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The information role of audit opinions in debt contracting

Peter F. Chen

School of Business & Management

Hong Kong University of Science & Technology

acpchen@ust.hk

Shaohua He

Department of Accounting & Finance

Lancaster University

s.he@lancaster.ac.uk

Zhiming Ma

Guanghua School of Management

Peking University

mazhiming@gsm.edu.cn

Derrald Stice*

School of Business & Management

Hong Kong University of Science & Technology

acstice@ust.hk

Draft: July, 2014

JEL Classification: G01, M4, M49

Keywords: Debt Contracting, Audit Opinions, Going Concern Opinion, Explanatory

Language

__________________________________

We thank Valerie Li, Michael Minnis, Tomomi Takada, and Hansang Yi for helpful

comments and suggestions. We appreciate comments and suggestions from participants at

the 2013 European Accounting Association Conference in Paris, France; 2013 Korean

Accounting Association Conference in Gyeongju, South Korea; 2013 AAA Annual Meeting

and from workshop participants at Singapore Management University.

*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: acstice@ust.hk.

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ABSTRACT

This study examines the effect of audit opinions on private debt contracting. We use the

auditor’s explanatory language to partition modified audit opinions into Inconsistency, caused

by an accounting change or restatement, and Inadequacy, arising from material uncertainty or

a going concern opinion. Using the loan contracts of firms with modified audit opinions

during the period 1992-2009; we find that, compared with loans initiated after clean opinions,

loans initiated after modified opinions 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 being secured. 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 increase of 107 basis points for going concern opinions. Additional

analyses of GC opinions using propensity score matching show that auditor opinions are still

associated with price and non-price contract terms. Overall, our empirical results suggest that

audit reporting communicates auditors’ private information to lenders.

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1. Introduction

The audit opinion communicates to financial report users an auditor’s degree of

assurance that the financial statements faithfully reflect a client’s underlying economic

activities. Under the Security and Exchange Commission (SEC) requirement and the

Generally Accepted Auditing Standards (GAAS), the auditor’s reporting options are either an

unqualified opinion or an unqualified opinion with explanatory language – commonly called

a modified audit opinion (MAO).1 This discretionary explanatory language is the only

difference between a standard clean opinion and an MAO. 2 In fact, it is the only practical

channel for the auditor to communicate private information discovered during the auditing

process to outsiders.3 With the exception of going concern (GC) opinions, there is limited

evidence on whether and how audit opinions are informative to financial statement users. In

this study, we examine the information role of MAOs in private debt contracting by

incorporating the auditor’s explanations for modification.

To investigate the effect of audit opinions on debt contracting, we manually classify

MAOs in to categories relating to accounting changes, restatements, material uncertainty, and

going concern opinions. We predict that these four types of MAOs will have different

implications for evaluating a borrower’s credit risk and for designing appropriate monitoring

mechanisms. Generally, audit opinions may be useful to lenders for at least two reasons. First,

audit opinions may inform lenders about the reliability of financial reports. Accounting

1 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, our MAOs are almost exclusively unqualified with explanatory language (e.g., Butler et al., 2004).

2 The auditor’s discretion is based on the professional standard 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 (AU Section 508.11).

3 One exception is the auditor change 8-K. However, prior research has found 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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information is useful in evaluating a borrower’s liquidity and asset values and in predicting

future cash flow and the likelihood of bankruptcy. Additionally, accounting numbers can be

used in contracting with relative ease and low cost. Unreliable financial information increases

uncertainty about borrowers and the costs of contracting.

Second, through the audit report, an auditor reveals to financial statement users

private information about a client’s risks and future expenses of interest to investors (ex.

future costs related to ongoing litigation). In particular, a GC opinion may signal an auditor’s

judgment that a client is closer to financial distress or bankruptcy than what is conveyed

elsewhere in the financial report. For these reasons, we predict that loans initiated after

MAOs will have higher loan spreads, compared to loans initiated after clean opinions, and

that this effect on spreads will be higher following GC opinions than after the other three

MAO types.

The use of covenants to monitor borrowers’ performance is an essential component of

debt contracting (Jensen and Meckling, 1976; Smith and Warner, 1979; Watts and

Zimmerman, 1986). A necessary condition for financial covenants to be effective is that these

financial statement numbers faithfully reflect a borrower’s economic activities. If an MAO

signals a borrower’s lower financial reporting quality, lenders may prefer to use general

covenants that do not use accounting numbers as inputs. Specifically, we predict that lenders

will choose to rely less on accounting numbers and more on non-accounting monitoring

mechanisms - resulting in a decrease in financial covenants and an increase in general

covenants - after MAOs. In addition, we expect the effect on covenants to be stronger for GC

opinions than for other MAO types.

Audit opinions can also be informative to lenders in negotiating other loan terms such

as loan size, collateral requirements, and loan maturity. For example, if a GC opinion signals

higher than expected borrower default risk, then lenders will be more likely to require

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collateral because covenants are unlikely to provide effective lender protection. In the same

vein, we further predict that loans initiated after MAOs are likely to be smaller in size and

shorter in maturity length.

To examine the effects of audit opinions on debt contracting, we collect a sample of

firms with MAOs that have at least one loan issued during an MAO period and at least one

loan issued during a non-MAO period during the 18-year period from 1992 to 2009. For the

2,056 modified opinions in the final sample, we read through the explanatory language

contained in the 10-K filings in order to classify the modified opinions. We classify MAOs in

to two general categories that are further partitioned into four types: Inconsistency caused by

an Accounting Change or a Restatement; and Inadequacy related to Material Uncertainty or a

GC Opinion.

MAOs labeled Inconsistency alert financial statement users about the potential

incomparability, perhaps even quality, of data contained in the financial statements arising

from restatements and accounting changes. Inadequacy MAOs express auditors’ more serious

concern about the validity of the financial statements as a whole.4 Material Uncertainty

concerns are related to the resolution of future economically relevant unknowns (ex.

contingent liabilities, litigation risk, and business uncertainty), and going concern opinions

indicate that a key assumption of the accounting model is violated (i.e. that the firm will

continue as a going concern for a period of at least one year). Relative to the standard clean

opinion, these four types of MAOs, convey differential degrees of negativity about a client’s

financial reporting quality and risk and increase in severity as follows: Accounting Change,

Restatement, Material Uncertainty and GC Opinion. As discussed, we predict the greatest

effect on loan terms to come from GC opinions.

4 According to AU Section 508, 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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Consistent with our predictions, 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 the other determinants of the interest rate. In addition, the effects of

MAOs on loan spreads vary significantly depending on the type of modified opinion. The

loan spread effect (in basis points) increases from 0 after an Accounting Change MAO to 25,

49, and 107 after Restatement, Material Uncertainty, and GC Opinion MAOs, respectively.

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 by 4.2%, on average,

after an MAO. Again, the effect of an MAO depends on the type of modification. In general,

we find that Inconsistency MAOs have a smaller, but still significant, effect on the use of

financial and general covenants than do Inadequacy MOAs. As expected, GC Opinion has a

large effect on the use of covenants - the number of financial covenants decreases by 9.1%

and the number of general covenants increases by 12.3% in loans issued after GC opinions.

Material Uncertainty opinions are associated with the largest effect on the use of general

covenants (an increase of 18.2%), but they are not associated with the use of financial

covenants.

We perform additional tests on other loan terms beyond covenants - we find that

lenders reduce loan sizes and increase the likelihood of requiring collateral following an

MAO. The results on loan maturity are mixed and indicate a slight decrease (increase) in loan

maturities after GC Opinion (Material Uncertainty). In addition, as predicted, the effect of an

MAO on each of the loan terms is largest for GC opinions. Overall, these results are

consistent with differential non-price costs for firms receiving MAOs.

To further establish the unique value of the audit report, we perform separate analyses

on the different types of GC opinions, because these opinions in particular signal more of the

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auditor’s private information likely to be relevant to lenders. 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 on the loan contract terms previously

investigated. We find that each of these categories of GC opinions influences the composition

of loan terms, reinforcing the value of the private information provided by the auditor in the

audit opinion.

A key inference drawn in this study is that auditors communicate private information

to lenders through the audit opinion. To mitigate concerns that our results are driven by

financial stress of the borrower we employ a variety of control variables in each of our tests.

However, managers of firms preparing to secure financing are likely to be in contact with

potential lenders preceding a loan issuance, and we are unable to directly control for

information that managers privately provide to lenders. To alleviate the concern this raises,

we construct a new indicator variable Before_MAO that 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

actually preempted by managers communicating to lenders before an MAO, then we would

expect a significant coefficient on this new variable. However, Before_MAO is insignificant

in tests of each of the contract terms we investigate. This result alleviates the concern that our

results are driven by unobserved information leakage instead of by the private information

communicated by auditors.

We also match our firms with GC opinions with out-of-sample firms based on the

determinants of receiving a GC opinion and bankruptcy using variables from prior studies

(Zmijewski, 1984; DeFond et al., 2002). If GC opinions simply capture the probability of

financial distress or bankruptcy, as predicted by these mechanical models, they should have

no incremental effect on loan contract terms in the matched-sample specifications. However,

we find that GC opinions continue to have a significant effect on all of the loans terms

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examined except financial covenants. Overall, these results suggest that the information value

of GC opinions to lenders likely arises from the auditor’s private information about the client.

Our study makes several contributions to the existing literature. First, this is the first

study to document the information role of audit opinions in debt contracting by incorporating

the auditor’s explanatory language. The empirical results provide evidence that modified

audit opinions along with the explanatory language of the audit report are informative to

lenders in negotiating loan agreements. These results complement the finding in prior studies

that the voluntary use of auditing or the employment of Big 5 auditors is associated with a

lower cost of debt (Fortin and Pittman, 2007; Lennox and Pittman, 2011; Minnis, 2011). In

addition, our empirical results have implications for the current regulatory initiative to

expand the scope of audit opinions to include a discussion of “critical audit matters” (PCAOB,

2013) and other items to enhance the value of auditing (Carson et al., 2013; DeFond and

Zhang, 2013). One implication of our study is that regulators need to weigh the informative

role of additional disclosures in audit reports against the potential reluctance (cost) of

auditors to reveal private information about clients, given the economic consequences of

MAOs documented here.

