THE ROLE OF ACCOUNTING IN DEBT CONTRACT RENEGOTIATIONS:
EVIDENCE FROM POSITIVE SHOCKS
by
MASSACHUSETTS INSTITUTE
OF TECHNOLOLGY
Kexin Zheng
MAR 112015
BA, Nankai University, 2004
MBA, Boston College, 2009
LIBRARIES
SUBMITTED TO THE SLOAN SCHOOL OF MANAGEMENT IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY IN MANAGEMENT
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
SEPTEMBER 2014
0 2014 Massachusetts Institute of Technology. All rights reserved
Signature redacted
Signature of Author
MIT Sloan School of Management
August 8, 2014
Signature redacted
Certified by
Joseph Weber
George Maverick Bunker Professor of Management
Professor of Accounting
Thesis Supervisor
Signature redacted
Accepted by
Ezra Zuckerman
Nanyang Technological University Professor
Chair, MIT Sloan PhD Program
I
2
THE ROLE OF ACCOUNTING IN DEBT CONTRACT RENEGOTIATIONS:
EVIDENCE FROM POSITIVE SHOCKS
by
Kexin Zheng
Submitted to the Sloan School of Management
on August 8, 2014 in Partial Fulfillment of the Requirements
for the Degree of Doctor of Philosophy in Management
ABSTRACT
Using a hand-collected sample of private debt contracts between U.S. publicly traded firms and
financial institutions, I examine the role of accounting in the renegotiation of debt contracts
following a positive shock to the borrower's credit quality. I find that, following a positive shock
to their credit quality, firms with more timely reporting of good news are more likely to
renegotiate their loan contracts and they do so sooner than firms with less timely good news
reporting. Further, these effects are more pronounced for firms whose positive shocks can be
more credibly communicated through financial reporting. My paper contributes to the literature
on the role of accounting information in debt contract renegotiations.
Thesis Supervisor: Joseph Weber
Title: George Maverick Bunker Professor of Management, Professor of Accounting
3
Acknowledgements
The journey to this thesis represents an important chapter of my life. During the five years I have
spent at MIT, I have been very fortunate to have received the help and support of numerous
people. First and foremost, I would like to thank the members of my dissertation committee: S.P.
Kothari, Rodrigo Verdi, and Joseph Weber (Chair) for their guidance, feedback, and suggestions
on this paper. I appreciate the amount of time that they have spent in guiding me through the
PhD coursework, thesis, and job market. Their support has been invaluable.
I am also grateful to the other current and former faculty members of the Accounting Group at
the MIT Sloan School of Management: John Core, Anna Costello, Joao Granja, Michelle
Hanlon, Scott Keating, Mozaffar Khan, Jeffery Ng, Christopher Noe, Reining Petacchi, Sugata
Roychowdhury, Nemit Shroff, Ewa Sletten, Eric So, and Ross Watts, as well as my fellow
Accounting PhD students and friends: Brian Akins, Josh Anderson, Nick Guest, Derek Johnson,
Zawadi Lemayian, Rebecca Lester, Lynn Lei Li, Mihir Mehta, Patricia L. Naranjo, Heidi
Packard, Daniel Saavedra, Benjamin Yost, and Luo Zuo. I must also thank Nancy Leonelli and
Hillary Ross for their kindness and assistance to me.
I received many valuable comments from the workshop participants at the FDIC Center for
Financial Research, University of Connecticut, University of Minnesota, University of Toronto,
and Washington University in St. Louis. I am grateful that the professors and economists at these
institutes thought enough of me to give me the opportunity to interview to be a potential
colleague. My paper has also benefitted from my interaction with fellow graduate students and
friends: Winston Wei Dou, Song Lin, Indrajit Mitra, Hong Ru, Yu Xu, and Yang Sun. I thank
my cousin-in-law, Jia Li at Duke University, for our helpful discussions on econometrics.
I could not have made it to MIT without the support of and guidance from Professors Peter
Wilson and Mary Ellen Carter at Boston College. They have always believed in me and
encouraged me to realize my potential. I am also deeply grateful to all my friends both in the
U.S. and back in China (too many to mention by name), who have consistently supported me
and given me a great deal of joy. Particularly, I want to thank Dongwei Hao, Lynn Lei Li, Qian
Liu, Yang Sun, Miao Yan, and Qiujie Zheng. Their friendship, compassion, and support have
helped me through many challenges. I feel truly blessed to have found them to share my
passions and outlook in life.
Most importantly, I wish to thank my family. I owe a debt of gratitude to my parents Xiaoguang
Wang and Li Zheng, for their unconditional love and many sacrifices in raising me to believe in
myself, to endeavor, and to persevere. I thank my in-laws for lending a hand whenever asked. I
thank my son, Rubin Xiao, who has brightened every aspect of my life since he came into the
world. He has been a source of endless joy and comfort for me, and for our whole family. My
last acknowledgement goes to my beloved husband, Bin Xiao, who has supported me in more
ways than I can count, and kept my life balanced by showing me the beauty of the world.
Beyond all doubt, his love and optimism have helped make this moment unforgettable.
I dedicate this thesis to my family.
4
Table of Contents
1. In tro d uctio n .................................................................................................................................
7
2. Background Literature and Hypothesis Development...........................................................
12
2.1 Renegotiations after Positive Shocks...............................................................................
12
2.2 Timely Reporting of Good News and Renegotiations after Positive Shocks ................... 13
2.3 Credibility of Accounting Information ............................................................................
15
3. Sample and Research Design .................................................................................................
18
3.1 Data and Sample Selection ...............................................................................................
18
3.2 M easure of Renegotiation.................................................................................................19
3.3 M easure of Timely Reporting of Good News..................................................................
21
3.4 Test of Likelihood of Renegotiations ..............................................................................
22
3.5 Test of Time to Renegotiate.............................................................................................
24
3.6 Cross-Sectional Tests on the Credibility of Accounting Information..............................
25
4. Empirical Results.......................................................................................................................27
4.1 Descriptive Statistics............................................................................................................27
4.2 Likelihood and Timeliness of Renegotiations after Positive Shocks...............................
28
4.3 Cross-Sectional Test Results...........................................................................................
29
4.4 Additional Tests ...................................................................................................................
30
4.4.1 Economic Impact of Timely Reporting of Good News on Renegotiations after Positive
30
S h o ck s ....................................................................................................................................
4.4.2 Implications for Accounting Conservatism................................................................33
4.4.3 Nature of Good News Received ...............................................................................
34
4.4.4 Alternative Measure of Timely Reporting of Good News .........................................
35
4.4.5 Timely Reporting of Good News Interacted with the Size of Positive Shock..........36
4.4.6 Firm Loans without Earnings-Based Performance Pricing Provision............36
4.4.7 Switch of Lenders......................................................................................................
37
4.4.8 Control for Changes in Firm Characteristics.................................................................37
5 . C on clu sion .................................................................................................................................
37
Referen ces......................................................................................................................................3
9
Appendix I: Variable Definitions ...............................................................................................
45
Appendix II: Example of Amendment Report...........................................................................
49
5
List of Tables
T able 1: Sam ple Selection ........................................................................................................
53
Table 2: D escriptive Statistics ...................................................................................................
54
Table 3: C orrelation M atrix ......................................................................................................
56
Table 4: The Likelihood and Timeliness of Renegotiations after Positive Shocks...................57
T ab le 5 ...........................................................................................................................................
58
Panel A: Cross-Sectional Tests on the Likelihood of Renegotiations ...................................
58
Panel B: Cross-Sectional Tests on the Timeliness of Renegotiations ...................................
59
T able 6: E conomic Impact.............................................................................................................6
1
Table 7: Likelihood and Timeliness of Renegotiations on Conservatism.................................62
Table 8: Nature of Good News Received .................................................................................
63
Table 9: Alternative Measure of Timely Reporting of Good News ..........................................
64
Table 10: Interact Timely Reporting of Good News with the Size of Positive Shock..............65
Table 11: Firm Loans Without Earnings-Based Performance Pricing Provision..................... 66
Table 12: Refinancing from Outside Lenders ..........................................................................
Table 13: Control for Changes in Firm Characteristics.............................................................68
List of Figures
Figure 1: Timeline of the Measurement Period for the Variables.............................................52
6
67
1. Introduction
Renegotiations play a critical role in corporate financial contracting (Aghion and Bolton
1992; Bolton and Scharfstein 1996; Hart and Moore 1998; Roberts and Sufi 2009a). While the
majority of the previous literature focuses on renegotiations in the context of corporate default,
recent empirical studies show that debt contracts are frequently renegotiated even when a default
has not occurred (Roberts and Sufi 2009b; Roberts 2012). In fact, numerous renegotiations are
voluntarily initiated by borrowers, especially when the borrower's credit quality improves. But
while recent research has provided evidence of the pervasiveness of such renegotiations, the role
of accounting in such a setting is not well understood. In my paper, I examine whether and how
the timely reporting of good news influences contract renegotiations following a positive shock
to the borrower's credit quality (hereafter, 'renegotiations after positive shocks').
I first hypothesize that, following a positive shock to their credit quality, firms with more
timely reporting of good news are more likely to renegotiate their loan contracts and they do so
sooner than firms with less timely good news reporting. This hypothesis is motivated by the
incomplete contracting literature. When a positive shock to credit quality occurs after the
origination of loan contracts, borrowers are motivated to renegotiate any constraining contract
terms that do not reflect the current credit quality (Hart and Moore 1998; Maskin and Moore
1999). However, lenders may not agree to renegotiate with borrowers if the information
concerning the borrower's credit quality is not verifiable (Petersen 2004). In that case, the
borrower's accounting system is expected to influence renegotiations because accounting
information is scrutinized by auditors and regulators, which makes the recognition of the good
news verifiable. Therefore, it can serve as a key source of verifiable information for updating the
lender's prior belief about the borrower's credit quality (Armstrong, Guay, and Weber 2010;
7
Minnis 2011). Further, by disclosing good news in their financial statements in a more timely
manner, borrowers can provide lenders with more timely and verifiable data on improvements in
credit quality. This allows lenders to better re-assess a borrower's credit quality, improving the
probability and the timeliness of renegotiations.
In my second hypothesis, I predict that the effects of the timely reporting of good news
on debt contract renegotiations after positive shocks are more pronounced for firms whose
positive shocks can be more credibly communicated through financial reporting. While firms
can report good news in a timely manner, such news can influence renegotiations only to the
extent that lenders find it credible (Maines and Wahlen 2006; Minnis 2011). To test this
prediction, I build on prior research that shows that reported information is perceived to be more
credible when the firm does not have a history of financial misreporting, proxied by financial
restatements (Graham et al. 2008; Dechow et al. 2010; Kravet and Shevlin 2010); when the
firm's auditor provides more effective audits, proxied by industry specialist (Levitt 2000;
Dechow et al. 2010; Defond and Zhang 2013); and when the improvement in performance is less
transitory (Ramakrishnan and Thomas 1998; Baber et al. 1998; Li 2010). I then hypothesize that
the effects of the timely reporting of good news on renegotiations after positive shocks are more
pronounced when the firm does not have prior financial restatements, when the firm is audited
by an industry specialist, and when the improvement in performance is less transitory.
I test the above hypotheses on a sample of 466 firm loans for the years 1996 through
2005. Sample firms must have experienced a significant positive shock to credit quality in the
two quarters following contract origination. I proxy for such a shock by requiring that a firm
experience a 1% decrease in the probability of bankruptcy, measured by the Black-ScholesMerton option-pricing model (BSM-Prob) (Hillegeist et al. 2004). After obtaining a sample of
8
firms that experienced a positive shock, I hand-collect information on renegotiation by
examining each borrower's SEC filings (10-Ks, 10-Qs and 8-Ks) for the six quarters after the
positive shock.
I use the probit model and the hazard model to test respectively the probability and the
timeliness of renegotiations. In the probit model, the dependent variable is an indicator variable
that takes the value of one if the original contract is amended within the six quarters subsequent
to the positive shock. In the hazard model, the dependent variable is a continuous variable
measuring the time that elapses between a positive shock to credit quality and a later
renegotiation. The mean probability of renegotiation in my sample is 50%, and the mean time
between positive shock and renegotiation is 1.58 quarters.
As my hypothesis relates to the timely reporting of good news, I use the Khan and Watts
(2009) G-Score measure, which captures how quickly good news is reflected in accounting
earnings. The existing debt contracting literature focuses largely on cross-sectional variation in
the degree of total timely loss recognition and in asymmetric timeliness of loss recognition.
However, as Guay and Verrecchia (2006) note, under the assumption that the timely reporting of
information is a key property of accounting reports, the timely reporting of gains should also
play an important role in corporate behavior. This is particularly the case for my study, as I focus
on the timeliness with which firms can credibly recognize a positive shock to credit quality.
Consistent with my first hypothesis, G-Score is positively associated with the probability
of renegotiations after positive shocks and negatively associated with the time to renegotiate.
The results are also economically significant. A one standard deviation increase in G-Score is
associated with a 6.2% increase in the predicted probability of renegotiation compared to 50%
unconditional probability of renegotiation. In addition, a one standard deviation higher G-Score
9
translates into a 2.7% higher renegotiation hazard rate, which is equivalent to 1.06 quarters
shorter time to renegotiate. To put this in perspective, among firms that report a renegotiation
after a positive shock (50% of my sample), 30% of firms receive a reduction in interest rates,
38% of firms receive an increase in loan amount, and 34% of firms extend their loan maturity.
On average, firms can receive a reduction of 86 bps in interest rates (relative to the average
initial interest rate of 265.71 bps), an increase of 53% of original loan amount, or an increase of
92% of original loan maturity during renegotiations. Thus, my results suggest that by increasing
the chance of a renegotiation, firms with a higher G-Score are more likely to obtain better
contract terms.
Consistent with my second hypothesis, the timely reporting of good news plays a more
important role in renegotiations after positive shocks when the firm does not have prior
restatements (my proxy for prior misreporting), when it is audited by industry specialists (my
proxy for auditing quality), and when the reported income is less affected by one-time items (my
proxy for transitory earnings). In terms of economic significance, a one standard deviation
increase in G-Score is associated with a 7.6% increase in the predicted probability of
renegotiation (Reneg) when firms do not have prior restatements, versus a 3.5% decrease when
firms have restated their financial statements. Similarly, a one standard deviation increase in GScore is associated with a 7.7% increase in Reneg for firms that are audited by an industry
specialist, as opposed to a 5.0% increase for firms audited by non-specialists. Lastly, a one
standard deviation increase in G-Score is associated with an 8.3% increase in Reneg when the
proportion of one-time items is below the sample median, in contrast to a 3.7% increase when it
is above the sample median.
