unethical behavior and debt contracting: evidence from ......culture. our approach is validated by...
TRANSCRIPT
Unethical Behavior and Debt Contracting: Evidence from
Backdated Option Grants
Veljko Fotak
SUNY Buffalo – [email protected]
Feng Jiang
SUNY Buffalo – [email protected]
Hae Kwon Lee
SUNY Buffalo – [email protected]
Current draft: July 13, 2016
We study the impact of unethical behavior on debt contracting. We find that, after the revelation
of option backdating, borrowers pay higher spreads on loans, by about 19 bp. The increase is larger
for loans originating from lenders with no prior relationship with the borrower, for geographically
distant lenders, and for opaque borrowers. Our results are consistent with the notion that unethical
behavior leads to an increase in perceived information risk. On the other side, we do not find any
impact on the cost of public debt and find that, after the revelation, backdating borrowers rely more
on public than on private debt.
JEL Classification: G32
Keywords: Corporate culture; Cost of debt; Option backdating; Soft information; Unethical
behavior
Please address correspondence to:
Feng Jiang
344 Jacobs Management Center, Buffalo, NY 14620-4000
Tel: (716) 645-3225
e-mail: [email protected]
* We thank Michael Dambra, Sahn-Wook Huh, William Kross, William Megginson, Inho Suk,
Cristian Tiu, Brian Wolfe, Fei Xie, and seminar participants at the University at Buffalo for their
valuable feedback and comments.
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1. Introduction
It has been long recognized that culture plays an important role in explaining individual behavior
and economic growth (e.g., Weber, 1930; North, 1990; Greif, 1994; Lal, 1999; Lehrer, 2004; Stulz and
Williamson, 2003). The impact of firm culture on corporate decisions and asset prices, however, is less
understood (Hermalin, 2001).1 As a consequence, there is a growing interest in understanding the effect
of corporate culture on corporate behavior and performance (e.g., Guiso, Sapienza, and Zingales, 2015;
Pan, Siegel, and Wang, 2014; Benmelech and Frydman, 2015; Biggerstaff, Cicero, and Puckett, 2015).
In this paper, we extend this line of research to investigate the impact of corporate culture on debt
contracting. In particular, we examine the impact of unethical corporate culture on the perceived level
of information risk of the firm and, consequently, on the price and non-price terms of bank loans, on the
price of public debt and, ultimately, on the choice of private versus public debt financing.
Stein (2003) argues that failures in the collection, processing, and sharing of information are the
“most pervasive and important” violations of the Modigliani and Miller (1958) perfect capital market
assumption. Hence, in the absence of perfect information, lenders are concerned about the quality of
disclosure of borrowers and about the perceived level of information risk of the firm. Extant literature
on debt contracting highlights the importance of information transparency as a determinant of a firm’s
cost of capital (Diamond and Verrecchia, 1991), while a stream of studies on information transparency
has established that unethical behavior affects the credibility of corporate disclosure (Karpoff, Lee, and
Martin, 2008). Accordingly, we expect that unethical behavior will increase the perceived information
risk of the firm and, in turn, affect debt contracting.
One empirical challenge in this area is that ethics and corporate culture are hard to observe and
quantify. We build on prior literature finding that executive option backdating may be a manifestation
of lax ethical norms in the firm (e.g., Armstrong and Larcker, 2009) to identify firms with unethical
1 Hirshleifer (2014) argues that “Most importantly, there is a need to move from behavioral finance to
social finance (or social economics). Social finance includes the study of how social norms, moral
attitudes, religions and ideologies affect financial behaviors […], and how norms that affect financial
decisions form and spread” (p. 44).
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culture. Our approach is validated by Biggerstaff, Cicero, and Puckett (2015), who use option backdating
as a proxy for unethical culture and report evidence that firms with CEOs who personally benefit from
options backdating are more likely to engage in other corporate misbehaviors, suggestive of an unethical
corporate culture. We accordingly exploit the revelation of executive option backdating in 2006 to study
the impact of revealed unethical culture on debt contracting.
Lie (2005) defines the backdating of stock options as the practice of retroactively choosing a
favorable date (i.e., when the stock price was low) as the date on which stock options were supposedly
granted.2 Extant research shows that backdating was usually initiated by CEOs or other high-level
executives and widespread around the turn of the century, with as many as 30% of public firms engaging
in the practice (Heron and Lie, 2007; Heron and Lie, 2009; Bizjak, Lemmon, and Whitby, 2009; Collins,
Gong, and Li, 2009; Bebchuk, Grinstein, and Peyer, 2010). The main advantage of using the revelation
of prior backdating of option award grants as a proxy for unethical behavior is that, while indicative of
unethical behavior, it does not reveal new information about the underlying performance of the firm and
has limited cash flow implications (Bernile and Jarrell, 2009), which hence allows us to separate a
“wealth effect” from an “information effect” (Graham, Li, and Qiu, 2008).
We focus on syndicated loan contacting for three main reasons. First, bank loans are an
inherently important market to investigate as they constitute, worldwide, the largest source of external
funding for corporations (Lin et al., 2010; Chui et al., 2010). In 2013, global syndicated lending for the
full year reached USD 4.7 trillion (USD 2.3 trillion in the USA), while global bond issues, the second
2 Because the value of an option is higher if the exercise prize is lower, executives should prefer being
granted options when the exercise prize is at its lowest. Backdating would not be illegal if it were clearly
communicated to shareholders, adequately accounted for in both earnings and taxes, and no document
was forged. However, this is rarely true in practice, making most instances of backdating illegal.
Academic interest in option grant dates started with the finding that returns are abnormally low leading
up to the grants and positive afterwards (Yermack, 1997). Early research documented abnormal stock
returns around stock option grants but it was only in Lie (2005) established that official grant dates had
been timed retroactively by many firms.
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largest source of financing for corporations, reached approximately USD 3.5 trillion.3 About 80% of
public companies headquartered in the USA have outstanding bank loans compared to only 20% with
outstanding public debt. Thus bank loans provide an optimal testing ground offering evidence
generalizable to a large cross-section of firms. Second, bank loan contracts allow for the identification
of both borrowers and lenders, allowing us to test whether the observed impact is due to an increase in
perceived information risk. Extant literature finds that lenders that have previous relationships with the
borrower (Bhattacharya and Chiesa, 1995; Boot, 2000; Bharath et al, 2009), or lenders that are
geographically closer to the borrower (Agarwal and Hauswald, 2010), have superior access to
information about the borrower and thus enjoy lower information asymmetry. Accordingly, we posit
that such lenders would be less sensitive to information risk and test whether the impact of unethical
behavior on loan terms differs for lenders with prior relationships and who are geographically close to
borrowers. Third, according to Melnik and Plaut (1986), loan contracts are effectively a bundle of
contract terms including both price and nonprice terms such as maturity, collateral and covenant
requirements, seniority levels, and others. In this sense, syndicated loans allow for the investigation of
non-price mechanisms employed to mitigate perceived information risk.
Our main sample includes 7,509 loans to US firms over the years spanning the interval between
2000 and 2012, of which approximately 34% are to firms identified as likely backdaters.4 We find strong
evidence of an increase in spreads (over LIBOR) for loans to borrowers identified as likely backdaters.
Our results are both statistically significant and economically meaningful, as the estimated increase in
the cost of loans is of approximately 19 basis points (bp), or about 8% of average loan spreads. We
further find that the increase in the cost of loans is related to the number of option grants that are likely
to have been backdated.
3 The totals are, respectively, from the “Thomson Reuters Debt Capital Markets Review (Full Year
2014)” and the “Thomson Reuters Global Syndicated Loans Review (Full Year 2014),” both available
at http://dmi.thomsonreuters.com/ 4 We follow Lie (2005) and Heron and Lie (2007, 2009) in identifying firms that are likely to have
modified option grant dates ex-post. Here, and in the remainder of the paper, we refer to such firms as
“backdaters” for brevity—and we conversely refer to all other firms as “non-backdaters.”
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Further, we find that the impact of backdating on the cost of loans is mitigated by lenders with
lower information asymmetry. In particular, we find that the increase in the cost of loans for backdaters
is much smaller if they have previous lending relationships with the borrowers. While the increase in
the cost of the loan is of approximately 34 bp for loans originating from “new” lenders, the increase is
of only 6 bp for loans originating from relationship lenders. Similarly, we find that the increase in the
cost of debt is smaller for borrowers who are geographically close to the lender. After revelation of
option backdating, we find that the cost of loans increases by 2 bp for every 100 miles of distance
between lender and borrower who is a likely backdater. In additional tests, we find that the revelation
of option backdating has a stronger impact on the cost of debt of firms that are ex-ante less transparent:
smaller firms, firms with a smaller analyst following, and firms not included in the S&P 500 index.
Overall, these findings suggest that the increase in the cost of debt is due to a revised perception of
information risk following the revelation of unethical behavior, rather than to the revelation of failures
of internal governance and control mechanisms.
In contrast, we find no significant impact on the non-price terms of loans, suggesting that lenders
do not try to mitigate the perceived increase in information risk by relying on risk-mitigating loan
features such as financial covenants, collateral, or shorter maturities.
While our main interest lies in studying the impact of managerial unethical behavior on the
terms of loans, we extend our investigation to public debt markets. Given that bond investors have both
lower incentives to monitor borrowers and generally do not share the superior access to firm level
information that banks have, we would expect them to react even more forcefully to a deterioration in
perceived disclosure quality. On the other side, extant literature finds that, while banks value soft
information (Berger and Udell, 2002; Berger et al, 2005), public debt markets tend to price debt mostly
on the basis of hard information. Accordingly, this might suggest that the revelation of unethical
behavior would have a stronger impact on bank lenders than on public debt investors. Consistent with
the latter explanation, our tests reveal no change in the cost of public debt following the revelation of
the backdating practice.
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Given that the bond market appears not to penalize firms after the revelation of option
backdating, we expect an increased reliance on public over private debt by firms likely to have backdated
option grants. Our findings support this conjecture, as we document that, post-revelation, backdaters are
more likely to seek public debt financing post-revelation and issue a greater share of public debt. This
migration to public debt markets is particularly marked for large borrowers and borrowers with a large
analyst following, suggesting that large, transparent borrowers who have access to bond-market
financing rely less on bank financing post-revelation, while smaller, more opaque borrowers maintain
their reliance on bank financing and suffer a greater increase in the cost of debt.
Extant literature further finds that lenders respond to higher information risk by charging higher
interest rates, by employing non-price risk mitigating loan terms, but also by rationing capital (Stiglitz
and Weiss, 1981). Accordingly, we document that the revelation of option backdating leads to an
increased sensitivity of investments to cash flows, which has been interpreted in prior studies as a metric
of financial constraints (Almedia, Campello, and Weisbach, 2004; Fazzari, Hubbard, and Petersen,
1998). Our evidence suggests that unethical behavior not only increases the cost of debt of backdaters,
but that it also leads to capital rationing and increased financial constraints.
