Externalities of Credit Default Swaps on Corporate ... · 1 1 Introduction In this study, we...

Externalities of Credit Default Swaps on Corporate Disclosure

Matthew Cedergren [email protected] University of Pennsylvania Philadelphia, Pennsylvania

Ting Luo [email protected]

Tsinghua University Beijing, China

Yue Zhang [email protected]

Lingnan University Hong Kong, China

April, 2018

Abstract

We investigate the effects of credit default swap (CDS) trading on customer firms on management forecasts by the supplier firms. We find that firms which derive a greater proportion of their revenue from CDS-referenced customers tend to lower forecast issuance, suggesting that enhanced information revelation in customers’ CDS market decreases suppliers’ disclosure benefits, creating a disincentive for managers to issue forecasts. We further find that this effect manifests for good news forecasts, but not for bad news forecasts, because of the litigation risk associated with withholding bad news. Our results are robust to a variety of sensitivity tests that control for potential self-selection in CDS-referenced customers, and our results strengthen when we focus on supplier firms which themselves are not referenced by CDSs. Our findings add to the literature examining the externality effects of CDSs on corporate decisions of entities outside of those directly referenced by CDSs.

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1 Introduction

In this study, we examine whether credit default swap (CDS) trading on a firm's major

customers influences the corporate disclosure policy of the supplier firms. The development of

the CDS market has been one of the most significant financial market developments in recent

years, with the notional amount of underlying CDS-referenced instruments growing from $918

billion in 2001, to an all time high of $62.2 trillion at the end of 2007.1 Although the CDS

market has contracted since the 2008 financial crisis, the size of the market, in terms of notional

amounts upon which CDSs have been written, remained above $10 trillion at the end of 2015.

Meanwhile, CDSs continue to be a primary mechanism through which financial institutions

manage and distribute risk to other parties. The CDS market has been shown to impact the

information environment and corporate policies of the firms that CDSs are traded on. For

example, CDS trading reduces analyst forecast errors (Eli Batta et al. 2016), increases dividend

payout to equity holders (Landsman et al. 2017), and increases management forecast activity of

the reference firms (Kim et al. 2017a).

One concern on CDSs is that they generally trade on larger firms, making it difficult to

generalize the documented effects of CDS trading to other entities of smaller size. A recent

study of Li and Tang (2016) has tried to address this issue by investigating whether the effects of

CDS trading extend to the reference firms’ supply chain partners. Taking advantage of SFAS

131, which requires firms to disclose the identity of major customers, Li and Tang find that CDS

trading on major customers has externality effects that improve the information environment of

the supplier firms, reducing equity issuance costs and leading to lower leverage.

We examine whether externality effects of CDSs apply to the realm of corporate

disclosure. It has been well established in the literature that disclosure is linked to other channels 1 International Swaps and Dealers Association 2010 Market Survey.

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of information in the market, affecting them and being affected by them in turn (Einhorn 2017).

CDS trading facilitates information production regarding the firms referenced by the CDS

instrument, because of informed trading in the CDS market (Li and Tang 2016) and increased

disclosure by the reference firms (Kim et al. 2017a). Given that revealed information and

promoted disclosure can also reflect the performance of firms having substantive economic ties

to the firm directly referenced by the CDS instrument, CDS trading provides an additional

information source regarding these economically connected firms. This will affect connected

firms’ disclosure behavior because: (1) additional information serves to reduce information

asymmetry of these firms; (2) the presence of alternative information sources generates an

opportunity cost for disclosure in that such alternative information may provide a more favorable

picture of the firm than does the information disclosed by the firm itself (Einhorn 2017).

Reduced information asymmetry and increased disclosure cost create a disincentive for the

linked firms to supply voluntary disclosure.

We follow Li and Tang (2016) and choose supply chain partnership as the setting to

examine CDSs’ externality effects on corporate disclosure. Particularly, we investigate how a

firm’s forecast behavior changes in response to varying levels of sales to CDS-referenced

customers. When firms make more sales to CDS-referenced customers, they have a closer

economic relationship with these customers, and information created by customers’ CDS trading

can better reflect (and is more useful in evaluating) the performance of the supplier firms. As

such, we expect higher sales to CDS-referenced customers leads to lower demand for the firms’

voluntary disclosure and thus less frequent issuance of management forecasts. We assemble a

sample of 10,865 firm-year observations, representing 2,433 unique firms, both with and without

sales to CDS-referenced customers, spanning the period from 2002 through 2012. We conduct

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our tests using ordinary least squares (OLS) and logistic regressions—depending on whether the

dependent variable is forecast frequency or forecast likelihood—that control for firm and time

fixed effects. We find that firms decrease their overall level of management forecast activity

when a greater proportion of their revenue is derived from CDS-referenced customers. This

evidence is supportive for CDS market’s externalities on corporate disclosure policy.

We next explore whether customers’ CDS trading influences the nature of the news

conveyed through the supplier firms’ management forecasts. We argue that the overall

externality effect documented above is mainly driven by good news forecasts rather than bad

news forecasts, because sales to CDS-referenced customers generate two countervailing

incentives on bad news disclosure. On one hand, the alternative information source, generated by

informed trading in the CDS market and promoted disclosure from the reference customers,

decreases information asymmetry and increases disclosure cost with regard to bad news for a

major customer, as discussed above. This information effect will spill over to a supplier which

derives a significant portion of its business from the customer, leading to less of a need for the

supplier firm in terms of bad news disclosure. In other words, there is less frequent issuance of

bad news management forecasts from firms having more revenue derived from CDS-referenced

customers.

On the other hand, withholding bad news is subject to litigation risk (Skinner 1994). Such

litigation risk can be higher when customers are CDS-referenced because the enhanced

production of information may raise the concern that managers have private information that is

not disclosed to the public. The threat of litigation associated with not disclosing bad news would

induce the firms with more revenue derived from CDS-referenced customers to increase bad

news forecasts. Therefore, the information effect and litigation effect acts as countervailing

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forces with regards to bad news forecasts. This is what we find in our tests: the overall negative

relation between management forecasts and sales to CDS-referenced customers is driven by good

news forecasts, while bad news forecasts do not show this pattern.

To alleviate potential concern on self-selection in the supplier-customer relationship, we

perform a difference-in-differences analysis by identifying a treatment group of supplier firms

which make sales to a CDS customer for the first time over a four-year window. We then

measure the change in forecast issuance from the last two years of the window (in which

treatment firms have sales to CDS-referenced customers) to the first two years of the window

(where treatment firms do not have such sales) and compare this change to a matched control

group of firms which do not have any sales to CDS-referenced customers during the same four-

year window. We also perform an instrumental variable approach where customers’ CDS status

is instrumented by the average bond trading volume of customers’ industry peers, proxied for

bond investors’ hedging and speculative demand in the CDS market. We find that our main

results hold in each of these analyses.

Expanding our analyses, we then explore the role of litigation risk and its influence on the

relation between management forecasts and sales to CDS-referenced customers. Using a

measure of ex ante litigation risk developed by Kim and Skinner (2012), we find that increased

litigation risk does not significantly influence the relation between good news forecasts and sales

to CDS-referenced customers. However, we find a significantly positive impact for bad news

forecasts, suggesting that litigation risk restricts the externalities of the information effect of

CDSs on suppliers’ bad news forecasts.

Finally, we conduct a variety of robustness tests to enrich our analyses. Notably, we

focus on supplier firms that are themselves not CDS-referenced, as the CDS trading on their

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customers is likely to be more incrementally informative with regards to these firms. The results

strengthen when we exclude CDS-referenced supplier firms.

Our study contributes to the growing literature examining the impact of financial

innovations on corporate policies. We expand upon Li and Tang's (2016) study examining the

externality effects that CDSs have along the supply chain. Li and Tang find that greater

information availability through customers’ CDS referencing influences a firm' financing policy,

and we extend the externality of CDSs to disclosure behavior. The externality on disclosure

policy, together with that documented in Li and Tang, suggests that although CDSs exist only for

a handful of large firms, their effects are not confined to the reference firms; rather, the effects

can extend to economically linked entities as well.

