Madsen Summer Paper 2009

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Credit Default Swaps and the Asymmetric Credit-RiskResponse to Accounting Information 1

Joshua Madsen

Ph.D. Candidate

University of Chicago Booth School of Business

November 20, 2009

(First Draft September 25, 2009)

Abstract

Using changes in credit default swap spreads, a signed earnings surprise variable, and firm-

level risk measures, I study the effect of accounting information (earnings surprises) on credit

risk. I document an asymmetric credit-risk response to positive and negative news for low-

risk firms. In particular, low-risk firms have an insignificant decrease in credit risk follow-

ing positive-news events and a 4% increase in credit risk following negative-news events. In

contrast to low-risk firms, high-risk firms experience a significant 8% decrease in credit risk

following a positive earnings surprise. Thus, credit risk for high-risk firms does not exhibit

asymmetry. My findings differ from Callen et al. (2009) and are in line with Easton et al.

(2009). I further document that the negative correlation between CDS spreads and equity prices

is increasing in negative news and firm-level risk.

1I would like to thank Ray Ball, Phil Berger, Erv Black, Ted Christensen, Joao Granja, Jeff McMullin, Valeri

Nikolaev, Regina Wittenberg-Moerman, and seminar participants at the 2009 BYU Accounting Symposium for helpful

comments and suggestions. The author can be contacted at [email protected].

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Theory predicts that debt is more sensitive to negative economic information. Ceteris paribus,

the arrival of negative news decreases the likelihood a firm will make fixed payments to debt holders

which increases credit risk. The arrival of positive news, however, has limited benefits for debt

holders since their maximum payoff is fixed. The sensitivity of credit risk to economic news,

however, also depends on firm-level risk. For negative news, it is not clear ex ante whether high- or

low-risk firms would experience a greater increase in credit risk. Credit risk for high-risk firms can

already be sufficiently high, such that additional negative news has a trivial impact. Alternatively,

for low-risk firms it is unclear whether the arrival of negative news will impact credit risk in a

significant fashion. The credit risk of a firm on a theoretical risk boundary is most likely to respond

significantly to negative news, but it is unclear how credit risk responds away from this boundary.

Similarly, the impact of positive news on credit risk will also be a function of firm-level risk.

For low-risk firms, positive news should have a negligible impact on credit risk. These firms have

significant equity, such that the likelihood of defaulting on debt payments is low. Positive news

provides no benefit to these debt holders, so credit risk should be (nearly) unaffected. The same is

not true for high-risk firms. These firms will default on debt payments with some positive prob-

ability, and the arrival of positive news reduces that probability. For positive news, credit risk is

decreasing in firm-level risk, yet it is unclear at what level of risk credit risk begins to measurably

respond. In this study I address these questions empirically.

Using CDS spreads I provide empirical support for the theoretically described impact of positive

and negative news across various risk measures. I analyze changes in credit risk for the 60 trading

days (the equivalent of a quarter) surrounding a quarterly earnings announcement to determine the

timing and magnitude with which debt values respond to economic news. Earnings announcements

are a summary signal of economic performance and to the extent they are useful for debt holdersprovide a relevant means to measure the impact of accounting information on credit risk. I use a 60-

day window surrounding the announcement in order to capture any information leakage or delayed

reactions to earnings information. I measure expected earnings as the median consensus analyst

forecast and use the signed difference of expected and realized earnings as a proxy for changes in

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the information set of debt holders.

I partition my data in several ways to analyze the impact of accounting information on credit

risk. First, I use all firms and find a strong reaction in the CDS market following an earnings

announcement for both a positive and negative earnings surprise. For the entire sample, negative

news results in a 4% increase and positive news a 2% decrease in credit risk, as proxied by CDS

spreads. Next, I include the entity-level S&P long-term credit rating as a measure of firm-level risk.

Splitting my sample into two risk groups, I find a smaller impact on credit risk for high-risk firms

(credit rating below BBB-) with negative news. However, the small sample of high-risk, negative-

news firms with a CDS spread results in low power for this test. Low-risk firms (credit rating above

BBB-) continue to realize a significant 4% increase in credit risk for negative-news events. For

positive news, I find only a marginal reduction in credit risk for low-risk firms, while high-risk

firms experience a significant reduction of credit risk (and increase in debt value) of nearly 8%. I

corroborate these results using other credit rating partitions, market leverage rankings, and CDS

spread rankings.

I substantiate my findings using the correlations between CDS spreads and equity prices for var-

ious event-time intervals. There is a natural negative correlation between the two markets, such that

CDS spreads decrease with positive news while equity prices increase, and vice versa. I hypothe-

size, however, that the degree of correlation around earnings announcements will be a function of

the type of news and firm-level risk. Over the 10 days following an earnings announcement, I docu-

ment a significant correlation of -0.21 for negative news and -0.09 for positive news, substantiating

the claim of greater negative correlation during negative-news events. Over this same interval, the

correlation is -0.06 for low-risk firms with positive news, and -0.25 for high-risk firms with positive

news. Positive news thus also affects the credit risk of high-risk firms. I find and document similarresults over different intervals.

My study differs from Callen, Livnat, and Segal (2009) (CLS hereafter), in three respects. First,

while CLS constrain their analysis to have a symmetric response to an earnings surprise, my design

allows the response to vary by both the signed surprise and risk. I find that for low-risk firms

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with negative news there is a statistically significant increase in credit risk, while CLS do not find

a similar result. This asymmetric response for low-risk firms is in line with Easton, Monahan,

Vasvari (2009) and DeFond and Zhang (2008). Second, I highlight the inherent risky nature of

the CDS spread and the implicit need to measure the abnormal change in CDS spreads rather than

the raw change. CLS use as their dependent variables raw CDS levels, quarterly changes, and

three-day window changes, which have a significant positive return. I propose a market measure

consisting of the most liquid CDS contracts (defined as those that trade each day of a given month)

and accordingly adjust my CDS spreads to measure abnormal changes. Finally, in addition to using

credit scores I supplement my analysis with accounting and market risk measures and the risk

implicit in the spread level. CLS use credit ratings and the level of ROA relative to the industry

median. Given the sticky nature of credit ratings (less than 10% of my sample changes credit

ratings over a given quarter) additional risk measures provide timely and corroborating evidence

on the role of risk in debt markets.