10
My paper makes two main contributions. First, it enhances our understanding of the role
of accounting information in incomplete contracting. Incomplete contract theory posits that
contract renegotiation is often triggered by the arrival of new information. While the majority of
the accounting literature has focused on renegotiation triggered by negative news such as
covenant violations (Healy and Palepu 1990; Sweeney 1994; DeFond and Jiambalvo 1994;
Dichev and Skinner 2002; Zhang 2008; Tan 2013), there is little evidence on the role of
accounting information in renegotiations voluntarily initiated by borrowers when their credit
quality improves.' My paper fills this gap, suggesting that accounting information, proxied by
the timely reporting of good news, plays an important role in such a setting.
Second, by providing evidence on the role of accounting in ex post contract
renegotiations after positive shocks, my paper also provides a rationale for prior findings on the
ex ante benefits of accounting conservatism (i.e., conservative firms receive better contract terms
at the time of the loan initiation) (Zhang 2008; Wittenberg-Moerman 2008). Specifically, I find
that firms that accelerate disclosing good news (i.e., less conservative firms) are more likely to
renegotiate their contracts after a positive shock to credit quality.2 This represents a risk to the
lenders because renegotiations are associated with better contract terms from the borrower's
perspective (e.g., lower interest rates). As a result, lenders anticipate and price protect against
My paper is related to Dou (2013), which investigates the impact of accounting information on the likelihood of
all renegotiations. However, since Dou (2013) does not distinguish between renegotiations after positive shocks and
those after negative shocks, it focuses on other attributes of accounting, such as the debt contracting value of
accounting information. My paper, in contrast, focuses on renegotiations after positive shocks and on the role of the
timely reporting of good news, which is (arguably) more suitable for this setting.
2 Technically speaking, the timely reporting of good news is not necessarily inversely related to conservatism. For
example, the standard estimation of conservatism (i.e., asymmetric timeliness as in Basu 1997) is measured
incrementally to good news. So it is possible that firms could increase their reporting timeliness of good news
without affecting their asymmetric timeliness. Empirically and in my study, however, these constructs are
negatively correlated, so the results related to the timely reporting of good news are likely to have implications for
accounting conservatism. In fact, in robustness tests, I show that for more conservative firms (measured as
asymmetric timeliness), renegotiations after positive shocks are both less likely and less timely.
11
this risk by charging higher interest rates for less conservative firms at the time of the loan
initiation.
The rest of the paper proceeds as follows. Section 2 reviews the literature and develops
my hypotheses. In Section 3, 1 introduce the sample and research design, and Section 4 presents
empirical evidence. Section 5 summarizes my findings.
2. Background Literature and Hypothesis Development
2.1 Renegotiations after Positive Shocks
Renegotiation occurs when parties to a contract are unable to commit to the initial terms
of their agreement after the relationship has been initiated. The theory of incomplete contracts
posits that renegotiation is driven mainly by exogenous uncertainty.3 When unanticipated (i.e.,
non-contractible) states of the world occur, the initial terms of the contract are no longer optimal,
and renegotiations will be required (Hart and Moore 1998; Maskin and Moore 1999; Gorton and
Kahn 2000; Garleanu and Zwiebel 2009).4 While early studies focus on renegotiations in
financial distress or corporate default such as covenant violations, Roberts and Sufi (2009b)
study all renegotiations.5 They find that 90% of long-term loan contracts are renegotiated before
maturity, and fewer than 18% of renegotiations are directly or indirectly associated with a
covenant violation or payment default. Consistent with the theoretical model, their analysis of
what triggers renegotiation indicates that new information concerning credit quality is a strong
3 When drawing up a contract, it is not viable to specify all the relevant contingencies. Hence, the contract will often
be incomplete.
4 In addition to exogenous uncertainty, the original contract design plays an important role in renegotiation (Aghion
et al. 1994). Empirically, therefore, I control for the original contract tenns.
5 Prior literature on renegotiations triggered by corporate default studies the outcome and implications of covenant
violations. For example, creditors could either waive the covenant violations or demand certain conditions such as
an increase in the interest rate, a reduction in the borrowing base, or a shift of control rights to the creditors (Beneish
and Press 1993; Chen and Wei 1993; Smith 1993; Chava and Roberts 2008; Nini et al. 2009).
12
predictor of the incidence and outcomes of renegotiation. In particular, numerous renegotiations
are voluntarily initiated by borrowers, especially when the borrower's credit quality improves.
Extending the work of Roberts and Sufi (2009b), which focuses on the probability of
renegotiation, Roberts (2012) and Nikolaev (2013) further investigate the dynamics of
renegotiation, i.e., the timing (scope) of renegotiation.
While recent finance research has provided evidence of renegotiation outside of default,
the extant accounting literature focuses on the role of accounting in the context of covenant
violations. For example, several studies provide evidence that managers make accounting
choices or take real actions to avoid covenant violations (Healy and Palepu 1990; Defond and
Jiambalvo 1994; Sweeny 1994; Dichev and Skinner 2002; Daniel et al. 2008). In addition,
Zhang (2008) shows that accounting conservatism benefits lenders by accelerating covenant
violations, and Tan (2013) finds that firms' financial reporting becomes more conservative
immediately after covenant violations. My paper contributes to the accounting literature by
examining the role of accounting information in renegotiations after positive shocks.
2.2 Timely Reporting of Good News and Renegotiations after Positive Shocks
While previous literature emphasizes that lenders bear downside risk but no upside
potential, improvement in the borrower's credit quality is not inconsequential to them. With a
credible threat to leave current lenders in favor of alternative sources of financing, borrowers can
voluntarily initiate renegotiations with lenders when their credit quality improves (Hart and
Moore 1998; Gorton and Kahn 2000; Garleanu and Zwiebel 2009; Roberts and Sufi 2009b). 6 In
other words, a positive shock to credit quality can shift bargaining power in the borrower's
6 In practice, many lenders refer to borrowers' wishing to "go outside the deal" as a motivation for an amendment.
Roberts and Sufi (2009b) suggest that competition among lenders and the existence of alternative sources of
financing, such as external equity, increase borrowers' bargaining power by increasing the credibility of any threat
to switch lenders.
13
favor, so the borrower may be able to renegotiate any constraining contract terms that do not
reflect the firm's current credit quality (e.g., reduce interest rates, increase loan amounts, extend
loan maturity, and loosen the restrictiveness of loan covenants). While new information
concerning the borrower's credit quality is an important determinant of renegotiations, lenders
may not agree to renegotiate with borrowers if this information is not verifiable. Petersen (2004)
suggests that decision makers place more weight on verifiable information (i.e., "hard"
information) because it is more objective and precise; in contrast, "soft" information is
subjective and usually cannot be verified at low cost, which makes it less reliable.
The borrower's accounting system is expected to significantly influence renegotiations
because accounting information can serve as a key source of verifiable information to update a
lender's prior belief regarding a borrower's credit quality (Armstrong, Guay, and Weber 2010;
Minnis 201 1).7 The reason is that accounting information is scrutinized by auditors and
regulators, which makes the recognition of the good news verifiable.
Further, by disclosing
good news in their financial statements in a more timely manner, borrowers can provide lenders
with more timely and verifiable data on improvements in credit quality. This allows lenders to
better re-assess the credit quality of such firms in a timely manner, which improves the
probability and timeliness of renegotiations (Ball and Shivakumar 2005; Wittenberg-Moerman
2008; Cassar 2011; Kim et al. 2011; Minnis 2011).8 While borrowers that do not report good
news in a timely fashion may voluntarily disclose private information (i.e., soft information)
7 Financial statement verification is considered by regulators and exchanges to be so important that all firms with
publicly available filings are required to have their financial statements audited. The role of an audit is to assure
financial statement users that the statements are compiled and presented according to Generally Accepted
Accounting Principles (GAAP) and faithfully represent the client's underlying economic activities (Ball 2001; Ball,
Jayarman, and Shivakumar 2009). Per FASB's Statement of Financial Accounting Concepts No. 2: "the purpose of
verification is to provide a significant degree of assurance that accounting measures represent what they purport to
represent" (FASB 1980, paragraph 8 1).
8 I focus on the probability that firms renegotiate their loan contracts within a given time window (e.g., six quarters
after positive shocks).
14
about gains to lenders, such voluntary disclosures are usually more costly to verify, leading to
higher verification cost. Given the same magnitude of positive change in credit quality, higher
verification cost may prevent borrowers to renegotiate their loans with lenders in time.
Therefore, firms with less timely good news reporting may have to wait and renegotiate their
contracts at a later point, when the good news becomes hard information for lenders. This leads
to my first set of hypotheses:
H1(a): Ceteris paribus, following a positive shock to credit quality, firms with more
timely reporting of good news are more likely to renegotiate their loan contracts.
H1(b): Ceteris paribus, following a positive shock to credit quality, firms with more
timely reporting of good news are more timely in renegotiating their loan contracts.
2.3 CredibilityofAccounting Information
My second hypothesis exploits the extent to which a good news shock can be credibly
communicated. While firms can report good news in a timely manner, such news will influence
the contract renegotiation only to the extent that lenders find it credible (Maines and Wahlen
2006; Minnis 2011). In this section, I discuss three characteristics - financial misreporting, audit
quality, and transitory earnings - that may affect the credibility of accounting information and
thereby affect the impact of the timely reporting of good news in renegotiations after positive
shocks.
First, I argue that reported financial information is perceived by lenders to be more
credible when the firm does not have a history of financial misreporting (e.g., restatements of
financial reports). Prior literature has shown that restatements of financial reports are associated
with more earnings quality problems (i.e., both intentional misstatements and unintentional
errors) and affect the credibility of financial reporting (Hribar and Jenkins 2004; Bums and
15
Kedia 2006; Hennes et al. 2010; Dechow et al. 2010; Kravet and Shevlin 2010). Although a
restatement might increase the uncertainty about one particular accounting item, it also causes
lenders to question other aspects of the firm's operations and reported performance, increasing
the overall uncertainty about a company's financial information. As a result, the perceived
information asymmetry between borrowers and lenders increases after restatement (Graham et
al. 2008; Costello and Wittenberg-Moerman 2011).9 The increased information asymmetry
reduces financial statements' perceived credibility and makes any gains promptly reported
therein seem less credible to lenders, leading to fewer renegotiations after positive shocks.
In addition, the effectiveness of an audit in identifying misstatements is also positively
associated with reporting credibility (Levitt 2000; Dechow et al. 2010).10 Prior literature
suggests that industry specialists provide more effective audits than non-specialists do, leading
to enhanced financial statement credibility (Wright and Wright 1997; Balsam et al. 2003;
Krishnan 2003; Dunn and Mayhew 2004)." This is because industry specialists' expertise,
which is derived from serving other clients in the same industry and learning and sharing best
practices across the industry, enables them to more effectively identify misstatements. In
addition, to protect their market shares, specialists also have more of an incentive to correct or
report identified misstatements. Therefore, lenders are more likely to trust the gains specialist
auditors have verified and are thus more likely to agree on the borrower-initiated renegotiations.
Finally, I examine the extent to which the improvement in performance is transitory.
While not directly capturing the credibility of financial information, the performance's transitory
Costello and Wittenberg-Moerman (2011) find that when a firm reports an internal control weakness, lenders
decrease their reliance on financial-ratio-based performance pricing provisions and financial covenants. That is,
lenders place less weight on questionable financial data.
10 As noted by Levitt (2000), investor perceptions of audit quality play a critical role in maintaining systemic
confidence in the integrity of financial reporting. The higher perceived audit quality, the more credible the auditee's
financial statements.
" Big 4 auditors represent another widely used proxy for audit quality. However, I find that since the Big 4 audit
approximately 90% of my sample, there is very little cross-sectional variation in this variable.
9
16
components relate to the extent to which good news can be used for re-contracting purposes.
Specifically, debt contracts are more likely to use core earnings (e.g., EBITDA) which are
reported earnings minus transitory earnings (Li 2010; Kothari et al. 2010; Demerjian 2011).
One important reason is that core earnings are more credible for measuring firm performance
and less susceptible to manipulation (Leftwich 1983; Watts and Zimmerman 1986; Ohlson 1999;
Collins et al. 1997; McVay 2006).13 Thus, to the extent that good news is transitory (e.g., a gain
from asset sales), while it will affect earnings in a timely manner, it might be excluded for recontracting purposes, leading to a lower probability of renegotiations. Therefore, all else equal,
the higher proportion of core earnings, the more likely it is that the gains promptly recognized in
financial statements will trigger renegotiations.
Taken together, this argument motivates the following hypotheses:' 4
H2(a): Ceteris paribus, the effects of the timely reporting of good news on
renegotiations after positive shocks are more pronounced when the firm does not
have prior financial restatements.
H2(b): Ceteris paribus, the effects of the timely reporting of good news on
renegotiations after positive shocks are more pronounced when the firm is audited
by an industry specialist.
1 Transitory earnings measure certain earnings components that are generally viewed as non-recurring in standard
financial statement analysis (e.g., extraordinary items and income from asset sales, asset revaluation, discontinued
operations, accounting changes); and more than half of debt contracts exclude transitory earnings when defining net
income (Li 2010).
1 Transitory earnings are less informative for forecasting firms' future performance and are usually used as an
income smoothing device (Collins et al. 1997). In addition, Leftwich argues that GAAP numbers are adjusted (e.g.,
transitory earnings are excluded) to reduce managers' ability to circumvent covenant violation through accounting
manipulation.
" When financial reporting is less credible, firms with more timely reporting of good news may not trigger
renegotiations, leading to longer time elapsed until a renegotiation occurs (i.e., infinite time). Therefore, the second
hypothesis applies to both the likelihood and the timeliness of renegotiations.
17
H2(c): Ceteris paribus, the effects of the timely reporting of good news on
renegotiations after positive shocks are more pronounced when the improvement in
performance is less transitory.
3. Sample and Research Design
3.1 Data and Sample Selection
Table 1 summarizes the sample selection process. I begin with a sample of loan deals
from Reuters Loan Pricing Corporations' DealScan database that are matched with firms'
financial characteristics from Standard and Poor's Compustat database.' 5 The sample is
restricted to deals initiated during the years 1996 - 2005. My starting year corresponds to the
initial SEC-required electronic filing year; prior to 1996, electronic filings are only sparsely
available on EDGAR.
16
This sample period is also consistent with that in Roberts and Sufi
(2009b).
As previously mentioned, I require the sample firms experience a significant positive
shock to credit quality two quarters subsequent to the origination of loan contracts. The credit
quality change is calculated as the change in the probability of bankruptcy (i.e., BSM-Prob),
measured by the Black-Scholes-Merton option-pricing model (Hillegeist et al. 2004).7 The
rationale for this measure is that the shock to credit quality is calculated by a market model, and
The extracted loan deals consist of both original credit agreements and the agreements' major amendments
and
restatements. I perceive an amended/restated loan agreement as a new loan. This method is consistent with the
DealScan data collection principle.