Our study also contributes to the literature of corporate disclosure by documenting that

peer firm financial market innovation in the form of CDS trading is an important influence on the

decision to issue management forecasts. Note that CDSs can exercise different effects on

disclosure activity of the reference firms themselves versus their peers. First, managers of the

reference firms are insiders and are believed to be more informed than external peers. If

information is revealed through the CDS market instead of through firm disclosure, managers of

the reference firms are very likely to be challenged or even sued for hiding information. In

contrast, peer firms such as supplier firms are outsiders. They are not necessarily exposed to the

same litigation risk as the reference firms in this regard. Second, the CDS market reduces

lenders’ monitoring on the reference firms, but does not yield the same effect on their peer firms,

which may motivate different disclosure responses from the reference firms and peers. Indeed,

what we find is a decreased propensity for management forecasts from the reference entities’

suppliers, rather than an increased propensity for forecasts from the reference entities as in Kim

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et al. (2017a). Therefore, the peer effect as examined in this study and the direct effect on the

reference firms themselves as examined in Kim et al. together provide a more comprehensive

understanding of the impact of CDSs on corporate disclosure.

Another stream of research studies peer effects in various corporate decisions, such as

capital structure choice (Leary and Roberts 2014), financial misconduct (Kedia et al. 2015) and

executive compensation (Shue 2013). Our study adds to this line of research by providing

evidence on the peer effect in corporate disclosure decisions.

The rest of the study proceeds as follows. Section 2 discusses related literature and

develops hypotheses. Section 3 describes the sample selection and presents descriptive statistics.

Section 4 presents research design, empirical results and robustness tests, and Section 5

concludes the study.

2 Related literature and hypotheses

2.1 Information environment and the CDS market

In a typical CDS contract, the buyer of credit risk protection transfers the risk to the

seller. The reference entity is not a party to the contract. When the reference entity experiences a

credit event such as default, the seller compensates the buyer for the loss either by the buyer

physically delivering the defaulted debt to the seller and receiving payment of par value from the

seller, or by auction where the buyer receives the auction proceeds and the seller pays the buyer

the cash difference between par value and auction proceeds (Griffin 2014).

Given its dramatic growth in recent years, the CDS market has become an increasingly

important area of academic study.2 A number of studies investigate how CDS trading affects the

information environments of firms. Banks, mutual funds and hedge funds are influential players 2 More thorough reviews of the CDS literature can be found in Augustin et al. (2014) and Augustin et al. (2016).

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in the CDS market. They often have access to non-public information about the reference firms

through lending activities or tight connections with large financial institutions (Acharya and

Johnson 2007). CDS trading and price discovery are able to facilitate information production

regarding the reference firms. For example, Acharya and Johnson find evidence of significant

incremental information revelation in the CDS market by informed banks. Similarly, Eli Batta et

al. (2016) document that the effect of CDSs’ price discovery prior to earnings announcements is

related to the presence of private information, and the CDS market conveys information valuable

to financial analysts. Zhang and Zhang (2013) find that the CDS market anticipates earnings

surprises up to a month in advance of the earnings announcement. Besides facilitating revelation

of performance information, the CDS market is also informative about the reference firms’

information quality. Ertan et al. (2016) show that CDS term structure signals the reference firms’

likelihood of earnings management. Griffin (2014) reports that the quality of financial

information affects CDS spreads, and Tang and Yan (2015) find that companies experiencing

material weaknesses in internal control exhibit higher CDS spreads than companies with

effective internal control.

Other studies show that CDS trading can influence corporate decisions of the reference

firms. Saretto and Tookes (2013) find that firms with traded CDS contracts maintain higher

leverage ratios and longer debt maturities because CDSs alleviate frictions on the supply side of

credit market. Meanwhile, Landsman et al. (2017) find that following CDS trading initiation,

reference firms increase dividend payouts to mitigate the agency conflicts between managers and

equity holders that arise because of lowered incentives of banks to monitor firms with CDS

trading. Kim et al. (2017b) find that introduction of CDSs increases creditors’ tolerance for tax

avoidance, and borrowing firms significantly increase their level of tax avoidance when they

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have CDS trading on their debts. From an accounting perspective, Gong et al. (2015)

hypothesize that CDS trading reduces lenders' incentives to monitor borrowers and reduces

demand for conservative reporting. They find that borrowing firms' reporting conservatism

indeed declines following the initiation of CDS trading. Kim et al. (2017a) find that intensified

shareholders’ monitoring when CDSs are actively traded makes managers at the reference firms

more likely to issue management forecasts.

2.2 Information externality effects

Many studies examine information externalities using the setting of the supply chain.

Information from a supply chain partner is value-relevant because it helps investors revise

expectations about future cash flows, reduces uncertainty about those cash flows, or both (Pandit

et al. 2011). Olsen and Dietrich (1985) were the first to examine information transfers along the

supply chain. They demonstrate that supplier firms’ stock prices react to the monthly sales

announcements of their major customers.

Along this line, some recent studies examine how supplier firms react to the earnings-

related information of major customers. For example, Pandit et al. (2011) find that supplier firms

have a stronger return reaction to the quarterly earnings announcements of their major customers

when the economic link between the suppliers and the customers is stronger. Using a similar

setting, Cheng and Eshlman (2014) find that shareholders are less likely to overreact (in terms of

price reaction by the supplier firm) to a customer’s earnings announcements when the customer

accounts for a larger percentage of the supplier firm's revenue. Ma (2017) tests the theoretical

framework of Lambert et al. (2007) and finds that higher earnings quality of economically linked

firms reduces firms’ systematic market risk. Radhakrishnan et al. (2014) find a positive

association between suppliers’ operating performance and greater provision of earnings forecasts

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by major customers. Other studies focus on non-earnings information and find that this type of

information also affects supplier firms. For example, Fee and Thomas (2004) find that suppliers

experience significant abnormal returns to customers’ horizontal merger announcements. Hertzel

et al. (2008) find that pre-filing and filing of bankruptcy are associated with a significantly

negative stock price reaction of suppliers.

The externality effect of information regarding customers is not only limited to suppliers

and their investors, but also applies to other parties. Guan et al. (2015) and Luo and Nagarajan

(2011) find that when analysts cover firms along a supply chain, their forecast accuracy

improves and is superior to forecasts of firms outside the supply chain. Johnstone et al. (2014)

find that auditors’ supply chain knowledge shared at the local-office level is associated with

higher audit production efficiencies, resulting in higher audit quality and lower audit fees for the

suppliers. Gong and Luo (2014) examine the value of supply-chain information in lenders’

decision making and find that through pre-existing lending relationships with major customers,

lenders tend to reduce their reliance on suppliers’ conservative accounting. Cen et al. (2016) find

that supplier firms having a longer relationship with major customers are perceived as “safer”

firms by banks, and thus enjoy smaller loan spreads and looser loan covenants.

Most relevant to our study, recently Li and Tang (2016) examine the externality effects of

CDS trading along the supply chain. They argue and find that customers’ CDS trading acts as a

vehicle for improved price discovery and thus reduces information asymmetry of the supplier

firms. This in turn reduces suppliers’ equity costs, facilitates equity issuance, and reduces

leverage.

2.3 Hypothesis development

We extend Li and Tang's research by investigating whether and how these CDS

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externality effects extend to voluntary disclosure decisions. It is well-established in the

disclosure literature that disclosing forward-looking news involves various costs and benefits.

Managers have to trade off costs and benefits in determining their disclosure policy (e.g.,

Verrecchia 2001; Cheng and Lo 2006). If customers’ CDS trading can affect costs and benefits

associated with disclosure for supplier firms, we would expect suppliers’ forecast behavior to

vary with changes in the level of sales to CDS-referenced customers.

CDS trading can facilitate information production on the firms referenced by the CDS

instrument. First, traders on the CDS market very often have non-public information about the

reference firms and such information is revealed to the public through informed traders’ trading.