Second, our empirical evidence on the role of private information conveyed by GC

opinions contributes 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). Our empirical results on the effect of GC opinions on loan terms shed light on the

unique value of auditors’ private information that is not only unavailable elsewhere in the

financial statements but is also inaccessible through other channels of sophisticated lenders

(Mutchler, 1985; Menon and Schwartz, 1987).

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Last, our study is related to the literature on the role of financial reporting quality in

debt contracting. Bharath et al. (2008) provide evidence that a borrower’s access to the

private versus public debt market and its loan terms depend on the quality of the borrower’s

accounting information, and Costello and Wittenberg-Moerman (2011) document that lenders

trade-off between different monitoring mechanisms when internal control weakness

information is disclosed. However, these studies are silent about the specific role of the audit

function within financial reporting. Our study provides empirical evidence on how audit

opinions are incrementally informative to lenders in the private debt market.

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 empirical

results of our hypotheses 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 protecting the timely repayment of the

loan and interest that are claims on the borrower’s future cash flow and assets. When

contemplating a loan facility, banks and other lenders perform a credit analysis of the

borrower on several dimensions. They analyze the risk of default, estimate the market value

and liquidation values of assets, and evaluate the character and ability of the management

(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 that compensate them for risk and allow them

to monitor the borrower’s performance over the life of the loan. Audit opinions are

potentially informative to lenders in evaluating the borrower’s default risk and negotiating

loan terms that lead to more efficient monitoring.

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Financial statements provide important information to lenders in evaluating the

borrower’s credit worthiness and default risk. Lenders use financial statements to analyze the

financial data and to estimate the value of a firm’s assets. Financial ratios are useful in

predicting the likelihood of borrower default (e.g., Beaver, 1966; Altman, 1968; Ohlson,

1980). Research has shown that accounting measures can predict the losses that will be

sustained by lenders in the event of borrower default (Varma and Cantor, 2005; Acharya et

al., 2007), and recent work has demonstrated that accounting numbers also possess

significant ability to predict future loss given default at the debt issuance date (Amiram,

2012).

An independent audit is a critical component of financial reporting (Watts and

Zimmerman, 1983). Through the audit opinion, the auditor communicates to users its degree

of assurance that the financial statements faithfully reflect the firm’s underlying economic

activities. Under the current professional standards, the auditor’s degree of assurance is

communicated through the inclusion of explanatory language within an unqualified report.

Lenders are likely to find auditors’ disclosures useful for at least two reasons.

First, auditors’ disclosures inform lenders about the quality of financial data by

revealing an auditor’s judgment about qualitative aspects of the financial reports such as

potential bias and assumptions that may not faithfully reflect the firm’s economic activities.5

Czerney et al. (2013) provide evidence that explanatory language mentioning accounting

changes and restatements is associated with a higher probability of subsequent misstatements.

Graham et al. (2008) find that earnings restatements are associated with increases in price and

5 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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non-price costs of debt, consistent with financial restatements leading to higher information

uncertainty.6

Second, auditors can reveal private information about the borrower’s risks and

financial health directly in their audit opinions. For lenders, Inadequacy opinions are

particularly relevant because they convey an auditor’s judgment about default risks that are

not reflected elsewhere in the financial statements – these risks may pertain to future events

(ex. contingent liabilities). Therefore, the effect of MAOs related to Inadequacy opinions

should be stronger than the effect of Inconsistency opinions because they signal greater

default risk of the borrower. Also, prior studies find that GC opinions communicate auditors’

private information about higher default risk beyond information publically available and are

useful in predicting bankruptcy (Mutchler, 1985; Menon and Schwartz, 1987; Mutchler et al.,

1987; Hopwood et al., 1989; Raghunandan and Rama, 1995). 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 loan

spreads will be greater than the other three opinion types.

Given the potential economic consequence of MAOs, auditors are likely to experience

pressure from clients to issue a clean opinion.7 On the other hand, considering auditors’ legal

liability, auditors also have incentives to be more conservative in issuing MAOs as a way of

protecting themselves from potential litigation risk. GC opinions may be most affected by

this conservative bias because auditors are more likely to be sued when a clean opinion is

issued before bankruptcy. If auditors’ issuance of MAOs is conservative, it should bias

6 The sample of restatements in Graham et al. (2008) is based on restatements announced in the filed financial

reports. These restatements are not always mentioned in the explanatory language in the audit report. The

restatements in our sample are exclusively restatements mentioned in the explanatory paragraph of the audit

report.

7 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 (2013) for a review of the literature.

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against our finding an effect of MAOs on loan terms as lenders rationally adjustment for the

conservative bias of MAOs. As a result, to what extent audit opinions are informative to

lenders in negotiating the interest rates of loan contracts is an empirical question.

The discussion above leads to our first empirical hypothesis, stated as follows:

H1: Compared with loans issued to firms with clean opinions, loans issued to firms with

modified audit opinions have higher loan spreads. In addition, the effect on spread is

greater for going concern 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 quickly transferred to lenders. Accounting information and financial

ratios are widely used to monitor a borrower’s performance in debt covenants. A pre-

condition for using financial covenants is the assurance that the accounting information used

reflects the actual performance of the borrower.

On the other hand, if accounting numbers are unreliable or of low quality, lenders

may use general covenants that do not rely on accounting information. General covenants

often specify events that will 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 type of modified opinion. If

Inconsistency opinions are associated with a high probability of subsequent misstatements,

then financial covenants may be less effective as a monitoring tool (Czerney et al., 2013).

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However, the effects of Inadequacy opinions on covenant choices should be stronger than the

effects of Inconsistency opinions, because Inadequacy MAOs convey an auditor’s

information about the adequacy of financial statements to reflect a firm’s economic activities.

Financial statements with a Material Uncertainty MAO may not reflect the potential risks of

corporate events or contingent liabilities. GC Opinion MAOs may indicate that a basic

assumption of the accounting model (i.e. that the firm will persist as a going concern) is

violated, decreasing the usefulness of the financial statements to lenders as an effective

monitoring tool. Lenders may need to 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 to firms with clean opinions, loans issued to firms with

modified audit opinions are associated with a decrease in the number of financial

covenants and/or an increase in the number of general covenants contained in debt

contracts. In addition, the effect of GC opinions is stronger than those of other MAO

types.

To assess the total effect on contract design of an MAO, it is important to consider the

many different contract components that lenders can choose from (Gigler et al., 2009). Up

until this point we have only considered the use of spread and covenants in contract design.

In reality, lenders have other options to consider when designing a firm-specific contract. We

consider the effects of an MAO on three additional contracting options available to lenders:

loan size, the requirement of collateral, and the duration of the loan contract. We view an

MAO as a disclosure event that communicates a negative signal about financial reporting

quality and information risk, and we predict that lenders will be more likely to reduce loan

size, require collateral and shorten the loan maturity after an MAO.

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However, the effects should vary for different types of MAOs. Economic theory on

credit rationing (see e.g., Jaffee and Russell, 1976; Stiglitz and Weiss, 1981) shows that loan

size is not a linear function of the default risk signaled in a borrower’s going concern opinion

because credit risk may increase if the loan size can only finance small projects that are more

risky. As a result, rather than reducing the size of loan, lenders may simply deny the loan

application after an MAO, especially after a GC opinion. Moreover, Diamond’s (1991)

theory shows that debt maturity is a non-monotonic function of a firm’s risk; with low and

high risk firms obtaining short-term debt. Finally, we expect GC opinions to have a stronger

effect on the likelihood of a loan being secured than the other three types of MAO.

Stated in the alternative form, we predict that:

H3: Compared with loans issued to firms with clean opinions, loans issued to firms with

MAOs are associated with decreases in the size of loans granted, increases in the

likelihood that lenders will require collateral, and decreases in the average length of

maturity in debt contracts. In addition, the effect of GC opinions is greater than those

of other MAO types.

3. Research Design and Sample Selection

3.1 Research Design

To examine the information role of audit opinions in debt contracting, we perform

empirical tests of the effect of an MAO on the contract terms of loans issued to firms with

MAOs 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 in each of our tests including: interest spread, the number of

financial covenants, the number of general covenants, loan size, whether or not a loan is

secured, and the maturity length of a loan. MAO is an indicator variable equal to one if the

loan is initiated after the borrower receives an MAO, and zero otherwise. To examine

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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 clean opinion but had an MAO equal to 1 in 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 the loan being secured, but a negative (β1) effect on the use of

financial covenants, loan size, and maturity length.

To investigate the differential effect of different types of MAOs on debt contracts, we

replace the generic MAO with our classified MAOs.

For the 2,056 modified opinions in the final sample, we read through the explanatory

language contained in the 10-K filings in order to classify the modified opinions. Following

Butler et al. (2004), we find that most of the modified opinions are related to Inconsistency

issues (1,881, or 91.5%) with the rest of the opinions related to a client’s Inadequacy (175,

8.5%). Out of these Inconsistency issues, most modifications (1,680) are related to an

accounting change (Accounting Change) and the remaining opinions (201) are related to

restatements (Restatement) mentioned by the auditor in the audit reports. For Inadequacy

MAOs, the majority is related to going concern opinions (GC Opinion), 131 or 74.9% - the

rest are related to material uncertainty (Material Uncertainty), 44 or 25.1%.8

We first examine the incremental effect of MAOs on loan spreads after controlling for

the determinants of loan spreads. We control for firm size because small firms have greater

information asymmetry and a 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

8 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.

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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 debt contracting (Bharath et al., 2008). We include a control

for revolvers because these loans have lower loan spread than term loans (Zhang, 2008).

Institutional loans, relative to bank loans, have higher loan spread because of the higher

information symmetry with the borrower. We control for the existence of performance

pricing provisions because a performance pricing provision signals higher adverse selection

and moral hazard costs of the borrower (Asquith et al., 2005). We also control for the number

of lenders in the loan. A larger number of participants in the loan syndicate is an indication of

a higher quality borrower with less information symmetry. Last, we control for other

contracting devices available to lenders: loan maturity, collateral, and the number of financial

covenants.9

We include a similar set of control variables in testing the effects of MAOs on the

use of financial and general covenants. We replace the number of financial covenants with

loan interest rate as a control variable since 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 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).