Our main contribution is to the literature on unethical behavior, as we show that the revelation
of malfeasance increases perceived information risk, which leads to both a higher cost of debt and
increased financial constraints. In this sense, we add to the findings by Graham, Li, and Qiu (2008), who
show that earnings restatements have an impact on loan contracting terms. Yet, while Graham, Li, and
Qiu (2008) recognize being empirically unable to separate new information about the underlying cash
flows of the firm from the impact of increased information risk, our empirical setup more clearly
identifies the impact on loan contracting as a consequence of a break in trust.5 Our second contribution
lies in offering evidence that unethical behavior by managers has long lasting consequences on the
5 Bernille and Jarrell (2009) extensive examine the channels through which option backdating impact
firms and conclude that the practice has a strong impact on firm value due to an increase in information
risk related to lower perceived quality of disclosure, while having little impact on firms’ fundamentals
and cash flows.
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perceived information quality of the firm’s disclosure, which indicates a persistent impact on firm
culture. We further contribute to the literature on option backdating, which has examined how the
practice impacts firms from the vintage point of equity holders, but has so far failed to capture the impact
on debt contracts. We also add to the literature on relationship lending, by providing evidence that banks
that engage in multiple transactions with borrowers or that are located in geographical proximity can
mitigate the negative impact of a shock to information risk. Finally, we provide insights into firms’
choice of private vs. public debt, by showing that private debt markets react more strongly to the
revelation of unethical behavior, which leads to an increase in the cost of private debt of unethical firms
and a shift in borrowing from private to public debt markets.
This paper is organized as follows. Section 2 develops testable hypotheses. Section 3 describes
the data sources and identification standards. Section 4 focuses on the empirical analysis. Section 5
presents our conclusions.
2. Hypotheses development
Early studies have documented unusual patterns in stock prices surrounding option grant dates
(Yarmack, 1997; Aboody and Kasznik, 2000; Chauvin and Shenoy, 2001). Yet, it was Lie (2005) that
first postulated a “backdating hypothesis” to explain that positive abnormal stock price returns following
option grant dates were due to the ex-post manipulation of the dates. This led to strong attention in the
media, including a story published in The Wall Street Journal on March 18, 2006, which has been cited
as having attracted public interest in the backdating scandal (Bernille and Jarrell, 2009). As in extant
studies, we hypothesize that it is only after this initial revelation that investors become aware of the
practice and of the identity of specific firms likely to have backdated option grants.
The revelation of unethical behavior by managers can increase a lender’s uncertainty about the
firm’s financial information (Graham, Li, and Qiu, 2008). Extant literature finds that investor’s
perception of disclosure quality and, in turn, uncertainty about the quality of financial information have
an impact on a firm’s cost of capital and firm value (Diamond and Verrecchia, 1991; Duffie and Lando,
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2001; Easly and O’Hara, 2004; Yu, 2005; Kumar et al., 2006; Epstein and Schneider, 2008). Bernille
and Jarrell (2009) find that option backdating leads to a loss of investors’ confidence in a firm’s
management and a consequent increase in perceived information risk. We accordingly hypothesize that
the revelation of option backdating leads to an increase in the cost of debt of firms.
Hypothesis H1: The revelation of option backdating increases the cost of debt of firms.
Our main hypothesis is that option backdating affects firm’s cost of capital because of an
increase in information risk. Yet, option backdating might also reveal new information about the quality
of governance and internal controls of the firm. Bebchuk, Ginstein, and Peyer (2010) find that option
backdating is associated with weak internal governance—few independent directors, no outside
blockholders, and entrenched CEOs. Accordingly, we recognize that option backdating might have an
impact on bank loan contracting terms as investors revise their expectations regarding agency costs
stemming from the separation of ownership and control of the firm (Jensen and Meckling, 1976). In
order to verify that the main impact is due to an increase in perceived information risk, rather than on
the perceived quality of internal governance, we test whether the impact of option backdating on loan
contracts is conditional on the level of information asymmetry between borrower and lender. The
underlying assumption is that, if option backdating affects loan terms because of an increase in
information risk, this effect should be weaker for lenders who have superior access to information about
the borrower. Prior studies find that lenders that have repeat interactions with the same borrowers suffer
from lower information asymmetry (Berger and Udell, 1995; Bharath et al, 2009). Similarly, extant
literature finds that lenders that are physically close to the borrower are capable of mitigating
information asymmetry problems via closer monitoring. Hence, we hypothesize that the impact on the
cost of debt due to the revelation of option backdating will be lower for lenders who have a prior
relationship with the borrower or who are located in geographical proximity to the borrower.
Hypothesis H2: The impact of option backdating on the cost of loans is weaker if the lender has
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a prior relationship with the borrower or if the lender is geographically close to the borrower.6
In a similar spirit, we recognize that some borrowers are more transparent than others. If option
backdating affects firms’ cost of debt mainly by increasing information risk, we would expect this effect
to be stronger for less transparent borrowers. Extant literature finds that large borrowers, borrowers with
a large analyst following, and borrowers included in the S&P 500 index are generally more transparent.7
Accordingly, we hypothesize that the impact on the cost of debt due to the revelation of option
backdating will be lower for borrowers that are larger, have a greater analyst following, and are included
in the S&P 500 index.
Hypothesis H3: The impact of option backdating on the cost of loans is weaker if the borrower
is large, has a large analyst following, or is included in the S&P 500 index
Extant literature further finds that lenders not only respond to an increase in information
asymmetry by requiring higher compensation in the form of higher spreads. Rather, lenders employ
other non-price mechanisms to mitigate the risk level of a lending contract. Qian and Strahan (2007)
and Bae and Goyal (2009) find that borrowers use loan maturity as a risk-mitigating mechanism. Chava
and Roberts (2008) discuss in detail the use of financial covenants to mitigate loan risk. Accordingly,
we posit that:
Hypothesis H4: After the revelation of option backdating, lending contracts will include non-
price risk-mitigating features, including: shorter maturity, more frequent use of collateral, and a higher
number of covenants.
Esty and Megginson (2003) and Qian and Strahan (2007) find that risk affects the size and
concentration of a lending syndicate. Their evidence is consistent with concentrated lending leading to
6 Finding an interaction between borrower sensitivity to information asymmetry and the revelation of
backdating in determining the firm’s cost of debt would provide strong evidence in support of option
backdating increasing cost of debt via information risk. We nonetheless recognize that we cannot,
ultimately, rule out the possibility that the impact is at least partially due to revise expectation about
future agency costs. 7 Hong, Lim, and Stein (2000) find that information transfer to public is slower for firms with low
analyst coverage. Seasholes and Zhu (2010) find that there is a great overlap between alternative
measures of information asymmetry and S&P 500 inclusion.
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better monitoring incentives and greater re-contracting flexibility. On the other side, riskier loans could
also lead to larger lending syndicates, as lenders attempt to spread risk by retaining a smaller share of
the loan. Accordingly, the net impact of information risk on the size of the lending syndicate cannot be
easily predicted and the link between the revelation of option backdating and the size of the lending
syndicate is worthy of empirical investigation.
Hypothesis H5: The revelation of option backdating affects the size and concentration of the
lending syndicate.
As a further test of the information asymmetry channel, we recognize that banks are generally
less sensitive to information asymmetry problems than public debt holders. First, banks have access to
better information about borrowers (Fama, 1985; James and Smith, 2000). Second, banks have a stronger
incentive to engage in costly monitoring (Berlin and Loeys, 1988; Diamond, 1991; Houston and James,
1996). Finally, banks have the ability to discipline firms more effectively than public debt holders
(Gertner and Scharfstein, 1991; Denis and Mihov, 2003; Park, 2000). As a consequence, banks are less
sensitive to changes in information quality than public debt holders (Leland and Pyle, 1977; Diamond,
1984; Boyd and Prescott, 1986; Houston and James, 1996; Denis and Mihov, 2003). Accordingly, we
expect to see a stronger impact (a larger increase in spreads) for bonds issued by backdaters, in
comparison to loans received by backdaters. We do recognize, however, that extant literature finds that
banks tends to rely more on “soft” information, such as borrower character (Berger and Udell, 2002;
Petersen, 2004; Berger et al, 2005), than public sector lenders, who generally tend to give greater
emphasis to “hard information” such as operating performance metrics. As the revelation of option
backdating confers information about unethical firm culture—“soft” information—we could observe a
stronger reaction from banks than from bond investors. Ultimately, these two streams of the literature
offer conflicting predictions about the relative reaction of private versus public debt markets to the
revelation of unethical behavior. Accordingly, we recognize that the matter is open for empirical
investigation.
Hypothesis H6: The revelation of option backdating impacts the cost of public debt of firms to
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a different degree than the cost of private debt.
Not only we expect a different impact on public debt markets, but we note that a difference in
market reaction to the revelation of unethical behavior should affect the firm’s optimal choice between
public and private debt (e.g. Leland and Pyle, 1977; Campbell and Kracaw, 1980; Diamond 1984, 1991;
Fama, 1985; Berlin and Loeys, 1988; Rajan, 1992; Park, 2000). Accordingly, we expect the revelation
of option backdating to impact the firm’s choice between bank financing and public debt markets, with
reliance increasing on the type of debt whose cost is less affected by the revelation of unethical behavior.
Hypothesis H7: The revelation of option backdating affects the proportion of private loans to
public debt issues.
Finally, extant literature further finds that lenders respond to higher information risk by charging
higher interest rates, by employing non-price risk mitigating loan terms, but also by rationing capital
(Stiglitz and Weiss, 1981). We accordingly hypothesize that backdaters could suffer from higher capital
constraints. One of the proxies used in the literature for capital constraints is the sensitivity of
investments to cash flows (Almedia, Campello, and Weisbach, 2004; Fazzari, Hubbard, and Petersen,
1998). We accordingly expect an increased sensitivity of investments to cash flows for backdaters
following the revelation of the practice.
Hypothesis H8: The revelation of option backdating increases the sensitivity of firm’s
investments to cash flows.
3. Data
3.1. Backdating of executive option grants
We follow the methodology in Lie (2005) and Heron and Lie (2007, 2009) to construct the
sample to identify firms that are likely to have modified option grant dates ex-post. Here, and in the
remainder of the paper, we refer to such firms as “backdaters” for brevity—and we conversely refer to
all other firms as “non-backdaters.”
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We first obtain the sample of stock option grants to CEOs from the Thomson Financial Insider
Filing database. This database captures insider transactions reported on SEC forms 3, 4, 5, and 144. We
restrict the sample to transactions that occurred from January 1996 to December 2002.8 We further
require that stock returns be available from 20 trading days before to 20 trading days after the grant date.
Finally, following Heron and Lie (2009), we only include grants to the CEO, President, or Chairman of
the Board. We include all three categories because in many instances, CEOs identify themselves by an
alternate title (such as the President) in their SEC filings (Heron and Lie, 2009).
We eliminate any duplicate grants that occur on a given grant date, so for each firm we have
only one grant “event” for a given date. Like other studies, we focus on unscheduled awards because
these grants are much more likely to be manipulated (Heron and Lie, 2007 and 2009). A grant is
identified as scheduled if a grant is issued on the same date, plus or minus one day, during the preceding
year; otherwise, it is classified as unscheduled. Our final CEO option grants sample consists of 29,421
grants across 4,326 companies over the period 1996–2002.