As documented in Acharya and Johnson (2007), information possessed by loan officers is

transmitted to the market as they trade in the CDS market. Second, CDSs can promote more

voluntary disclosure from the reference customers. The effect of CDSs is to mitigate lenders’

risk exposure and weaken their incentives to monitor borrowers, i.e., the reference firms. This

leads to intensified monitoring from shareholders that have higher demand for voluntary

disclosure. Consistent with this view, Kim et al. (2017a) find that managers are more likely to

issue earnings forecasts when firms have actively traded CDSs.

Because of the close economic link between customers and their suppliers, customers’

information can partly reflect supplier performance. Therefore, enhanced information flow about

the reference customers can provide an additional public information source for investors of the

supplier firms and help such investors more efficiently evaluate the supplier firms’ future

prospects. The presence of additional information reduces the benefits of disclosure and

generates disincentives for suppliers to issue management forecasts for the following reasons.

First, the additional information reduces the overall level of information asymmetry of

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the supplier firms. Theoretical models such as Diamond (1985) imply that managers may commit

to disclose more information than is mandated in order to reduce information asymmetry.

Consistent evidence is also provided in the survey study by Graham et al. (2005). As in the

context of management forecasts, the supply and demand of forecasts have been documented to

be driven by information asymmetry, with managers issuing forecasts to reduce the asymmetry

in information between managers and outside investors (Verrecchia 2001). Coller and Yohn

(1997) find that firms issuing management forecasts tend to have higher levels of information

asymmetry. In a recent study by Balakrishnan et al. (2014), the authors use plausible exogenous

variation in the supply of public information to show that firms actively shape their information

environment by voluntarily issuing more management forecasts. With the information

asymmetry reduction brought about by the development of customers’ CDS market, there are

less benefits of disclosure for supplier firms, which in turn reduces the issuance of management

forecasts.

Second, additional information associated with customers’ CDS trading increases the

disclosure cost for supplier firms. The recent theoretical paper by Einhorn (2017) develops a

framework with the presence of competing information sources for corporate disclosure. Under

this framework, there is an opportunity cost for disclosure in that managers lose the opportunity

that the additional information may be more favorable than the disclosed information, in which

case withholding of disclosure would be a more optimal strategy. Einhorn documents that

managers would be willing to refrain disclosure to gamble on the unknown content of

forthcoming information. This is primarily the same case as when suppliers face more

information produced through their customers’ CDS market. The higher disclosure cost makes

suppliers less likely to issue management forecasts.

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We expect that these impacts are stronger for the suppliers that are more closely

connected with the reference customers. When a firm makes more sales to CDS-referenced

customers, it has a closer economic relationship with these customers and information created by

customers’ CDS trading can better reflect its performance. Therefore, the information effect

would be more pronounced for these firms. As such, our first hypothesis is stated formally as:

H1: Ceteris paribus, firms with higher sales to CDS-referenced customers are less likely

to issue management forecasts, compared to other firms.

We further argue that customers’ CDS trading differentially influences the issuance of

good news and bad news management forecasts of the supplier firms, because CDSs generate

two countervailing incentives for bad news forecasts, but not so for good news forecasts. As

discussed above, informed trading in the CDS market and promoted disclosure from the

reference customers produce more information, including bad news, to the public. With more

timely revelation and more production of bad news regarding customers, the supplier firms’

information asymmetry and disclosure cost in terms of bad news change significantly, and

incentives for the supplier firms to release bad news forecasts are mitigated. Hence, the

information effect predicts a negative relation between sales to CDS-referenced customers and

issuance of bad news management forecasts.

On the other hand, withholding bad news is subject to litigation risk (Skinner 1994). With

more negative information being revealed in customers’ CDS market, investors are more likely

to suspect supplier firms may have private information. Suppliers would be exposed to higher

litigation threats resulting from accusations that management was hiding bad news. This will

discipline managers of the firms with more revenue derived from CDS-referenced customers to

increase bad news forecasts. The above discussion suggests that the two incentives resulting

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from information effect and litigation risk generated by customers’ CDS trading would act as

countervailing forces for bad news forecasts. However, for good news forecasts, information

incentive would dominate, as there is less litigation risk associated with delayed disclosure of

good news. Thus, our hypothesis is directional for good news forecasts and non-directional for

bad news forecasts.

H2a: Ceteris paribus, firms with higher sales to CDS-referenced customers are less likely

to issue good news management forecasts, compared to other firms.

H2b: Ceteris paribus, firms with higher sales to CDS-referenced customers do not differ

in the issuance of bad news management forecasts, compared to other firms.

3 Data and sample

3.1 Sample construction

We construct a sample of firm-years using the intersection of several databases. We

obtain data on credit default swaps from the Markit database. For a given reference entity,

Markit contains information on the existence of CDS contracts, liquidity measures, and spreads.

We obtain information on supplier-customer relationships from the Compustat Segments

database. Statement of Financial Accounting Standards No. 131, Disclosures about Segments of

an Enterprise and Related Information, which became effective for fiscal years beginning after

December 15, 1997, requires firms to separately disclose the identity and amount of sales

revenue derived from any individual customer accounting for 10% or more of the supplier firm's

total sales revenue.3 We then link the CDS-referenced firms with the customers identified in the

Compustat Segments database to determine the percentage of each supplier firm's sales to

3 The provisions of SFAS 131 are now largely contained in Section 280 of the FASB's Accounting Standards Codification (ASC).

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customers with CDS contracts being traded.4

We obtain firm-year level accounting data from the Compustat Fundamentals Annual

database. Because the Compustat Segments file provides only the customer name reported by the

firm, we manually match identified customers where appropriate to the corresponding gvkey

identifier in Compustat. We obtain daily return data from the Center for Research on Security

Prices (CRSP), and analyst forecast data from I/B/E/S. For management forecasts, we utilize the

I/B/E/S management guidance database. When a firm reports more than one CDS-referenced

customer in one year, we aggregate customer data for each supplier year. Our empirical analyses

are on firm-year basis.

Table 1 Panel A outlines our sample selection criteria and composition. After obtaining

firm-year observations with available CDS data in the Markit database, and after linking the

CDS reference entities with the Compustat customer segments database and retaining those

observations with available data from the other above mentioned databases, we arrive at a final

sample of 10,865 firm-year observations, representing 2,438 unique firms, over the period from

2002 through 2012. Of these firm-years, slightly more than half (5,437 firm-years, representing

1,758 unique firms) are firm-years in which the firms had positive sales to CDS-referenced

customers.

Panel B of Table 1 provides a year-by-year breakdown of the supplier firms and CDS-

referenced customers in our sample. Typically, a firm discloses sales about two major customers,

with sales to major disclosed customers accounting for 33.9% of total sales.5 In most years there

are typically slightly more than one thousand unique customer firms which have active CDS 4 It is possible that firms have sales to CDS-referenced customers not disclosed by the firms because such sales constitute less than 10% of the firms’ total revenue. Thus, this figure represents a lower bound on the percentage of sales made to CDS-referenced customers. 5 Since our measure of sales to CDS-referenced customers relies on customer identification, each firm-year in the sample must have at least one disclosed customer. In our sample, 49.4% of firm-years disclose one customer, 24.1% of firm-years disclose two, 12.5% disclose three, and 6.3% disclose four or more.

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trading and for which we are able to match to our sample of supplier firms. As mentioned above,

more than half of all firm-years in our sample have positive sales to CDS-referenced customers,

and this pattern generally holds on a year-to-year basis.

3.2 Descriptive statistics

Panel A of Table 2 presents descriptive statistics for variables of interest in our study, as

well as control variables. Consistent with Li and Tang (2016), we use a firm's sales to disclosed

CDS-referenced customers to measure the firm's overall exposure to CDS-referenced customers.

Specifically, we measure the firm's exposure to CDS-referenced customers in a given year as the

sales to customers with CDS trading in the current year, divided by the firm's total sales for that

year. In our sample, 5,437, or 50.3%, of firm-years (representing 1,758, or 72.3%, of all firms)

have positive sales to CDS-referenced customers. The overall mean percentage of sales to CDS-

referenced customers is 19.2% (among firm-years with positive sales to CDS-referenced

customers, this mean is 28.9%). There is considerable cross-sectional variation in this figure,

ranging from 12.6% at the overall median, to 68.0% at the 95th percentile.