To test hypothesis H3, we examine the effects of MAOs on loan size, likelihood of

requiring collateral, and loan maturity. If MAOs signal the borrower’s financial reporting

quality, lenders may reduce loan size, require collateral, and reduce loan maturity as

compensation for the increased cost and reduced efficiency of monitoring after loan initiation.

If some MAOs, such as GC opinions, signal an increased default risk of the borrower, the

9 All variables are defined in Appendix A.

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effect of the default risk on the individual loan terms may vary. If GC opinions communicate

higher default risk, it is more likely that the loan will be secured to insure the recoverability

of the principal. In addition, GC opinions will have larger effects on the decrease in loan size

and in loan maturity length than other MAOs.

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 2009.10

We match our MAO sample with public firms in the

Dealscan database that contains contractual terms such as interest rate, size, and covenants of

loans issued by public firms in the United States.11

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) and outside of this MAO window

(either before the MAO or more than three years after the first clean opinion following a

MAO). This requirement is to ensure that our results are not driven by a change in the sample

composition over time and also to avoid comparing MAO to non-MAO borrowers. After

eliminating observations with missing data needed in our analyses, our final sample includes

8,473 loans issued to 5,377 borrowers during the period 1992-2009.12

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

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Audit Analytics provides going concern opinions after 2000 - we use these data to check our sample of MAOs

from COMPUSTAT. We found three cases in which Audit Analytics classified an observation as a going

concern but we did not find a going concern statement in the audit report. We also identified 11 cases in which

Audit Analytics classified an observation as “non-going concern,” but we observed a going concern statement in

the audit report. Including or excluding these observations does not affect our results.

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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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Syndicated loans often bundle multiple facilities in to 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

borrower’s 10-K on EDGAR or LexisNexis. Following Butler et al. (2004), we classify the

reason for each MAO into two broad categories: Inconsistency and Inadequacy. Inconsistency

MAOS refer to a lack of consistency according to GAAP accounting principles (AU 508

section) - we breakdown Inconsistency into Accounting Change and Restatement depending

on whether the auditor mentions accounting changes or restatements in the explanatory

paragraph.13

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 – 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

MAOs takes place starting in 2002 when the Sarbanes-Oxley Act (SOX) was implemented.

This is consistent with SOX increasing the pressure for auditors to issue MAOs. Consistent

with Butler et al. (2004), most MAOs are related to Inconsistency with less than 10% related

to Inadequacy. Our sample of MAOs contains a higher proportion of Inconsistency

modifications than does the Butler et al. sample – this is likely because firms in our sample

are relatively large and were able to obtain financing. Within Inconsistency MAOs, most are

Accounting Change with about 10% being Restatement MAOs. Within Inadequacy

modifications, GC Opinion MAOs are about 3 times the number of Material Uncertainty

modifications. In addition, GC opinions have a wider variation over the years with some

13

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 MAO as in their study.

17

concentrations in 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., 2002;

DeFond at al., 2002) - this is likely a result of our requirement that firms must have obtained

debt financing in the post-MAO period to be included in our sample. If a firm was denied a

loan or filed 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. We define our variables in Appendix A. 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. The average loan size is $379.12M for firms with an MAO and

$258.72M for firms with a clean audit opinion. Loans mature in an average of 48.12 and

47.58 months for firms with and without MAOs, respectively. The debt contracts of firms

with (without) an MAO include an average of 2.34 (2.63) financial covenants and 5.67 (5.22)

general covenants. Most loans include a performance pricing provision, require collateral,

and are a revolver, regardless of whether a firm has an MAO or not.

Panel B of Table 2 reports the descriptive statistics of firm characteristics for our

sample of loan observations. The mean firm size for firms with (without) an MAO is

$3,562.07M ($1,796.54). The means of profitability, leverage and tangibility are 0.12 (0.13),

0.26 (0.25) and 0.34 (0.32), respectively, for firms with (without) an MAO. The MAO period

accounts for 38% of observations (2,056 / 2,056 + 3,321), 22% of observations fall into the

after-MAO category, and the remaining 40% of observations are non-MAO observations.

18

Table 2 Panel C provides a correlation matrix. Many of the contracting terms are

significantly correlated. As expected, spread is positively associated with the number of debt

covenants and with requiring collateral in the univariate; and it is negatively correlated with

loan size, profitability, firm size, and Z-score. These univariate results are consistent with

lenders having many different mechanisms through which to design contracts (Melnik and

Plaut, 1986), not just through interest spreads. We expect lenders to incorporate the

information provided by audit opinions in the negotiation of debt agreements.

4. Empirical Results

4.1 Audit Opinions and Loan Spreads

Table 3 presents the effects of modified audit opinions on loan spreads. We regress

loan spread on MAO, After_MAO, and a set of control variables. Our first hypothesis predicts

that if lenders view the auditor’s MAO as a negative signal of financial reporting quality

and/or of the borrower’s risk, then loans issued to firms with an MAO will have a higher

interest rate than loans issued to firms with a clean audit opinion. Additionally, if there is a

lingering effect of an MAO even after it has been cured, 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 during the MAO period have a spread

over LIBOR that is 17.31 basis points higher than loans issued during the non-MAO period.

This represents an increase in the interest spread of 8.6%.14

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. However, Column 3 reports that firms

with GC Opinion, Material Uncertainty, and Restatement MAOs all experience significant

14

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

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 relative mechanical in nature and

providing little incremental spread-related information about the borrower to lenders. In

contrast, the significant coefficients of the other three types of MAOs suggest that they

increase 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.15

These results

support H1.

Many of the included control variables are statistically significant. Spreads are

negatively associated with borrower profitability, firm size, market-to-book, Z-score, loan

size, the inclusion of a performance pricing provision, loan maturity, and whether or not the

loan is a revolver. Spreads are positively associated with leverage, cash flow volatility, credit

spread, abnormal accruals, whether or not the loan is secured, and whether or not the loan is

institutional. 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. The variables of interest and the control variables capture much of

the variation in the dependent variable - the R-squared is over 51% across all specifications.

4.2 Audit Opinions and the Use of Financial and General Covenants

Table 4 presents the effects of MAOs on the use of financial and general covenants.

Hypothesis 2 predicts that lenders will be less willing to rely on financial covenants in debt

contracts after the reliability of the financial statements is brought in to question by a

modified opinion from an auditor. The first column in Table 4 provides evidence that the

15

The tests of the difference between the coefficient on GC Opinion and those of each of the other opinion types

are significant.

20

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 the decreases in the use

of financial 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 and

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 some lingering effect of MAOs on

the use of financial covenants.

H2 also predicts that lenders will be more likely to include general covenants.

Column 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 general covenants

of 4.2%. This finding provides evidence consistent with our prediction that when lenders are

less willing to use accounting numbers in debt contracts, they will increase their use of the

non-accounting contracting mechanisms that they have at their disposal. In contrast to the use

of financial covenants, the coefficient on After_MAO is insignificant. Column 5 shows that

the general covenants increase for 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 an uncertain environment is through the increased use of general covenants. As in

tests of H1, many of the control variables are statistically significant. Overall, our empirical

21

results on the effects of MAOs on the use of financial and general covenants are consistent

with H2.

4.3 Audit Opinions and the 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

employ in debt contracts: loan size, requirement of collateral, and loan maturity length and

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 any MAO except those related to material

uncertainties. Additionally, After_MAO is significant across each loan size specification.

These results are consistent with lenders reacting to an auditor’s concerns about the client’s

risk and financial statement quality by reducing loan sizes. The difference between the

coefficient of GC Opinion and any of the other three is not significant. This suggests that the

loan size component of the loan contract is insensitive to the differential signal of MAOs on

the borrower’s default risk or financial reporting quality. However, this finding is consistent

with the credit rationing literature (see e.g., Jaffee and Russell, 1976; Stiglitz and Weiss,

1981) in that lenders’ reaction to a signal of default risk in a non-linear fashion. Our

empirical results are biased against finding the differential effect of GC opinion from those of

other MAOs since loan applications that were denied because of GC opinions are not

represented.16

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 opinion 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

16

As a result, our estimate of the effect of an MAO on debt terms underestimates the true cost of an MAO

because borrowers are likely to incur higher costs of financing if they are denied a loan, especially because of a

GC opinion, from lenders in the private debt market.

22

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 the other monitoring mechanisms, such as covenants, are

unlikely to be effective when the ability of the firm to continue as a going concern is in

question or when there is serious uncertainty regarding a borrower’s prospects.

Our last test of H3 investigates loan maturity choices that lenders make at the contract

initiation - we predict that lenders will decrease loan lengths after an MAO. Columns 7 and 8

indicate there is no effect on loan maturity after an MAO, even when broken down in to

Inadequacy and Inconsistency modifications. However, Column 9 indicates a decrease in

maturity length for firms with GC opinions but an increase in maturity length for those with

material uncertainty modifications. While it is not surprising that lenders prefer to decrease

loan lengths after going concern opinions, it is less clear why an MAO related to a material

uncertainty would lead to an increase in loan maturity. One explanation is that Material

Uncertainty MAOs do not communicate auditors’ information about client risk as it pertains

to loan maturities. An alternative explanation is that these borrowers have greater bargaining

power relative to lenders in negotiating the maturity terms of the loan because of managers’

information advantage about future cash flows. Overall, the loan maturity results are weaker

than those of the other contract devices.

5. Additional Analyses

5.1 The Effect of Different Types of Going Concern Opinions on Loan Terms

A large body of auditing literature has examined the information role of GC opinions.

This is understandable because the consequence of GC opinions is most severe and GC

opinions reveal auditors’ private information a client’s financial health. In the main tests of

23

our empirical analyses, we study the information content of GC opinions in relation to other

MAOs in order to provide evidence of the broad value as well as the differential value of

audit opinions in the context of debt contracting. We provide further evidence on the unique

value of auditors’ GC opinions in debt contracting by investigating the differential effects of

different types of GC opinions.