16 The sample selection procedure balances the need for a sufficiently large sample
to yield reasonable power (and
to ensure that the results are somewhat generalizable) against the costs in time and effort to manually obtain
renegotiation information. Due to time constraints, the sample period is temporarily restricted to deals initiated
during the years 1996 to 2005. This sample period will be extended in future work.
17 Hillegeist et al. (2004) estimate a market-based measure of the probability of bankruptcy based on the BlackScholes-Merton option-pricing model, BSM-Prob. They show that BSM-Prob provides considerably more
information than either of the two popular accounting-based measures, Altman's (1986) Z-Score and Ohlson's
(1980) O-Score. They estimate the default distance at the finn-year level. I compute this measure at the firm-quarter
level by replacing their yearly variables with quarterly variables (i.e., equity volatility, the risk-free rate, market
value, long-term debt, and dividend yield). I choose a two-quarter window to measure the credit quality
improvement because BSM-Prob does not change significantly within a shorter window.
15
18
I
assess whether accounting systems can disclose such good news in financial statements in a
timely manner. I assume that lenders do not base contracts exclusively on market-based
measures and argue that financial reporting provides incremental, verifiable information to
lenders (Sloan 1996; Ball et al. 2008).18 1 include only firm deals that have experienced at least a
1% decrease in BSM-Prob, yielding a sample of 1,195 firm deals. 19
I require that each deal have information about the interest spread and the loan amount of
all tranches in the deal. Deals must also have a maturity greater than 12 months, allowing
enough time for firms to renegotiate their loans before maturity. From Compustat, I construct
financial statistics (Appendix I) as the average over the four quarters prior to the signing of a
loan agreement. I include only deals for which these borrower-level variables are present. In
addition, data must be available to calculate the measure of the timely reporting of good news.
These requirements yield the final sample of 466 firm loans.
To obtain the restatement data, I use the GAO Financial Statement Restatement Database
(e.g., Desai et al. 2006). This database was constructed using a Lexis-Nexis text search based on
variations of the word "restate" and contains approximately 2,309 restatements between January
1997 and December 2007. Therefore, my sample period for cross-sectional tests on restatements
is from 1998, leading to a sample of 409 firm loans.
3.2 Measure of Renegotiation
After obtaining a sample of firms that experienced a positive shock, I then hand-collect
the information on renegotiation by examining the SEC filings (10-Ks, 10-Qs, and 8-Ks) of each
18 By collecting and summarizing the financial effects of firms' investment, operating, and financing activities,
accounting systems convey detailed information about the underlying sources of changes in credit quality.
'9 The average probability of bankruptcy (BSM-Prob) of all compustat firms is 5%. Similar to the findings in
Hillegeist et al. (2004), 1 find that approximately 60% of firm deals did not experience any change in the BSM-Prob
(i.e., firms continue to have a zero probability of bankruptcy) two quarters after loan origination. Among firms that
experienced a decrease in the probability of bankruptcy, only 15% of firms experienced not less than 1% decrease.
Therefore, this 1% cut-off rate is economically significant, yielding reasonable power.
19
borrower for the six quarters after the positive shock. 2()A six-quarter window comprises a period
sufficient for testing the timeliness of renegotiation as accounting systems that report good news
'
less promptly may renegotiate at a later point when the good news becomes verifiable.2
To investigate the probability of renegotiation, I identify the ending date of the positive
shock to credit quality (hereafter, 4the end of the positive shock'). The indicator variable Reneg
takes the value of one if the original contract is amended between the end of the positive shock
and the maturity date or between the end of the positive shock and the end of the sample period
(i.e., six quarters after the shock), whichever is shorter, and zero otherwise. In case the credit
quality changes again after the positive shock, I also calculate the BSM-Prob at the quarter of
renegotiation and delete those renegotiations that are not related to positive shocks in credit
quality.
22
Further, to analyze the time to renegotiate, I identify the date of the first renegotiation
after the positive shock. If the borrower reports a renegotiation, I define the dependent variable
TimetoReneg as the number of quarters between the end of the positive shock and the
renegotiation.2 3 If the firm does not report a renegotiation within the search period, I define
TimetoReneg as the number of quarters between the end of the positive shock and the maturity
date or between the end of the positive shock and the end of the sample period (i.e., six quarters
after the shock), whichever is shorter.2 4
The SEC requires disclosure of any material changes to the debt agreements in the exhibits of company filings.
By following the explanations of debt agreements through time. I can detect whether the original loan contracts are
amended.
21 Results are robust if I use a shorter window of four quarters or a longer window of eight quarters.
22 Specifically, if the reduction in BSM-Prob by the quarter of renegotiation drops
more than half of the reduction in
BSM-Prob within the first two quarters, then I do not consider it as renegotiation triggered by a positive shock in
credit quality. My data shows that only 26 out of 234 renegotiations are this case, not affecting my results.
2' Time to Reneg is measured in quarters to be consistent with the quarterly measure
of positive shock. However,
the results are robust to monthly measure.
2 Nikolaev (2013) was among the first to explore the time to renegotiation, but his setting focuses on contract
renegotiations that require lender majority consent, as opposed to unanimous consent. However, renegotiations that
2
20
3.3 Measure of Timely Reporting of Good News
I use the Khan and Watts (2009) G-Score to measure the timely reporting of good news.
Khan and Watts (2009) develop their firm-year measure of good news timeliness based on the
Basu (1997) cross-sectional model:
X =,81 + /q2Di + /33R+ P4D1R + ej,
(1)
where X is earnings, R is returns (measuring news), and D is a dummy variable equal to I when
R<0 (bad news), zero otherwise. The good news timeliness is represented by 83, and the
incremental timeliness for bad news over good news is represented by #4.
To estimate the
timeliness of good news at the firm-year level, they further define G-Score as a linear function of
firm-specific characteristics (firm size, market-to-book ratio, and leverage) each year:
G-Score, =3= pi + u2Sizei + p3 M/Bi + p 4Levi.
(2)
Substituting G-Score into equation (1), an annual cross-sectional regression model is
derived to estimate the parameters (tI-P4) in equation (2).
X =1
+/8 2D, + Ri(pi + p2Sizei + p 3M/Bi
+(6jSizei + 6 2M/B,
+ 63 Levi
+
P 4Levi) + DiRi(
+
A2Size, + )M/Bi + X4Lev)
+ 6 4DiSizei + 5DiM/Bi + 6 6DLevi) + Ei.
(3)
The estimated parameters (u-p4) can be used to calculate the G-Score in equation (3).
G-Scorei shows how good news is reflected in net income, where a larger coefficient
indicates that earnings exhibit a greater response for a given amount of good news in returns.
require unanimous consent are more likely to be due to significant events (e.g., a sufficient change in credit quality),
which are important to my study. In general, Nikolaev (2013) provides evidence on the link between time to
renegotiation and contracting frictions (as characterized by agency conflicts and information problems).
25 The discretion in accounting earnings that relates to the timeliness of good news recognition has not been
discussed sufficiently in previous literature. The earnings recognition could leave managers discretion. For example,
some products are combined with services. When firms already sold this kind of products, firms that report good
news in a more timely manner may choose to recognize both the sale of products and the sale of services, but firms
that report good news in a less timely manner may simply recognize the revenue from the sale of products and
recognize the services revenue later.
21
Figure 1 provides a timeline of the measures of the main variables. As shown in Figure 1,
I measure the timeliness of good news reporting G-Score at the year-end before the loan
origination. 26 This method assumes that borrowers can commit to a certain level of reporting
timeliness of good news and that they do not subsequently deviate due to either a lack of
accounting slack or reputation concerns.
3.4 Test of Likelihood of Renegotiations
Hi(a) hypothesizes that following a positive shock to credit quality, firms with more
timely reporting of good news are more likely to renegotiate their loan contracts. I test this
hypothesis by estimating the following probit model:
Renegi =,8o + 1iG-Score +X'1 ( + ei.
(4)
The dependent variable Reneg measures the probability of renegotiations after positive
shocks and G-Score captures how timely borrowers reflect good news in their financial
statements. H 1(a) predicts that P > 0.
In terms of control variables X, I first include the size of a positive shock to credit quality
(ZBSM Prob), measured by the absolute value of the change in BSM-Prob two quarters after the
loan origination. The larger the positive shocks to credit quality, the more likely firms are to
initiate renegotiation with their lenders. Then, drawing from the quarterly data before the loan
origination, I use the following firm-specific characteristics to proxy for the original credit
quality of each firm. The natural log of the total assets (LNASSET) captures the firm's ability to
secure or collateralize its debt, and proxies for the liquidation value in distress. A larger firm
usually has more bargaining power, leading to a higher likelihood of renegotiations after positive
shocks. I use the ratio of debt to EBITDA (DTE) and the ratio of debt to total book assets (LEV)
One important reason for measuring G-Score at the loan origination is to partially alleviate endogeneity concerns.
For robustness, I also measure G-Score at the year-end before the positive shock; the results are robust.
26
22
to proxy for the financial health of each firm. The market-to-book ratio (MTB) measures future
investment opportunities and the ratio of EBITDA to assets (ROA) captures the short-term
liquidity necessary for repayment and firm profitability. Stronger financial health and better
future opportunities can provide firms with more bargaining power, leading to more
renegotiations after positive shocks. In addition, I include the standard deviation of
EBITDA/Assets (STDROA), which captures profitability uncertainty. Finally, I include
Altman's Z-score (ZSCORE) to proxy for credit risk. Previous literature has shown that riskier
firms renegotiate their contracts sooner and more frequently (e.g., Berlin and Mester 1992;
Garleanu and Zwiebel 2009; Nikolaev 2013).
The terms of the initial contract are also included as controls and consist of the average
interest rate spread weighted by the amount of each tranche (INTEREST), the natural log of the
loan amount scaled by firm total assets (AMOUNT),
the natural log of the maturity
(MATURITY), the number of lenders in the lending syndicate (NUMLENDERS), the number of
financial covenants (NUMCOV).
I also include indicator variables for the presence of a
revolving line of credit (REVL V), a borrowing base (BORROWBASE), collateral (SECURED), a
pricing grid (PPP),and a prior lending relationship with lenders (RELINT).
Loan renegotiations can also be triggered by changes in market conditions. Therefore, I
also include two macroeconomic factors. I use both the change in one year treasury yield
(A Treasury Yield) and the change in BB-AAA credit spread on publicly traded bonds
(A Credit Spread) over the two quarters subsequent to loan origination to measure the change in
credit market conditions (Roberts and Sufi 2009b).27 An increase in credit spread indicates a
decrease in demand for corporate debt and higher lender bargaining power in the renegotiation
One year treasury yield data is from Federal Reserve Bank 1-Year Treasury Constant Maturity Rate. Credit
spread data is calculated by subtracting the Bank of America Merrill Lynch US Corporate AAA Effective Yield
from the Bank of American Merrill Lynch US High Yield BB Effective Yield.
27
23
process. Finally, I incorporate the Fama-French 12-industry fixed effects to capture the
institutional structure of syndicated lending and year fixed effects to capture any common trends
in the data. The standard errors are also clustered by firm and year. All continuous variables are
winsorized at the top and bottom 1% levels.
3.5 Test of Time lo Renegotiale
H1(b) also hypothesizes that firms with more timely reporting of good news are more
timely in renegotiating their contracts after a positive credit quality shock. Since the variable of
interest is the time to renegotiate, I use survival analysis which is also called time to event
analysis. As I only search for renegotiations in the six quarters after a positive shock, my data
has right censoring at six quarters. Therefore, I estimate the widely used COX proportional
hazard model (Cox 1972), which corrects for right-censoring problem and yields unbiased
coefficient estimates of the covariates. 28 Recent treatments of survival analysis tend to focus on
the hazard function. The hazard function allows us to approximate the probability of exiting the
initial state within a short interval (i.e., hazard rate), conditional on having survived up to the
starting time of the interval.2 9 Specifically, I test this hypothesis by estimating the following
hazard model:
(5)
In hi(t) = P(t) + 81G-Scorei + X'4 + el.
Wooldrige (2010) specifically explains the survival analysis with right censored data, and shows that hazard
model corrects for it (Section 20.3). For programming, I use the stata command "setset TimetoReneg, failure
(Reneg)" to correct for right-censoring problem.
29 If the data is with discrete time (time measured in large intervals such as months, quarters, years or even decades),
we can get an intuitive idea of the hazard rate. For discrete time the hazard rate is the probability that an individual
will experience an event at time t while that individual is at risk for having an event. Thus, the hazard rate is really
just the unobserved rate at which events occur. If the hazard rate is constant over time and it was equal to 1.5 for
example this would mean that one would expect 1.5 events to occur in a time interval that is one unit long.
Furthermore, if a person had a hazard rate of 1.2 at time t and a second person had a hazard rate of 2.4 at time t then
it would be correct to say that the second person's risk of an event would be two times greater at time t. It is
important to realize that the hazard rate is an un-observed variable yet it controls both the occurrence and the timing
of the events. It is the fundamental dependent variable in survival analysis.
28
24
The term hi(t) represents the instantaneous risk of a renegotiation at time t for borrower i
conditional on i surviving to time t; p(t) is the baseline hazard function. H 1(b) predicts that /3 >
0, i.e., the hazard rate of renegotiation following a positive shock to credit quality increases with
the timeliness of good news reporting.
Compared to the probit regression, the hazard model uses the information in the timing
of the renegotiations rather than just the incidence of the renegotiations, providing more insight
about the interaction between the timely reporting of good news and renegotiations following a
positive shock to credit quality. The control variables are the same as in the probit regression in
Section 3.4.2.
3.6 Cross-Sectional Tests on the Credibility ofAccounting Information
My second hypothesis posits that the effects of the timely reporting of good news on
renegotiations after positive shocks are more pronounced when the good news can be more
credibly communicated. I test this hypothesis using equations (4) and (5), adding interaction
terms.30
Renegi =,8o + PIG-Scorei + P2 Crediblei + /3 3G-Score * Credible + X'i + ci.
(6)
in hi(t) =,8(t) + ,81G-Scorei+ /2 Crediblei + /3G-Score * Credible, + X'1 ( + ci.
(7)
Crediblei are variables that proxy for the credibility of accounting information. First, I
use Restatement to proxy for the financial misreporting. Restatement is an indicator variable that
takes the value of one if the firm reports any restatement prior to renegotiation, zero otherwise. If
the firm does not report a renegotiation within the search period, Restatement equals one if the
Norton et al. (2004) points out and explains why computing the marginal effect of a change in two variables is
more complicated in nonlinear models than in linear models. Given this paper, therefore, I use command inteff and
margins that computes the correct marginal effect of a change in two interacted variables for a logit or probit model,
as well as the correct standard errors.
30
25
firm reported any restatement before the maturity date or the end of the sample period, (i.e., six
quarters after the shock), whichever is shorter, and zero otherwise. H2(a) predicts /3 <0.