We follow the methodology in Bizjak, Lemmon, and Whitby (2009) to identify backdated
option grants. The key assumption used by Bizjak, Lemmon, and Whitby (2009) is that if option grant
dates are chosen randomly instead of manipulated, there will not be any unusual performance pattern in
the stock price surrounding the grant date. Alternatively, if firms use hindsight to identify past dates
with particularly low stock prices when setting option grant dates, the stock prices will exhibit a reversal
around the reported grant date. Consistent with backdating, Lie (2005); Heron and Lie (2007);
Narayanan and Seyhun (2008); Bizjak, Lemmon, and Whitby (2009) find that, on average, stock option
grants are preceded by a fall in the stock price, with a subsequent increase in the stock price following
the reported grant date.
8 Similar to Bizjak, Lemmon, and Whitby (2009), we begin with 1996 because it is the first year
Thomson began collecting data on option grants, and we end our sample period in 2002 because Heron
and Lie (2009) report that the incidence of backdating drops dramatically after the implementation of
new insider reporting guidelines associated with the passage of SOX in August of 2002.
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As discussed in Section 2, we recognize that investors become aware of the practice of option
backdating only after the “backdating scandal” originating from a series of academic and media articles.
Given that multiple articles appeared in the press from the early spring of 2006 and the strong media
coverage continuing through the year, we identify 2006 as a “watershed” period and label the pre-2006
period (ending on December 31, 2005) as “pre-revelation” and the post-2006 period (starting January 1,
2007) as the “post-revelation” period.
3.2. Syndicated loans
The main source of data for syndicated loans used in this study is the Thomson Reuters Loan
Pricing Corporation Deal Scan database (“DealScan”). DealScan includes loans, high-yield bonds, and
private placement transactions spanning the globe. The version of the database used in this study covers
loans initiated between January 1980 and December 2013. The database includes data on loan pricing,
contract details, terms and conditions, plus information on loan participants (borrower and lender
identities and sparse accounting data). The loans are organized by “package” and by “facility.” Each
package represents a loosely-defined “deal” and may contain one or multiple facilities—on an average,
there are approximately 1.5 loans in each package. All loans within the same package share the same
borrower, but the identity of the lender, or composition of the lending syndicate, type of loan, loan
initiation date and other contract characteristics can all vary between loans from the same package.9
For each loan, we obtain, as an estimate of cost to the borrower, the all-in-drawn spread. We
further record the loan maturity (recording at initiation, in months), the facility amount (in USD), the
number of lenders, headquarter addresses of lenders, indicator variables identifying collateralized loans
and senior loans, and information on the number of financial and general covenants. We also create
indicator variables based on the database fields identifying “loan type” and “loan purpose.”
We limit our sample to loans identified as “364-Day Facility,” “Bridge Loan,” “Term Loan” of
9 Chava and Roberts (2008) describe the database extensively. Some recent empirical studies using data
from this database include Guner (2006), Qian and Strahan (2007), Sufi (2009), Bae and Goyal (2009)
and Haselmann and Wachtel (2010).
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all types, “Revolver line” of all maturities and “Other Loan,” thus excluding not only bonds and private
placements, but also credit letters and guarantees. We further exclude loans whose status is “Cancelled”
or “Rumor.” Further, we exclude from our sample all loans for which data on the composition of the
lending syndicate is missing and loans with conflicting information (for example, loans marked as
single-lender loans for which multiple lenders are listed). Finally, we include in the sample only loans
to US firms that issue options to executives as part of their compensation packages—related data is
discussed in more detail in the following sections. The rationale for this restriction is to make sure we
have a truly comparable benchmark of firms: given that our backadaters, by definition, issue options,
we want to make sure that factors correlated with the decision to issue options are not inducing sample
selection biases in our analysis. Our final sample includes 7,509 loans to 1,847 firms.
3.3. Bonds
In additional tests, we compare and contrast the impact of the revelation of unethical behavior
between private and public debt markets. The bond sample is the newly issued bonds by U.S. companies
provided by Thomson Reuters’s SDC Global New Issues database. For each bond issue, SDC provides
detailed information, including the cost of the bond in terms of spread, the issue date, yield-to-maturity
(YTM), maturity, proceeds, and ratings. Bond spread is the difference between the yield-to-maturity of
the corporate bond of interest and that of a U.S. Treasury bond with comparable maturity, measured in
bp. Bond issues without information on spread and maturity are excluded. We further exclude
convertible bonds and bonds for which we are unable to obtain necessary financial statement
information. Our final sample includes 2,820 bonds issued by 641 firms.
3.4. Additional data
We obtain accounting information from the COMPUSTAT database. Size (log of total assets),
coverage (coverage ratio), leverage (debt-to-asset ratio), profitability (ROA), valuation (Tobin’s Q),
firm age, cash volatility and bankruptcy risk (Altman Z-score) are estimated and used as a firm level
control variables. The Dealscan-Compustat link file provided by Professor Roberts is used to merge the
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syndicated loan data and the option grant data.10 We exclude bonds and loans issued by financial
institutions and utilities. The number of analysts covering the firm is measured by the number of analysts
issuing earnings forecasts for the relevant firm during the previous year. The information on analysts
are obtained from Thomson Reuters I/B/E/S database. A full list of variables, with definition and
sources, is included in Table 1.
*** Insert Table 1 about here ***
4. Empirical analysis
4.1. Descriptive statistics
As a first step in analyzing the impact of option backdating on loan contracting, we first present
summary statistics regarding price and non-price loan terms in Table 2. Our total sample includes 7,509
loans with complete data. Of those, 34% are to borrowers we identify as “backdaters.” Approximately
half (49%) are classified as relationship loans—that is, loans from lenders that have previously lent to
the same borrower. The average all-in-drawn spread is 229 bp over LIBOR. The average maturity is of
47.42 months, or approximately 4 years. The average loan size is of USD 379.08 million. The average
syndicate is composed of 8.69 members. 72% of the loans are collateralized. Loans in our sample have
an average of 2.15 financial covenants and 4.14 general covenants.
Table 2 also contains descriptive statistics of borrowers. We note that borrowers in our sample
are fairly large firms, with average assets equal to USD 3.92 billion and are often mature firms, with an
average firm age of 24 years.
*** Insert Table 2 about here ***
10 Michael Roberts extended the link data used in Chava and Roberts (2008) that includes Dealscan and
COMPUSTAT links for the period between 1983 and August 2012. The file is available at
http://finance.wharton.upenn.edu/~mrrobert
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4.2. Price and non-price loan terms for backdating borrowers and matched borrowers
To investigate the impact of the revelation of managerial unethical behavior on loan terms, we
compare the terms of loans to borrowers likely having backdated options (“backdaters”) issued after the
revelation of the practice of option backdating (that is, after 2006) to loans to backdaters issued prior to
the revelation of option backdating (that is, prior to 2006). As a metric of the cost of the loan, we rely
on the all-in-drawn spread. Our analysis of the non-price loan terms includes metrics of loan maturity,
the number of lenders participated in the syndicated loan deals, size of the loan, a binary variable
identifying collateralized loans (secured vs not-secured), and the number of financial and general
covenants. To ensure that we are comparing loan terms to truly comparable loans, we match loans
initiated after the revelation of backdating to loans to backdating borrowers from the same industry
(first-digit SIC code) and classified as having the same Loan purpose and Loan type. Our analysis relies
on a comparison of loans issued post-revelation to loans issued prior to the revelation of the practice of
option backdating. As such, it suffers from the empirical problems of all analysis relying on a single
event in time—that is, the need to separate temporal trends from the impact of the event that we are
investigating. Accordingly, we rely on a difference-in-difference comparison by comparing changes in
loans to backdating firms to changes in loans to a sample of non-backdaters.
We present means of variables related to price and non-price loan terms by sub-periods and t-
tests for differences in means in Table 3, Panel A. For backdaters, we find that spreads increase post-
revelation, from a mean of 189.84 bp to 252.48 bp. The approximately 63 bp increase is highly
statistically significant and clearly economically meaningful. While we observe an increase in spreads
in the benchmark sample of non-backdaters, this increase is smaller in magnitude (37 bp). The
difference-in-difference indicates that, post revelation of option backdating, the spread on loans to
backdaters increases by 25.54 bp more than the spread on loans to non-backdaters, and the result is
statistically significant at 1%. We further observe an increase in loan maturity for both samples, but it
is of similar magnitude (4.8 months for backdaters, 4.6 months for non-backdaters) and the difference-
in-difference analysis is not statistically significant. Average loan size increased by USD 78.57 million
17
for backdaters and by USD 131.35 million for non-backdaters; the difference-in-difference is not
statistically significant. We further find that lending syndicate shrink post-revelation for both backdaters
(a loss of 3 lenders, on average) and non-backdaters (a loss of 2 lenders). The difference-in-difference
is statistically significant at 5% and suggests that, after revelation of unethical behavior, lending
syndicates tend to shrink in size. This is consistent with the prior literature finding that higher
information risk borrowers optimally contract with smaller syndicates that, in virtue of their size, have
superior monitoring incentives and advantages in renegotiating eventual covenant violations. We
observe no change in the frequency of use of collateral in either sample. Finally, in both sample we see
fewer financial and general covenants, but no significant difference between backdaters and non-
backdaters.
One potential weakness of the matching test above is that it does not match loans to backdaters
and non-backdaters by initiation year. Given the importance of controlling for temporal trends in
analyzing lending patterns, we construct a second set of matched tests by matching to each loan to a
backdaters a loan to a non-backdater from the same industry (first-digit of the primary SIC code)
initiated during the same year and sharing the same Loan purpose and Loan type. We do so both for pre-
revelation and post-revelation loans and then compute difference-in-differences estimators. In this
second set of tests, we find no statistically significant difference in the spreads on loans to backdaters
and non-backdaters in the pre-revelation period, but a 16.25 bp difference in the spreads post-revelation.
The difference in difference is statistically significant at the 5% level. Our difference-in-difference
analysis further reveals that backdaters receive larger loans both pre- and post-revelation of unethical
behavior—but the difference-in-difference is not statistically significant.
Overall, the results in Table 3 indicate a statistically and economically significant increase in
the cost of loans to backdaters after the revelation of the option backdating practice, which is consistent
with the hypothesized increase in perceived information risk. We also find strong evidence of larger
loan sizes for loans to backdaters even after the revelation. While extant theory does not offer any clear
predictions regarding loan size, the larger loan size is somehow at odds with the idea that unethical
18
borrowers, once identified as such, are likely to suffer from borrowing constraints. The analysis of the
non-price terms of the loan indicates, for the most part, that lenders do not rely on non-price loan terms
to mitigate the impact of unethical behavior and the consequent increase in information risk. In this
sense, the only statistically significant result is an indication that lending syndicates are more
concentrated post-revelation, which is consistent with our priors—but the result is not robust to the two
different matching methodologies we employ.