Menon and Williams (2010) use a large sample of going concern audit reports and

document significantly negative stock returns when these going concern opinions are

disclosed. This negative reaction is more negative when the audit report specifically mentions

a problem obtaining financing. We manually collect and categorize all the going concern

opinions contained in our sample from 1992 to 2009 following Menon and Williams (2010)

and create three new variables: 1) GC_Performance if a going concern opinion mentions

poor financial performance or operating problems (ex. loss of a major customer/supplier) 2)

GC_Financing if a going concern opinion mentions financing problems and 3) GC_Other if a

going concern opinion mentions other issues (ex. litigation risk or regulatory issues).17

We

investigate the effects of the different types of going concern 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 reasons for 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. This is consistent

with Menon and Williams (2010) who report that the most negative stock price reactions are

caused by disclosure of going concern opinions that mention financing problems. Column 2

provides evidence that the decrease in the use of financial covenants after a going concern

17

Menon and Williams (2010) separate their sample of going concern opinions into four categories. We

combined their poor financial performance and operating problem variables because we had only one operating

problem observation that did not also mention poor financial performance. Our going concern sample consists

of 90, 80, and 6 observations for GC_Performance, GC_Financing, and GC_Other, respectively. Following

Menon and Williams, we allow GC opinions to appear in more than one category.

24

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 going concern opinions that mention problems arranging financing have more

general covenants. A firm facing financing difficulties is likely to be asked by lenders to

restrict dividend payments, commit to not take on additional debt, pay off loans when capital

assets are sold, and to 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 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 value of different types of audit opinions.

These results suggest that auditors’ reasons for GC opinions in the explanatory paragraph are

informative to lenders in designing efficient debt contracts.

5.2 Information Leakage Before Modified Audit Opinions Are Issued

A key inference drawn in this study is that auditors communicate private information

to lenders through the audit opinion. However, our tests cannot perfectly identify whether it is

the unique value of auditors’ private information discovered during the auditing process or

information accessible to lenders through private channels that are unavailable to the public

driving our results. Such information might include private communication from top

managers related to major company developments such as an upcoming MAO.

25

To mitigate concerns that our results are driven by financial stress of the borrower we

employ a variety of control variables in each of our tests. However, managers of firms

preparing to secure financing are likely to be in contact with potential lenders preceding a

loan issuance, and we are unable to directly control for information that managers privately

provide to lenders. To address the concern that this unobserved information transfer from

managers to lenders is driving our results, we construct a new indicator variable Before_MAO

that 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 actually preempted by managers communicating to

lenders before an MAO, 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 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 private information

communicated by auditors.

5.3 The Information Role of Going Concern Opinions with Matched Samples

We further provide evidence that GC opinions communicate to lenders the auditor’s

private information about the client’s financial health. 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 firm outside of our sample that was predicted to receive a GC

opinion but did not. We also require that the matching firm must obtain a loan 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 going concern opinion using

two different specifications and present the results in Appendix B.

26

The variables included in the both specifications explain much of the variation in the

dependent variable – the pseudo r-squared is over 50% in each specification. We present

descriptive statistics of our sample firms before and after matching in Panel A of Table 8.

While most variables were statistically different between GC and non-GC firms before

propensity score matching, most are indistinguishably different after using both the baseline

and extended models. These descriptive statistics provide comfort that the matching process

was 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 report, if there is no private information there

should be no difference in contract terms between the sample and matched firms. However,

the coefficients on GC Opinion in Table 8 remain significant in both panels in the predicted

direction for five of the six contract terms we examine – the exception being the financial

covenants 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 do not signal

incrementally useful information about the diminished contractibility of financial data already

conveyed in the financial reports, consistent with the results in Table 7. It is possible that

lenders are able to detect a decreased contracting usefulness of financial information leading

up to an MAO generally, and a GC opinion in particular. Overall, our additional analyses

with matched samples show that GC opinions are incrementally informative to lenders,

27

beyond the two mechanical models that predict GC opinions and bankruptcy using

information available in the financial report.

5.4 The Differential Reporting of MAOs by Large and Small Audit Firms

Prior studies find that large auditors have greater reputation assets than small auditors,

and therefore have higher incentives to provide a high audit quality. Large auditors have been

used as a proxy for audit quality (Pittman and Fortin, 2004). We investigate whether an MAO

from a large audit firm causes a greater impact on the contractual terms of the 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 have examined. Therefore,

conditional on an MAO being issued, we do not find any significant difference in the effects

on contracts terms between large and small auditors. This does not necessarily contradict the

conclusions drawn in prior studies that big audit firms have higher audit quality because the

propensity to issue an MAO may be different across large and small auditors or there may be

endogeneity in the match between auditor size and client quality. For our sample of 8,473

observations, 93.5% of the total observations are audited by the big auditors. The propensity

to issue an MAO for the big auditors is 38.1%, and is 33.6% for small auditors. This result,

while providing neither necessary nor sufficient evidence, is consistent with big auditors

yielding less to client pressure and providing higher audit quality.

5.5 The Effect of Auditor Opinions after Controlling for Internal Control Weaknesses

We investigate an additional source of information in the financial statements.

Beginning in 2004, managers are required to test and report on the quality of the firm’s

internal controls. Prior studies show that disclosures of internal control weakness under

section 302 or section 404 of the Sarbanes-Oxley Act 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

28

that even after controlling for the disclosure of an internal control weakness our variables of

interest remain statistically significant, indicating that MAOs informs 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 to firms with clean opinions,

loans issued to firms with MAOs are associated with higher loan spreads and less favorable

non-price loan terms. More importantly, we predict that the effect of MAOs on loan terms

varies by type of MAO, with GC opinions having the strongest effect.

Using the loan contracts of firms with modified audit opinions during the period

1992-2009; we find empirical results that support our predictions. Specifically, we find that,

compared with loans issued to firms with clean opinions, loans issued to firms with modified

opinions are associated with higher interest spreads, fewer financial covenants, more general

covenants, smaller loan sizes, and a higher likelihood of being secured. 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 increase of 107 basis points

for GC opinions. 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

29

favorable loan terms after controlling for these effects. Taken together, our empirical results

suggest that audit reporting communicates auditors’ private information to lenders.

An independent audit is an essential part of the financial reporting process. Our

empirical analyses contribute to our understanding of the value-adding function of audit

reporting by incorporating the auditor’s discretionary explanatory language. Our results

suggest that the explanatory language in audit reports informs lenders. As the market demand

increases for more disclosures from auditors, regulators are contemplating whether to include

a discussion of “critical audit matters” in order to enhance the value of the audit report

(PCAOB, 2013). An implication of our study is that regulators must weigh the informative

role of additional disclosures in audit reports with the potential resistance from auditors to

reveal private information about clients, given the economic consequences of MAOs

documented here.

The empirical results we document are not obvious given 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. Our research contributes to our

understanding of the economic value of auditing in an important market that has, up until

now, received little attention. Given the multiple features of debt contracts and the frequent

use of debt financing, debt markets provide a fertile ground for future studies. As an example,

future research can extend the investigation into whether and how an MAO affects a

borrower’s ability to access the public versus private debt markets. Additionally, it would be

interesting to examine whether the role of auditor opinions in debt contracting varies across

countries where institutional infrastructures are divergent.

30

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33

Appendix A: Variable Definitions

Auditor Opinion Variables MAO An indicator variable equal to 1 if the firm receives an opinion from the

auditor other than unqualified in the current year, and zero otherwise.

After_MAO An indicator variable equal to 1 if the firm does not has a MAO this

year but had a MAO equal to 1 in the previous three years, and zero

otherwise.

Before_MAO An indicator variable equal to 1 if the firm does not has a MAO this

year but has a 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 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 financial

statements of the firm 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 methods change or reliance on another auditor in the

auditor report, and zero otherwise.

GC_Performance3 An indicator variable equal to 1 if a going concern opinion issued by an

auditor mentions poor financial performance or operating problems (ex.

loss of a major customer/supplier), and zero otherwise.

GC_Financing An indicator variable equal to 1 if a going concern opinion issued by an

auditor mentions financing problems, and zero otherwise.

GC_Other An indicator variable equal to 1 if a going concern opinion issued by an

auditor mentions other issues (ex. litigation risk or regulatory issues),

and zero otherwise.

Borrower-Specific Variables Size The natural log of total assets, estimated in the year prior to entering

into a loan contract.

Market-to-book Market value of equity plus the book value of debt over total assets at

the year prior to entering into a loan contract.

Leverage Long-term debt divided by total assets, estimated in the year prior to

entering into a loan contract.

Profitability EBIDTA divided by total assets, estimated in the year prior to entering

into a loan contract.

Cash flow volatility Standard deviation of quarterly cash flows from operations over

previous four fiscal years, scaled by total assets.

34

Tangibility Net PPE divided by total assets, estimated in the year prior to entering

into a loan contract.

Z-score Probability of bankruptcy score (Zmijewski 1984). We exclude the

Market-to-book component since we have Market-to-book in the

regression.

Abnormal_Accruals Absolute abnormal accruals calculated as the residual of a cross-

sectional version of the Jones (1991) model for each (two-digit SIC)

industry and year.

Credit spread The difference between BAA corporate bond yield and AAA corporate

bond yield.

Term spread The difference between the 10-year Treasury yield and the 2-year

Treasury yield.

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.

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.

Interest Rate 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.

Loan Size Amount borrowed in millions of dollars.

Maturity The number of months between the facility’s issue date and the loan

maturity date.

Number of Lenders Number of participants in the loan syndicate.

PP Indicator An indicator variable equal to 1 if the loan contract includes a

performance pricing provision, and zero otherwise.

Revolver An indicator variable equal to 1 if the loan is a revolver, and zero

otherwise.

Secured An indicator variable equal to 1 if the loan is backed by collateral, and

zero otherwise.

Loan Purpose Effect 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.

Going concern observations are not limited to one sub-classification.