To test H2(b), I define industry specialization as an increasing function of market share. I
measure market share using the total sales examined by an audit firm within an industry
(Palmrose, 1986). 3I define an industry as all companies within each two-digit primary Standard
Industry Classification (SIC) code in the Compustat database. Following the prior literature, I
create an indicator variable AudilorSpecialist;it equals one if the client's audit firm examines at
least 20% of sales and the largest proportion of sales in the client's two-digit SIC code industry,
as reported by Compustat, and zero otherwise (Mayhew and Wilkins 2003; Dunn and Mayhew
2004).32 This variable is measured at the fiscal year-end immediately prior to loan origination.
H2(b) predicts
#3 > 0.
Finally, to test H2(c), I use Transitory to proxy for the degree to which accounting
earnings are affected by transitory earnings. Following Collins, Maydew, and Weiss (1997), I
calculate Transitory as the ratio of the value of one-time items to the value of core earnings over
the two quarters subsequent to loan origination, where core earnings are reported earnings minus
one-time items. One-time items refer to discontinued operations, extraordinary items, and special
items. This constructs shows that the higher the ratio of Transitory, the greater proportion of
transitory earnings might be excluded from reported earnings, leading to fewer renegotiations.
Therefore, H2(c) predicts
133
< 0.
Appendix I is the list of variable definitions.
Prior research shows a positive correlation between audit fees and client size measured by
sales or assets
(Simunic, 1980; Palmrose, 1986). Therefore, my definition of specialization assumes that audit firns with large
market shares, and hence industry specific audit fees, have strong incentives to deliver high quality audits and
services.
32 For robustness, I also measure Auditor Specialist in another standard manner. That is, Auditor Specialist takes
the value of one if the client's audit finn audits the largest proportion of the sales as well as the largest number of
firms in the client's two-digit SIC code industry as reported by Compustat, zero otherwise.
31
26
4. Empirical Results
4.1 Descriptive Statistics
Table 2 presents descriptive statistics of all the variables used in both the main and crosssectional tests. Panel A presents the descriptive statistics of full sample, Panel B for subsample
of firms that renegotiate their contracts, and Panel C for subsample of firms that do not. The
mean value of Reneg indicates that approximately 50% of the sample firms renegotiate their loan
contracts with existing lenders after their credit quality improves. 33 Within the sample of firms
that renegotiate their contracts, the mean value of TimetoReneg reveals that on average, a
borrower initiates a renegotiation 1.58 quarters after the positive shock.
The distribution of G-Score is generally consistent with the statistics in Khan and Watts
(2009). The distribution of interaction terms shows that on average, 17% of firms have a history
of restatement before either renegotiation or the end of sample period and 23% of my sample
firms are audited by industry specialists. In addition, the mean value of Transitory reveals that
the sample firms have an average 31% of one-time items as a percent of core earnings. Finally,
Panels D and E present the distribution of deals across years and industries.
Table 3 reports Pearson (above the diagonal) and Spearman (below the diagonal)
correlations of the key variables. The panel reveals a positive correlation between G-Score and
the likelihood of renegotiations after positive shocks, indicating that firms with more timely
reporting of good news are more likely to renegotiate their contracts. The panel also reports a
33 This number is lower than the 90% found in Roberts and Sufi (2009b), mainly because I only explore
renegotiation from the end of the positive shock rather than from loan origination. This helps alleviate the
endogeneity problem, as it is otherwise difficult to distinguish between a credit quality shock causing a
renegotiation and a renegotiation causing a credit quality shock. In addition, Roberts (2012) finds that renegotiations
in the early stage of contracts are mostly related to the initial contract's restrictiveness, while later-stage
renegotiations are largely triggered by the uncertainty of the borrower's credit quality.
27
negative correlation between Reneg and Restatement, indicating that firms with a misreporting
history are less likely to renegotiate their contracts. In addition, the likelihood of renegotiations
after positive shocks is positively correlated with the size of the credit quality shock
(JBSM Prob), as well as with loan characteristics such as initial interest rates (Interest), prior
lending relationship (Relint), the number of financial covenants (Num_Cov), and performance
pricing provision (PPP). As TimetoReneg is inversely correlated with Reneg, the relations
described above are generally the opposite for TimetoReneg.
4.2 Likelihood and Timeliness of Renegotiations after Positive Shocks
Table 4 reports the results of my first set of hypotheses. 34 Consistent with both H 1(a) and
Hi (b), the probit model and hazard model regressions show that G-Score is positively associated
with both the likelihood and timeliness of renegotiations after positive shocks. These results are
also economically significant: a one standard deviation increase in G-Score is associated with a
6.2% increase in the predicted probability of renegotiation and a 2.7% increase in the hazard
rate.
In terms of the control variables, I find the change in credit quality shock (JBSM Prob) and firm
size (LNASSET) have significant positive effects on both the likelihood and timeliness of
renegotiations. This is consistent with my prediction, as both a larger improvement in credit
quality and firms' being a larger size provide borrowers with more bargaining power in the
renegotiation process, leading to more renegotiations. Other firm characteristics do not load in
my regressions, which is consistent with Roberts and Sufi (2009b). In terms of loan
characteristics, I find that loans with higher interest rates and larger deal amounts, as well as
The timely reporting of good news may affect the likelihood of renegotiations through two potential channels.
First, the timely reporting of good news may influence the initial contract design choice. Second, the timely
reporting of good news influences ex post renegotiations. However, my study focuses on the second channel, so I
hold constant the initial design choice by controlling for ex ante loan characteristics in the regression analyses.
1
28
revolving loans, are more likely to be renegotiated. In addition, the coefficients on PPP are
positive and significant in both of the table's regressions, implying that loans with performance
pricing provisions are more likely to be renegotiated. These results are consistent with Roberts
and Sufi (2009b, 181), that demonstrate that "contingencies are designed to shape the
renegotiation game rather than to avoid the renegotiation game." However, they also caveat their
result: the choice of covenant on which to contract is endogenous with respect to a particular
renegotiation outcome. That is, it is possible that a performance pricing provision is included in
contracts that are more likely to be renegotiated ex ante.3 5 In general, this table provides strong
evidence for my main hypotheses.
4.3 Cross-Sectional Test Results
Panel A of Table 5 presents the cross-sectional test results of the probit model regression.
The dependent variable in all regressions is an indicator variable (Reneg) that is equal to one if
the contract is renegotiated between the end of the positive shock and either maturity or the six
quarters after the positive shock, whichever is shorter. Consistent with my second set of
hypotheses, I find that the effects of the timely reporting of good news on the likelihood of
renegotiations after positive shocks are more pronounced for firms that do not have prior
restatements, that are audited by industry specialists, and whose performance improvement is
less transitory. When adding all the interaction terms into the regression , the coefficients on
each interaction terms remain significant. In terms of economic significance, a one standard
deviation increase in G-Score is associated with a 7.6% increase in the predicted probability of
renegotiation (Reneg) when firms have no prior restatements, versus a 3.5% decrease for firms
that restated. In addition, a one standard deviation increase in G-Score is associated with a 7.7%
35
f performance pricing provisions (PPP) are used to avoid renegotiation, I predict renegotiation would have been
even more likely had the PPPnot been incorporated into the contract.
29
increase in Reneg for firms audited by an industry specialist, as opposed to a 5.0% increase for
those with non-specialist auditors. Finally, a one standard deviation increase in G-Score is
associated with an 8.3% increase in Reneg when the proportion of one-time items is below the
sample median, in contrast to a 3.7% increase when it is above. The results of control variables
in the main regression (Table 4) generally still hold in these cross-sectional tests.
Panel B of Table 5 presents the Cox proportional hazard model of the time to renegotiate
(Time to Reneg) for each cross-sectional test. Similar to Panel A, the results about the
interaction terms are consistent with my second set of hypotheses, implying that the effects of
the timely reporting of good news on the timeliness of renegotiations after positive shocks are
more pronounced when the firm does not have a history of financial misreporting, when it is
audited by an industry specialist, and when its improvement in performance is less transitory.
4.4 Additional Tests
4.4.1 Economic Impact of Timely Reporting of Good News on Renegotiations after Positive
Shocks
Up till this point, my paper shows that firms with more timely reporting of good news are
more likely to renegotiate their debt after positive shocks, and are likely to renegotiate their debt
in a more timely manner. It indicates that the timely reporting of good news can benefit
borrowers by increasing the chance of renegotiations. Naturally, it is also interesting to quantify
how much firms can benefit from renegotiating their contracts by reporting good news more
quickly. Specifically, how are contract terms amended during renegotiations after positive
shocks?
To investigate this economic impact of timely reporting of good news on renegotiations
after positive shocks, I collect renegotiation data in two ways. If only a part of the original loan
30
contract is amended, then an amendment report will be issued and disclosed in the exhibit of a
financial report (e.g., 10-K, 10-Q, 8-K). From the amendment report, I can manually collect
information on how different contract terms have been changed. In my sample, there are 165
amendment reports issued among 234 renegotiations. In contrast, borrowers and lenders may
choose to replace the whole original loan contract with a new one (i.e., restated loan contract),
and also attach the restated loan contract in the exhibit of a financial report. 36 In this case, I can
rely on the Dealscan database to collect information on how different contract terms have been
changed. This is because restatements of original loan contracts generate independent
observations in Dealscan, from which I can compare contract terms (e.g., interest rates, loan
amount, and maturity) between the original loan contract and the restated loan contract.
Appendix II shows an example of an amendment report and the corresponding part from
the original contract. In total, four contract terms have been changed in this amendment (i.e.,
Clayton Williams Energy Inc. - Amendment report on July 1, 2000). First, the performance
pricing grid is changed. While the structure of the performance pricing grid is not changed, the
interest rate in each grid has been reduced by 50 bps. This is equivalent to the fact that the
borrower can obtain a 50 bps lower interest rate after the renegotiation. Second, the maturity has
been extended by one year till July 31, 2002. Third, the limitation on the sale of collateral has
been relaxed from $500,000 to $1,000,000. Lastly, the restriction on investment has also been
relaxed so that the borrower can make up to $1,000,000 other investments. From this example,
we can see that the amendment of original contracts can be very diverse, involving changes in
different kinds of contract terms.
Whether issuing an amendment report or a restated loan depends on how material the changes to original loan
contracts are. In addition, renegotiations must be approved by a certain percentage of lenders, depending on the
proposed changes. Amendments usually require a simple majority (typically 51% of the votes). Restated loan
contracts usually involve changes to the rate, term, or collateral which are considered material changes, and
therefore often require a unanimous vote (Standard & Poor's 2006; Roberts and Sufi 2009b; Nikolaev 2013).
36
31
Panel A of Table 6 summarizes the occurrence of change in each contract term in 165
amendment reports. The two most frequent changes to original loan contracts are changes to
loan amount, in 49% of contracts, and changes to financial covenants (e.g., minimum EBITDA,
minimum interest coverage, maximum leverage), in 41% of contracts. In addition, 52% of
amendments charge an amendment fee, which is consistent with the argument that renegotiation
is not costless (Roberts and Sufi 2009b). Lenders need to spend time and effort in understanding
the new condition of borrowers, so they usually charge an amendment fee during renegotiations.
I notice that the percentages of changes to interest rates and changes to maturity are relatively
small, only 17% and 15%, respectively. This is because changes to interest rates and maturity are
typically considered material changes and often require a unanimous vote, so borrowers and
lenders may choose to restate the loan contract if they decide to change them (Roberts and Sufi
2009b; Nikolaev 2013). My data on restated loans show that 65% of them involve changes to
interest rate and 85% involve changes to maturity.
While changes to contract terms during renegotiations can be very diverse, in this paper I
focus on investigating changes to three contract terms during renegotiations - interest rate, loan
amount, and loan maturity. This is mainly because changes to these three contract terms are
generally considered material changes, and they are also easy to quantify. This method is also
consistent with Roberts and Sufi (2009b), which focus on examining renegotiations of these
three contract terms.
Panel B of Table 6 reports the frequency of the occurrence of changes to these three
major contract terms. Among 234 firms that report a renegotiation (either issuing an amendment
report or issuing a restated loan) after positive shocks, 30% of firms receive a reduction in
interest rates, 38% of firms receive an increase in loan amount, and 34% of firms extend their
32
maturity. A few firms receive seemingly unfavorable outcomes (an increase in interest rates, a
decrease in loan amount, or a shortened maturity) during renegotiations. There may be several
reasons. For instance, borrowers want to reduce commitment fees on the unused portion of any
credit line. Therefore, they may be motivated to reduce the loan amount. Banks, however, prefer
firms to maintain the original size and therefore may increase the interest rates to compensate for
this reduction in interest rates. In addition, banks may also charge higher interest rates if
borrowers want to shorten the loan maturity (Roberts and Sufi 2009b). However, these cases are
uncommon, especially increases in interest rates (2%) and shortening of maturity (1%),
indicating that firms usually obtain more favorable outcomes during renegotiations after positive
shocks.
Panel C of Table 6 presents descriptive statistics of changes to these three contract terms.
On average, firms can receive a reduction of 86 bps in interest rates (relative to the average
initial interest rate of 265.71 bps), an increase of 53% of original loan amount, or an increase of
92% in original loan maturity during renegotiations. Thus, my results indicate a large economic
significance of reporting of good news, and suggest that by increasing the chance of a
renegotiation, firms with more timely reporting of good news are likely to obtain considerably
better contract terms.
4.4.2 Implicationsfor Accounting Conservatism
While my paper examines the impact of timely reporting of good news in debt
contracting, it has important implications for accounting conservatism (i.e., the asymmetric
timeliness of bad news recognition). Conceptually, the timely reporting of good news is not
necessarily inversely related to accounting conservatism. In my study, however, the two
constructs are negatively correlated (Table 3). This suggests that more conservative firms tend to
33
delay reporting good news, and thus are less likely to renegotiate their loans after positive
shocks. Consistent with my prediction, I find that the estimated coefficients on C-Score (i.e.,
Khan and Watts (2009) measure of the asymmetric timeliness of bad news recognition) are
significantly negative, indicating that after positive shocks to their credit quality, more
conservative firms are less likely to renegotiate their loan contracts and when they do, to do so
later (Table 7). This finding provides a rationale for prior findings on the ex ante benefits of
accounting conservatism (Zhang 2008; Wittenberg-Moerman 2008). Specifically, to the extent
that these less likely and less timely renegotiations may benefit lenders ex post by protecting
them from interest rates reductions, one would expect lenders to share these benefits by charging
lower interest rates ex ante (i.e., at the time of loan origination) to more conservative firms. In
addition, this finding provides a nice complement to the existing research on accounting
conservatism. Previous literature (Zhang 2008) shows that conservative reporting benefits
lenders by accelerating covenant violations after negative shocks, whereas my paper indicates
that conservative reporting benefits lenders by delaying renegotiations after positive shocks.
Therefore, my paper contributes to the literature on accounting conservatism by enhancing our
understanding of the complete role of accounting conservatism in debt contracting.