*** Insert Table 3 about here ***
4.3. The impact of backdating on loan spreads in regression analysis
In this section, we conduct multivariate analysis to gauge the impact of the revelation of option
grant backdating on the cost of bank loans for backdaters. In the regression, the dependent variable is
all-in spread, which is defined by DealScan as the total annual cost, including a set of fees and fixed
spread, paid over LIBOR for each dollar used under the loan commitment. The variable of interest is the
interaction term between a binary variable identifying a backdating borrower and a binary variable
identifying the post-revelation period (Backdater × Post revelation). We also control for firm
characteristic that have been found to be associated with the cost of debt in prior literature: Total assets,
Coverage, Leverage, Profitability, Tobin’s Q, Firm age, Speculative rating, Analysts, Cash volatility,
and Altman Z, as defined in Table 1. We further control for loan characteristics, including loan size, loan
maturity and use of collateral as in Graham, Li, and Qiu (2008) and Engelberg, Gao, and Parsons
(2012).11 We further control for macroeconomic factors by including the default spread (the yield spread
between BAA and AAA corporate bond indices) and term spread (the yield spread between a ten-year
Treasury bond and three-month Treasury bond). We include fixed effects for years, industries (2-digit
11 We recognize that price and non-price terms of the loans are co-determined during negotiations
between members of the lending syndicate and the borrower. However, it is difficult to disentangle each
component without any theory-based simultaneous model. Our previous analysis based on matched
loans indicates that loans to backdaters do not differ substantially in terms of non-price loan
characteristics, which ensures that this simultaneous determination should not affect our findings in
regards to loan spreads.
19
SIC codes), loan type, loan purpose, and debt seniority. Standard errors are adjusted for
heteroskedasticity and loan package level clustering.
Estimated coefficients and levels of significance are reported in column 1, panel A of Table 4.
We find that the coefficient associated with Backdater is not statistically significant, indicating that,
prior to the revelation of unethical behavior, backdaters are paying a similar cost of loans as non-
backdaters. The variable identifying the post-revelation period is associated with a large and statistically
significant coefficient of approximately 57 bp, likely due to the fact that the post-revelation period
coincides with the onset of the financial crisis—while we do not explore the matter further, academic
and anecdotal evidence indicates that a drop in funding liquidity in the banking sector during the crisis
lead to an increase in loan spreads for all borrowers. Finally, the coefficient of the interaction (Backdater
× Post revelation) is significantly positive, suggesting that the revelation of option backdating
significantly increases the cost of bank loans for backdaters. The impact is also economically significant.
While the cost of loans increases for all borrowers by 57 bp after 2006, the cost of bank loans for
backdaters increase by an additional 19 bp after the revelation period. Given that the average firm in the
study has a loan spread of 229 bp, this constitutes roughly an 8 percent increase in loan spreads for
backdaters.
In column 2, panel A of Table 4, we replace backdater by backdated grants (the number of
backdated grants) to test if the frequency of backdating matters. We find that, post revelation, there is a
significantly positive relation between the cost of loans and the number of option grants that are likely
to have been backdated, with each additional backdated option grant leading to a 17 bp increase in the
cost of debt.
We note that firm size might affect the relation between option backdating (and the consequent
perceived increase in information risk post revelation) and cost of loans. On one side, small firms are
considered to be less informational transparent and subject to greater information asymmetry between
insiders and investors. On the other side, large firms are likely to have access to multiple sources of
capital, benefiting both from multiple banking relationships and the access to the bond market. Large
20
firms might be able to mitigate the impact on the cost of debt thanks to greater bargaining power, or by
increasing their reliance on sources of capital that are less sensitive to soft information (for example, by
relying on public debt markets rather than private loans). Hence, we expect the impact of the revelation
of option backdating on the cost of loans to be greater in small, rather than in large firms. We accordingly
subset our sample by total assets and report the results in column 1-4, panel B of Table 4. We find that,
for small borrowers (total assets below the median) the impact is much greater; loan spreads increase by
about 27 bp. On the other side, we find no statistically significant impact for large borrowers.
To further test whether the increase in cost of debt is linked to information risk we report results
using alternative measures of borrower transparency. In column 5-8, panel B of Table 4, we divide our
sample into firms with more analysts following (analysts following above the median) and the ones with
less analysts following (analysts following below the median). We find that loan spreads increase by 26
bp for firms with a smaller analysts following while the impact is not statistically significant for the
borrowers with more analysts. Results in column 9-12, panel B of Table 4 also show consistent findings,
using membership in the S&P 500 index as a proxy for transparency. Firms that are not members of
S&P 500 face higher cost of loans (21 bp) while there is no statistically significant impact for the
members of S&P 500. For all subsets, we document robust results when Backdater is replaced with
Backdate grants (the number of backdated grants).
*** Insert Table 4 about here ***
4.4. The impact of backdating on loan spreads for lenders with low information asymmetry in
regression analysis
The results thus far show that the cost of bank loans are higher for backdaters after the revelation
of the backdating practice in 2006, which is consistent with the notion that banks are concerned about
the quality of disclosure and the perceived level of information risk. Yet, the increase in the cost of debt
is also consistent with the revelation of option backdating bringing to light weaknesses in internal
governance and oversight. Accordingly, to test whether the impact of option backdating on loan spreads
21
is really due to an increase in perceived information asymmetry, we rely on an additional set of
regression-based tests. In particular, we identify borrowers who are less affected by the decrease in
perceived firm transparency, due to having gained superior knowledge about the firm via prior lending
interactions.
To explore this issue, we follow the prior literature to construct a dummy variable (Relationship
loan) identifying lenders with prior lending relationship with the borrowers. We include Relationship
loan by itself and an interaction term Backdater × Post revelation × Relationship loan. For
completeness, we include also all the relevant two-way interaction terms. If prior lending relationship
mitigates the adverse effect of increased information risk, we expect to see a negative coefficient
associated with the three-way interaction term.
The regression results are reported in Table 5. In this model, we again find that backdaters pay
a similar cost of loans as non-backdaters prior to the revelation of the practice. We further find, in
accordance with extant literature, that relationship loans are associated with lower spreads, by about 7
bp, for both backdaters and non-backdaters. Most importantly, we find that backdaters who receive loans
from a “new” (non-relationship) lenders incur, post revelation, an increase in the cost of loan of
approximately 34 bp. This result is highly statistically significant—and the magnitude of the finding
indicates that borrowers with no prior relationships with lenders are penalized even more than the
average borrower. On the other side, the coefficient associated with the three-way interaction term is a
negative 28 bp, indicating that the increase in the cost of loan for backdaters receiving relationship loans
post revelation is tiny, approximately, 6 bp. We find similar result in column 2 of Table 5 by replacing
backdater by backdated grants (the number of backdated grants). Consistent with our expectations, our
evidence indicates that the increase in the cost of loans is mitigated by the existence of prior lending
relationships with the borrower.
*** Insert Table 5 about here ***
Extant literature finds that lenders who are physically closer to borrowers are capable of
mitigating information risk via more efficient monitoring (Agarwal and Hauswald, 2010). We hence test
22
whether the impact of the revelation of unethical behavior is mitigated by geographical proximity. In
particular, we add a three-way interaction term between Backdater, Post revelation, and Average
distance, measured as the average distance between the states in which the headquarters of the lender
and borrower are located, in miles. The regression results are reported in Table 6. While the coefficient
associated with the Backdater and Post revelation interaction variable is not statistically significant, the
coefficient associated with the three-way interaction variable is positive and statistically significant. The
cost of the loan to backdating borrowers increases, post revelation, by 2 bp for every 100 miles of
distance between borrower and lender. Consistent with our expectations, we find that the increase in the
cost of loans is mitigated by geographical closeness of borrowers and lenders.
*** Insert Table 6 about here ***
4.5. The impact of backdating on bond spreads in regression analysis
The results presented so far strongly indicate that lenders react to the revelation of unethical
behavior by charging a higher cost of debt in response to a perceived increase in information risk.
Consistently, our findings indicate that lenders with better access to firm-level information react less
forcefully. In further analysis, we rely on the findings of extant literature, which identifies public-debt
lenders (bond investors) as having both weaker incentives to monitor borrowers and inferior access to
information. Based on this stream of extant literature, our expectation is that, given their greater
exposure to information risk, public-debt lenders will react even more forcefully to the revelation of
unethical behavior. On the other side, a second stream of extant research indicates weaker reliance by
public debt markets on soft information, which could translate into a weaker impact at the revelation of
unethical behavior.
We replicate the analysis for the cost of debt presented in Table 4 by using a sample of public
debt issues. Firm characteristics are controlled as was done in loan analysis. Bond characteristics are
included in our model: Bond size, Bond maturity, Callable bond (binary variable), Puttable bond (binary
variable), Subordinated bond (binary variable), and Bond rating. Prior bond issuance is also controlled
which identifies whether the firm had issued bond before or not.
23
Our findings, presented in Table 7, show that the cost of bond (bond spread) increases for all
firms, which is consistent with the result from loan sample. However, we find no evidence of a
statistically significant incremental increase in the cost of public debt for backdaters before or after the
revelation of unethical behavior. In unreported tests, we further subset the sample on the basis of proxies
for borrower transparency (size, analyst following, and S&P500 index inclusion). We similarly find no
significant incremental increase in the cost of bonds to backdaters in any of the subsamples.
*** Insert Table 7 about here ***
4.6. Does backdating affect the choice of private over public debt?
Given that we find that the cost of loans to backdaters increases post revelation of the practice,
while the cost of public debt appears unaffected, we question whether the revelation increases
backdaters’ reliance on public debt, compared to private loans. Accordingly, we follow Bharath, Sunder,
and Sunder (2008) and model the choice of debt issue (public vs. private) in a probit framework. The
response variable is a binary variable equal to one if the debt issue is public (bond) and equal to zero if
the debt issue is private (loan). We control for firm characteristics, by including the variables Total
assets, Coverage, Leverage, Profitability, Tobin’s Q, Firm age, and Altman Z, as defined in Table 1. We
further control for Prior bond issuance, using a binary variable equal to one if the firm has issued bonds
at any point of time prior to the debt issuance in question. Our main variable of interest is, as before, the
interaction between Backdater and Post revelation. The sample includes both private and public debt
issues; the criteria for inclusion is as described in Section 3. Our findings are presented in Table 8.
*** Insert Table 8 about here ***
Coefficient estimates associated with control variables are consistent with the findings of prior
studies. For instance, larger firms, with more leverage, higher valuation, and more tangible assets are
more likely to rely on public rather than private debt. Prior bond issuances are also strongly associated
with the probability of issuing new public debt. Backdaters are equally likely to choose public debt over
private debt as non-backdaters, prior to the revelation of the option backdating practice. Consistent with
our expectations, we find that, following the revelation of the option backdating practice, the likelihood
24
of issuing public debt increases for backdaters. Our results are both statistically and economically
significant. All else equal, we find that the probability of a public debt issue increases by over 21
percentage points for backdaters following the revelation of the option backdating practice.