35

Appendix B: The Determinants of Going Concern Opinions

Dependent Variable = GC Opinion

Predicted Sign Baseline PSM Extended PSM

Firm Size - -0.15*** -0.15***

(-3.53) (-3.20)

Market-to-book - -1.61*** -1.85***

(-4.99) (-6.71)

Total liability + 3.32*** 3.01***

(10.14) (9.24)

Profitability - -2.09** -1.88*

(-2.30) (-1.84)

Cash flow volatility + 1.32 2.42

(0.51) (0.84)

Tangibility - 0.05 -0.19

(0.14) (-0.54)

Z-score - -0.26*** -0.26***

(-3.58) (-3.49)

Abnormal_Accruals + 1.78*** 1.80***

(3.27) (3.05)

Loss + 0.32**

(2.55)

Age - -0.04

(-0.43)

Big + 0.33

(1.43)

CLeverage + 0.83**

(2.23)

Cash - -1.61**

(-1.97)

Operating cash flow - 0.26

(0.31)

Intercept -0.70 -0.57

(-0.94) (-0.76)

Year FE Included Included

Industry FE Included Included

Observations 5,377 5,377

Pseudo R2 0.502 0.520

This panel presents the probit regression results of modeling the probability of receiving a going concern

opinion at firm level to generate the score for matching. We include all firm level control variables used in our

main regression in baseline PSM (we replace leverage with Total liability which is defined as total liability

scalded by total assets since going concern firms have more short term debts instead of long term debts). We

include more firm-level control variables in the extended PSM. Loss is defined as one if the firm reports a loss at

year t and zero otherwise. 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. CLeverage is the change of leverage 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. Industry and year fixed effects are included in both regressions and standard errors are

heteroskedasticity robust.

36

Table 1: Sample Distribution

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

Year Facility Firm MAO Inadequacy Inconsistency GC Opinion Material Uncertainty Restatement Accounting Change

1992 63 42 3 2 1 0 2 0 1

1993 139 92 22 1 21 0 1 0 21

1994 205 129 54 2 52 1 1 1 51

1995 293 191 81 9 72 3 6 2 70

1996 459 299 94 5 89 3 2 4 85

1997 551 327 42 4 38 3 1 3 35

1998 558 322 28 2 26 2 0 2 24

1999 602 330 23 10 13 7 3 1 13

2000 582 353 39 21 18 17 4 2 17

2001 658 412 48 16 32 15 1 5 30

2002 678 450 101 23 78 22 1 39 73

2003 702 448 318 23 295 15 8 50 256

2004 711 481 345 11 334 9 2 43 284

2005 679 448 218 10 208 7 3 28 165

2006 573 370 129 16 113 13 3 13 85

2007 530 320 229 7 222 5 2 7 209

2008 320 226 179 7 172 4 3 1 165

2009 170 137 103 6 97 5 1 1 96

Total 8,473 5,377 2,056 175 1,881 131 44 201 1,680

37

Table 2: Descriptive Statistics

Table 2 presents the descriptive statistics for the total sample. Panel A provides loan

characteristics and Panel B provides firm characteristics. See the appendix A for the variable

definitions.

Panel A: Loan Characteristics

MAO=1(N=3,203) MAO=0(N=5,270)

Mean Median Std. dev. Mean Median Std. dev.

Interest Rate 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

Secured 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

Panel B: Firm Characteristics

MAO=1(N=2,056) MAO=0(N=3,321)

Mean Median Std. dev. Mean Median Std. dev.

Firm Size (in millions) 3562.07 931.19 6833.59 1796.54 473.05 4305.90

Firm Size 6.93 6.84 1.63 6.17 6.16 1.64

Market-to-book 0.12 0.12 0.09 0.13 0.13 0.10

Leverage 0.26 0.24 0.22 0.25 0.22 0.21

Profitability 0.03 0.02 0.03 0.03 0.02 0.03

Cash flow volatility 2.37 1.81 3.46 2.71 2.06 3.51

Tangibility 0.34 0.28 0.24 0.32 0.26 0.23

Z-score 3.00 2.50 2.70 3.90 3.15 3.33

Abnormal_Accruals 0.08 0.06 0.08 0.09 0.06 0.09

Credit spread 1.00 0.90 0.46 0.86 0.81 0.29

Term spread 1.24 1.33 0.91 0.77 0.44 0.84

38

Panel C: Correlation Matrix

Table 2 Panel C presents the Pearson correlation matrix. All variables are defined in the 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

1 Interest Rate

2 Number of Financial Covenants 0.15

3 Number of General Covenants 0.26 0.39

4 Number of Lenders -0.25 -0.06 0.11

5 Loan Size (in millions) -0.16 -0.15 0.00 0.29

6 Maturity (in months) -0.02 0.11 0.32 0.17 0.02

7 PP Indicator -0.36 0.15 0.15 0.18 0.06 0.13

8 Secured 0.50 0.23 0.36 -0.19 -0.18 0.15 -0.18

9 Firm Size -0.30 -0.22 0.11 0.52 0.40 0.08 0.17 -0.37

10 Market-to-book -0.19 -0.05 -0.09 0.01 0.02 -0.01 0.04 -0.15 -0.06

11 Leverage 0.15 0.08 0.24 0.16 0.03 0.22 -0.02 0.16 0.18 -0.10

12 Profitability -0.30 0.02 0.04 0.08 0.04 0.11 0.20 -0.19 0.05 0.41 0.01

13 Cash flow volatility 0.12 -0.04 -0.13 -0.19 -0.12 -0.16 -0.13 0.09 -0.35 0.06 -0.20 -0.05

14 Tangibility -0.03 -0.09 -0.04 0.07 0.06 0.04 0.02 -0.05 0.15 -0.10 0.21 0.13 -0.21

15 Z-score -0.29 -0.02 -0.09 -0.01 -0.03 -0.03 0.17 -0.19 -0.03 0.12 -0.35 0.48 0.16 -0.21

16 Abnormal_Accruals 0.14 0.01 -0.06 -0.11 -0.04 -0.07 -0.08 0.11 -0.19 0.11 -0.07 -0.13 0.16 -0.09 -0.11

17 Credit spread 0.22 0.02 0.08 -0.07 0.00 -0.14 -0.01 0.04 0.11 -0.07 -0.06 -0.06 0.00 -0.01 -0.03 0.01

18 Term spread 0.19 0.01 -0.01 -0.04 -0.05 -0.16 -0.09 0.04 0.04 -0.12 0.01 -0.03 0.05 0.01 -0.04 0.00 0.47

19 MAO 0.07 -0.10 0.08 0.03 0.07 0.01 -0.03 -0.02 0.22 -0.10 0.02 -0.06 -0.02 0.04 -0.11 -0.03 0.19 0.26

39

Table 3: The Effect of Modified Audit Opinions on Loan Spreads Interest Rate

Predicted Sign (1) (2) (3)

MAO + 17.31***

(3.24)

Inadequacy + 92.14***

(7.77)

Inconsistency + 5.95

(1.52)

GC Opinion + 107.13***

(7.67)

Material Uncertainty + 49.37**

(2.56)

Restatement + 25.27***

(3.21)

Accounting Change + 3.64

(0.88)

After_MAO +/? 2.03 0.68 0.90

(0.43) (0.15) (0.20)

Institutional Investor + 47.15*** 47.32*** 46.79***

(6.97) (6.83) (6.72)

Revolver - -27.07*** -27.21*** -27.23***

(-6.89) (-6.87) (-6.86)

Financial Covenants - 1.09 1.20 1.27

(0.76) (0.84) (0.88)

Loan Size - -12.62*** -12.84*** -12.74***

(-5.90) (-5.94) (-5.79)

Maturity + -0.25*** -0.22** -0.21**

(-2.74) (-2.49) (-2.21)

Number of Lenders - -0.31 -0.36 -0.37

(-1.42) (-1.54) (-1.52)

PP Indicator - -48.57*** -45.41*** -45.23***

(-7.57) (-7.78) (-7.80)

Secured + 78.88*** 76.87*** 77.08***

(13.49) (13.69) (13.98)

Firm Size - -8.84*** -8.39*** -8.28***

(-3.26) (-3.14) (-3.09)

Market-to-book - -7.97*** -7.17*** -7.01***

(-4.17) (-4.11) (-3.93)

Leverage + 66.96*** 76.16*** 76.41***

(8.08) (8.65) (8.70)

Profitability - -146.32*** -144.17*** -142.65***

(-5.95) (-6.12) (-6.19)

Cash flow volatility + 218.32*** 190.13*** 183.18**

(3.04) (2.58) (2.44)

Tangibility - 7.51 6.88 7.06

(0.66) (0.60) (0.61)

Z-score - -8.34*** -6.23*** -5.93***

(-5.60) (-4.98) (-4.59)

40

Abnormal_Accruals + 46.78** 34.08* 34.07*

(2.38) (1.70) (1.70)

Credit spread + 29.49* 31.20* 31.11*

(1.69) (1.73) (1.74)

Term spread + 3.71 3.50 3.78

(0.60) (0.56) (0.62)

Intercept 490.08*** 474.72*** 471.73***

(12.92) (12.44) (12.29)

Loan Purpose FE Included Included Included

Year FE Included Included Included

Observations 8,473 8,473 8,473

Adj. R2 0.515 0.526 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 the 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. ∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%,

and 10% levels, respectively.