4.4.3 Nature of Good News Received
An alternative interpretation of the findings in this paper is that it is the variation in the
nature of information received, rather than the variation in the timeliness with which firms
incorporate good news into financial statements, that influences contract renegotiations
following positive shocks. For example, for high growth firms, a greater proportion of their
market value shock may be due to growth options which cannot be incorporated into financial
statements, leading to lower probability of renegotiations. Another example could be firms in
34
R&D-intensive industries (e.g., software, biotechnology and telecommunications). This is
because the granting of a patent to these firms could induce a positive shock to their credit
quality, which also cannot be incorporated into accounting records in a timely manner, leading to
fewer renegotiations. To address these concerns, I conduct two sensitivity tests (Table 8). First, I
re-run probit and hazard model regressions only on firms that have no increase in market-tobook ratio (my proxy for expansion in investment opportunity). All results still hold. Second, I
eliminate firms in four R&D-intensive industries with two-digit SIC codes of 28, 35, 36 and 38
from my sample (Lev and Sougiannis 1996; Shi 2003; Jones 2010).37 I re-run both regressions
and also find robust results.
4.4.4 Alternative Measure of Timely Reporting of Good News
I modify the approach in Ball et al. (2008) to measure how well accounting numbers
predict future credit rating upgrades (Table 9). The debt-contracting value of accounting in the
context of credit rating upgrades, DCV Upgrade, is calculated at the industry level. Specifically,
for any given year, I estimate a probit model using quarterly data in the past five years for each
Fama-French industry (48 categories):
P(Upgrade,, = 1) =
P(ao + aiAE,_.,i + a2AE -2. + a3 AEt-3 ,i + a 4AEt-4 )
(10)
Where Upgrade is an indicator variable equal to one if firm i's credit rating is upgraded
in the current quarter I and equal to zero otherwise. AE-k.i is the seasonally adjusted change in
quarterly earnings before extraordinary items scaled by total assets in the kth quarter prior to the
current quarter, t. Somers' D, also known as the accuracy ratio, is a popular statistic that is used
to measure the quality of credit-rating systems. Somers' D measures the extent of concordance
between the model-predicted upgrades and the actual upgrades. The higher the Somers' D, the
Four R&D-intensive industries are chemicals and pharmaceuticals (Standard Industrial Classification [SIC] 28),
machinery and computer hardware (SIC 35), electrical and electronics (SIC 36), and scientific instruments (SIC 38).
3
35
higher the prediction ability of earnings changes (Altman and Sabato 2007). This measure
captures how well accounting numbers predict future credit rating, implicitly signaling how
quickly accounting numbers reflect current good news. Replacing G-Score with DCV Upgrade
does not change my results.
4.4.5 Timely Reporting of Good News Interactedwith the Size of Positive Shock
One way to confirm the mechanism that financial statements provide verification value to
lenders and thereby affect renegotiations is to have the timely reporting of good news interacted
with the size of positive shock. It can be expected that the larger the size of positive shock, the
more pronounced are the effects of timely reporting of good news on the likelihood of
renegotiations. This is because the larger the size of positive shock, the more the gains that can
be incorporated in financial statements if firms report good news in a timely manner, leading to
higher probability of triggering renegotiations. I find results consistent with my prediction,
implying that accounting properties do play an important role in contract renegotiations (Table
10).
4.4.6 Firm Loans without Earnings-BasedPerformancePricingProvision
According to Asquith, Beatty, and Weber (2005), in a performance pricing contract the
lender and the borrower agree at loan inception to a pricing grid that designates how interest
rates will change in response to changes in financial performance. The pricing grid provides
important mechanisms that can reduce the re-contracting costs associated with loans, namely,
reducing the probability of renegotiations, especially those related to interest rate changes.
Therefore, if the loan includes an earnings-based performance pricing grid (e.g., debt-toEBITDA ratio, interest coverage, fixed charge coverage), it is less likely that the timely
reporting of good news in accounting earnings will affect renegotiations after positive shocks.
36
Therefore, I exclude loans that have earnings-based performance pricing (21 loans) and re-run
the probit and hazard model regressions. All results still hold (Table 11).
4.4.7 Switch of Lenders
Although I examine the renegotiation of existing loan contracts, one concern is that as
firms have outside financing options, they may switch to outside banks to refinance their current
loans as opposed to renegotiating with existing lenders after the credit quality improves. To
alleviate this concern, I identify new loans that are "refinancing from outside lenders" as
renegotiations and re-run the probit and hazard model regressions. All results still hold (Table
12).
4.4.8 Controlfor Changes in Firm Characteristics
Roberts and Sufi (2009b) find that ex post changes in firm-specific variables are
important determinants of renegotiations (e.g., LNASSET, LEV). As I already include the change
in the probability of bankruptcy (JBSM-Prob), which is a comprehensive proxy for the change
in credit quality, I do not expect the changes in firm-specific variables to affect my previous
results. Nevertheless, I still measure the change in each of the firm-specific characteristic
variables from loan origination to the two quarters after origination and add them to my
regressions. Consistent with my prediction, all results still hold, and the coefficients on these
change variables are not statistically significant (Table 13).
5. Conclusion
Renegotiations play a critical role in corporate financial contracting. Despite the
extensive research on renegotiations in the context of corporate default, recent empirical studies
show that numerous renegotiations are voluntarily initiated by borrowers, especially when the
37
borrower's credit quality improves. In my paper, I extend this literature by investigating the role
of accounting in renegotiations after positive shocks.
Using a hand-collected sample of private debt contracts between U.S. publicly traded
firms and financial institutions, I examine whether and how the timely reporting of good news
affects the renegotiation of debt contracts following a positive shock to the borrower's credit
quality. I find that firms with more timely reporting of good news are more likely to renegotiate
their loan contracts and they do so sooner than firms with less timely good news reporting.
Further, these effects are more pronounced for firms whose positive shocks can be more credibly
communicated through financial reporting.
My paper contributes to the literature on the role of accounting in incomplete contracting.
In particular, my paper fills a gap in the literature by examining whether and how accounting
information, proxied by the timely reporting of good news, plays a critical role in renegotiations
following a positive shock to a borrower's credit quality. In addition, an implication from my
paper is that conservative firms tend to delay disclosing good news and thus are less likely to
renegotiate their contracts after positive shocks. To the extent that this benefits lenders ex post,
my paper provides a rationale for the evidence in previous literature on the ex ante benefits of
accounting conservatism, i.e., lower initial interest rates charged for firms with delayed good
news disclosure (Zhang 2008; Wittenberg-Moerman 2008).
38
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44
Appendix I: Variable Definitions
Variable
Dependent variables
Reneg
Time toReneg
Treatment variables
G-Score and
C-Score
Definition
Indicator variable that equals one if the original contract is amended
between the end of the positive shock and the maturity date or
between the end of the positive shock and the end of the sample
period (i.e., six quarters after the shock), whichever is shorter, and
zero otherwise.
The number of quarters between the end of the positive shock and
renegotiation. If the firm does not report a renegotiation within the
search period, TimetoReneg is the number of quarters between the
end of the positive shock and the maturity date or between the end
of the positive shock and the end of the sample period (i.e., six
quarters after the shock), whichever is shorter.
Khan and Watts (2009) Measure: Xi=Pi+P2Di43R+
DRiei;
G-Score=p3=i+ji 2 Sizei+p 3 M/Bi+p4 Levi;
C-Score=P4=3%+X2Sizei+k3M/Bi+4Levi.
Where X is earnings scaled by the lagged market value of equity, R
is annual returns compounded from monthly returns beginning the
fourth month after fiscal year-end. D is a dummy variable equal to
one if returns (R) are negative, and zero if returns are positive. M/B
is the ratio of the market value of equity to the book value of equity
at the end of the year. Size is the natural log of the market value of
equity. Lev is leverage, defined as long-term and short-term debt
deflated by the market value of equity.
DCV Upgrade
+ U3 AEt 3,i + a 4 AEt4,i)
Where Upgrade is an indicator variable equal to one if firm i's
credit rating is upgraded in the current quarter t and equal to zero
otherwise. AE,ki is the seasonally adjusted change in quarterly
earnings before extraordinary items scaled by total assets in the kth
quarter prior to the current quarter, t. Somers' D, also known as the
accuracy ratio, is a popular statistic that is used to measure the
quality of credit-rating systems.
Restatement
Indicator variable that takes the value of one if the firm reported any
restatement prior to renegotiation, and zero otherwise. If the firm
does not report a renegotiation within the search period,
Restatement equals one if the firm reported any restatement before
the maturity date, or the end of the sample period, (i.e., six quarters
after the shock), whichever is shorter, and zero otherwise.
P(Upgradeti= 1)= cD(ao + ai AEt-, i+ U2 AEt-2,
45
A udilor Specialist
Indicator variable that equals one if the client's audit firm audits at
least 20% of the sales and also the largest proportion of the sales in
the client's two-digit SIC code industry as reported by Compustat,
zero otherwise
Transitory
The ratio of the value of one-time items to the value of core
earnings, where core earnings are reported earnings minus one-time
items. One-time items refer to discontinued operations,
extraordinary items, and special items.
Hillegeist et al.'s (2004) probability of bankruptcy (BSM-Prob) is
based on Black-Scholes-Merton option-pricing model at firm-year
level. I compute this measure at firm-quarter level by replacing their
yearly variables with quarterly variables. ABSMProb is the
absolute value of changes in the probability of bankruptcy in the
two quarters after the origination of loan contracts.
BSM-Prob = N(-
UA VT
)
Firm-specific variables
ABSM Prob
Where VA is the market value of assets which is defined as total
liabilities plus the market value of equity; uA is the standard
deviation of asset returns; It is the expected return on assets; 6 is the
dividend rate which is calculated as the sum of the prior year's
common and preferred dividends divided by the market value of
assets VA; X is the face value of debt maturing at time T.
The average of natural log of book assets (atq) over quarter q - 3 to
q, where q is the quarter before loan origination.
DTE
The average of the debt-to-EBITDA ratio ((dlcq + dlttq)/oibdpq)
over quarter q - 3 to q, where q is the quarter before loan
origination.
LEV
The average of the debt-to-book-assets ratio ((dlcq + dlttq)/atq) over
quarter q - 3 to q, where q is the quarter before loan origination.
MTB
The average of the market-to-book ratio (((ltq + pstkl - txditcq)
(prccq*cshoq))/atq) over quarter q - 3 to q, where q is the quarter
before loan origination.
ROA
The average of the EBITDA-to-book assets ratio (oibdpq/atq) over
quarter q - 3 to q, where q is the quarter before loan origination.
+
LNASSET
46
The standard deviation of the EBITDA-to-book assets ratio
(oibdpq/atq) over the past eight quarters.
ZSCORE
The average Z-score (1.2*((actq - lctq)/atq) + 1.4*(req/atq)
3.3*(piq/atq) + 0.6*((prccq*cshoq)/ltq) + 0.999*(saleq/atq)) over
quarter q - 3 to q, where q is the quarter before loan origination.
A LNASSET
Change in the value of LNASSET in the two quarters after the
origination of loan contracts.
J DTE
Change in the value of DTE in the two quarters after the origination
of loan contracts.
JLEV
Change in the value of LEV in the two quarters after the origination
of loan contracts.
JMTB
Change in the value of MTB in the two quarters after the origination
of loan contracts.
JROA
Change in the value of ROA in the two quarters after the origination
of loan contracts.
ASTDROA
Change in the value of STD ROA in the two quarters after the
origination of loan contracts.
AZSCORE
Change in the value of ZSCORE in the two quarters after the
origination of loan contracts.
+
STD_ROA
Macroeconomic variable
4 Credit Spread
The value of change in the BB-AAA credit spread on publicly
traded bonds over the two quarters subsequent to loan origination.
Credit spread data is calculated by subtracting the Bank of America
Merrill Lynch US Corporate AAA Effective Yield from the Bank of
American Merrill Lynch US High Yield BB Effective Yield.
A Treasury Yield
Loan-specific variables
Interest Spread
Change in one year treasury yield, which data is from Federal
Reserve Bank 1-Year Treasury Constant Maturity Rate.
The average all-in-drawn spread over LIBOR for all tranches in the
deal, weighted by the amount of each tranche.
AInterest
Change in interest rate spread during renegotiations.
Amount
The sum of the amounts of all tranches in each deal scaled by total
assets.
47
zAimount
Percentage change in loan amount from original loan amount during
renegotiations.
Log(Amount)
The natural log of the sum of the amounts of all tranches in each
deal scaled by total assets.
Maturity
The length of the loan in months of all tranches in the deal,
weighted by the amount of each tranche.
AMaturity
Percentage change in loan maturity from original loan maturity
during renegotiations.
Log(Maturity)
The natural log of the length of the loan in months of all tranches in
the deal, weighted by the amount of each tranche.
NumLenders
The number of lenders in lending deal.
Revolver
Indicator variable that equals one if the deal involves a revolver
loan.
Secured
Indicator variable that equals one if the deal is secured and zero
otherwise.
PPP
Indicator variable that equals one if the deal involves a performance
pricing provision and zero otherwise.
Relint
Indicator variable that equals one if there is a prior lending
relationship with the lender for the past five years, zero otherwise.
Num Cov
The number of financial covenants.
48
Appendix II: Example of Amendment Report
Clayton Williams Energy Inc. - Excerpts from Amendment Report on July 1, 2000
1. Section 1(v) is hereby deleted in its entirety and the following inserted in lieu thereof:
"(v) EURODOLLAR MARGIN - the fluctuating Eurodollar Margin in effect from day to day,
shall be:
(i) two percent (2%) per annum whenever the Total Outstandings are greater than 75%
of the Elected Borrowing Limit in effect at the time in question;
(ii) one and three-quarters percent (1.75%) per annum whenever the Total
Outstandings are greater than 51%, but less than or equal to 75%, of the Elected Borrowing
Limit in effect at the time in question;
(iii) one and one-half percent (1.50%) per annum whenever the Total Outstandings are
greater than 25%, but less than or equal to 50%, of the Elected Borrowing Limit in effect at the
time in question;
(iv) one and one-fourth percent (1.25%), whenever the Total Outstandings are 25% or
less of the Elected Borrowing Limit in effect at the time in question."
The corresponding part from the original contract:
(v) Eurodollar Margin - The fluctuating Eurodollar Margin in effect from day to day shall be:
(i) two and one-half percent (2.50%) per annum whenever the Total Outstandings are
greater than 75% of the Elected Borrowing Limit in effect at the time in question;
(ii) two and one-quarter percent (2.25%) per annum whenever the Total Outstandings
are greater than 50%, but less than or equal to 75%, of the Elected Borrowing Limit in effect at
the time in question;
(iii) two percent (2%) per annum whenever the Total Outstandings are greater than
25%, but less than or equal to 50%, of the Elected Borrowing Limit in effect at the time in
question;
(iv) one and three-quarters percent (1.75%), whenever the Total Outstandings are
25% or less of the Elected Borrowing Limit in effect at the time in question.