In Panel B of Table 8, we subset our sample based on metrics of borrower opacity. We find that
large borrowers (with total assets above the median) are more likely to increase their reliance on public
debt markets post-revelation of backdating (although results are significant only at the 10% level). This
finding is consistent with the idea that small borrowers are more reliant on bank loans and are thus
forced to accept the increase in the cost of loans following the revelation of option backdating, while
large borrowers can mitigate the increase in the cost of debt by shifting some borrowing to the bond
market, which gives lower weight to soft information (the revelation of dishonest behavior). We
similarly find evidence that borrowers with greater-than-median analyst following increase their reliance
on public debt markets, while results are not statistically significant for borrowers with smaller analyst
following. Finally, we find evidence that borrowers that are included in the S&P500 are more likely to
increase their reliance on public debt markets, although the result has weak statistical significance.
As an additional test of backdaters’ increased reliance on public debt markets, we create a metric
measuring the proportion of new public debt as a fraction of total new debt issues. Accordingly, we
compute, for each firm-year, the ratio between the dollar amount of new public debt issues over the sum
of new public and private debt issues. We then regress this metric on the same set of explanatory
variables used in Table 8. Results are reported in Table 9. We find that all borrowers increase the
proportion of public debt issuance post-revelation (likely, due to the lower liquidity in bank lending
during the financial crisis), by approximately 36 percentage points. Yet, backdaters’ ratio of public to
total debt increases by a further 3 percentage points. The finding is statistically significant at the 10%
level. In robustness tests, we document similar results using the number of backdated option grants in
place of the binary variable identifying backdaters.
*** Insert Table 9 about here ***
4.7. Joint determination of loan price and non-price terms
25
Price and non-price loan terms are possibly co-determined, as they are the result of a complex
negotiation between borrowers and loan arrangers. Bharath et al. (2011) address the join determination
of loan contract terms, focusing on loan spreads, loan maturity, and loan collateral requirements. They
do so within an instrumental variable (IV) framework. Following Dennis, Nandy, and Sharpe (2000),
Bharath et al. (2011) assume a unidirectional relationship between the spread and the non-price terms
of loans. In their model, while maturity and collateral affect each other, loan spreads are only affected
by maturity and collateral, but do not, in turn, affect non-price terms. As discussed by Bharath et al.
(2011), this framework is consistent with most syndication processes, as lead banks tend to first
negotiate non-price terms with borrowers, then set interest rates on loans in discussion with other
potential syndicate members. We employ a similar two stages least squares instrumental variable
framework. In particular, we use Asset maturity as the instrument variable for loan maturity (Bharath et
al., 2011; Barclay, Marx and Smith, 2003). The intuition is that longer life assets are likely to be financed
by longer term debt (Hart and Moore, 1994). We use Term spread as another instrument variable for
loan maturity (Brick and Ravid, 1985, 1991; Bharath et al., 2011). Bharath et al. (2011) find the positive
correlation between loan maturity and term spread which was consistent with the prediction made in
Brick and Ravid (1985, 1991). We use Loan concentration, the ratio of that loan facility amount to the
total firm’s debt, as the instrument for loan collateral. As discussed in Bharath et al. (2011), this is
consistent with the finding that if a particular loan facility is a large portion of a firm’s debt, it is more
likely to be secured (as in Berger and Udell, 1990; Boot, Thakor and Udell, 1991; and Dennis, Nandy
and Sharpe, 2000). Finally, we use the average all-in-drawn spread on loans over the previous six
months as an instrument for loan spreads. As Bharath et al. (2011) discuss, it is not likely that the past
average spread would affect the non-pricing terms of the particular loan. 12
12 We provide an overview of the model in this section. For additional detail on the estimation of this
simultaneous equation system and tests for instrument validity, we refer interested readers to Bharath et
al (2011). Bharath et al. (2011) use a second instrument for loan maturity—a variable identifying
regulated industries as in Barclay and Smith (1995). That is due to the fact that higher regulatory
oversight should reduce agency costs of debt and lead to longer maturity. We do not include such
instrument, as we exclude regulated firms from our sample.
26
The results are reported in Table 10. For brevity, we only report the results for loan spread. The
results in Table 10 suggest that our earlier analysis of cost of bank loans are robust to the joint
determination of loan price and non-price terms. We find that, post-revelation, the cost of debt for
backdaters increases by 24 bp more than for non-backdaters. When replacing the binary variable
identifying backdaters with the count of backdated option grants, we find that, for each backdated option
grant, the cost of debt increases by 22 bp post-revelation.
*** Insert Table 10 about here ***
4.8. Financial constraints
Extant literature further finds that lenders respond to higher information risk by charging higher
interest rates, by employing non-price risk mitigating loan terms, but also by rationing capital (Stiglitz
and Weiss, 1981). This should lead to increased capital constraints for borrowers. Multiple studies have
documented that financially constrained firms display an increased sensitivity of investments to cash
flow (Almeida and Campello, 2007; Almeida, Campello, and Weisbach, 2004; Fazzari, Hubbard, and
Petersen, 1988). Consistently, we test whether the sensitivity of investments to cash flow is greater for
backdaters post revelation of the practice.
We test this increase in financial constraints in regression analysis. We exclude firms with
negative cash flows as in Allayannis and Mozumdar (2004). Following Guner, Malmendier, and Tate
(2008), we use capital expenditures (property, plant, and equipment) scaled by lagged capital as the
response variable. We include, as explanatory variables, Cash flow, Backdater, Post revelation, and their
two- and three-way interactions. We further add control variables consistent with extant literature: firm
characteristics (Tobin’s Q and log of Total assets and their interactions with Cash flow), and fixed effects
for years, ratings, and industry. The main variable of interest in our tests is the three way interaction
(Backdater × Cash flow × Post revelation). We expect that an increase in capital constraints for
backdaters should lead to a positive coefficient estimate associated with this three-way interaction.
Coefficient estimates are presented in Table 10. Consistent with prior literature, we find a
positive relation between investments and cash flows. Most importantly, we find that this relation is
27
much stronger for backdaters post-revelation, indicating that the revelation of option backdating leads
to increased capital constraints. This increase is both statistically and economically significant, as the
coefficient measuring the sensitivity of investments to cash flows more than doubles for backdaters post-
revelation. Further, we find that the sensitivity of investments to cash flows is related not only to the
binary variable identifying backdaters, but also to the number of backdating grant contracts, suggesting
that more frequent and rampant backdating leads to more capital constraints.
*** Insert Table 11 about here ***
4.9. Lucky CEOs
As a robustness test, we employ an alternative method to identify backdaters. In particular, we
use the list of option grants that are likely to have been backdated in Bebchuk, Grinstein, and Peyer
(2010) to construct a binary variable (Lucky CEO) which equals to 1 if the firm is likely to have
backdated option grants to CEOs or 0 otherwise.13 Bebchuk, Grinstein, and Peyer (2010) identify a grant
as “lucky” if it was given at the lowest stock price of the month—for more details on the exact
methodology and on the sample construction, interested readers should refer to their manuscript.
In untabulated analysis, we find that the coefficient on the interaction variable Lucky CEO× Post
revelation is significant and positive in the loan sample, implying an increase in cost of bank loans for
backdaters post revelation. In contrast, we find no statistically significant evidence in the bond sample,
consistent with our earlier findings.
5. Conclusion
Our study extends research on the impact of corporate culture on corporate behavior and
performance (e.g., Guiso, Sapienza, and Zingales, 2015; Pan, Siegel, and Wang, 2014; Biggerstaff,
Cicero, and Puckett, 2015) by examining the impact of corporate (unethical) culture on the cost of debt
and on the choice of private versus public debt as financing vehicles. We build on the prior literature on
13 The data are available at Lucian Bebchuk’s webpage: http://www.law.harvard.edu/faculty/bebchuk/data.shtml
28
executive option backdating to measure a firm’s unethical culture. Armstrong and Larcker (2009) argue
that backdating may be a manifestation of lax ethical norms in the firm. Biggerstaff, Cicero, and Puckett,
(2015) show that firms with CEOs who personally benefit from options backdating are more likely to
engage in other corporate misbehaviors, suggestive of an unethical corporate culture. Numerous papers
on unethical culture of firms suggests that employees, especially the top executives, in fraudulent firms
are more likely to be involved in extensive corporate malfeasance.
Our findings indicate that the revelation of unethical behavior—option backdating— does
indeed lead to a higher cost of private debt for borrowers, leading to an increase in spreads by 19 bp,
which is approximately a 8% increase relative to the pre-revelation period. We recognize that this impact
could be driven by an increase in information risk or, possibly, by revised expectations about future
agency costs. Accordingly, to ensure that the results are driven by the information channel, we further
show that the results are mitigated by lenders suffering from lower sensitivity to information
asymmetry—that is, lenders who, in virtue of having established prior relationships with the borrowers,
have better information about the firm or lenders who, thanks to geographical proximity, are able to
lower information asymmetry via (less expensive) monitoring. Finding an interaction between borrower
sensitivity to information asymmetry and the revelation of backdating in determining the firm’s cost of
debt provides strong evidence in support of option backdating increasing cost of debt via information
asymmetry. We nonetheless recognize that we cannot, ultimately, rule out the possibility that the impact
is at least partially due to revise expectation about future agency costs.
As additional tests of the information channel, we document that the increase in the cost of debt
post-revelation of backdating is stronger for more opaque borrowers (smaller firms with a lower analyst
following and not included in the S&P 500 index).
We further hypothesize that lenders could also react by requesting stricter non-price loan terms
to mitigate the increase in information risk. Accordingly, we expect shorter loan maturities, a high
number of both general and financial covenants, and greater reliance on collateral. Yet we do not find,
29
in our analysis, robust evidence of lenders mitigating this increase in information risk by contracting
stricter non-price loan terms.
We also recognize that bond investors could react differently from banks at the revelation of
unethical behavior. A greater sensitivity to information asymmetry documented in extant literature
would suggest that bond investors could react even more forcefully. On the other side, extant literature
also finds that banks rely more on soft information in their lending decisions, while public debt markets
rely mostly on hard information (reported financial statements), which might suggest a weaker reaction
to the revelation of unethical behavior for public-debt lenders. Consistent with the letter explanation, we
find no robust increase in the cost of public debt, indicating that banks give greater emphasis to
managerial character and firm culture than public-debt investors. Consistently, we find a significant shift
in the choice of public versus private debt for option backdaters following the revelation of the practice.
Our findings have implication for bond investors, as they suggest that, by ignoring soft information,
they might suffer from adverse selection costs, since unethical behavior tend to increase reliance on
public debt markets.
Finally, we document that the revelation of option backdating leads to an increased sensitivity
of investments to cash flows, which has been interpreted in prior studies as a metric of financial
constraints. Accordingly, we provide evidence that unethical behavior not only increases the cost of debt
of backdaters, but that it also leads to capital rationing and increased financial constraints.