41

Table 4: The Effect of Modified Audit Opinions on Debt Covenants Financial Covenants General Covenants

Predicted

Sign (1) (2) (3)

Predicted

Sign (4) (5) (6)

MAO - -0.10** + 0.22***

(-2.04) (3.01)

Inadequacy - -0.17** + 0.72***

(-1.99) (3.21)

Inconsistency - -0.10* + 0.15*

(-1.74) (1.75)

GC Opinion - -0.24** + 0.64**

(-2.56) (2.18)

Material Uncertainty - 0.04 + 0.95***

(0.17) (4.12)

Restatement - -0.09 + 0.17

(-0.99) (0.88)

Accounting Change - -0.09* + 0.15*

(-1.71) (1.77)

After_MAO -/? -0.16*** -0.16*** -0.16*** +/? -0.04 -0.04 -0.04

(-3.24) (-3.25) (-3.23) (-0.47) (-0.57) (-0.56)

Institutional Investor + 0.45*** 0.45*** 0.45*** + 0.88*** 0.89*** 0.89***

(5.39) (5.40) (5.43) (6.87) (6.88) (6.87)

Revolver ? -0.00 -0.00 -0.00 ? -0.44*** -0.45*** -0.45***

(-0.10) (-0.08) (-0.08) (-6.52) (-6.63) (-6.60)

Interest rate - 0.00 0.00 0.00 + 0.00*** 0.00*** 0.00***

(0.80) (0.89) (0.91) (10.72) (10.82) (11.26)

Secured + 0.35*** 0.35*** 0.35*** + 1.93*** 1.93*** 1.93***

(5.30) (5.31) (5.16) (15.49) (15.32) (15.38)

Loan Size - -0.04 -0.04 -0.04 ? 0.36*** 0.35*** 0.35***

(-1.64) (-1.62) (-1.63) (6.85) (6.87) (6.87)

Maturity + 0.00*** 0.00*** 0.00*** + 0.01*** 0.01*** 0.01***

(3.31) (3.30) (3.32) (6.12) (6.26) (6.15)

Number of Lenders ? 0.01*** 0.01*** 0.01*** ? -0.00 -0.00 -0.00

(2.63) (2.66) (2.69) (-0.11) (-0.21) (-0.19)

PP Indicator + 0.40*** 0.40*** 0.40*** + 1.03*** 1.04*** 1.04***

(6.74) (6.75) (6.73) (11.14) (11.30) (11.14)

Firm Size - -0.18*** -0.18*** -0.18*** - 0.03 0.03 0.03

(-7.01) (-7.01) (-7.02) (0.73) (0.78) (0.77)

Market-to-book - -0.10*** -0.10*** -0.10*** - -0.15*** -0.14*** -0.14***

(-3.98) (-4.02) (-4.04) (-2.84) (-2.81) (-2.76)

Leverage + 0.35*** 0.34*** 0.34*** + 0.92*** 0.99*** 0.99***

(2.84) (2.71) (2.71) (4.04) (4.23) (4.22)

Profitability - 1.50*** 1.50*** 1.50*** - 2.86*** 2.85*** 2.85***

(5.32) (5.31) (5.30) (4.60) (4.67) (4.69)

Cash flow volatility + -4.86*** -4.84*** -4.81*** + -4.66*** -4.82*** -4.79***

(-4.85) (-4.83) (-4.78) (-2.86) (-2.86) (-2.85)

Tangibility ? -0.54*** -0.54*** -0.54*** ? -1.03*** -1.04*** -1.04***

(-5.08) (-5.06) (-5.07) (-6.54) (-6.57) (-6.55)

42

Z-score - -0.02 -0.03 -0.03 - -0.01 0.00 0.00

(-1.29) (-1.37) (-1.47) (-0.22) (0.10) (0.05)

Abnormal_Accruals - -0.45*** -0.44*** -0.44*** - -1.34*** -1.42*** -1.42***

(-3.11) (-3.12) (-3.15) (-3.83) (-4.09) (-4.11)

Credit spread ? 0.03 0.03 0.03 ? 0.21** 0.23** 0.23**

(0.40) (0.37) (0.39) (2.33) (2.48) (2.51)

Term spread ? 0.11* 0.11* 0.11* ? 0.20 0.20 0.20

(1.70) (1.71) (1.67) (1.56) (1.58) (1.55)

Intercept 0.72 0.72 0.73 -8.41*** -8.44*** -8.43***

(1.45) (1.45) (1.47) (-7.54) (-7.63) (-7.61)

Loan Purpose FE Included Included Included Included Included Included

Year FE Included Included Included Included Included Included

Observations 8,473 8,473 8,473 8,473 8,473 8,473

Adj. R2 0.336 0.337 0.337 0.466 0.467 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 firm-

specific control variables in Column 2 and Column 5. In Columns 3 and 6,we test the effects of GC Opinion, Material

Uncertainty, Restatement, and Accounting Change.

All variables are defined in the 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. ∗∗∗,

∗∗, ∗ denote significance at the 1%, 5%, and 10% levels, respectively.

43

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

Loan Size Secured Log(Maturity)

Predicted

Sign (1) (2) (3)

Predicted

Sign (4) (5) (6)

Predicted

Sign (7) (8) (9)

MAO - -0.05*** + 0.07 - 0.00

(-3.62) (1.01) (0.12)

Inadequacy - -0.05*** + 0.96*** - -0.12

(-2.87) (3.81) (-1.47)

Inconsistency - -0.05*** + 0.01 - 0.02

(-3.67) (0.13) (0.83)

GC Opinion - -0.06*** + 0.98*** - -0.20**

(-2.75) (2.83) (-1.99)

Material Uncertainty - -0.04 + 0.95** - 0.12*

(-1.04) (2.12) (1.66)

Restatement - -0.04*** + 0.09 - -0.04

(-2.76) (0.83) (-1.11)

Accounting Change - -0.05*** + 0.00 - 0.03

(-3.59) (0.00) (1.13)

After_MAO - -0.02* -0.02* -0.02* + 0.14* 0.13* 0.13* - -0.00 0.00 -0.00

(-1.67) (-1.67) (-1.68) (1.82) (1.74) (1.76) (-0.07) (0.00) (-0.02)

Institutional Investor + 0.07*** 0.07*** 0.07*** + 1.17*** 1.17*** 1.17*** + 0.56*** 0.56*** 0.56***

(3.09) (3.08) (3.06) (5.46) (5.34) (5.31) (13.83) (13.70) (13.60)

Revolver ? 0.09*** 0.09*** 0.09*** ? 0.07 0.07 0.07 ? 0.18*** 0.19*** 0.19***

(9.06) (9.03) (8.94) (0.99) (0.95) (0.95) (4.65) (4.68) (4.69)

Loan Size - -0.15*** -0.15*** -0.15*** ? 0.06*** 0.06*** 0.06***

(-3.63) (-3.64) (-3.61) (5.06) (4.99) (4.93)

Financial Covenants ? 0.01 0.01 0.01 ? 0.13*** 0.13*** 0.13*** ? 0.04*** 0.04*** 0.04***

(1.43) (1.42) (1.39) (4.53) (4.39) (4.36) (4.89) (4.82) (4.92)

Maturity + 0.00*** 0.00*** 0.00*** + 0.00*** 0.00*** 0.00***

(4.35) (4.33) (4.02) (3.06) (3.00) (3.00)

Number of Lenders ? -0.00 -0.00 -0.00 ? 0.01** 0.01* 0.01* + 0.01*** 0.01*** 0.01***

44

(-0.22) (-0.19) (-0.19) (2.04) (1.91) (1.90) (2.82) (2.85) (2.98)

PP Indicator ? 0.03*** 0.03*** 0.03*** - -0.28*** -0.26*** -0.26*** + 0.25*** 0.24*** 0.24***

(3.38) (3.36) (3.35) (-4.28) (-3.96) (-3.97) (7.37) (7.11) (7.03)

Secured + 0.06*** 0.06*** 0.06*** + 0.16*** 0.16*** 0.15***

(6.14) (6.12) (6.01) (6.21) (6.22) (5.97)

Interest rate - -0.00 -0.00 -0.00 + -0.00** -0.00* -0.00

(-0.26) (-0.19) (-0.15) (-2.10) (-1.72) (-1.56)

Firm Size - -0.35*** -0.34*** -0.34*** + -0.06*** -0.06*** -0.06***

(-10.08) (-10.06) (-10.03) (-4.22) (-4.22) (-4.18)

Market-to-book + 0.04*** 0.04*** 0.04*** - -0.13*** -0.13*** -0.13*** + -0.03*** -0.03*** -0.03***

(4.20) (4.18) (4.16) (-4.44) (-4.29) (-4.22) (-2.66) (-2.77) (-2.85)

Leverage - -0.05** -0.05** -0.05** + 1.57*** 1.65*** 1.65*** - 0.27*** 0.25*** 0.25***

(-2.22) (-2.18) (-2.18) (8.19) (8.41) (8.47) (6.61) (5.60) (5.57)

Profitability + 0.25*** 0.25*** 0.25*** - -1.65*** -1.59*** -1.58*** + 0.50*** 0.51*** 0.50***

(3.01) (3.01) (3.02) (-3.62) (-3.47) (-3.44) (3.88) (3.95) (4.01)

Cash flow volatility + 1.40*** 1.40*** 1.40*** + 4.04*** 3.87** 3.86** - -1.84*** -1.81*** -1.77***

(8.83) (8.89) (8.86) (2.65) (2.55) (2.54) (-5.15) (-5.08) (-5.03)

Tangibility + 0.04*** 0.04*** 0.04*** - -0.20 -0.20 -0.20 + -0.01 -0.01 -0.01

(4.20) (4.18) (4.16) (-1.42) (-1.43) (-1.42) (-0.31) (-0.29) (-0.32)

Z-score - -0.05** -0.05** -0.05** - -0.13*** -0.12*** -0.12*** + 0.01 0.00 0.00

(-2.22) (-2.18) (-2.18) (-3.56) (-3.22) (-3.21) (0.65) (0.41) (0.26)

Abnormal_Accruals + 0.25*** 0.25*** 0.25*** + 0.89*** 0.77*** 0.77*** - -0.11 -0.09 -0.10

(3.01) (3.01) (3.02) (6.14) (5.67) (5.94) (-1.02) (-0.83) (-0.87)

Credit spread ? -0.01 -0.01 -0.01 ? 0.23 0.24 0.24 ? -0.12*** -0.12*** -0.12***

(-0.47) (-0.48) (-0.47) (1.21) (1.24) (1.24) (-2.76) (-2.78) (-2.83)

Term spread ? 0.00 0.00 0.00 ? 0.00 0.01 0.01 ? -0.01 -0.01 -0.01

(0.11) (0.11) (0.10) (0.06) (0.12) (0.12) (-0.37) (-0.35) (-0.45)

Intercept -0.10** -0.10** -0.10** 6.08*** 5.92*** 5.92*** 3.09*** 3.10*** 3.11***

(-2.20) (-2.17) (-2.17) (8.71) (8.34) (8.27) (15.43) (15.46) (15.74)

Loan Purpose FE Included Included Included Included Included Included Included Included Included

Year FE Included Included Included Included Included Included Included Included Included

Observations 8,473 8,473 8,473 8,473 8,473 8,473 8,473 8,473 8,473

Adj. R2/Pseudo R

2 0.238 0.238 0.238 0.339 0.345 0.345 0.294 0.295 0.297

45

In Table 5 we present results for the following specifications:

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

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

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

In Column 1 we estimate the effect on loan size of a 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 lenders require a loan to be secured. The dependent variable is equal to one if the loan is secured and zero otherwise. Column 7 presents results of our

estimate of the effect on loan maturity of a 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.

