.08 (-0.87)

-0.18 (-0.22) -6.65*** (-2.58) 4.85 (1.09) 3.68*** (2.61) -0.09 (-0.50) -6.66*** (-4.25) 0.37 (0.66) 0.02 (0.09) 3.94 (1.41)

-0.46** (-2.36) 0.54 (0.83) -1.64 (-0.96) 0.04 (0.19) 0.00 (0.11) -0.16 (-0.36) -0.09 (-0.36) -0.12 (-0.64) 3.04*** (4.51)

Included Included 425 0.394

Included Included 425 0.326

Included Included 425 0.576

Included Included 425 0.351

Excluded Excluded 425 0.422

Included Included 425 0.408

Panel C: Extended PSM Regression Result

Dependent Variable =

GC Opinion Institutional Investor

Revolver Loan Size Financial Covenants

Maturity Number of Lenders PP Indicator Collateral

(1) (2) Loan Financial Spread Covenants 85.91*** (4.14) 106.92*** (2.81) -69.56*** (-3.19) -17.73 (-1.48) -2.05 (-0.27) -1.39*** (-2.99) 0.66 (0.72) -32.93 (-1.52) 126.22* (1.86)

Interest rate Firm Size

-10.11

(3) General Covenants

(4) Loan Size

1.07** (2.53) 0.34 (0.69) 0.05 (0.18) 0.76*** (4.30)

-0.06* (-1.67) 0.05 (1.24) 0.08* (1.67)

0.01 (0.02) 0.12 (0.48) -0.06 (-0.36) 0.15 (1.57)

0.01*** (2.66) 0.01 (0.96) 0.39 (1.42) 0.61 (1.44) -0.00 (-0.27) -0.21*

0.03*** (2.76) -0.00 (-0.03) 0.06 (0.11) 2.45 (1.31) 0.00 (1.46) 0.03

1

-0.01 (-0.72) 0.00 (0.77) 0.00 (0.22) 0.15** (2.56) -0.68 (-1.23) -0.00 (-0.72)

(5)

(6)

Collateral Log(Maturity) 2.50*** (3.76)

-1.64*** (-6.11) 0.78*** (4.12) 0.02 (1.60) -0.01 (-0.76) -1.19 (-1.58)

0.82***

-0.18** (-2.02) -0.03 (-0.26) -0.42*** (-5.02) 0.06 (1.20) 0.11*** (3.96)

-0.02*** (-6.90) 0.37*** (4.66) 0.01 (0.04) -0.00*** (-3.13) 0.03

Market-to-book Leverage Profitability Cash flow volatility Tangibility Z-score Abnormal_Accruals Credit spread Term spread Intercept Loan Purpose FE Year FE Observations Adj. R2/Pseudo R2

(-0.84) 1.77 (0.10) 62.57** (2.00) -41.27 (-0.37) -770.28** (-2.29) 12.19 (0.26) -0.66 (-0.09) 33.92 (0.42) -13.46 (-0.32) 24.09 (0.58) 605.86*** (3.09)

(-1.94) 0.24 (0.91) -0.25 (-0.58) 2.57* (1.92) -1.74 (-0.44) 0.42 (0.88) -0.01 (-0.09) -1.06 (-0.97) 0.11 (0.32) 0.53 (1.30) -2.33 (-1.15)

(0.13) -0.64* (-1.76) 1.95*** (3.33) 5.74*** (2.71) -1.58 (-0.27) -1.37 (-1.45) -0.06 (-0.43) 1.09 (0.59) 0.15 (0.20) -0.50 (-0.73) -16.13*** (-4.46)

-0.02 (-0.45) 0.01 (0.19) 0.10 (0.28) 2.17* (1.81) 0.08 (0.65) -0.03 (-1.01) -0.16 (-0.66) 0.35*** (3.30) 0.12 (1.62) 0.41 (0.65)

(3.34) 0.07 (0.21) 3.40* (1.94) -1.17 (-0.34) -5.47 (-0.66) -0.83 (-1.06) 0.42** (2.24) -8.71*** (-3.60) 1.63*** (3.41) 1.46*** (3.10) 24.14*** (6.34)

(0.56) -0.10 (-1.09) -0.29* (-1.85) 0.50 (0.95) -0.79 (-0.52) 0.16 (0.83) -0.00 (-0.07) 0.28 (0.84) -0.06 (-0.35) 0.20 (1.32) 2.93*** (4.00)

Included Included 409 0.352

Included Included 409 0.306

Included Included 409 0.489

Included Included 409 0.266

Excluded Excluded 409 0.592

Included Included 409 0.389

Table 8 presents the effectiveness of the matching process and the regression results. We implement propensityscore-matching by first estimating a probit regression to model the probability of receiving a GC opinion. Following prior research, we limit our sample to financially distressed firms when modeling the likelihood of receiving a GC opinion. Financially distressed firms are defined as those with negative earnings or operating cash flow in the current fiscal year (Reynolds and Francis, 2000; DeFond et al., 2002). We include firm-level variables used in our main regressions in the basic PSM and include additional firm-level control variables in the extended PSM from prior literature (DeFond et al., 2002). The results of the first stage are provided in Appendix B. We estimate the propensity score for each firm using the predicted probabilities from the probit model. Given the minimal overlap between GC opinion and non-MAO firms, we use the nearest-neighbor method and match with replacement. Panel A shows the efficiency of the matching process. Panels B and C provide the results of the basic and extended PSM samples separately. The names of the columns provide the dependent variables. All variables are defined in Appendix A. Firmspecific financial variables are winsorized at the 0.01 level. Regressions include loan purpose and year fixed effects (except the collateral regression), and standard errors are heteroskedasticity robust. Z-statistics are reported in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

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