30
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35
Appendix A
We follow the methodology in Bizjak, Lemmon, and Whitby (2009) to identify backdated option
grants. The key assumption used by Bizjak, Lemmon, and Whitby (2009) is that if option grant dates
are chosen randomly instead of manipulated, there will not be any unusual performance pattern in the
stock price surrounding the grant date. Alternatively, if firms use hindsight to identify past dates with
particularly low stock prices when setting option grant dates, the stock prices will exhibit a reversal
around the reported grant date. Consistent with backdating, Lie (2005); Heron and Lie (2007);
Narayanan and Seyhun (2008); Bizjak, Lemmon, and Whitby (2009) find that, on average, stock option
grants are preceded by a fall in the stock price, with a subsequent increase in the stock price following
the reported grant date.
In order to identify individual grant dates that are likely to have been manipulated, we employ the
following statistical approach similar to that used by Bizjak, Lemmon, and Whitby (2009). First, we
randomly select 1,000,000 trading days from our final sample of firm years and define these as
hypothetical option grant dates. We calculate the cumulative raw stock returns over the 20 trading-day
periods before and after the randomly selected grant dates. To measure reversals around the hypothetical
grant dates, we compute the difference between the post-grant and pre-grant 20-day cumulative returns.
Next, we sort firms into quartiles based on the monthly standard deviation of stock returns calculated
over the two-year period preceding the hypothetical grant date. Separating firms into groups based on
the volatility of returns controls for the tendency that firms with higher stock-price volatility exhibit
more frequent and larger reversals on average, even in the absence of backdating. For the sample of
random grant dates in each volatility quartile, we identify the magnitude of the post- to pre-grant return
difference that corresponds to a pre-specified confidence level (e.g., 95% or 99%).
Finally, to identify whether an actual option grant date is likely to have been backdated, we
compute the difference in the post-grant and pre-grant 20-day cumulative stock returns around the actual
reported grant date and compare this value to the cutoff level corresponding to the desired confidence
level based on randomly assigned grant dates. If the magnitude of the return difference around the actual
36
grant date exceeds the cutoff level, the grant is classified as backdated. At the firm level, we classify a
firm as having backdated options in a given year if we classify any of the option grant dates by that firm
in that year as backdated. Table A1 presents the cutoff levels for returns around the grant date that
correspond to a given confidence level within each of the volatility quartiles. In general, the magnitude
of the forty-one-day cumulative returns (–20 to +20) that are necessary to identify a grant as having been
backdated are large and increase significantly with return volatility. For example, to identify backdated
grants at the 95% confidence level, cumulative returns around the grant date must be larger than 18.39%
for firms with low volatility and must exceed 61.95% for firms with high volatility. At the 99%
confidence level, the corresponding return cutoffs are 30.60% and 113.71% for low and high volatility
firms, respectively.
*** Insert Table A1 about here ***
The number of firms identified as backdaters using difference confidence levels, by year, is
presented in Table A2.
*** Insert Table A2 about here ***
37
Table 1. Variable definitions
Variable Name Definition Source
Terms of the loans
Maturity Time to maturity (in months) at issuance. Dealscan
Loan size The total size of the facility committed in US dollars. Dealscan
Spread Amount the borrower pays in basis points over LIBOR for each dollar drawn down. Dealscan
Number of lenders Number of participants (including lead arranger) in the facility. Dealscan
Collateral A binary variable equal to one if the facility is secured or zero otherwise. Dealscan
Financial covenants Number of financial covenants in a loan. Dealscan
General covenants Number of general covenants in a loan. Dealscan
Relationship loan A binary variable that equals one if the borrower received a loan arranged by the lender in the past five years or zero otherwise. We refer to Bharath, Dahiya, Saunders, and Srinivasan (2011).
Dealscan
Average distance The average distance between the headquarter states of the syndicate lenders and the headquarter state of the borrower, in miles.
Dealscan
Option backdating
Backdater A binary variable that equals one if the borrower is likely to have backdated option grants or 0 otherwise. As in Heron and Lie (2007).
Thomson Financial Insider Filing database
Backdated grants Number of backdated option grants. Thomson Financial Insider Filing database
Lucky CEO A binary variable that equals one if the borrower has granted an option to CEO on a day which had the lowest price of the month or 0 otherwise.
Bebchuk, Grinstein, and Peyer (2010)
Post revelation A binary variable that equals one if the loan is initiated after 2006 or zero otherwise. Dealscan
Firm characteristics (as of Dec 31 of the year preceding loan initiation)
Total assets Book value of assets of the firms. Compustat
Coverage Ratio of EBITDA to interest expenses from the year preceding loan initiation. Compustat
Leverage Ratio of book value of debt (total) to book value of assets from the year preceding loan initiation. Compustat
Profitability Ratio of earnings before interest, taxes, depreciation and amortization to sales from the year preceding loan initiation.
Compustat
Tobin’s Q Ratio of (book value of assets - book value of equity + market value of equity) to book value of assets from the year preceding loan initiation.
Compustat
38
<Table 1 continued>
Variable Name Definition Source
Firm characteristics
Firm age Number of years since first appearance in Compustat database. Compustat
Altman Z 1.2 (Net working capital/Total assets) +1.4 (Retained earnings/Total assets) +3.3 (Earnings before interest and taxes/Total Assets) +0.6 (Market value of equity/Book value of liabilities) +1.0 (Sales/Total assets).
Compustat
Cash volatility Ratio of standard deviation of quarterly net operating cash flows over sixteen quarters before the loan initiation year to book value of assets from the year preceding loan initiation.
Compustat
Speculative rating A binary variable that equals one if the firm has speculative grade (lower than BBB-) in S&P long term credit rating or 0 otherwise.
Compustat
Not rated A binary variable that equals one if the firm has S&P long term credit rating or 0 otherwise. Compustat
Cash flow Ratio of (income before extraordinary items + depreciation and amortization) to lagged capital (property, plant, and equipment).
Compustat
Investment Ratio of (capital expenditures) to lagged capital (property, plant, and equipment). Compustat
Analysts Number of analysts following the firm during previous year. I/B/E/S
Bond variables
Bond spread Difference between yield to maturity of a bond and yield to maturity of a risk-free bond measured in bp.
SDC Platinum
Bond size Amount of bonds measured in millions of dollars. SDC Platinum
Bond maturity Maturity of the bond measured in months. SDC Platinum
Callable bond A binary variable that equals one if the bond is callable or zero otherwise. SDC Platinum
Puttable bond A binary variable that equals one if the bond is puttable or zero otherwise. SDC Platinum
Subordinated bond A binary variable that equals one if the bond is subordinated bond or 0 otherwise. SDC Platinum
Prior bond issuance A binary variable that equals one if the firm had issued a bond or zero otherwise. SDC Platinum
Bond rating The bond rating by S&P at issuance SDC Platinum
Public debt / Total debt
Ratio of (aggregate volume of newly issued bonds in each year) to (aggregate volume of newly issued bonds in each year + aggregate volume of newly issued loans in each year)
SDC Platinum, Dealscan
39
<Table 1 continued>
Variable Name Definition Source
Choice between public and private debt
Public debt A binary variable that equals one if the debt issued by the firm is public debt or zero otherwise. SDC Platinum / Dealscan
Macroeconomic controls
Credit spread The difference between yield of AAA corporate bond and that of BAA corporate bond. Federal reserve board of governors
Term spread The difference between yield of 10-year Treasury bond and that of 2-year Treasury bond. Federal reserve board of governors
40
Table 2. Sample descriptive statistics
Table 2 presents summary statistics of observations for the variables listed in Table 1 for the sample of loans from
2000 to 2012. All loans are to US borrowers issuing option grants to executives. The number of observations,
mean, 25 percentile, median, 75 percentile, and standard deviations are reported in the table.
N Mean 25th % Median 75th % Std
Loan characteristics
Relationship loan (binary) 7509 0.49 0.00 0.00 1.00 0.50
Spread 7509 229.00 125.00 200.00 300.00 154.58
Maturity (Months) 7509 47.42 36.00 54.00 60.00 21.46
Facility amount (USD M) 7509 379.08 50.00 150.00 400.00 918.46
Number of lenders 7509 8.69 3.00 6.00 12.00 8.73
Collateral (binary) 7509 0.72 0.00 1.00 1.00 0.45
Financial covenants (Number) 7509 2.15 1.00 2.00 3.00 1.33
General covenants (Number) 7509 4.14 2.00 3.00 7.00 2.59
Borrower Characteristics
Backdater (binary) 7509 0.34 0.00 0.00 1.00 0.47
Backdated grants 7509 0.46 0.00 0.00 1.00 0.76
Total assets (USD M) 7509 3915.18 336.21 986.62 2859.40 11585.33
Coverage 7509 35.14 2.98 6.17 14.39 311.70
Leverage 7509 0.32 0.16 0.28 0.43 0.24
Profitability 7509 0.14 0.07 0.13 0.20 0.92
Tobin's Q 7509 1.67 1.10 1.38 1.86 1.19
Firm age 7509 23.55 10.00 17.00 36.00 16.23
Altman Z 7509 1.57 0.85 1.65 2.41 1.81
Analysts 7509 7.98 0.00 5.00 12.00 9.03
Cash volatility 7509 0.05 0.03 0.04 0.06 0.03
41
Table 3. Matching test results
Table 3 compares means of variables related to price and non-price terms of matched loans. Tests for significance of mean differences are implemented as paired t-tests with
standard errors clustered at the borrower and year level; tests of significance are two-sided. All variables are as defined in Table 1; the sample includes loans to US borrowers
issuing option grants to executives spanning the years 2000 to 2012. In Panel A, loans initiated before the revelation of backdating are compared to loans initiated after the
revelation. Loans are matched by loan purpose, loan type, and industry (one-digit SIC code); from this set, the loan with the closest initiation date is selected. The difference
in difference between the firms identified as “backdaters” and “non-backdaters” are reported in the last column. In Panel B, loans to “backdaters” are compared to the ones
lent to “non-backdaters.” Loans are matched by loan purpose, loan type, industry (one-digit SIC code), and loan initiation year; from this set, the loan with the closest
initiation date is selected. The difference in difference between pre-revelation and post-revelation is reported in the last column. Significance levels are denoted as follows:
“*” indicates significance at the 0.10 level; “**” indicates significance at the 0.05 level; “***” indicates significance at the 0.01 level.