2. Section 1 (jj) is hereby deleted in its entirety and the following inserted in lieu thereof:
"(jj)
MATURITY DATE - July 31, 2002."
The corresponding part from the original contract:
(jj) Maturity Date - July 31, 2001.
3. Section 13(e) is hereby deleted in its entirety and the following inserted in lieu thereof:
49
"(e) LIMITATION ON SALE OF COLLATERAL. Neither Borrower nor Guarantor
will sell, assign or discount any of the Collateral or Negative Pledge Property other than (i) sales
of oil and gas production in the ordinary course of business, (ii) sales or other disposition of
obsolete equipment which are no longer needed for the ordinary business of Borrower or
Guarantor or which are being replaced by equipment of at least comparable value and utility, and
(iii) sales or other dispositions not exceeding $1,000,000 in the aggregate between Borrowing
Base redeterminations. If and as any of such Collateral or Negative Pledge Properties and
interests are sold, conveyed or assigned during the term of the Revolving Commitment,
Borrower or Guarantor will prepay against the Notes or Guarantor's obligation under its guaranty
agreement, as the case may be, 100% of the Release Price, provided, however, that no such
payments shall be required from Borrower or Guarantor until the aggregate proceeds received
between any Borrowing Base redetermination exceeds $1,000,000. Provided, however,
notwithstanding the foregoing, if an Event of Default has occurred and is continuing all such
amounts received by Borrower and/or Guarantor from such sale during the continuance of an
Event of Default shall be paid to the Agent for the ratable benefit of the Banks. The term
"Release Price" as used herein shall mean the loan value of the Collateral or the Negative Pledge
Property being sold as determined by the Agent. Any such prepayment of principal on the Notes
required by this Section 13(e) shall not be in lieu of, but shall be in addition to, any Monthly
Commitment Reduction or any mandatory prepayment of principal required to be made pursuant
to Section 9(b) hereof. Any such prepayment shall be applied pro rata to the principal due on the
Revolving Notes until such Revolving Notes are paid in full, principal, interest and other
amounts."
The corresponding part from the original contract:
(e) Limitation on Sale of Collateral. Neither Borrower nor Guarantor will sell, assign
or discount any of the Collateral or Negative Pledge Property other than (i) sales of oil and gas
production in the ordinary course of business, and (ii) sales or other disposition of obsolete
equipment which are no longer needed for the ordinary business of Borrower or Guarantor or
which are being replaced by equipment of at least comparable value and utility. If and as any of
such Collateral or Negative Pledge Properties and interests are sold, conveyed or assigned during
the term of the Revolving Commitment, Borrower or Guarantor will prepay against the Notes or
Guarantor's obligation under its guaranty agreement, as the case may be, 100% of the Release
Price. The term "Release Price" as used herein shall mean the loan value of the Collateral or the
Negative Pledge Property being sold as determined by the Agent. Any such prepayment of
principal on the Notes required by this Section 13(e) shall not be in lieu of, but shall be in
addition to, any Monthly Commitment Reduction or any mandatory prepayment of principal
required to be made pursuant to Section 9(b) hereof. Any such prepayment shall be applied pro
rata to the principal due on the Revolving Notes until such Revolving Notes are paid in full,
principal, interest and other amounts. Provided, however, that the Borrower and Guarantor may,
without consent of Banks and Agent and without prepaying the Notes, sell Negative Pledge
Properties where the sales proceeds from any such sale do not exceed $500,000 on an annual
basis.
50
4. Section 13(k) is hereby amended by deleting the period (".") at the end of Subsection
13(k)(vii) and substituting a semi-colon and the word "and" ("; and") in lieu thereof and
by the addition of a new Subsection (viii) thereto as follows:
"(viii) other investments not exceeding $1,000,000 in the aggregate made from and
after the First Amendment Effective Date."
The corresponding part from the original contract:
13(k) Investments. Neither Borrower nor Guarantor shall make any investments in any person or
entity, except that the foregoing restriction shall not apply to:
(i) investments and direct obligations of the United States of America or any agency
thereof;
(ii) investments in certificates of deposit issued by the Agent or certificates of deposit
with maturities of less than one year issued by other commercial banks in the United States
having capital and surplus in excess of $500,000,000 and have a rating of (A) 50 or above by
Sheshunoff and (B) "B" or above by Keef-Bruett;
(iii) investments such as insured money market funds, Eurodollar investment
accounts and other similar accounts with the Agent or such investments with maturities of less
than ninety (90) days at other commercial banks in the United States having capital and surplus
in excess of $500,000,000 and having a rating of (A) 50 or above by Sheshunoff and (B) "B" or
above by Keef-Bruett;
(iv) investments in the Subsidiaries (other than Guarantor) existing on the Effective
Date;
(v) Borrower's investments in Guarantor;
(vi) the repurchase of Borrower's stock as permitted by Section 13(j) hereof; and
(vii) Guarantor's initial investment of $425,000 in Clayton Williams Acquisition
Partnership, Ltd. ("Acquisition") and additional investments in Acquisition equal to one percent
(1 %) of all capital contributions made to Acquisition by its general and limited partners, but only
to the extent such contributions are required by the partnership agreement of Acquisition,
provided that immediately before and after giving effect to such additional investments no
default or Event of Default shall exist.
51
Figure 1
Timeline of the Measurement Period/lbrthe Variables
JBSM-Prob
A Treasury Yield
ACredit Spread
Transitory
Time _toReneg
2 quarters
Loan origination
End of positive shock
Renegotiation
End of sample period
4',
G-Score, C-Score, DCV Upgrade
are
and
A uditor Specialist
measured at the fiscal year-end
to
loan
prior
immediately
origination.
2.
variables
are
Firm-specific
measured at the quarter-end
prior
immediately
to
loan
origination.
3.
Loan-specific variables are taken
original
loan
from
the
agreements.
4.
If firms report a renegotiation, then
Restatement is measured at the
year-end
before
renegotiation.
Otherwise, Restatement is measured
at the year-end before maturity or
the
end
of
sample
period,
whichever is shorter.
52
Table 1
Sample Selection
No. of Obs.
Remaining
Selection Criteria
Firm loans that experienced a decrease in the probability of bankruptcy for the years
1996-2005
3,127
Firm loans that experienced at least a 1% decrease in BSM-Prob
1,195
Firm loans with available data to calculate loan characteristics
786
Firm loans with data available to calculate firm characteristics and required accounting
attributes
466
466
Final Sample
* Among the 466 firm loans in the final sample, 230 disclose a renegotiation in their 10-K, 10-Q, or 8-K
filings within sample period.
* As the starting year for the restatement data is 1998, the sample for testing H2(a) is reduced to 409
firms.
53
Table 2
Descriptive Statistics
Variable
Dependent variables
Reneg
TimetoReneg (Quarters)
Treatment variables
G-Score
C-Score
DCV Upgrade
Restatement
Auditor Specialist
Transitory
Firm-specific control variables
JBSM Prob
LNASSET
DTE
LEV
MTB
ROA
STDROA
ZSCORE
Macroeconomic control variable
ACredit Spread
A TreasuryYield
Loan-specific control variables
Interest Spread
Amount
Maturiy (Months)
NumLenders
Revolver
Secured
PPP
Relint
Num Cov
All variables are defined in Appendix
N
Panel A: Full Sample
STD
Mean
Median
N
Mean
Median
STD
N
Panel B: Reneg=1
Panel C: Reneg=O
Mean
Median
STD
466
466
0.50
3.60
1.00
4.44
0.50
2.52
234
234
1.00
1.58
1.00
1.02
0.00
1.33
232
232
0.00
5.95
0.00
6.00
0.00
0.23
466
466
466
409
466
466
0.04
0.22
0.22
0.17
0.23
0.31
0.04
0.20
0.17
0.00
0.00
0.00
0.05
0.21
0.19
0.34
0.42
1.61
234
234
234
206
234
234
0.05
0.20
0.24
0.12
0.26
0.05
0.05
0.20
0.19
0.00
0.00
0.00
0.05
0.13
0.20
0.29
0.44
0.90
232
232
232
203
232
232
0.03
0.23
0.20
0.20
0.20
0.56
0.03
0.20
0.17
0.00
0.00
0.00
0.05
0.27
0.18
0.38
0.40
2.23
466
466
466
466
466
466
466
466
0.08
5.44
11.89
0.31
1.42
0.02
0.02
0.04
5.32
8.73
0.30
1.12
0.02
0.01
1.56
1.16
0.09
1.86
43.21
0.18
0.95
0.03
0.02
2.94
234
234
234
234
234
234
234
234
0.09
5.62
9.99
0.31
1.41
0.02
0.02
1.66
0.05
5.48
8.37
0.30
1.10
0.02
0.01
1.14
0.10
1.77
42.11
0.19
1.11
0.03
0.02
3.44
232
232
232
232
232
232
232
232
0.07
5.25
13.67
0.31
1.42
0.01
0.02
1.46
0.04
5.08
8.93
0.30
1.15
0.02
0.01
1.18
0.08
1.93
45.30
0.18
0.75
0.04
0.02
2.34
466
466
-0.16
-0.47
-0.17
-0.38
1.12
1.14
234
234
-0.21
-0.52
-0.26
-0.49
1.15
1.17
232
232
-0.10
-0.43
-0.17
-0.29
1.08
1.11
466
466
466
466
466
466
466
466
466
265.71
0.34
41.43
6.60
0.88
0.77
0.45
0.48
3.77
255.00
0.24
36.00
2.00
1.00
1.00
0.00
0.00
4.00
122.46
0.42
20.06
13.35
0.33
0.42
0.50
0.50
2.90
234
234
234
234
234
234
234
234
234
274.19
0.32
39.86
7.63
0.93
0.79
0.56
0.53
4.24
275.00
0.25
36.00
3.00
1.00
1.00
1.00
1.00
4.00
108.74
0.30
16.02
15.60
0.25
0.41
0.50
0.50
2.85
232
232
232
232
232
232
232
232
232
257.13
0.35
43.03
5.55
0.83
0.75
0.35
0.42
3.28
250.00
0.23
36.00
2.00
1.00
1.00
0.00
0.00
3.00
134.64
0.51
23.39
10.52
0.38
0.43
0.48
0.49
2.87
I.
54
Table 2 (Continued)
Panel D: Deal Distribution by Initiation Years
Year
Freg.
Percent
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Total
17
40
18
77
64
86
57
83
10
14
466
3.65
8.58
3.86
16.52
13.73
18.45
12.23
17.81
2.15
3.00
100.00
Freg.
16
17
59
36
14
96
18
Percent
3.43
3.65
12.66
7.73
3.00
20.60
3.86
Utilities
13
2.79
Wholesale, Retail, and Some Services
Healthcare, Medical Equipment, and Drugs
72
48
15.45
10.30
Other
77
466
16.52
100.00
Panel E: Deal Distribution by Industry composition
Industry
Consumer NonDurables
Consumer Durables
Manufacturing
Oil, Gas, and Coal Extraction and Products
Chemicals and Allied Products
Business Equipment
Telephone and Television Transmission
Total
55
Table 3
CorrelationMatrix
This table presents Pearson (above the
significant at the 5% level or higher). All
(1)
Variable
1.00
Reneg
(1)
-0.92
TimetoReneg
(2)
0.12
G-Score
(3)
-0.07
C-Score
(4)
-0.12
Restatement
(5)
0.07
AuditorSpecialist
(6)
0.07
Transitory
(7)
0.11
JBSMProb
(8)
0.10
LNASSET
(9)
-0.04
A Treasury Yield
(10)
0.07
InterestSpread
(11)
0.05
(12) Log(Amount)
-0.04
Log(Maturity)
(13)
0.21
(14) PPP
0.12
Relint
(15)
0.17
Num Cov
(16)
diagonal) and Spearman (below the diagonal)
variables are defined in Appendix I.
(7)
(6)
(5)
(4)
(3)
(2)
0.12
0.10 -0.01 -0.12 0.07
-0.91
-0.07 -0.12
0.11
0.01
1.00 -0.09
0.38
1.00 -0.46 0.08 -0.03
-0.11
1.00 -0.07 0.02 -0.16
0.07 -0.47
0.14
0.06
1.00
0.10 -0.04
0.11
0.03
1.00
0.06
0.04
-0.07 -0.06
1.00
0.10 -0.01
-0.06 0.19 -0.02
0.01
0.04
0.02
-0.08 -0.06 0.10
0.21
0.13
-0.10 0.49 -0.35 0.19
-0.24 0.06 -0.03 -0.03 -0.15
0.03
0.04
0.03
0.11
0.05
-0.08 0.05
-0.04 -0.36 0.18 -0.18 -0.17 -0.21
-0.04
0.01
0.02
0.08 -0.02 -0.02
0.04
-0.04
-0.05
-0.17 0.08 -0.04
0.05
-0.01
0.07
-0.12
0.19
-0.10
0.04
0.05 -0.09 -0.05
-0.15 0.04
56
correlations of the key variables (correlation coefficients in bold are
(8)
0.09
-0.08
-0.13
0.23
0.05
0.03
-0.05
1.00
-0.10
0.01
0.14
-0.03
-0.05
-0.06
-0.05
0.01
(9)
0.11
-0.09
0.37
-0.52
0.18
0.09
0.41
-0.18
1.00
-0.08
-0.11
-0.49
0.18
0.18
0.48
0.02
(10)
-0.04
0.04
-0.31
-0.03
-0.04
-0.04
-0.28
0.02
-0.09
1.00
0.03
0.11
0.03
-0.01
0.00
-0.01
(11)
0.11
-0.13
-0.01
0.12
0.06
0.04
0.06
0.21
-0.18
0.02
1.00
-0.05
-0.09
-0.16
-0.07
0.11
(12)
0.04
-0.02
-0.30
0.22
-0.20
-0.14
-0.37
0.00
-0.41
0.16
-0.06
1.00
0.08
0.19
-0.07
0.22
(13)
-0.03
0.09
-0.01
-0.10
0.04
-0.02
-0.01
-0.09
0.23
0.03
-0.09
0.13
1.00
0.14
0.02
0.06
(14)
0.21
-0.15
0.08
-0.06
-0.05
-0.04
0.10
-0.08
0.23
-0.02
-0.16
0.19
0.15
1.00
0.18
0.53
(15)
0.12
-0.09
0.17
-0.21
0.07
-0.01
0.23
-0.07
0.50
-0.01
-0.07
-0.06
0.05
0.18
1.00
0.16
(16)
0.17
-0.15
0.03
0.02
-0.09
-0.06
0.06
0.00
0.04
-0.01
0.12
0.21
0.04
0.53
0.17
1.00
Table 4
The Likelihood and Timeliness of Renegotiationsafter Positive Shocks
Reneg
Variables
Inhi()
Coef.
t-stat
Coef.