Our dependent variable is voluntary disclosure in the form of management earnings

forecasts. We employ two measures of management forecasts. First, we define the variable

(MF_N) as the number of instances of management earnings forecasts provided in a given firm-

year. This variable has an overall mean value of 1.956 (among firm-years with management

guidance activity during the year, this mean value is 5.713). Second, we use an indicator variable

(MF_D) taking a value of one if the firm issues at least one earnings forecast during the fiscal

year, and zero otherwise. This variable has a mean value of 0.342, indicating that slightly over

one-third of our firm-years have management guidance activity.

We additionally classify management forecasts into good news and bad news forecasts.

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As discussed earlier, we utilize the I/B/E/S management guidance database. I/B/E/S classifies

management forecasts as good or bad based on a comparison of the forecasted earnings amount

to the consensus analyst forecast as the time of issuance.6 In our sample, 23.8% of firm-years

have good news forecasts, with a mean frequency of 0.632, while 28.4% of firm-years have bad

news forecasts, with an average frequency of 0.945.

The median firm-year has four analysts covering (with 72.7% of all firm-years having at

least one analyst covering), market capitalization of $335 million, and a market-to-book ratio of

1.962. In terms of operating performance, the median firm-year has return on assets (ROA) of

2.6%, and 38.7% of the firm-years in our sample have loss. The return patterns in our sample

show considerable variation, with the middle 50% of firm-year returns ranging from −24.2% to

+27.3%, and daily volatility ranging from 2.32% to 4.17%.

Panel B of Table 2 displays univariate correlations among our control variables, and

primary independent variables of interest, customer CDS sales. We find that customer CDS sales

is not significantly correlated with our control variables. Other correlations are usually not large

except that between return on assets and loss (−0.633) which is reasonable considering the

mechanical relation between these two variables.

4 Empirical results

4.1 Customer CDS trading and management forecast activity

In our initial analyses, we examine the relation between the likelihood and frequency of

management forecasts during the fiscal year and the extent to which CDSs are actively traded on

6 In untabulated analyses, we manually classify a management forecast as good (bad) news if the point estimate, or the mid-point of the range forecast, is above (below) the average of analyst forecasts issued in the 90 days before the management forecast. For open-ended management forecasts, the forecast is classified as good (bad) news when its bottom (upper) bound is higher (lower) than average analyst forecast. The main results hold.

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the firm's major customers. In our main tests, we regress management forecast issuance on sales

to CDS-referenced customers and a set of firm controls, along with firm and year fixed effects:

εeffectsfixedyearandFirmcontrolsFirmβ

SalesCDSββMF_N(MF_D)

i

10 (1)

Management forecast issuance is represented by either the frequency variable of MF_N

or the indicator variable of MF_D(as described earlier). Equation (1) takes the form of an

ordinary least squares (OLS) model when the dependent variable is MF_N, and of a logistic

model when the dependent variable is MF_D. Our main variable of interest is CDS Sales, which

is the percentage of the firm's sales during the fiscal year that went to customers with CDS

trading during that year. Following prior research (Rogers and Van Buskirk 2009; Chen et al.

2011), we control for other known determinants of management forecasts, including analyst

following, firm size (as measured by market capitalization), market-to-book ratio, return

volatility (measured as the standard deviation of daily returns), operating performance (measured

by return on assets, as well as a loss indicator variable), and stock return. All balance-sheet

variables are measured at the beginning of the year, and income statement variables and stock

return variables are measured over the previous year. We additionally control for firm and year

fixed effects, and in all our regressions we cluster standard errors at the firm level.

Table 3 reports the regression results from estimating Equation (1). We find a

significantly negative coefficient (p-value = 0.021) on CDS Sales when the dependent variable is

MF_N, and an insignificantly negative coefficient on CDS Sales when the dependent variable is

MF_D. The coefficient on CDS Sales in the MF_N specification implies that on average a one

percent increase in the percentage of sales to CDS-traded customers is associated with a decrease

of 0.529 in instances of management forecasts during the year. This is consistent with our

prediction in our first hypothesis that, in general, when more information is available about the

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viability of a firm's major customers, with benefits to disclose decreasing, managers will issue

less frequent earnings forecasts. The insignificantly negative coefficient on CDS Sales when

MF_D is the dependent variable suggests that a higher amount of CDS trading on major

customers may not necessarily induce guiders to discontinue guidance. Discontinuing forecasts is

costly because it might be perceived by the market as suggesting poor future performance or

higher systematic risk, and is associated with negative market reaction (Chen et al. 2011).

Consistent with this notion, research has shown an inertia in the decision to provide guidance:

once firms start to provide guidance, they tend to continue to do so (Lang and Lundholm 1996;

Anilowski Feng and Skinner 2007; Einhorn and Ziv 2008).

4.2 Nature of forecast news

Our results thus far suggest that active CDS trading on a firm's customers has externality

effects, reducing disclosure benefits of the supplier firms. This potentially reduces the need for

managers to provide earnings forecasts, and is thus negatively associated with the overall level of

the firm's management forecast activity, at least in terms of forecast frequency. In this section,

we explore whether the nature of the news conveyed in management forecasts is influenced by

the level of sales to customers with CDS trading.

To investigate how CDS trading on a firm's customers affects the nature of news

conveyed in management forecasts, we use a similar regression model as Equation (1), except we

now use the dependent variables, MF_N and MF_D, to represent issuance of good news and bad

news earnings forecasts, rather than overall management forecasts. Bad news and good news

earnings forecasts are defined similarly as described in Section 3.2. We perform our regressions

separately for good news and bad news forecasts.

Table 4 presents the results for forecast issuance, separately for good news and bad news

19

forecasts. We find a significantly negative coefficient on sales to CDS-referenced customers for

good news management forecasts. Specifically, in the regression using MF_N, a one percentage

increase in sales to CDS-referenced customers is associated with an average decrease of 0.15

instances of good news forecasts. In the regression using MF_D, and in contrast to the results on

the likelihood of overall management forecasts, CDS Sales loads significantly negative.

In contrast, for bad news management forecasts, we find that the coefficient on CDS

Sales is insignificantly different from zero in both specifications of MF_N and MF_D.

Combined with Table 3, Table 4 show that the negative relation between customer CDS sales

and overall management forecasts is driven primarily by good news forecasts. The results

suggest that while the CDS market provides a better information environment about the firm,

leading supplier firms to reduce their overall disclosure levels, CDSs also have a disciplining

effect that leads managers to hold off on reduction in bad news disclosure.

4.3 Change analyses and instrumental variable tests

One concern in our setting is that sales to customers with CDS trading and management

forecast issuance may be jointly determined. To address this concern, we perform a difference-

in-differences analysis using the introduction of CDS trading on certain customers, and we also

perform an instrumental variable regression.

First, we perform a difference-in-differences analysis to examine how supplier firms alter

forecast issuance when they begin to have CDS-referenced customers. We take an approach

similar to that used in Li and Tang (2016). Specifically, we begin by examining all four-year

windows from all firms in our sample. We then define a treatment firm as a firm which has sales

to CDS-referenced customers in the third and fourth years of the window, and no sales to CDS-

referenced customers in the first and second years of the window. We are able to identify 113

20

unique firms in our sample that have an introduction of sales to CDS-referenced customers in

this fashion. A control firm is defined as having no sales to CDS-referenced customers during the

same four-year window as the treatment firm. We match each treatment firm to the control firm

in the same two-digit SIC industry with the closest level of firm size and information uncertainty

among all potential matches. We use market value of equity to measure firm size and stock

return volatility to measure information uncertainty. Using this approach, we arrive at a sample

of 906 firm-years.

We then run Equation (1) except we replace CDS Sales with two indicator variables,

Treatment and Post, and their interaction, Treatment × Post. Treatment take a value of one for

treatment firms, and zero for control firms. Post takes a value of one for the third and fourth

years, and zero for the first and second years of the four-year window.