Panel A: First digit of SIC, loan purpose, and loan type matching
Backdater Difference Non-backdater Difference Diff in Diff
(1) Pre revelation
(2) Post revelation
(2)-(1) (3) Pre
revelation (4) Post
revelation (4)-(3) [(2)-(1)]-[(4)-(3)]
Spread 189.84 252.48 62.64 *** 201.45 238.55 37.11 *** 25.54 ***
6.16 5.79 3.12
Maturity (Months) 48.19 52.97 4.78 *** 47.49 52.13 4.64 *** 0.14
3.73 6.03 0.13
Facility amount (USD M) 485.75 564.32 78.57 * 412.69 544.03 131.35 *** -52.78
1.87 4.08 -0.99
Number of lender 12.16 8.88 -3.29 *** 10.73 8.60 -2.13 *** -1.16 **
-6.36 -6.47 -2.54
Collateral 68.05 69.05 1.00 67.10 69.31 2.21 -1.20
0.31 1.12 -0.48
Financial covenants (Number)
2.35 1.69 -0.66 *** 2.31 1.71 -0.60 *** -0.06
-9.12 -10.71 -0.85
General covenants (Number)
4.27 3.76 -0.52 *** 4.44 3.80 -0.64 *** 0.12
-3.17 -5.70 0.89
Observations 798 798 798 1678 1678 1678
42
Panel B: First digit of SIC, facility start year, loan purpose and loan type matching
Pre revelation Difference Post revelation Difference Diff in Diff
(1) Backdater
(2) Non-backdater
(1)-(2) (3)
Backdater (4) Non-
backdater (3)-(4) [(3)-(4)]-[(1)-(2)]
Spread 218.02 218.82 -0.80 250.22 233.97 16.25 * 17.05 **
-0.15 1.92 2.45
Maturity (Months) 46.08 46.00 0.08 53.04 53.13 -0.09 -0.17
0.10 -0.11 -0.17
Facility amount (USD M) 325.10 241.67 83.43 ** 550.79 427.78 123.01 ** 39.59
2.35 2.14 0.80
Number of lender 9.37 8.43 0.93 ** 8.97 8.51 0.46 -0.48
2.16 1.07 -1.03
Collateral 74.33 75.93 -1.60 68.99 72.22 -3.23 -1.63
-1.00 -1.27 -0.78
Financial covenants (Number)
2.47 2.40 0.07 1.70 1.73 -0.03 -0.10
1.17 -0.39 -1.39
General covenants (Number)
4.38 4.42 -0.04 3.72 3.78 -0.06 -0.02
-0.33 -0.40 -0.16
Observations 1691 1691 1691 774 774 774
43
Table 4. Regression analysis, loan spreads
Table 4 reports regression results. The response variable is the all-in-drawn spread (Spread). All variables are as
defined in Table 1. The sample includes loans to US borrowers issuing option grants to executives spanning the
years 2000 to 2012. In Panel A, results for the overall sample is reported. Results for subsamples are reported in
Panel B: loans granted to borrowers with total assets below median and loans granted to borrowers with total
assets above median (column 1 to 4), loans granted to borrowers with number of analysts following below median
and loans granted to borrowers with number of analysts following above median (column 5 to 8), and loans granted
to non S&P 500 firms and loans granted to S&P 500 firms (column 9 to 12). We control for non-price terms of
loans by including Loan size (log), Maturity (log), and Collateral (unreported in Panel B). Firm characteristics
are controlled by adding, as (unreported) control variables, Total assets (log), Coverage (log), Leverage,
Profitability, Tobin’s Q, Firm age (log), Speculative rating, Analysts (log), Cash volatility and Altman Z. Credit
spread and Term spread are included as (unreported) macroeconomic control variables. The model includes fixed
effects for loan initiation year, two-digit SIC code, loan type, and loan purpose. t-statistics from two-sided tests
of significance are reported under the parameter estimates, in grey italics. Significance levels are denoted as
follows: “*” indicates significance at the 0.10 level; “**” indicates significance at the 0.05 level; “***” indicates
significance at the 0.01 level.
Panel A: Overall sample
Spread Spread
Post revelation 57.12 *** 59.24 ***
5.48 5.72
Backdater -2.42
-0.55
Backdated grants 0.03
0.01
Backdater × Post revelation 18.98 ***
2.69
Backdated grants × Post revelation 16.77 **
2.06
Loan size (log) -15.23 *** -15.16 ***
-8.53 -8.48
Maturity (log) -24.40 *** -24.37 ***
-5.00 -4.99
Collateral 62.47 *** 62.37 ***
16.80 16.79
Intercept Yes Yes
Firm characteristics Yes Yes
Macroeconomic controls Yes Yes
Fixed effects (year, industry, loan type, loan purpose)
Yes Yes
Observations 7509 7509
Adjusted R2 0.5570 0.5568
44
Panel B: Data subsets, transparent vs. opaque borrowers
Total assets < median Total assets >= median Analysts < median Analysts >= median Non S&P 500 S&P 500
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Post revelation 44.41 *** 46.60 *** 71.46 *** 72.56 *** 36.72 ** 37.55 ** 68.55 *** 71.49 *** 66.43 *** 68.35 *** 37.94 *** 40.24 ***
2.41 2.54 6.01 6.09 2.06 2.11 5.24 5.48 5.13 5.33 3.17 3.27
Backdater 1.71 -2.79 -8.49 2.54 -2.97 8.87
0.26 -0.57 -1.31 0.47 -0.60 1.33
Backdated grants 5.68 -2.05 -9.06 6.74 -0.66 12.49 0.75 -0.35 -1.13 1.12 -0.12 1.44 Backdater
× Post revelation 27.42 ** 5.93 26.05 ** 11.87 20.66 ** -2.11
2.39 0.72 2.31 1.40 2.53 -0.19
Backdated grants
× Post revelation 28.16 ** 3.91 30.28 ** 5.98 19.84 ** -9.63
2.07 0.40 2.36 0.60 2.08 -0.67
Intercept Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Other terms of the loans
Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Firm characteristics
Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Macroeconomic controls
Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Fixed effects (year, industry, loan type, loan purpose)
Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 3754 3754 3755 3755 3448 3448 4061 4061 6103 6103 1406 1406
Adjusted R2 0.4344 0.4344 0.6544 0.6543 0.5296 0.5296 0.5707 0.5705 0.4864 0.4861 0.6898 0.6900
45
Table 5. Regression analysis, loan spreads and relationship lending
Table 5 reports regression results. The response variable is the all-in-drawn spread (Spread). All variables are as
defined in Table 1. The sample includes loans to US borrowers issuing option grants to executives spanning the
years 2000 to 2012. We control for (unreported) firm characteristics as in Table 4. Credit spread and Term spread
are included as (unreported) macroeconomic control variables. The model include fixed effects for loan initiation
year, two-digit SIC code, loan type, and loan purpose. t-statistics from two-sided tests of significance are reported
under the parameter estimates, in grey italics. Significance levels are denoted as follows: “*” indicates significance
at the 0.10 level; “**” indicates significance at the 0.05 level; “***” indicates significance at the 0.01 level.
Spread Spread
Post revelation 56.37 *** 59.49 ***
4.94 5.24
Backdater × Post revelation 33.55 ***
3.26
Backdated grants × Post revelation 30.78 ***
2.66
Backdater × Post revelation × Relationship loan -27.67 **
-2.17
Backdated grants × Post revelation × Relationship loan -26.01 *
-1.79
Backdater -6.18
-1.18
Backdated grants -3.05
-0.49
Relationship loan -6.58 * -5.66
-1.70 -1.47
Backdater × Relationship loan 8.03
1.10
Backdated grants × Relationship loan 6.63
0.79
Relationship loan × Post revelation 3.08 0.97
0.42 0.13
Intercept Yes Yes
Other terms of the loans Yes Yes
Firm characteristics Yes Yes
Macroeconomic controls Yes Yes
Fixed effects (year, seniority, industry, loan type, loan purpose)
Yes Yes
Observations 7509 7509
Adjusted R2 0.5577 0.5573
46
Table 6. Regression analysis, loan spreads and geographical distance
Table 6 reports regression results. The response variable is the all-in-drawn spread (Spread). All variables are as
defined in Table 1. The sample includes loans to US borrowers issuing option grants to executives spanning the
years 2000 to 2012. We control for (unreported) firm characteristics as in Table 4. Credit spread and Term spread
are included as (unreported) macroeconomic control variables. The model include fixed effects for loan initiation
year, two-digit SIC code, loan type, and loan purpose. t-statistics from two-sided tests of significance are reported
under the parameter estimates, in grey italics. Significance levels are denoted as follows: “*” indicates significance
at the 0.10 level; “**” indicates significance at the 0.05 level; “***” indicates significance at the 0.01 level.
Spread Spread
Post revelation 51.44 *** 53.12 ***
4.17 4.33
Backdater × Post revelation -3.60
-0.28
Backdated grants × Post revelation -9.71
-0.64
Backdater × Post revelation × Average distance 0.02 **
2.02
Backdated grants × Post revelation × Average distance 0.03 **
1.99
Backdater 4.20
0.57
Backdated grants 9.45
1.09
Average distance 0.002 0.003
0.63 0.78
Backdater × Average distance -0.01
-1.33
Backdated grants × Average distance -0.01
-1.61
Average distance × Post revelation 0.004 0.004
0.58 0.62
Intercept Yes Yes
Other terms of the loans Yes Yes
Firm characteristics Yes Yes
Macroeconomic controls Yes Yes
Fixed effects (year, seniority, industry, loan type, loan purpose)
Yes Yes
Observations 7412 7412
Adjusted R2 0.5627 0.5625
47
Table 7. Regression analysis, bond spreads
Table 7 reports regression results. The response variable is the bond spread, computed as the difference between
the yield to maturity of a bond and the yield to maturity of a risk-free bond measured in basis points. All variables
are as defined in Table 1. The sample includes bonds issued by US firms issuing option grants to executives
spanning the years 2000 to 2012.We control for (unreported) firm characteristics as in Table 4 and bond
characteristics: Bond size, Bond maturity, Callable bond, Puttable bond, Subordinated bond, Bond rating. Prior
bond issuance. Credit spread and Term spread are included as (unreported) macroeconomic control variables.
The model includes fixed effects for bond issuance year, two-digit SIC code. t-statistics from two-sided tests of
significance are reported under the parameter estimates, in grey italics. Significance levels are denoted as follows:
“*” indicates significance at the 0.10 level; “**” indicates significance at the 0.05 level; “***” indicates
significance at the 0.01 level.
Bond spread Bond spread
Post revelation 36.83 ** 37.52 **
2.52 2.57
Backdater 11.36
1.32
Backdated grants 13.07
1.40
Backdater × Post revelation -1.75
-0.15
Backdated grants × Post revelation -4.63
-0.33
Bond size (log) 5.14 * 5.28 *
1.65 1.71
Bond maturity (log) 9.02 9.22
1.56 1.59
Callable bond -30.92 *** -30.87 ***
-4.34 -4.34
Puttable bond -7.87 -7.93
-1.13 -1.13
Subordinated bond -39.73 *** -39.37 ***
-3.10 -3.08
Bond rating -19.21 *** -19.17 ***
-6.54 -6.53
Prior bond issuance -9.44 -9.03
-1.01 -0.97
Intercept Yes Yes
Firm characteristics Yes Yes
Macroeconomic controls Yes Yes
Fixed effects (year, industry) Yes Yes
Observations 2820 2820
Adjusted R2 0.7422 0.7422
48
Table 8. Choice between public and private debt financing
Table 8 reports probit regression results. The response variable is equal to one if the debt issue is public and zero
otherwise. In Panel A, results for the overall sample are reported. Results for subsamples are reported in Panel B:
by total assets (column 1 to 4), by number of analysists (column 5 to 8), and by S&P 500 index membership
(column 9 to 12). All variables are as defined in Table 1. The sample includes both private debt issues (syndicated
loans) and public debt (bonds) to US firms issuing option grants to executives spanning the years 2000 to 2012.