Intercept
G-Score
-1.27
4.00**
(-1.38)
(2.11)
4.23**
(2.12)
ABSMProb
LNASSET
DTE
LEV
MTB
ROA
STDROA
2.09***
0.13*
0.00
-0.42
-0.02
-0.53
5.46
(2.68)
(1.75)
(-0.64)
(-0.91)
(-0.1)
(-0.19)
(1.09)
2.26***
0.12
0.00
-0.13
-0.09
0.23
6.33
(3.36)
(1.48)
(-1.01)
(-0.25)
(-0.37)
(0.07)
(1.18)
ZSCORE
0.04
(1.38)
0.06*
(1.86)
zJCreditSpread
ATreasury Yield
0.02
0.05
0.04
0.01
InterestSpread
0.00**
(0.2)
(0.39)
(2.2)
0.00**
(0.28)
(0.06)
(2.06)
Log(A mount)
Log(Maturity)
NumLenders
Revolver
Secured
PPP
Relint
NuinCov
0.18*
-0.13
0.00
0.5 1**
0.00
0.36**
0.05
0.03
(1.93)
(-0.80)
(-0.45)
(2.12)
(0.00)
(2.2)
(0.32)
(0.78)
0.20*
-0.28
0.00
0.41
-0.05
0.40**
0.05
0.02
(1.75)
(-1.46)
(-0.66)
(1.28)
(-0.23)
(2.07)
(0.28)
(0.61)
Year FE
Yes
Yes
Industry FE
Yes
Yes
N
466
466
16%
3%
Pseudo R
2
t-stat
The first column of this table presents the estimated coefficients and z-statistics (in parentheses) from a probit
regression of whether or not a renegotiation occurs after a positive shock, where the dependent variable is an
indicator variable (Reneg) that is equal to one if the original contract is amended between the end of the positive
shock and the maturity date or between the end of the positive shock and the end of the sample period (i.e., six
quarters after the shock), whichever is shorter, and zero otherwise. The second column of this table presents the Cox
proportional hazard model of the time to renegotiate (Time toReneg), which measures the number of quarters
between the credit quality shock and renegotiation. The dependent variable in this regression is h,(t), which
measures the instantaneous risk of a renegotiation at time t for borrower i conditional on i surviving to time t. All
variables are defined in Appendix 1. Continuous variables are winsorized at the 1" and 9 9 th percentiles. Statistical
significance at the 10%, 5%, and 1% levels is denoted by *, * and ***, respectively. All hypothesis tests are
conducted with standard errors robust to within-firm dependence and heteroskedasticity.
57
Table 5
Cross-SectionalTests
Panel A: Cross-Sectional Tests on the Likelihood of Renegotiations
Variables
Intercept
G-Score
Restatement
G-Score *Restatement
Auditor Specialist
G-Score*Auditor Specialist
Transitory
Coef.
-1.12
6.22***
-0.63***
-6.74**
t-stat
(-1.15)
(3.07)
(-2.77)
(-1.97)
Dependent Variable = Reneg
Coef.
t-stat
(-1.49)
-1.38
3.59**
(2.31)
0.36
1.32*
2.00**
0.13*
0.00
-0.45
-0.24
-0.96
6.21
0.04
-0.01
-0.01
0.00***
0.15
-0.17
-0.01
0.51*
0.03
0.42**
0.04
0.02
Yes
Yes
409
19%
(2.33)
(1.72)
(-0.83)
(-0.95)
(-1.02)
(-0.33)
(1.19)
(1.26)
(-0.06)
(-0.11)
(2.73)
(1.58)
(-0.94)
(-0.74)
(1.91)
(0.15)
(2.34)
(0.28)
(0.52)
58
2.14***
0.13*
0.00
-0.45
0.01
0.03
6.09
0.03
0.03
0.04
0.00**
0.18*
-0.14
0.00
0.52**
0.01
0.36**
0.07
0.02
Yes
Yes
466
17%
t-stat
(-1.17)
(3.02)
0.07
-3.08***
2.10***
0.13*
0.00
-0.36
-0.02
-0.49
5.16
0.04
0.05
0.03
0.00**
0.19**
-0.17
0.00
0.44*
0.01
0.34**
0.04
0.02
Yes
Yes
466
17%
(1.26)
(-2.72)
(2.63)
(1.77)
(-0.9)
(-0.78)
(-0.12)
(-0.18)
(1.03)
(1.47)
(0.37)
(0.22)
(2.34)
(2.03)
(-1.05)
(-0.43)
(1.83)
(0.03)
(2.09)
(0.27)
(0.75)
(1.38)
(1.81)
G-Score *Transitory
ABSM Prob
LNASSET
DTE
LEV
MTB
ROA
STDROA
ZSCORE
ACreditSpread
J Treasury Yield
InterestSpread
Log(Amount)
Log(Maturity)
NumLenders
Revolver
Secured
PPP
Relint
Nuni Cov
Year FE
Industry FE
N
Pseudo R 2
Coef.
-1.08
5.85***
(2.75)
(1.81)
(-0.64)
(-0.97)
(0.03)
(0.01)
(1.2)
(1.24)
(0.21)
(0.31)
(2.24)
(1.93)
(-0.87)
(-0.43)
(2.14)
(0.07)
(2.24)
(0.46)
(0.77)
Panel B: Cross-Sectional Tests on the Timeliness of Renegotiations
Variables
G-Score
Restatement
G-Score*Restatement
Auditor Specialist
G-Score*Auditor Specialist
Transitory
G-Score *Transitory
Coef.
5.42***
-0.73***
-6.00*
-
Table 5 (Continued)
t-stat
(2.68)
(-2.68)
(-1.76)
Dependent Variable = Inh;(t)
Coef.
t-stat
3.65**
(2.25)
0.25
1.26*
A BSMProb
LNASSET
DTE
LEV
MTB
ROA
STDROA
ZSCORE
z CreditSpread
A Treasury_Yield
Interest Spread
Log(A mount)
Log(Maturity)
NumLenders
Revolver
Secured
PPP
Relint
NumCov
1.72**
0.09
0.00
-0.04
-0.36
-1.32
9.06*
0.05*
-0.03
0.00
0.00**
0.12
-0.21
0.00
0.51
0.07
0.40*
0.09
0.01
Year FE
Industry FE
Yes
Yes
N
Pseudo R2
(2.41)
(1.10)
(-1.18)
(-0.09)
(-1.52)
(-0.4)
(1.68)
(1.76)
(-0.21)
(0.02)
(2.3)
(1.25)
(-1.06)
(-0.6)
(1.43)
(0.31)
(1.94)
(0.55)
(0.15)
2.29***
0.12
0.00
-0.09
-0.08
0.69
6.74
0.06*
0.03
0.00
0.00**
0.19*
-0.28
0.00
0.43
-0.05
0.41**
0.05
0.02
Coef.
6.04***
t-stat
(2.97)
0.07
-2.90***
(1.36)
(-2.87)
1.89***
0.13
0.00
0.00
-0.06
0.37
5.69
0.06*
0.00
0.02
0.00**
0.21*
-0.33*
-0.01
0.38
-0.04
0.38**
0.02
0.02
(2.72)
(1.51)
(-1.08)
(0)
(-0.26)
(0.11)
(1.06)
(1.74)
(-0.01)
(0.17)
(2.18)
(1.84)
(-1.65)
(-0.71)
(1.16)
(-0.19)
(1.96)
(0.09)
(0.61)
(1.49)
(1.70)
(3.46)
(1.45)
(-1.02)
(-0.16)
(-0.32)
(0.21)
(1.23)
(1.79)
(0.25)
(0.03)
(2.16)
(1.71)
(-1.45)
(-0.61)
(1.34)
(-0.22)
(2.12)
(0.34)
(0.54)
Yes
Yes
Yes
Yes
409
466
466
3%
3%
3%
59
Table 5 (Continued)
Panel A of Table 5 presents the probit model of the likelihood of renegotiations on three interaction terms. The dependent variable in all regressions is an
indicator variable (Reneg) that is equal to one if the original contract is amended after the positive shock and before the maturity date or the end of the sample
period, (i.e., six quarters after the shock), whichever is shorter and zero otherwise. Panel B of Table 5 presents the Cox proportional hazard model of the time to
renegotiate (Time to Reneg) on three interaction terms. The dependent variable in all regressions is hi(t), which measures the instantaneous risk of a
renegotiation at time t for borrower i conditional on i surviving to time t. The three regressions in each panel correspond to the tests of Hypotheses H2(a), H2(b),
and H2(c) respectively. As the sample period for testing restatements begins in 1998, the sample size is reduced to 409. All variables are defined in Appendix 1.
Continuous variables are winsorized at the l and 9 9 th percentiles. Statistical significance at the 10%, 5%, and 1% levels is denoted by *, **, and ***,
respectively. All hypothesis tests are conducted with standard errors robust to within-firm dependence and heteroskedasticity.
60
Table 6
Economic Impact
Panel A: The Occurrence of Changes to Contract Terms in Amendment Report
Change Items
Change in interest rates (includingperformancepricing)
Change in loan amount
Change in maturity
Change in collateral/security
Change in borrowing base
Change in investment restrictions
Change in asset sale
Change in financialcovenants
Change in other covenants
Charge amendmentfees
Total N umber of Amendment Reports
Freq.
Percent
28
82
24
15
17%
49%
15%
9%
1 5%
25
43
26%
21
68
13%
41%
10
6%
85
52%
165
100%
Panel B: The Occurrence of Changes to Three Major Contract Terms in Renegotiation Sample
Change Items
AInterest < 0
AInterest = 0
AInterest > 0
Total
AAmount > 0
AAmount = 0
AAmount < 0
Total
AMaturity > 0
AMaturity = 0
AMaturity < 0
Total
Freq.
Percent
69
161
30%
69%
4
2%
234
89
108
37
234
80
151
3
234
100%
38%
46%
16%
100%
34%
65%
1%
100%
Panel C: Descriptive Statistics of Changes to Three Major Contract Terms
N
Mean
P25
Median
P75
STD
AInterest (Reneg = 1 andzinterest 0)
73
-86.31
-105.88
-72.50
-43.00
72.53
AAmount (Reneg = I andAAmount# 0)
126
52.80%
-16.67%
25.00%
57.14%
1.61
Variable
99.50%
135.90% 0.50
91.78%
55.77%
83
AMaturity (Reneg = I and AMaturity # 0)
AInterest is the bps change in interest rate spread during renegotiations. AAmount is the percentage
change in loan amount during renegotiations. AMaturity is the percentage change in maturity during
renegotiations.
61
Table 7
Likelihood and Timeliness of Renegolialions on Conservatism
Reneg
Variables
Intercept
Coef.
Inhi(t)
t-stat
Coef.
t-stat
-0.98
(-1.09)
C-Score
-1.11**
(-2.49)
-1.13**
(-2.33)
A BSMProb
1.95**
(2.48)
2.06***
(3.02)
LNASSET
0.12
(1.61)
0.13
(1.63)
DTE
0.00
(-0.66)
0.00
(-1.01)
LEV
-0.24
(-0.51)
0.00
(0.00)
MTB
-0.19
(-0.9)
-0.28
(-1.12)
ROA
0.00
(0.00)
0.38
(0.12)
STDROA
7.36
(1.51)
8.77*
(1.66)
ZSCORE
0.04
(1.46)
0.06*
(1.94)
ACredit_Spread
0.04
(0.36)
0.02
(0.13)
Interest_Spread
0.00**
(2.25)
0.00**
(2.11)
Log(Amount)
0.15
(1.64)
0.17
(1.57)
Log(Maturity)
-0.15
(-0.93)
-0.29
(-1.56)
NumLenders
-0.01
(-0.66)
-0.01
(-0.82)
Revolver
0.54**
(2.29)
0.54*
(1.72)
Secured
0.00
(-0.02)
-0.02
(-0.09)
PPP
0.34**
(2.07)
0.35*
(1.80)
Relint
0.10
(0.69)
0.08
(0.47)
NumCov
0.03
(0.89)
0.02
(0.74)
Year FE
Yes
Yes
Industry FE
Yes
Yes
N
466
466
2
Pseudo R
20%
12%
The first column of this table presents the estimated coefficients and z-statistics (in parentheses) from a probit
regression of whether or not a renegotiation occurs after a positive shock, where the dependent variable is an
indicator variable (Reneg) that is equal to one if the original contract is amended after the positive shock and before
either the maturity date or the end of the sample period, i.e., six quarters after the shock, whichever is shorter, and
zero otherwise. The second column of this table presents the Cox proportional hazard model of the time to
renegotiate (Time to Reneg), which measures the number of quarters between the credit quality shock and
renegotiation. The dependent variable in this regression is hi(t), which measures the instantaneous risk of a
renegotiation at time t for borrower i conditional on i surviving to time t. All variables are defined in Appendix 1.
Continuous variables are winsorized at the Is' and 9 9 th percentiles. Statistical significance at the 10%, 5%, and 1%
levels is denoted by *, **, and ***, respectively.
62
Table 8
Nature of Good News Received
Variables
Intercept
G-Score
Panel A: Firms with AMTB <=0
Reneg
Inhi(t)
Coef.
t-stat
Coef.
t-stat
-1.63
(-1.56)
4.76**
(2.15)
6.85**
(2.57)
BSMProb
LNASSET
DTE
LEV
MTB
ROA
STDROA
ZSCORE
J Treasury_ Yield
ziCreditSpread
InterestSpread
Log(Amount)
Log(Maturity)
NumLenders
Revolver
Secured
PPP
Relint
NumCov
3.16***
0.19**
0.00
-0.57
-0.59
-2.08
-0.93
0.03
-0.10
-0.24
0.00
0.26**
0.51
0.00
0.34
0.13
0.73***
-0.06
-0.03
Year FE
Industry FE
Yes
Yes
(3.23)
(2.00)
(-0.76)
(-0.96)
(-1.28)
(-0.58)
(-0.16)
(0.80)
(-0.62)
(-1.63)
(1.06)
(2.11)
(1.39)
(0.06)
(1.13)
(0.49)
(3.38)
(-0.30)
(-0.62)
3.63***
0.19
0.00
-0.62
-0.49
-1.85
-1.35
0.06
-0.08
-0.26
0.00
0.33*
0.67
0.00
0.15
0.04
0.84***
0.01
0.00
(3.64)
(1.50)
(-0.77)
(-0.74)
(-1.45)
(-0.39)
(-0.18)
(1.10)
(-0.36)
(-1.42)
(0.89)
(1.93)
(1.16)
(-0.14)
(0.35)
(0.13)
(2.84)
(0.05)
(-0.07)
Panel B: R&D Non-Intensive Firms
Reneg
Inhi(t)
Coef.
t-stat
Coef.
t-stat
-1.27
(-1.20)
4.38**
(2.14)
5.15**
(2.41)
1.94**
0.12
0.00
-0.44
-0.07
-2.55
1.97
0.06**
0.07
0.08
0.00**
0.15
-0.19
0.00
0.56**
-0.02
0.26
0.13
0.03
(2.11)
(1.37)
(-0.04)
(-0.86)
(-0.28)
(-0.73)
(0.35)
(2.17)
(0.5)
(0.59)
(2.01)
(1.43)
(-0.98)
(0.22)
(1.98)
(-0.08)
(1.43)
(0.75)
(0.81)
1.87**
0.11
0.00
-0.09
-0.16
-2.64
1.95
0.09***
0.11
0.10
0.00**
0.16
-0.33
0.00
0.50
-0.10
0.21
0.09
0.05
(2.22)
(1.24)
(-0.4)
(-0.15)
(-0.54)
(-0.57)
(0.33)
(3.29)
(0.75)
(0.77)
(2.38)
(1.28)
(-1.51)
(-0.03)
(1.36)
(-0.41)
(0.94)
(0.48)
(1.15)
Yes
Yes
Yes
Yes
Yes
Yes
e.