Table 5 presents the results that have a similar picture as the main results in Table 4. The

significantly negative coefficient on Treatment × Post for good news forecasts suggests that

following the start of sales to customers referenced by CDSs, the treated firms decrease their

propensity and frequency of issuing good news forecasts, relative to control firms without sales

to CDS-referenced customers. In contrast, and also consistent with our main results, we see an

insignificant difference between treatment and control firms in the change in the provision of bad

news forecasts with the introduction of CDS-referenced customers.

Second, we employ an approach similar to that used in Kim et al. (2017a), where we use

an instrumental variable to proxy for hedging and speculation demand of customers’ bond

investors in the CDS market, which is expected to be related to CDS trading on customers but

not directly related to how the supplier firms of these CDS-referenced entities provide

management forecasts. Oehmke and Zawadowski (2013) and Boehmer et al. (2015) observe that

21

the development of CDS market and underlying bond market tend to be inversely related for

individual firms. That is, CDS trading tends to be deeper and more liquid when trading in the

underlying bonds is more difficult and less liquid. Traders looking for credit hedging will

naturally gravitate to the more liquid CDS market for any given firm. Similar to Boehmer et al.

(2015), we use average bond trading volume of customers’ two-digit SIC industry peers to proxy

for investors’ demand for the underlying bonds of customers, and thus as an inverse proxy for

the development of the CDS market of customers. Higher bond trading on a customer’s industry

peers should be negatively associated with the likelihood of CDSs being traded on that customer,

but it is not obvious ex ante why bond trading of customers’ industry peers should be related to

suppliers’ issuance of management forecasts.

Table 6 presents the results using this approach. Similar to our main results in Table 4,

we continue to find a significantly negative effect of sales to CDS customers for good news

management forecasts, and an insignificant effect for bad news forecasts. That is, when the

investors’ bond market demand is lower for customer firms, indicating higher likelihood of a

developed CDS market on that customer, provision of good news forecasts tends to decrease,

while provision of bad news forecasts does not significantly change.

4.4 Litigation risk

To further explore the mechanism through which sales to CDS referenced customers

affect the frequency and propensity for managers to provide management forecasts of different

news nature, we examine the role of litigation risk. Skinner (1994, 1997) finds that managers are

more likely to preemptively disclose forward-looking bad news than good news, and suggests

this may be due to managers’ aversion to legal liability.

We employ a measure of ex-ante litigation risk based on the model developed in Kim and

22

Skinner (2012). This model takes into account several variables documented to influence lawsuit

filings, including industry-based litigation risk, and lagged values of firm size, sales growth,

stock return, return skewness, return volatility and turnover. For each firm year, we measure the

ex ante litigation risk by first calculating the fitted values from Kim and Skinner’s model to our

sample, and then define an indicator variable, Litig which equals one if the fitted values are

above the sample median and zero otherwise.7 We include Litig in our regression, as well as an

interaction with CDS Sales to determine its influence on the relation between customer CDS-

referencing and supplier disclosure decisions. We estimate the following regression model

including CDS Sales, Litig, their interaction, along with controls and fixed effects separately for

good news and bad news management forecasts:

εeffectsfixedyearandFirmcontrolsFirmβ

LitigSalesCDSβLitigβSalesCDSββMF_N(MF_D)

i

3210 (2)

As before, this model takes the form of OLS or logistic regression depending on whether

MF_N or MF_D is used.

The results are reported in Table 7. For good news management forecasts, we continue to

find a negative coefficient on the main effect of CDS Sales when Litig is equal to zero, consistent

with our finding in Table 4. However, we find that the coefficients on the main effect of Litig as

well as its interaction with CDS Sales are both insignificantly different from zero, suggesting that

litigation risk does not influence managers' propensity to provide good news forecasts, nor does

it significantly influence the mitigating effect on issuing good news forecasts of sales to CDS-

referenced customers.

In contrast, for bad news management forecasts, we find a significantly positive

7 The model takes the form of Sued=α0+ α1Industry litigation risk+ α2Firm size+ α3Sales growth+ α4Stock return+ α5Return skewness+ α6Return volatility+ α7Turnover+µ. Higher fitted values represent high levels of ex ante litigation risk. In our sample, the fitted values range from -4.59 to 5.83, with the zero value occurring at the 69 percentile.

23

coefficient on the interaction between CDS Sales and Litig, suggesting that with the pressure of

timely bad new revelation from CDS-referenced customers, managers would make more bad

news management forecasts to reduce litigation risk. Thus, while CDS trading on customers

overall does not change managers’ bad news disclosure, litigation risk plays a role by making

firms increase their bad news forecasts in response to higher levels of sales to CDS-referenced

customers. As for the main effect of Litig, we find a significantly negative coefficient. This

suggests that when there is no alternative information source to discipline managers for timely

disclosure, firms facing higher ex-ante litigation risk are more reluctant to issue bad news

forecasts, consistent with Johnson et al.'s (2007) argument that bad news forecasts may trigger

lawsuit and this in turn deters bad news forecasts.

4.5 Robustness tests and alternative subsamples

We perform several additional tests and analyses on alternative subsamples, and find that

our results are generally robust to different approaches.

In our sample, 3,655 (33.6%) of the 10,865 firm-years represent supplier firms without

CDS sales to customers in any year during the sample period, while the other 7,210 (67.4%)

represent firms having sales to CDS-referenced customers in at least one year over the sample

period. To control for the potential systematic difference between these two samples, we perform

a test removing the 3,655 firm-years without CDS sales over the sample period.

The results are presented in Table 8. Panel A presents the results based on Equation (1).

For good news management forecasts, MF_N continues to load significantly negative. When we

use MF_D, the coefficient on CDS Sales continues to be negative but falls below conventional

significance levels. Thus, when we only focus on firms with sales to CDS customers in at least

one year during the sample period, variation in the level of such sales is strongly associated with

24

the frequency of good news forecasts, but the effect may not be strong enough to dominate the

cost of discontinuing guidance and push a guider to switch to a non-guider. The results on bad

news forecasts continue to be insignificant, consistent with our earlier results.

Panel B of Table 8 reproduces Table 7’s results, based on Equation (2), investigating the

interactive effect of litigation risk on the relation between sales to CDS-referenced customers

and management forecasts. Our results continue to hold—the interaction between CDS Sales and

Litig loads significantly positive for both MF_N and MF_D of bad news forecasts, and

insignificantly for good news forecasts.

The effect of sales to CDS customers on the supplier firms’ management forecasts might

be driven by whether CDSs are being traded on the supplier firms themselves. If one of a firm's

major customers becomes a CDS-referenced entity, then the effect of sales to CDS customers on

the management forecasts of the supplier would likely be contaminated if the supplier firm is

itself a CDS-referenced entity. In our sample, 2,008 of the 10,865 firm-years are from suppliers

which have CDS trading. In Table 9, we reperform our main tests after removing these firm-

years from our sample, and find our main results on the frequency of guidance activity continue

to hold, and our results on guidance propensity strengthen.

Panel A of Table 9 reproduces our main tests of Equation (1). The distinction between

good news and bad news forecast issuance remains: good news forecast issuance is negatively

related to CDS Sales, while bad news forecast issuance is insignificantly related. As for good

news forecasts, the coefficient for forecast frequency (MF_N) loads with similar magnitude and

statistical significance as before, and the coefficient for forecast likelihood (MF_D) strengthens

in both magnitude and statistical significance.

Panel B of Table 9 reproduces our litigation risk tests based on Equation (2), and we see a

25

similar main effect of CDS Sales: the results on good news forecast frequency continue to hold,

while the results on good news forecast likelihood strengthen. As for the interaction between

CDS Sales and Litig, the coefficient for bad news forecasts strengthens in both magnitude and

statistical significance when MF_D is used as the dependent variable. The interactions for good

news forecasts, both MF_N and MF_D, continue to remain insignificant.

In Table 10, similar to Kim et al. (2017a), we focus on an alternative measure of CDS

activity, CDS liquidity. As discussed above in our instrumental variable analysis, investors are

more likely to focus on the CDS market for their credit hedging needs if the underlying bond

market for a firm is illiquid or presents trading frictions. We classify a customer as having liquid

CDSs if the average number of distinct dealers providing CDS spread quotes for that customer is

above the sample median for that year. We then measure sales to customers with liquid CDS

trading based on this criterion. Our baseline results and litigation risk results are presented in

Panel A and B, respectively, and are consistent with the results using CDS Sales based on

whether a customer is a reference entity in the CDS market.