Prior bond issuance equals to one if a firm has issued bond in the past, and zero otherwise. We control for
(unreported) firm characteristics by adding, as control variables, Total assets (log), Coverage (log), Leverage,
Profitability, Tobin’s Q, Analysts (log), and Altman Z. Chi-square test statistics from two-sided tests of
significance are reported under the parameter estimates, in grey italics. Significance levels are denoted as follows:
“*” indicates significance at the 0.10 level; “**” indicates significance at the 0.05 level; “***” indicates
significance at the 0.01 level.
Panel A: Overall sample analysis
Public debt Public debt
Post revelation 0.11 * 0.12 **
1.84 2.13
Backdater -0.10
-1.39
Backdated grants -0.08
-1.00
Backdater × Post revelation 0.21 **
2.53
Backdated grants × Post revelation 0.20 **
2.18
Prior bond issuance 0.83 *** 0.82 ***
14.90 14.87
Total assets (log) 0.38 *** 0.38 ***
19.34 19.21
Tobin’s Q 0.05 ** 0.05 **
2.04 2.04
Profitability 0.76 *** 0.76 ***
4.02 4.03
Coverage (log) 0.07 ** 0.07 **
2.41 2.41
Leverage 0.46 *** 0.46 ***
3.15 3.15
Altman Z 0.06 ** 0.06 **
2.55 2.54
Analysts (log) -0.01 -0.01
-0.42 -0.41
Intercept Yes Yes
Observations 10066 10066
Pseudo R2 0.3150 0.3148
49
Panel B: Data subsamples, transparent borrowers vs. opaque borrowers
Total assets < median Total assets >= median Analysts < median Analysts >= median Non S&P 500 S&P 500
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Post revelation 0.10 0.11 0.16 ** 0.17 ** 0.09 0.12 0.13 * 0.14 * 0.03 0.04 0.26 *** 0.27 ***
1.24 1.41 2.09 2.34 1.06 1.40 1.74 1.83 0.45 0.59 2.84 3.03
Backdater -0.23 *** -0.05 -0.07 -0.13 -0.21 *** 0.05
-2.89 -0.47 -0.72 -1.43 -2.66 0.36
Backdated grants -0.23 ** -0.03 -0.02 -0.15 -0.19 ** 0.05
-2.28 -0.29 -0.14 -1.59 -2.16 0.37
Backdater
× Post revelation 0.15 0.21 * 0.18 0.24 ** 0.17 0.24 *
1.21 1.94 1.47 2.24 1.56 1.70 Backdated grants
× Post revelation 0.15 0.20 * 0.13 0.27 ** 0.16 0.23
1.05 1.73 0.88 2.43 1.41 1.44
Prior bond issuance
0.59 *** 0.59 *** 0.89 ***
0.89 *** 0.65 ***
0.65 *** 0.93 *** 0.93 *** 0.74 *** 0.73 *** 0.98 *** 0.99 ***
8.46 8.41 11.78 11.79 8.14 8.10 12.57 12.56 11.85 11.79 8.88 8.93
Intercept Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Firm characteristics
Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 5032 5032 5034 5034 4776 4776 5290 5290 6873 6873 3193 3193
Pseudo R2 0.1895 0.1886 0.1732 0.1730 0.3705 0.3702 0.2696 0.2696 0.2205 0.2196 0.1543 0.1537
50
Table 9. Public debt issues over total debt issues
Table 9 reports regression results. The response variable is ratio of the annual dollar amount of newly issued bonds
scaled by the total annual dollar amount of newly issued total debt (both public and private). All variables are as
defined in Table 1. The sample includes US firms with option grants for executives that issue either bonds or loans
spanning the years 2000 to 2012. We control for Firm characteristics by adding Total assets (log), Coverage (log),
Leverage, Profitability, Tobin’s Q, Firm age (log), and Altman Z. The model include fixed effects for year and
two-digit SIC code. t-statistics from two-sided tests of significance are reported under the parameter estimates, in
grey italics. Significance levels are denoted as follows: “*” indicates significance at the 0.10 level; “**” indicates
significance at the 0.05 level; “***” indicates significance at the 0.01 level.
Public debt / Total debt Public debt / Total debt
Post revelation 0.36 *** 0.36 ***
13.34 13.43
Backdater -0.01
-0.88
Backdated grants -0.01
-0.83
Backdater × Post revelation 0.03 *
1.77
Backdated grants × Post revelation 0.04 *
1.73
Total assets (log) 0.09 *** 0.09 ***
23.12 23.05
Coverage (log) 0.002 0.002
0.46 0.46
Leverage 0.16 *** 0.16 ***
5.71 5.71
Profitability 0.01 0.01
1.64 1.64
Tobin’s Q 0.01 *** 0.01 ***
3.31 3.31
Firm age (log) 0.03 *** 0.03 ***
3.40 3.39
Altman Z 0.004 0.004
1.14 1.14
Intercept Yes Yes
Fixed effects (year, industry) Yes Yes
Observations 5901 5901
Adjusted R2 0.3130 0.3130
51
Table 10. Co-determination of price and non-price loan terms
Table 10 reports regression results. The response variable of interest is the all-in-drawn spread (Spread). The
model is a system of three equations with responses being Spread, Maturity, and Collateral, as in Bharath et al
(2011). The results are estimated by two stages least squares (2SLS) approach. In the first stage, we model
Maturity and Collateral as co-determined. In the second stage, we model Spread. We use Asset maturity and Term
spread as instruments for loan maturity, as in Barclay, Marx and Smith (2003) and Brick and Ravid (1985, 1991).
Loan concentration, the ratio of size of the loan facility to the total firm’s debt, is included as an instrument for
loan collateral. Average spread, the six months average all-in-drawn spread for all loans issued in the U.S., is
included as an instrument for Spread. Credit spread is included as (unreported) macroeconomic control variables.
The model includes fixed effects for loan initiation year, two-digit SIC code, loan type, and loan purpose. All
variables are as defined in Table 1. We control for (unreported) firm characteristics as in Table 4. Simple OLS
regression results without the instruments are included in columns 1 and 2, while instrumental-variable (IV)
estimates are presented in columns 3 and 4. t-statistics from two-sided tests of significance are reported under the
parameter estimates, in grey italics. Significance levels are denoted as follows: “*” indicates significance at the
0.10 level; “**” indicates significance at the 0.05 level; “***” indicates significance at the 0.01 level.
OLS IV
Spread Spread Spread Spread
Post revelation 59.06 *** 61.14 *** 28.15 31.07
5.66 5.89 1.34 1.49
Backdater -2.39 -8.74
-0.54 -1.17
Backdated grants 0.15 -7.76
0.03 -0.97
Backdater × Post revelation 18.47 *** 23.63 **
2.61 2.58
Backdated grants × Post revelation 16.16 ** 22.35 **
1.98 2.21
Loan size (log) -13.94 *** -13.87 *** -7.87 -7.57
-8.10 -8.04 -1.03 -0.99
Maturity (log) -24.87 *** -24.84 *** -54.65 -59.13
-5.10 -5.10 -0.41 -0.44
Collateral 62.56 *** 62.46 *** 295.32 * 290.73 *
16.88 16.87 1.86 1.82
Average spread 0.44 ** 0.43 **
2.29 2.27
Intercept Yes Yes Yes Yes
Firm characteristics Yes Yes Yes Yes
Macroeconomic controls Yes Yes Yes Yes
Fixed effects (year, seniority, industry, loan type, loan purpose)
Yes Yes
Yes Yes
Observations 7502 7502 7502 7502
Adjusted R2 0.5566 0.5563 0.2848 0.2929
52
Table 11. Financial constraints
Table 11 reports regression results. The response variable is Investment, measured as capital expenditures scaled
by lagged capital (property, plant, and equipment). All variables are as defined in Table 1. The sample includes
US firms with option grants for executives spanning the years 2000 to 2012. Firms with negative cash flows are
excluded. We control for firm characteristics by adding Tobin’s Q, Total assets (log) and their interaction with
Cash flow. The model include fixed effects for year, rating, and two-digit SIC code. t-statistics from two-sided
tests of significance are reported under the parameter estimates, in grey italics. Significance levels are denoted as
follows: “*” indicates significance at the 0.10 level; “**” indicates significance at the 0.05 level; “***” indicates
significance at the 0.01 level.
Investment Investment
Post revelation 0.10 *** 0.08 **
3.34 2.48
Backdater × Cash flow -0.02
-0.49
Backdated grants × Cash flow -0.03
-0.84
Cash flow × Post revelation -0.06 ** -0.04
-2.47 -1.64
Backdater × Cash flow × Post revelation 0.15 **
2.20
Backdated grants × Cash flow × Post revelation 0.16 **
2.30
Backdater 0.02
0.52
Backdated grants 0.03
0.95
Cash flow 0.10 * 0.11 *
1.93 1.95
Backdater × Post revelation -0.20 **
-2.41
Backdated grants × Post revelation -0.25 **
-2.42
Intercept Yes Yes
Lagged firm characteristics (Tobin's Q, logged Total assets)
Yes Yes
Lagged firm characteristics (Tobin's Q, logged Total assets) × Cash flow
Yes Yes
Macroeconomic controls Yes Yes
Fixed effects (year, rating, industry) Yes Yes
Observations 20872 20872
Adjusted R2 0.4513 0.4293
53
Appendix A1. Firms identified as “backdaters”: threshold levels and stock price volatility
Number of firms identified as “backdaters” using various thresholds. This table reports the required level of return reversals for a firm to be identified as a
backdater in each volatility quartile. Volatility is measured as the monthly standard deviation of stock returns calculated over the two-year period preceding the
grant date. Return levels are reported for the 95, 97.5, and 99% confidence levels. Confidence levels are derived from 1,000,000 randomly selected trading days
that are assigned as hypothetical option grant dates.
Confidence level for defining backdating
95% 97.5% 99%
Return cutoff levels for defining backdaters
Monthly standard deviation
of returns
Post- to pre-grant cumulative
stock return cutoff level
Less than or equal to 8.92% 18.39% 23.54% 30.60%
8.92% < STD <= 13.45% 29.16% 37.01% 48.05%
13.45% < STD <= 20.27% 42.61% 55.15% 73.75%
Greater than 20.27% 61.95% 82.26% 113.71%
54
Appendix A2. Firms identified as “backdaters”: number of firms by sample year
This table reports the number of firms in our sample each year and the number of firms identified as backdaters using different confidence levels. Firms are classified as
backdaters using cumulative raw returns over the forty-one-day period beginning twenty days prior to through twenty days following the option grant date. To be a backdater,
the cumulative stock returns around the grant date must be larger than the cutoff for a given confidence level and volatility quartile. Summary statistics for defining backdating
firms using various return cutoffs are provided in Appendix.
Number of backdating firms by sample year
Number of firms identified as backdaters
Confidence level for defining backdating
Year Total number of firms 95% 97.50% 99%
1996 2,223 181 78 24
1997 2,447 206 103 33
1998 2,519 361 196 87
1999 2,416 282 154 68
2000 2,404 416 241 106
2001 2,465 354 195 87
2002 2,261 182 108 44
Total firm-years 16,735 1,982 1,075 449