N
339
339
355
355
Pseudo R2
19%
6%
14%
3%
Panel A of Table 8 presents the probit model of the likelihood of renegotiations (Reneg) and the hazard model of the time to renegotiate (Time toReneg) for
firms with AMTB <= 0. Panel B of Table 8 presents the probit model of the likelihood of renegotiations (Reneg) and the hazard model of the time to renegotiate
(Time to Reneg) for R&D Non-Intensive firms. All variables are defined in Appendix 1. Continuous variables are winsorized at the 1s' and 9 9 h percentiles.
Statistical significance at the 10%, 5%, and 1% levels is denoted by *, **, and ***, respectively.
63
Table 9
Alternative Measure of Timely Reporting of Good News
Inhi(t)
Reneg
Variables
Intercept
DCV Upgrade
Coef.
-1.59**
t-stat
(-2.03)
Coef.
t-stat
0.79**
(2.00)
0.67*
(1.75)
JBSMProb
LNASSET
DTE
LEV
MTB
ROA
STDROA
ZSCORE
1.91**
0. 18***
0.00
-0.57
(2.41)
(2.66)
(-0.20)
(-1.30)
(-0.04)
(0.01)
(1.19)
(1.07)
(-0.59)
(-0.71)
(2.77)
(1.61)
(-1.01)
(-0.44)
(1.96)
(-0.19)
(2.28)
(0.51)
(0.88)
2.15***
0.19***
0.00
-0.38
0.01
1.06
6.84
0.04
-0.04
-0.03
0.00**
0.22*
-0.33*
-0.01
0.49
-0.08
0.39**
0.01
0.03
(3.06)
(2.61)
(-0.74)
(-0.75)
(0.02)
(0.32)
(1.39)
(1.45)
(-0.32)
(-0.29)
(2.23)
(1.90)
(-1.69)
(-0.94)
(1.51)
(-0.35)
(2.05)
(0.03)
(0.85)
ACredit_Spread
J Treasury_Yield
InterestSpread
Log(A mount)
Log(Maturity)
NumLenders
Revolver
Secured
PPP
Relint
Num_Cov
-0.01
0.02
5.38
0.03
-0.04
-0.05
0.00***
0.15
-0.16
0.00
0.47**
-0.04
0.36**
0.07
0.03
Year FE
Industry FE
Yes
Yes
Yes
Yes
N
466
466
2
Pseudo R
11%
3%
The first column of this table presents the estimated coefficients and z-statistics (in parentheses) from a probit
regression of whether or not a renegotiation occurs after a positive shock, where the dependent variable is an
indicator variable (Reneg) that is equal to one if the original contract is amended between the end of the positive
shock and the maturity date or between the end of the positive shock and the end of the sample period (i.e.. six
quarters after the shock), whichever is shorter, and zero otherwise. The second column of this table presents the Cox
proportional hazard model of the time to renegotiate (Time toReneg), which measures the number of quarters
between the credit quality shock and renegotiation. The dependent variable in this regression is h,(1), which
measures the instantaneous risk of a renegotiation at time t for borrower i conditional on i surviving to time t. The
independent variable in both regressions is DCVUpgrade, measuring how well accounting numbers predict future
credit rating upgrades. All variables are defined in Appendix 1. Continuous variables are winsorized at the I" and
9 9 th percentiles. Statistical significance at the 10%, 5%, and 1% levels is denoted by *, **, and ***, respectively.
64
Table 10
Interact Timely Reporting of Good News with the Size of Positive Shock
Inhi(t)
Reneg
Variables
Coef.
-1](-
t-stat
42
Coef.
t-stat
Intercept
G-Score
2.85*
(1.77)
3.68**
(2.11)
ABSMProb
2.11**
(2.28)
2.18**
(2.35)
G-Score*ABSM Prob
12.38*
(1.71)
4.55*
(1.73)
LNASSET
DTE
LEV
MTB
ROA
STDROA
ZSCORE
0.13*
0.00
-0.41
-0.02
-0.47
5.64
0.04
(1.79)
(-0.55)
(-0.90)
(-0.08)
0.12
0.00
-0.12
-0.09
0.24
6.43
0.06*
ACreditSpread
0.05
0.02
(1.49)
(-0.98)
(-0.23)
(-0.35)
(0.07)
(1.20)
(1.81)
(0.25)
(0.05)
(2.05)
ATreasury Yield
Interest Spread
Log(Aniount)
Log(Maturity)
NumLenders
Revolver
Secured
PPP
Relint
NumCov
0.00**
0.18*
-0.12
0.00
0.5*
-0.01
0.35**
0.05
0.02
(-0.17)
(1.13)
(1.34)
(0.35)
(0.19)
(2.19)
(1.89)
(-0.75)
(-0.45)
(2.11)
(-0.03)
(2.18)
(0.33)
(0.75)
0.03
0.01
0.00**
0.19*
-0.27
0.00
0.41
-0.06
0.40**
0.04
0.02
Year FE
Industry FE
Yes
Yes
Yes
Yes
N
466
466
2
(1.73)
(-1.41)
(-0.67)
(1.29)
(-0.25)
(2.06)
(0.28)
(0.62)
14%
3%
The first column of Table 10 presents the probit regression that has G-Score to be interacted with JBSMProb,
where the dependent variable is an indicator variable (Reneg) that is equal to one if the original contract is amended
between the end of the positive shock and the maturity date or between the end of the positive shock and the end of
the sample period (i.e., six quarters after the shock), whichever is shorter, and zero otherwise. The second column of
Table 10 presents the Cox proportional hazard model of the time to renegotiate (Time to Reneg), which has GScore to be interacted with JBSMProb. The dependent variable in this regression is h,(t), which measures the
instantaneous risk of a renegotiation at time t for borrower i conditional on i surviving to time t. All variables are
defined in Appendix 1. Continuous variables are winsorized at the V4 and 9 9 th percentiles. Statistical significance at
the 10%, 5%, and 1% levels is denoted by *, **, and ***, respectively.
Pseudo R
65
Table 11
Firm Loans Without Earnings-BasedPertbrmancePricingProvision
Reneg
Variables
Inhi(t)
Coef.
t-stat
Intercept
-1.32
(-1.41)
G-Score
4.20**
Coef.
t-stat
(2.12)
4.43**
(2.17)
1.85**
0.12
0.00
(2.47)
(1.45)
-1.64
(2.00)
(1.75)
(-0.11)
(-0.76)
(-0.24)
(-0.60)
4.80
0.04
(0.94)
(1.56)
5.34
0.07**
0.07
0.03
0.00**
(0.53)
(0.27)
(2.36)
0.06
0.02
0.00**
(1.82)
(-0.92)
0.19
-0.29
NumLenders
0.17*
-0.15
0.00
Revolver
0.54**
(-0.61)
(2.24)
-0.01
0.46
Secured
PPP
Relint
Num Cov
0.03
0.40**
0.07
0.02
(0.17)
(2.33)
-0.03
0.41**
0.06
0.02
JBSMProb
1.66**
LNASSET
0.13*
DTE
0.00
LEV
-0.35
-0.05
MTB
ROA
STDROA
ZSCORE
ACredit_Spread
ATreasuryYield
Interest_Spread
Log(Amount)
Log(Maturity)
(0.44)
(0.60)
0.01
-0.13
-1.18
Year FE
Yes
Yes
Industry FE
Yes
Yes
N
466
466
2
(-0.57)
(0.02)
(-0.5)
(-0.36)
(1.01)
(2.25)
(0.42)
(0.17)
(2.19)
(1.61)
(-1.51)
(-0.78)
(1.42)
(-0.11)
(2.02)
(0.35)
(0.51)
Pseudo R
13%
3%
Table 11 shows the estimated coefficients and z-statistics (in parentheses) from both probit regression and hazard
regression on the sample of firm loans without earnings-based performance pricing provision. The first column of
Table I1 presents the probit regression, where the dependent variable is an indicator variable (Reneg) that is equal
to one if the original contract is amended between the end of the positive shock and the maturity date or between the
end of the positive shock and the end of the sample period (i.e., six quarters after the shock), whichever is shorter,
and zero otherwise. The second column of Table I1 presents the Cox proportional hazard model of the time to
renegotiate (TimetoReneg). The dependent variable in this regression is h,(t), which measures the instantaneous
risk of a renegotiation at time t for borrower i conditional on i surviving to time t. All variables are defined in
Appendix 1. Continuous variables are winsorized at the I" and 9 9th percentiles. Statistical significance at the 10%,
5%, and 1% levels is denoted by *, **, and ***, respectively.
66
Table 12
Refinancingfrom Outside Lenders
Inhi(t)
Reneg
Coef.
t-stat
Intercept
Variables
-1.28
(-1.40)
G-Score
3.94**
ABSM Prob
Coef.
t-stat
(2.10)
4.30**
(2.15)
1.71**
(2.19)
2.04***
(3.01)
LNASSET
0.12*
(1.68)
0.12
(1.43)
DTE
0.00
-0.40
0.00
-0.12
(-1.02)
LEV
(-0.65)
(-0.89)
MTB
-0.03
(-0.14)
-0.09
(-0.37)
ROA
STDROA
-0.21
4.83
(-0.08)
(0.98)
0.53
5.99
(0.16)
(1.13)
ZSCORE
0.04
(1.38)
0.06*
(1.87)
ACredit Spread
0.07
-0.02
(0.54)
(-0.20)
0.05
-0.01
(0.4)
(-0.12)
0.00**
(2.30)
0.00**
(2.13)
(1.90)
(-0.80)
0.19*
-0.30
(1.74)
Log(Maturity)
0.17*
-0.13
NumLenders
0.00
(-0.22)
0.00
(-0.49)
(1.70)
(0.03)
(1.04)
Secured
0.41*
0.01
0.33
-0.04
(-0.19)
PPP
0.39**
(2.40)
0.42**
(2.19)
Relint
0.05
(0.34)
0.04
(0.28)
NumCov
0.03
(0.88)
0.02
(0.65)
Year FE
Yes
Yes
Industry FE
Yes
Yes
N
466
466
J Treasury Yield
Interest Spread
Log(A mount)
Revolver
2
(-0.24)
(-1.58)
3%
14%
The first column of Table 12 presents the probit regression, where the dependent variable is an indicator variable
(Reneg) that is equal to one if the original contract is amended or a new loan issued by outside lenders replaces the
original loan between the end of the positive shock and the maturity date or between the end of the positive shock
and the end of the sample period (i.e., six quarters after the shock), whichever is shorter, and zero otherwise. The
second column of Table 12 presents the Cox proportional hazard model of the time to renegotiate (Timeto Reneg).
The dependent variable in this regression is h,(t), which measures the instantaneous risk of a renegotiation at time t
for borrower i conditional on i surviving to time t. All variables are defined in Appendix 1. Continuous variables are
winsorized at the s and 9 9 th percentiles. Statistical significance at the 10%, 5%, and 1% levels is denoted by *, *,
Pseudo R
and ***, respectively.
67
Table 13
Controlfor Changes in Firm Characteristics
Inhi(t)
Reneg
Variables
t-stat
(-1.56)
(1.95)
Coef.
t-stat
Intercept
G-Score
Coef.
-1.49
3.66*
4.10**
(2.10)
JBSMProb
LNASSET
DTE
LE V
MTB
ROA
STDROA
ZSCORE
ALNASSET
JDTE
JLEV
AMTB
zROA
ZSTDROA
JZSCORE
ACreditSpread
ATreasury Yield
Interest_Spread
Log(Amnount)
Log(Maturity)
NumnLenders
Revolver
Secured
PPP
Relint
NumCov
2.23***
0.17**
0.00
-0.74
0.13
2.58
4.30
-0.04
0.91*
0.00
-1.38
0.60***
19.83***
5.97
-0.27***
-0.01
-0.02
0.00*
0.20**
-0.13
0.00
0.60**
-0.02
0.44***
-0.01
0.02
(2.71)
(2.25)
(0.38)
(-1.55)
(0.60)
(0.81)
(0.80)
(-1.05)
(1.90)
(0.42)
(-0.95)
(3.29)
(3.53)
(0.65)
(-3.72)
(-0.09)
(-0.16)
(1.95)
(2.08)
(-0.78)
(-0.60)
(2.36)
(-0.09)
(2.70)
(-0.04)
(0.47)
2.22***
0.14*
0.00
-0.51
0.06
3.57
6.62
-0.01
0.44
0.00
-0.76
0.45***
13.63***
2.52
-0.20***
0.01
-0.02
0.00*
0.17
-0.26
0.00
0.48
-0.09
0.49**
-0.07
0.02
(3.16)
(1.78)
(0.10)
(-0.92)
(0.26)
(0.91)
(1.24)
(-0.35)
(1.33)
(0.95)
(-0.62)
(3.37)
(2.67)
(0.21)
(-3.97)
(0.04)
(-0.15)
(1.78)
(1.58)
(-1.32)
(-0.65)
(1.45)
(-0.41)
(2.54)
(-0.39)
(0.67)
Year FE
Industry FE
Yes
Yes
Yes
Yes
N
466
466
Pseudo R 2
18%
4%
The first column of this table presents the estimated coefficients and z-statistics (in parentheses) from a probit
regression of whether or not a renegotiation occurs after a positive shock, where the dependent variable is an
indicator variable (Reneg) that is equal to one if the original contract is amended between the end of the positive
shock and the maturity date or between the end of the positive shock and the end of the sample period (i.e., six
quarters after the shock), whichever is shorter, and zero otherwise. The second column of this table presents the Cox
proportional hazard model of the time to renegotiate (Time_to_Reneg), which measures the number of quarters
between the credit quality shock and renegotiation. The dependent variable in this regression is h,(t), which
measures the instantaneous risk of a renegotiation at time t for borrower i conditional on i surviving to time t. All
variables are defined in Appendix 1. Continuous variables are winsorized at the I" and 9 9 percentiles. Statistical
significance at the 10%, 5%, and 1% levels is denoted by *, **, and ***, respectively.
68