Finally, to address the potential concern that the nature of management forecasts issued

by customers and suppliers might be correlated, we re-estimate Equation (1) controlling for

customers' concurrent issuance of good news and bad news management forecasts. In

untabulated analyses, we find that the results remain essentially unchanged.

5 Conclusion

The market for CDSs has been one of the most significant developments in financial

markets in recent years. Prior research has shown that the information conveyed by CDSs has

externality effects beyond the reference firms. This is especially true since SFAS 131 requires

26

firms to disclose major customers, thus making public the strong economic linkages between

CDS reference entities and their suppliers.

We examine whether the externality effects of CDS trading on economically linked

customers extend to the provision of management forecasts. In our main tests, we find that firms

decrease their overall level of management forecast issuance when a greater proportion of their

revenue is derived from CDS-referenced customers. This evidence is consistent with CDSs

acting as an additional public information source with regard to the reference entities’ suppliers.

Furthermore, we find that this reduction in management forecasts in response to higher

sales to CDS-referenced customers varies non-randomly with the nature of the news conveyed in

forecasts. The negative relation is driven by good news forecasts, but not by bad news forecasts.

These results are robust to a number of subsample analyses and tests that address potential joint

determination of CDS customer sales and management forecast issuance.

Expanding our analyses, we then explore the role of litigation risk in moderating the

relation between management forecasts and sales to CDS-referenced customers. We find that

increased litigation risk does not significantly influence the relation between good news forecasts

and sales to CDS-referenced customers. However, we find a significantly positive interaction

between sales to CDS-referenced customer and the supplier firm’s bad news forecasts,

suggesting that heightened litigation risk disciplines managers of the supplier firm to make more

timely disclosure of bad news when the news would otherwise be revealed by an alternative

source of CDS market.

Our study contributes to both the literature on the determinants of management forecasts,

and the growing literature examining the impact of CDSs. We shed light on how the CDS market

can provide information that affects managers’ tradeoff between costs and benefits when

27

deciding whether to provide forecasts and, if so, what kind of forecasts to provide. We also show

that the determinants of management forecast issuance and its nature come not just from the

issuing firm itself, but from economically linked firms as well.

28

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Appendix A Variable Definitions MF_N — The number of management forecasts issued during a year. MF_D — The likelihood of issuing management guidance during a year. This is an indicator variable taking a value of 1 if managers issue at least one piece of guidance in the period, and 0 otherwise. Good news / bad news — Classification of the nature of the management guidance. We use the I/B/E/S provided classification which is based on a comparison of the forecasted earnings to the consensus analyst forecast as the time of issuance. CDS Sales — Sales to customers with CDS trading as a proportion of a supplier's total sales. Litig — Ex ante litigation risk based on Kim and Skinner (2012), calculated as the fitted values from the model Sued=α0+ α1Industry litigation risk+ α2Firm size+ α3Sales growth+ α4Stock return+ α5Return skewness+ α6Return volatility+ α7Turnover+µ. Analyst Following — The number of unique analysts who issue forecasts for the firm in the previous year. Size — Market value (in millions), measured at the end of the previous year; for regressions, we use the natural logarithm of this variable. M/B — The ratio of market value to book value of equity, measured at the end of the previous year. Return Volatility — Volatility of daily stock returns in the previous year. ROA — The ratio of earnings to total assets in the previous year. Loss — Indicator for loss in the previous year, defined as one if earnings are negative, and zero otherwise. Prior Return — Cumulative stock returns in the previous year.

33

Table 1 Sample Selection and Composition

The sample consists of 10,865 firm-years (representing 2,448 unique firms) over the period [2002, 2012]. Panel A lists the sample selection procedures. Panel B provides a breakdown of the number of firms per year, as well as the average number of customers per firm, and how many of them are CDS-referenced. Panel A Sample Selection Number of

firms Number of

firm-years (1) Observations with customers' CDS data

from 2002-2012 2,671

11,763

(2) Sample after eliminating observations

with missing data 2,448

10,865

Observations with customer firms having traded CDS contracts

1,758

5,437

Panel B Sample Firms over Time

Fiscal Year

Number of

supplier firms

Mean number of customers per supplier firm

Number of

customer firms with active CDS

Number of supplier

firms with CDS-referenced customers

2002 785 1.94 625 321 2003 1,153 1.96 1,037 540 2004 1,155 1.95 1,123 560 2005 1,100 1.93 1,172 560 2006 1,093 1.9 1,124 568 2007 1,090 1.95 1,191 575 2008 1,056 1.91 1,121 549 2009 1,044 2.02 1,139 561 2010 1,012 2.09 1,057 495 2011 947 2.16 1,023 488 2012 430 2.4 474 220

34

Tab

le 2

D

escr

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ve S

tati

stic

s P

anel

A

Des

crip

tive

Sta

tist

ics

on t

he

Reg

ress

ion

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iab

les

The

sam

ple

cons

ists

of

10,8

65 f

irm

-yea

rs (

repr

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ting

2,4

48 u

niqu

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rms)

ove

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[20

02,

2012

].

Pan

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P

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depe

nden

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to

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itio

ns.

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bols

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, an

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n

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%

S

td. d

ev.

MF

_N (

over

all)

1.95

6

0

0

0

3

9

3.51

5 M

F_D

(ov

eral

l)

0.

342

0

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474

MF

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good

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s)

0.

632

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1.

447

MF

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good

new

s)

0.

238

0

0

0

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1

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426

MF

_N (

bad

new

s)

0.

945

0

0

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1

5

1.

948

MF

_D (

bad

new

s)

0.

284

0

0

0

1

1

0.

451

CD

S Sa

les

0.

192

0

0

0.

126

0.

288

0.

68

0.

224

Ana

lyst

Fol

low

ing

7.

095

0

0

4

10

25

8.

712

Size

(in

mil

lion

s)

2,

620

14

83

33

5

1,25

8

9,48

2

11,6

29

M/B

2.70

2

0.27

3

1.18

1.96

2

3.34

2

8.65

4

3.90

0 R

etur

n V

olat

ilit

y

3.33

3

1.46

5

2.32

1

3.13

4.16

5

5.87

3

1.34

2 R

OA

−0.

054

−

0.57

3

−0.

078

0.

026

0.

073

0.

169

0.

261

Los

s

0.38

7

0

0

0

1

1

0.48

7 P

rior

Ret

urn

(%)

−

0.07

3

−82

.302

−

24.2

43

1.17

8

27.2

71

74

.331

46.8

38

35

Tab

le 2

D

escr

ipti

ve S

tati

stic

s (c

onti

nu

ed)

Pan

el B

C

orre

lati

ons

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dep

end

ent

Var

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les

C

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sale

s

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/B

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RO

A

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lyst

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ing

0.01

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36

Table 3 Sales to CDS Customers and Management Forecast Issuance

This table reports the effect of sales to a CDS-referenced customer on the supplier's management forecast issuance. We use OLS (Logit) regression specification when MF_N (MF_D) is the dependent variable. Please see the Appendix for variable definitions. The p-values are reported to the right of each coefficient, and are two-sided and based on standard errors adjusted for firm-level clustering. The symbols •••, ••, and • represent statistical significance at the 1%, 5%, and 10%, levels, respectively.