All variables are defined in the 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. ∗∗∗, ∗∗, ∗ denote significance at the 1%,

5%, and 10% levels, respectively.

46

Table 6: The Effect of Different Types of Going Concern Opinions

(1) (2) (3) (4) (5) (6)

Dependent Variable = Interest

Rate

Financial

Covenants

General

Covenants Loan Size Secured Log(Maturity)

GC_Performance 47.15*** -0.29* -0.28 -0.05** 0.19 -0.16

(4.10) (-1.92) (-0.64) (-2.18) (0.57) (-0.94)

GC_Financing 93.10*** -0.03 1.09*** -0.02** 0.90** -0.02

(5.93) (-0.21) (4.04) (-2.00) (2.57) (-0.09)

GC_Other 55.11 -0.28 -0.27 -0.00 -0.38 -0.40***

(0.58) (-0.55) (-0.36) (-0.13) (-0.85) (-3.92)

Material Uncertainty 44.23** 0.05 0.92*** -0.04 0.93** 0.15**

(2.33) (0.22) (3.94) (-0.99) (2.07) (1.98)

Restatement 23.20*** -0.09 0.15 -0.04*** 0.07 -0.04

(3.17) (-0.98) (0.76) (-2.73) (0.65) (-0.87)

Accounting Change 1.91 -0.09* 0.13 -0.04*** -0.01 0.04

(0.49) (-1.66) (1.56) (-3.55) (-0.23) (1.38)

After_MAO -0.41 -0.16*** -0.06 -0.02 0.11 0.00

(-0.09) (-3.24) (-0.81) (-1.60) (1.63) (0.14)

Institutional Investor 46.89*** 0.45*** 0.89*** 0.07*** 1.17*** 0.56***

(6.87) (5.19) (6.97) (3.06) (5.31) (13.28)

Revolver -27.02*** -0.00 -0.45*** 0.09*** 0.06 0.18***

(-6.93) (-0.09) (-6.51) (8.98) (0.89) (4.66)

Loan Size -12.63*** -0.04 0.35*** -0.15*** 0.06***

(-5.93) (-1.64) (6.84) (-3.63) (4.97)

Financial Covenants 1.23 0.01 0.13*** 0.04***

(0.85) (1.38) (4.38) (4.92)

Maturity -0.22** 0.00*** 0.01*** 0.00*** 0.00***

(-2.54) (3.23) (6.07) (4.24) (2.99)

Number of Lenders -0.36 0.01*** -0.00 -0.00 0.01** 0.01***

(-1.45) (2.76) (-0.14) (-0.21) (1.97) (2.91)

PP Indicator -45.52*** 0.40*** 1.04*** 0.03*** -0.26*** 0.24***

(-7.49) (6.73) (10.86) (3.38) (-3.97) (7.02)

Secured 77.24*** 0.35*** 1.92*** 0.06*** 0.15***

(13.71) (5.10) (14.95) (6.02) (5.95)

Interest rate 0.00 0.00*** -0.00 -0.00

(0.82) (10.76) (-0.22) (-1.41)

Firm Size -8.21*** -0.18*** 0.03 -0.34*** -0.06***

(-3.10) (-6.80) (0.80) (-10.15) (-4.26)

Market-to-book -6.96*** -0.10*** -0.14*** 0.04*** -0.13*** -0.03***

(-3.77) (-4.06) (-2.75) (4.16) (-4.32) (-2.75)

Leverage 77.44*** 0.34*** 0.99*** -0.05** 1.63*** 0.25***

(8.64) (2.66) (4.18) (-2.17) (8.57) (5.52)

Profitability -143.51*** 1.49*** 2.81*** 0.25*** -1.54*** 0.49***

(-6.01) (5.28) (4.59) (2.99) (-3.37) (3.96)

Cash flow volatility 195.22*** -4.80*** -4.58*** 1.40*** 3.92*** -1.79***

(2.67) (-4.77) (-2.75) (8.76) (2.61) (-5.02)

Tangibility 6.64 -0.53*** -1.03*** 0.10*** -0.22 -0.01

(0.60) (-5.11) (-6.39) (3.71) (-1.52) (-0.31)

Z-score -6.41*** -0.03 -0.00 -0.01 -0.12*** 0.00

47

(-4.83) (-1.44) (-0.11) (-1.29) (-3.44) (0.39)

Abnormal_Accruals 33.96* -0.42*** -1.37*** 0.20*** 0.82*** -0.09

(1.69) (-2.92) (-3.94) (2.77) (6.70) (-0.79)

Credit spread 31.42* 0.03 0.23** -0.01 0.24 -0.12***

(1.75) (0.37) (2.46) (-0.47) (1.25) (-2.81)

Term spread 3.60 0.11* 0.20 0.00 0.01 -0.01

(0.56) (1.65) (1.48) (0.09) (0.21) (-0.46)

Intercept 472.37*** 0.73 -8.41*** -0.11** 5.93*** 3.11***

(12.82) (1.48) (-7.46) (-2.18) (8.28) (15.67)

Loan Purpose FE Included Included Included Included Included Included

Year FE Included Included Included Included Included Included

Observations 8,473 8,473 8,473 8,473 8,473 8,473

Adj. R2/Pseudo R

2 0.527 0.337 0.468 0.238 0.349 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 + βiCONTROLS + ε

The names of the columns show the dependent variables. All variables are defined in the 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. ∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%, and 10% levels, respectively.

48

Table 7: The Effect of Information Leakage on MAOs (1) (2) (3) (4) (5) (6)

Dependent Variable = Interest

Rate

Financial

Covenants

General

Covenants

Loan

Size Secured Log(Maturity)

GC Opinion 107.88*** -0.28*** 0.62** -0.06*** 0.98*** -0.21**

(7.76) (-2.79) (2.05) (-2.87) (2.82) (-1.98) Material Uncertainty 50.03*** 0.00 0.93*** -0.04 0.95** 0.12

(2.64) (0.01) (3.98) (-1.12) (2.14) (1.51)

Restatement 25.99*** -0.13 0.15 -0.05*** 0.09 -0.05

(3.30) (-1.27) (0.76) (-2.97) (0.80) (-1.16)

Accounting Change 4.34 -0.13** 0.12 -0.05*** 0.00 0.02

(1.02) (-2.12) (1.48) (-3.41) (0.01) (0.71)

Before_MAO 2.39 -0.13** -0.08 -0.02 0.00 -0.02

(0.54) (-2.14) (-0.72) (-1.17) (0.03) (-0.77)

After_MAO 1.48 -0.19*** -0.06 -0.02* 0.13* -0.01

(0.32) (-3.79) (-0.75) (-1.85) (1.76) (-0.20) Institutional Investor 46.82*** 0.45*** 0.89*** 0.07*** 1.17*** 0.56***

(6.72) (5.41) (6.90) (3.04) (5.31) (13.69)

Revolver -27.23*** -0.00 -0.45*** 0.09*** 0.07 0.19***

(-6.82) (-0.06) (-6.55) (8.99) (0.95) (4.69)

Loan Size -12.74*** -0.04* 0.35*** -0.15*** 0.06***

(-5.78) (-1.65) (6.87) (-3.61) (4.93)

Financial Covenants 1.29 0.01 0.13*** 0.04***

(0.88) (1.36) (4.35) (4.95)

Maturity -0.21** 0.00*** 0.01*** 0.00*** 0.00***

(-2.35) (3.36) (6.12) (4.25) (3.01)

Number of Lenders -0.37 0.01*** -0.00 -0.00 0.01* 0.01***

(-1.58) (2.67) (-0.20) (-0.18) (1.90) (2.98)

PP Indicator -45.21*** 0.40*** 1.04*** 0.03*** -0.26*** 0.24***

(-7.76) (6.79) (11.16) (3.36) (-3.95) (7.04)

Secured 77.07*** 0.35*** 1.93*** 0.06*** 0.15***

(13.99) (5.15) (15.34) (6.02) (5.98)

Interest rate 0.00 0.00*** -0.00 -0.00

(0.92) (11.38) (-0.17) (-1.53)

Firm Size -8.30*** -0.18*** 0.03 -0.34*** -0.06***

(-3.07) (-6.90) (0.79) (-10.03) (-4.16)

Market-to-book -7.02*** -0.10*** -0.14*** 0.04*** -0.13*** -0.03***

(-3.94) (-4.05) (-2.75) (4.16) (-4.23) (-2.87)

Leverage 76.39*** 0.34*** 0.99*** -0.05** 1.65*** 0.25***

(8.68) (2.72) (4.23) (-2.17) (8.48) (5.59)

Profitability -142.35*** 1.48*** 2.84*** 0.25*** -1.58*** 0.50***

(-6.22) (5.13) (4.62) (2.98) (-3.44) (3.99)

Cash flow volatility 183.54** -4.83*** -4.80*** 1.40*** 3.86** -1.77***

(2.44) (-4.79) (-2.86) (8.92) (2.55) (-5.04)

Tangibility 6.98 -0.53*** -1.03*** 0.10*** -0.20 -0.01

(0.61) (-5.07) (-6.51) (3.68) (-1.43) (-0.30)

Z-score -5.92*** -0.03 0.00 -0.01 -0.12*** 0.00

(-4.57) (-1.50) (0.05) (-1.33) (-3.21) (0.25)

Abnormal_Accruals 34.05* -0.44*** -1.42*** 0.19*** 0.77*** -0.09

(1.70) (-3.14) (-4.13) (2.76) (5.94) (-0.86)

Credit spread 31.15* 0.03 0.23** -0.01 0.24 -0.12***

49

(1.74) (0.36) (2.51) (-0.49) (1.24) (-2.81)

Term spread 3.68 0.11* 0.20 0.00 0.01 -0.01

(0.60) (1.76) (1.57) (0.15) (0.12) (-0.40)

Intercept 471.28*** 0.75 -8.41*** -0.10** 5.92*** 3.11***

(12.22) (1.55) (-7.57) (-2.15) (8.26) (15.62)

Loan Purpose FE Included Included Included Included Included Included

Year FE Included Included Included Included Included Included

Observations 8,473 8,473 8,473 8,473 8,473 8,473

Adj. R2/Pseudo R

2 0.528 0.337 0.467 0.238 0.349 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 + βiCONTROLS + ε

The names of the columns show the dependent variables.