All Management Forecasts MF_N MF_D Coefficient p-value Coefficient p-value Intercept 0.589 0.146 CDS Sales −0.529 0.021•• −0.094 0.850 Analyst Following 0.029 0.000••• 0.037 0.022•• Size 0.339 0.000••• 0.826 0.000••• M/B 0.005 0.504 0.001 0.931 Return Volatility −0.123 0.001••• −0.095 0.244 ROA 0.357 0.014•• 0.863 0.060• Loss −0.193 0.007••• −0.340 0.022•• Prior Return −0.001 0.053• −0.003 0.018•• Firm fixed effects Yes Yes Year fixed effects Yes Yes N 10,865 10,865 Adjusted R2 4.45% 13.92%

37

Table 4 Sales to CDS Customers and Management Forecast Issuance — Nature of News

This table reports the effect of sales to a CDS-referenced customer on the supplier's disclosure of good news and bad news management forecasts. We use OLS (Logit) regression when MF_N (MF_D) is the dependent variable. Please see the Appendix for variable definitions. The p-values are reported below each coefficient, and are two-sided and based on standard errors adjusted for firm-level clustering. The symbols •••, ••, and • represent statistical significance at the 1%, 5%, and 10%, levels, respectively.

Good News Forecasts Bad News Forecasts MF_N MF_D MF_N MF_D Intercept 0.602••• −0.524••• 0.000 0.001 CDS Sales −0.150•• −0.772•• −0.072 −0.054 0.014 0.040 0.384 0.893 Analyst Following 0.006••• 0.027••• 0.001 −0.003 0.006 0.003 0.657 0.723 Size −0.032 0.064 0.233••• 1.078••• 0.131 0.544 0.000 0.000 M/B 0.001 0.021 −0.003 −0.004 0.717 0.142 0.368 0.810 Return Volatility −0.030••• −0.150•• −0.032•• −0.091 0.019 0.036 0.028 0.218 ROA −0.012 −0.077 0.142•• 0.882•• 0.725 0.813 0.012 0.032 Loss 0.011 −0.073 −0.128••• −0.359••• 0.622 0.594 0.000 0.008 Prior Return 0.001••• 0.001 −0.002••• −0.006••• 0.002 0.250 0.000 0.000 Firm fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes N 10,865 10,865 10,865 10,865 Adjusted/Pseudo R2 0.70% 1.78% 3.67% 9.83%

38

Table 5 Sales to CDS Customers and Management Forecast Issuance – Difference-in-Differences

This table reports the effect of sales to CDS-referenced customer on the supplier's disclosure of good news and bad news management forecasts, based on a matched sample of treated and control suppliers. A treated supplier is defined as having CDS-referenced customers in the third and fourth years of a four-year window and having no CDS-referenced customers in the first and second years. A control supplier is defined as having no CDS-referenced customers over the four-year window. Control firms are in the same two-digit SIC industry as the treated supplier and their firm size and stock return volatility are the closest to the treated supplier among all potential matches. Treat is equal to one for the treated supplier and zero otherwise. Post is equal to one for the third and fourth year, and zero otherwise. We use OLS (Logit) regression when MF_N (MF_D) is the dependent variable. The p-values are reported below each coefficient, and are two-sided and based on standard errors adjusted for firm-level clustering. The symbols •••, ••, and • represent statistical significance at the 1%, 5%, and 10%, levels, respectively. Good News Forecasts Bad News Forecasts MF_N MF_D MF_N MF_D Intercept 1.377 −0.214

0.130 0.680

Treat 0.418 1.018 0.228 1.572•• 0.223 0.229 0.337 0.046

Post 0.398• 0.880 0.083 0.9450.083 0.187 0.503 0.144

Treat × Post −0.196•• −1.021• −0.091 −0.3430.043 0.098 0.378 0.593

Analyst Following −0.007 0.017 0.014 0.0790.512 0.799 0.206 0.192

Size −0.151 −0.694 0.063 0.8500.254 0.346 0.435 0.173

M/B 0.004 0.070 0.003 0.1700.663 0.678 0.722 0.167

Return Volatility −0.086 −0.280 0.029 0.2860.180 0.391 0.549 0.436

ROA −0.120 −1.162 −0.014 6.735•• 0.401 0.463 0.930 0.010

Loss 0.077 −0.105 −0.357•• −0.5880.411 0.878 0.014 0.398

Prior Return 0.002•• 0.006 −0.001 −0.0030.014 0.208 0.583 0.623

Firm fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes N 904 904 904 904 Adjusted/Pseudo R2 9.73% 18.89% 8.02% 20.40%

39

Table 6 Sales to CDS Customers and Management Forecast Issuance –

Instrumental Variable Analysis This table reports the effect of sales to a CDS–referenced customer on the supplier’s disclosure of good news and bad news management forecasts, based on an instrumental variable analysis. CDS Sales is instrumented by Bond Investor Demand of Customers, proxying for bond investors’ hedging and speculative demand in the CDS market. We use OLS (Logit) regression when MF_N (MF_D) is the dependent variable. Please see the Appendix for variable definitions. The p-values are reported below each coefficient, and are two-sided and based on standard errors adjusted for firm-level clustering. The symbols •••, ••, and • represent statistical significance at the 1%, 5%, and 10%, levels, respectively. Good News Forecasts Bad News Forecasts MF_N MF_D MF_N MF_D Intercept 1.183••• −1.030• 0.001 0.054

CDS Sales −3.065• −29.929• 3.065 −0.973 0.093 0.058 0.344 0.955

Analyst Following 0.006•• 0.037••• 0.001 0.002 0.015 0.003 0.753 0.877

Size −0.025 0.164 0.220••• 1.009••• 0.344 0.216 0.000 0.000

M/B −0.005 −0.027 0.001 −0.019 0.142 0.324 0.877 0.536

Return Volatility −0.049••• −0.212••• −0.031• −0.103 0.001 0.006 0.056 0.217

ROA 0.094 1.167 0.071 1.081 0.258 0.107 0.614 0.183

Loss −0.018 −0.296 −0.099•• −0.345• 0.536 0.110 0.016 0.074

Prior Return 0.001••• 0.002 −0.002••• −0.006••• 0.002 0.139 0.000 0.000

Firm fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes N 9,512 9,512 9,512 9,512 Adjusted/Pseudo R2 0.65% 1.56% 3.87% 9.92%

40


Conditional on Litigation Risk This table reports the effect of sales to a CDS-referenced customer on the supplier’s management forecasts, conditional on Litig. We use OLS (Logit) regression when MF_N (MF_D) is the dependent variable. Please see the Appendix for variable definitions. The p-values are reported below each coefficient, and are two-sided and are based on standard errors adjusted for firm-level clustering. The symbols •••, ••, and • represent statistical significance at the 1%, 5%, and 10%, levels, respectively. Good News Forecasts Bad News Forecasts MF_N MF_D MF_N MF_D Intercept 0.631••• -0.543•••

0.000 0.001

CDS Sales -0.154•• -0.856 -0.212••• -0.4600.045 0.060 0.040 0.347

CDS Sales × Litig 0.005 0.141 0.249••• 0.706• 0.942 0.736 0.010 0.091

Litig 0.034 0.091 -0.091••• -0.2150.163 0.482 0.004 0.103

Analyst Following 0.006••• 0.027 0.001 -0.0040.006 0.003 0.691 0.705

Size -0.035 0.052 0.238••• 1.085••• 0.101 0.627 0.000 0.000

M/B 0.001 0.021 -0.003 -0.0050.692 0.141 0.323 0.733

Return Volatility -0.039••• -0.183•• -0.021 -0.0710.005 0.020 0.184 0.362

ROA -0.012 -0.081 0.138•• 0.868•• 0.721 0.802 0.014 0.033

Loss 0.012 -0.070 -0.129••• -0.364••• 0.606 0.610 0.000 0.007

Prior Return 0.001••• 0.001 -0.002••• -0.006••• 0.004 0.281 0.000 0.000


41


Excluding Suppliers with no CDS-referenced Customers This table reports the effect of sales to a CDS-referenced customers on the supplier’s disclosure of good news and bad news management forecasts, based on the subsample excluding suppliers without CDS-referenced customers over the sample period. We use OLS (Logit) regression when MF_N (MF_D) is the dependent variable. Please see the Appendix for variable definitions. The p-values are reported below each coefficient, and are two-sided and are based on standard errors adjusted for firm-level clustering. The symbols •••, ••, and • represent statistical significance at the 1%, 5%, and 10%, levels, respectively. Panel A Baseline Results Good News Forecasts Bad News Forecasts MF_N MF_D MF_N MF_D Intercept 0.548••• -0.551••• 0.004 0.010

CDS Sales -0.157• -0.594 -0.099 0.080 0.077 0.258 0.389 0.878

Analyst Following 0.007••• 0.032••• 0.003 0.001 0.010 0.008 0.394 0.947

Size -0.019 0.042 0.239••• 1.057••• 0.455 0.743 0.000 0.000

M/B -0.003 0.008 -0.003 -0.006 0.210 0.621 0.413 0.745

Return Volatility -0.026• -0.144 -0.033• -0.033 0.079 0.076 0.088 0.714

ROA -0.013 0.030 0.285••• 0.780 0.809 0.945 0.001 0.117

Loss 0.008 -0.073 -0.085•• -0.405•• 0.802 0.670 0.027 0.013

Prior Return 0.000 0.001 -0.002••• -0.006••• 0.238 0.689 0.000 0.000


42

Table 8 (continued) Sales to CDS Customers and Management Forecast Issuance –

Excluding Suppliers with no CDS-referenced Customers Panel B Litigation Risk Good News Forecasts Bad News Forecasts MF_N MF_D MF_N MF_D Intercept 0.587••• −0.526••

0.002 0.015

CDS Sales −0.161 −0.613 −0.327•• −0.6730.122 0.314 0.023 0.280

CDS Sales × Litig 0.005 0.033 0.409••• 1.315•• 0.957 0.951 0.001 0.012

Litig 0.046 0.203 −0.160••• −0.487•• 0.256 0.292 0.003 0.011

Analyst Following 0.007••• 0.032••• 0.003 0.0000.008 0.007 0.458 0.971

Size −0.024 0.018 0.245••• 1.073••• 0.352 0.891 0.000 0.000

M/B −0.003 0.008 −0.003 −0.0090.207 0.610 0.416 0.637

Return Volatility −0.037•• −0.203•• −0.023 −0.0070.018 0.027 0.275 0.938

ROA −0.013 0.041 0.254••• 0.7540.814 0.923 0.002 0.121

Loss 0.009 −0.065 −0.092•• −0.423••• 0.780 0.705 0.016 0.009

Prior Return 0.000 0.000 −0.002••• −0.006••• 0.322 0.760 0.000 0.000


43


Excluding Suppliers with Active CDS Trading This table reports the effect of sales to a CDS-referenced customers on the supplier's disclosure of good news and bad news management forecasts, based on the subsample excluding suppliers with active CDS trading. We use OLS (Logit) regression when MF_N (MF_D) is the dependent variable. Please see the Appendix for variable definitions. The p-values are reported below each coefficient, and are two-sided and are based on standard errors adjusted for firm-level clustering. The symbols •••, ••, and • represent statistical significance at the 1%, 5%, and 10%, levels, respectively. Panel A Baseline Results Good News Forecasts Bad News Forecasts MF_N MF_D MF_N MF_D Intercept 0.492••• −0.604••• 0.000 0.000

CDS Sales −0.187••• −1.405••• −0.042 −0.023 0.002 0.001 0.607 0.962

Analyst Following 0.008••• 0.037••• −0.001 −0.012 0.005 0.002 0.824 0.389

Size −0.024 0.082 0.244••• 1.331••• 0.207 0.476 0.000 0.000

M/B 0.000 0.008 −0.007••• −0.055•• 0.941 0.640 0.002 0.013

Return Volatility −0.026•• −0.146• −0.018 −0.031 0.023 0.072 0.216 0.710

ROA −0.032 −0.228 0.106• 0.522 0.348 0.502 0.065 0.234

Loss −0.010 −0.135 −0.150••• −0.435••• 0.680 0.390• 0.000••• 0.006

Prior Return 0.001••• 0.002 −0.001 −0.006••• 0.002 0.085 0.000 0.000


44

Table 9 (continued) Sales to CDS Customers and Management Forecast Issuance –

Excluding Suppliers with Active CDS Trading Panel B Litigation Risk Good News Forecasts Bad News Forecasts MF_N MF_D MF_N MF_D Intercept 0.515••• −0.622•••

0.000 0.000

CDS Sales −0.202••• −1.714••• −0.172• −0.6890.007 0.001 0.090 0.243

CDS Sales × Litig 0.024 0.529 0.230•• 1.179•• 0.738 0.258 0.019 0.016

Litig 0.021 0.023 −0.083•• −0.279• 0.378 0.878 0.011 0.072

Analyst Following 0.008••• 0.036••• −0.001 −0.0130.005 0.003 0.815 0.353

Size −0.026 0.070 0.249••• 1.343••• 0.174 0.546 0.000 0.000

M/B 0.000 0.008 −0.007••• −0.058••• 0.912 0.648 0.001 0.008

Return Volatility −0.032••• −0.180••• −0.008 −0.0200.007 0.040 0.616 0.821

ROA −0.033 −0.237 0.103• 0.4990.342 0.473 0.073 0.249

Loss −0.009 −0.134 −0.151••• −0.438••• 0.688 0.391 0.000 0.005

Prior Return 0.001••• 0.002 −0.001••• −0.006••• 0.003 0.110 0.000 0.000


45

Table 10 Sales to CDS Customers and Management Forecast Issuance — CDS Liquidity

This table reports the effect of sales to a CDS-referenced customer on the supplier’s disclosure of good news and bad news management forecasts, based on sales to customers with liquid CDSs. A customer is classified as with liquid CDSs if average number of distinct dealers providing CDS spread quotes for the firm during a year is above the sample median for that year. We use OLS (Logit) regression when MF_N (MF_D) is the dependent variable. Please see the Appendix for variable definitions. The p-values are reported below each coefficient, and are two-sided and are based on standard errors adjusted for firm-level clustering. The symbols •••, ••, and • represent statistical significance at the 1%, 5%, and 10%, levels, respectively. Panel A Baseline Results Good News Forecasts Bad News Forecasts MF_N MF_D MF_N MF_D Intercept 0.601••• −0.530••• 0.000 0.001

CDS Sales −0.151•• −0.672• −0.033 −0.006 0.024 0.094 0.715 0.989

Analyst Following 0.006••• 0.027••• 0.001 −0.004 0.006 0.003 0.659 0.719

Size −0.032 0.071 0.233••• 1.078••• 0.136 0.502 0.000 0.000

M/B 0.001 0.021 −0.003 −0.004 0.710 0.132 0.378 0.808

Return Volatility −0.030•• −0.144•• −0.032•• −0.091 0.021 0.044 0.029 0.220

ROA −0.014 −0.080 0.140•• 0.880•• 0.697 0.804 0.013 0.032

Loss 0.012 −0.064 −0.128••• −0.359••• 0.617 0.639 0.000 0.008

Prior Return 0.001••• 0.001 −0.002••• −0.006••• 0.003 0.284 0.000 0.000


46

Table 10 (continued) Sales to CDS Customers and Management Forecast Issuance — CDS Liquidity

Panel B Litigation Risk Good News Forecasts Bad News Forecasts MF_N MF_D MF_N MF_D Intercept 0.630••• −0.554•••

0.000 0.001

CDS Sales −0.157 −0.721 −0.165 −0.4480.067 0.133 0.155 0.369

CDS Sales × Litig 0.010 0.071 0.236•• 0.803• 0.902 0.873 0.027 0.059

Litig 0.033 0.109 −0.082••• −0.214• 0.153 0.388 0.008 0.093

Analyst Following 0.006••• 0.027••• 0.001 −0.0040.005 0.003 0.685 0.705

Size −0.035 0.059 0.238••• 1.090••• 0.104 0.580 0.000 0.000

M/B 0.001 0.021 −0.003 −0.0050.685 0.131 0.347 0.741

Return Volatility −0.038••• −0.178•• −0.021 −0.0710.005 0.024 0.180 0.363

ROA −0.014 −0.084 0.136•• 0.852•• 0.692 0.795 0.015 0.037

Loss 0.012 −0.061 −0.129••• −0.364••• 0.602 0.656 0.000 0.007

Prior Return 0.001••• 0.001 −0.002••• −0.006••• 0.004 0.316 0.000 0.000


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