All variables are defined in the 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. ∗∗∗, ∗∗, ∗ denote significance

at the 1%, 5%, and 10% levels, respectively.

50

Table 8: The Effect of GC Opinions on Loan Terms (Matched Samples)

Panel A: Test of the Effectiveness of Matching Process

Variable GC

Opinion=1

(N=131)

GC

Opinion=0

(N=5,246)

P value

for the

mean

equivalent

Baseline

PSM

(N=131)

P value

for the

mean

equivalent

Extended

PSM

(N=131)

P value

for the

mean

equivalent

Firm Size 5.864 6.473 <0.001 5.442 0.082 5.477 0.107

Market-to-book 1.258 1.710 <0.001 1.288 0.635 1.243 0.802

Total liability 1.012 0.578 <0.001 0.980 0.495 0.942 0.152

Profitability 0.024 0.134 <0.001 0.040 0.313 0.013 0.520

Cash flow volatility 0.048 0.031 <0.001 0.053 0.910 0.060 0.042

Tangibility 0.344 0.330 0.503 0.341 0.357 0.354 0.730

Z-score 0.157 1.854 <0.001 -0.057 0.932 0.207 0.815

Abnormal_Accruals 0.146 0.084 <0.001 0.116 0.033 0.105 0.004

Panel B: Basic PSM Regression Result

(1) (2) (3) (4) (5) (6)

Dependent Variable = Interest

Rate

Financial

Covenants

General

Covenants

Loan

Size Secured Log(Maturity)

GC Opinion 87.24*** -0.01 0.81* -0.07* 2.08** -0.25**

(2.72) (-0.04) (1.78) (-1.90) (2.51) (-2.02)

Institutional Investor 110.86** 0.17 -0.04 0.07* -0.03

(2.15) (0.73) (-0.10) (1.69) (-0.25)

Revolver -69.81*** -0.00 -0.49* 0.07** -0.32***

(-2.99) (-0.01) (-1.86) (1.96) (-3.42)

Loan Size -22.83** 0.14 0.61*** -0.31* 0.05

(-2.07) (1.63) (3.54) (-1.78) (1.16)

Financial Covenants -4.59 0.02* 0.20 0.12***

(-0.47) (1.77) (1.40) (3.21)

Maturity -1.07* 0.02*** 0.02** 0.00 0.02**

(-1.83) (3.07) (2.14) (0.74) (2.33)

Number of Lenders 1.19 0.01** 0.01 -0.00 -0.00 -0.02***

(1.14) (2.08) (1.01) (-1.05) (-0.43) (-4.93)

PP Indicator -22.78 0.00 0.16 0.11** -0.75* 0.29***

(-1.48) (0.00) (0.33) (2.30) (-1.75) (2.96)

Secured 103.13** 0.16 3.79*** 0.10 0.16

(2.18) (0.44) (3.21) (1.41) (0.63)

Interest rate -0.00 0.00 -0.00 -0.00**

(-0.51) (1.02) (-0.07) (-1.98)

Firm Size -13.14* -0.18** 0.12 -0.28 0.05

(-1.70) (-1.96) (0.58) (-1.61) (0.94)

Market-to-book -2.37 -0.12 -0.45 0.05 -0.30 -0.02

(-0.12) (-0.50) (-1.17) (1.04) (-0.68) (-0.21)

51

Leverage 83.09*** -0.21 1.46** -0.05 1.88* -0.21

(3.07) (-0.59) (2.38) (-0.82) (1.88) (-1.13)

Profitability -84.94 1.83 4.08* 0.02 -2.08 0.61

(-0.94) (1.41) (1.89) (0.07) (-1.06) (0.97)

Cash flow volatility -680.59** -2.42 -5.85 1.65* -4.46 -0.29

(-2.37) (-0.67) (-0.97) (1.80) (-1.09) (-0.17)

Tangibility 24.11 -0.01 -1.30 0.14* -0.40 0.04

(0.39) (-0.02) (-1.60) (1.75) (-0.50) (0.17)

Z-score -1.21 -0.10 -0.04 -0.01 -0.05 0.05

(-0.18) (-1.10) (-0.26) (-0.51) (-0.31) (1.18)

Abnormal_Accruals 1.16 -0.53 -0.03 0.13 -5.74*** 0.35

(0.01) (-0.57) (-0.01) (1.03) (-3.17) (0.74)

Credit spread 4.26 -0.45 -0.32 0.11 1.80* 0.03

(0.08) (-1.09) (-0.42) (0.83) (1.88) (0.12)

Term spread 17.62 0.51 0.03 0.04 0.01 0.21

(0.53) (1.60) (0.05) (0.55) (0.02) (1.30)

Intercept 764.13*** -2.07 -14.86*** -0.18 7.46*** 2.56***

(4.41) (-1.39) (-5.18) (-1.11) (2.99) (3.35)

Loan Purpose FE Included Included Included Included Excluded Included

Year FE Included Included Included Included Excluded Included

Observations 420 420 420 420 420 420

Adj. R2/Pseudo R

2 0.391 0.299 0.498 0.279 0.511 0.379

Panel C: Extended PSM Regression Result

(1) (2) (3) (4) (5) (6)

Dependent Variable = Interest

Rate

Financial

Covenants

General

Covenants

Loan

Size Secured Log(Maturity)

GC Opinion 107.18*** 0.13 1.05*** -0.06* 2.31*** -0.22*

(4.24) (0.49) (2.61) (-1.72) (3.93) (-1.75) Institutional Investor 125.63*** 0.43** -0.35 0.09** -0.10

(2.68) (2.14) (-0.78) (2.13) (-0.78)

Revolver -54.22*** -0.14 -0.23 0.08*** -0.27***

(-2.60) (-0.84) (-0.96) (2.64) (-3.22)

Loan Size -29.36*** -0.01 0.70*** 0.11 0.08

(-3.35) (-0.07) (4.32) (0.42) (1.61)

Financial Covenants -7.75 -0.01 0.52*** 0.13***

(-0.91) (-0.72) (3.51) (3.84)

Maturity -0.89 0.02*** 0.03*** 0.00 0.03***

(-1.38) (3.34) (2.78) (0.73) (3.62)

Number of Lenders 0.68 0.02** 0.02 -0.00 -0.00 -0.02***

(0.74) (2.00) (1.49) (-0.33) (-0.34) (-5.99)

PP Indicator -35.71** 0.33 0.29 0.09** -2.31*** 0.34***

(-2.41) (1.38) (0.63) (2.21) (-2.82) (3.65)

Secured 121.14* 0.53 3.83*** 0.06 0.35

(1.67) (1.33) (3.28) (0.78) (1.63)

Interest rate -0.00 0.00 -0.00 -0.00*

(-0.99) (1.10) (-0.98) (-1.71)

52

Firm Size -1.50 -0.06 0.07 -0.68*** 0.04

(-0.22) (-0.54) (0.37) (-2.63) (0.94)

Market-to-book 2.80 -0.29 -0.73** 0.03 -0.13 0.01

(0.15) (-1.23) (-2.05) (0.57) (-0.25) (0.15)

Leverage 110.57*** -0.05 0.85 -0.03 2.23** -0.12

(4.53) (-0.12) (1.46) (-0.63) (2.56) (-0.63)

Profitability 14.19 2.92** 6.70*** 0.02 -3.79 -0.06

(0.13) (2.17) (3.46) (0.06) (-0.96) (-0.11)

Cash flow volatility -604.52** -2.30 0.94 1.46** -0.56 -0.31

(-2.10) (-0.64) (0.17) (2.21) (-0.11) (-0.21)

Tangibility -18.59 -0.58 -0.29 0.08 3.60*** -0.13

(-0.39) (-1.14) (-0.41) (0.98) (2.60) (-0.67)

Z-score -0.86 -0.14 -0.30*** -0.01 0.26 0.08**

(-0.11) (-1.56) (-2.67) (-0.65) (1.21) (2.03)

Abnormal_Accruals 9.74 0.47 -2.13 0.23* -6.39*** 0.17

(0.09) (0.52) (-1.06) (1.78) (-3.16) (0.37)

Credit spread 3.80 -0.70 0.03 0.20 2.03** 0.02

(0.08) (-1.29) (0.04) (1.34) (2.31) (0.10)

Term spread 20.68 0.53 -0.57 0.07 -0.20 0.24

(0.47) (1.44) (-0.79) (1.10) (-0.77) (1.33)

Intercept 749.05*** -0.34 -16.13*** -0.19 0.59 1.90***

(4.61) (-0.21) (-5.80) (-1.25) (0.18) (2.65)

Loan Purpose FE Included Included Included Included Excluded Included

Year FE Included Included Included Included Excluded Included

Observations 422 422 422 422 422 422

Adj. R2/Pseudo R

2 0.406 0.358 0.530 0.312 0.462 0.420

Table 8 presents the effectiveness of matching process and the regression results. We implement propensity-

score-matching approach by first estimating a probit regression to model the probability of receiving a going

concern opinion at firm level. We include firm level variables used in our main regression in the basic PSM and

include more firm-level control variables in the extended PSM following DeFond et al. (2002). We include

industry and year fixed effects in both regressions and the results are shown in appendix B. We then estimate the

propensity score for each firm using the predicted probabilities from the probit model. Given the minimal

overlap between GC group and non-MAO group, we use the nearest neighbor method and match with

replacement. Panel A shows the efficiency of the matching process. Panel B and C show the results of

regressions for basic PSM sample and extended PSM sample separately.

The names of the columns show the dependent variables. All variables are defined in the appendix A. Firm-

specific financial variables are winsorized at the 0.01 level. Regressions include loan purpose and year fixed

effects (except the secured regression) and standard errors are heteroskedasticity robust and clustered at both the

firm and year level. z-statistics are reported in parentheses. ∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%, and

10% levels, respectively.