I contribute to the literature that studies debt market responses to accounting information (Eas-

ton, Monahan, and Vasvari (2009), CLS). I document an asymmetric credit-risk reaction to the

announcement of earnings in the more liquid CDS market. This liquidity allows me to capture

timely changes leading up to and following the earnings announcement. Such analysis was not

feasible in Easton et al.s study of bonds due to liquidity constraints. CDS spreads represent an iso-

lated measure of credit risk unencumbered by interest rate risk, options, and other facets of the bond

market. This direct measure implies changing CDS spreads reflect the implicit market return on

debt. Further, CDSs are not limited to only bonds but may reference any long-term debt instrument.

I thus provide evidence of a more general debt market reaction to earnings announcements.

My findings are also relevant to the literature on price discovery in the debt market. Informationmay be incorporated into CDS spreads previous to an earnings announcement through private chan-

nels (such as monthly covenant reports and loan syndicate reports). Acharya and Johnson (2007)

find insider trading in the CDS market that increases with the number of relationship banks. Using

the syndicated and secondary loan market, Bushman et al. (2009) present evidence that private

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communication among debt market participants plays a significant role in price discovery. Price

discovery in the CDS market is further complicated by interactions with the bond and equity mar-

kets.4 To account for the possibility of leading and lagged information flows into the CDS market,

I allow for a long event window over a quarter to capture relevant price discovery and quantify both

efficiency and information leakage relative to earnings announcement surprises.

2 The Credit Default Swap

The advent of the credit default swap (CDS) is one of the more significant financial developments in

the last decade. As summarized in Callen, Livnat, and Segal (2009), the CDS market is comprised

of three large groups. Commercial banks, the largest group, are net swap buyers and use CDSs

to diversify the credit risk of their loan portfolios without directly involving borrowers. Insurance

companies, the second largest group, are net sellers and use CDSs to enhance their investment

yields and diversify their market exposure. The last group, global hedge funds, speculates as both

buyers and sellers.

The most common CDS type is the CDX and iTraxx index, referencing North American and

European companies respectively. The next most liquid CDS type is the five-year single-name

swap. In contrast to index swaps, which cover a basket of corporate debts, single-name swaps

reference one corporate debt. Maturities typically range from 1 to 10 years. In a single-name

swap, two entities (buyer and seller) enter into a financial agreement regarding a specific debt of a

reference entity for a fixed maturity. The buyer will pay a periodic fixed fee on the notional amount

(until either maturity or a credit event) in return for a contingent payment from the seller following

a credit event of the reference entity. The most common notional amount is $10M. Typical credit

events include bankruptcy, default, moratorium, repudiation, or a material adverse restructuring of

debt (JP Morgan and T.R. Group, 1999).

To provide an illustration, consider a transaction involving a seller, buyer, and corporate debt.

A buyer interested in protecting against a $10M default of the corporate debt for five years can

4See Blanco et al., 2005; Hotchkiss and Ronen, 2002; Longstaff et al., 2005; Norden and Weber, 2004.

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agree to pay the seller a fixed annual payment in return for default protection. A CDS spread is

in terms of basis points per annum on the notional amount, so a hypothetical spread of 200 on the

$10M notional amount would be the same as 200 basis points or an annual payment of $200,000.

This payment would be made by the buyer each year until either a credit event occurs or maturity.

After a credit event two settlement options exist. If the buyer owns $10M of the defaulted debt, he

can deliver it to the seller in exchange for the $10M face value and the seller can then recover the

market value of the defaulted debt from the corporation. This is known as physical settlement. If

the buyer does not own the defaulted debt, then a cash settlement occurs where the seller delivers

to the buyer the difference between the face and market value of the defaulted debt.

Because CDSs are contractual arrangements, they are not homogeneous but potentially differ

in notional terms, contracted credit events, and counterparty terms. As a result, it is typically in-

feasible to simply resell a contract since finding a replacement party or canceling a CDS position

requires consent of the opposite party. Instead, a CDS buyer or seller can alter an exposure by

entering into subsequent offsetting positions and lock in a gain or loss. To continue the previous

illustration, suppose after entering into the first swap that the market assessed risk of the corpo-

rate debt increases. This implies a higher market CDS spread, reflecting the greater likelihood of

default. If the spread rises to 220, the original buyer can now act as a CDS seller to a third party.

The original buyer is now committed to pay $200,000 each year from the first contract, but receives

$220,000 each year from the second contract. If a credit event occurs such that both contracts re-

quire repayment, he will receive $10M from the original seller and pass it on to the second buyer.

The original buyer has locked in an annual gain of $20,000.

Heterogeneity among CDS contracts is potentially problematic. Contracts are typically nego-

tiated directly through investment banks, and thus are not publicly available. However, dealerstypically submit their deals to Bloomberg which then posts (when there is sufficient information)

a summary daily spread for each swap. Differing notional amounts among the various contracts

should not systematically affect daily spreads, but the extent to which contracts are based on differ-

ent credit events could result in systematic differences in daily spreads. In this paper I operate under

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the assumption that the Bloomberg spread is a general representation of the market assessed credit

risk. While I cannot directly observe changes in the value of a specific contract, summary spreads

for similar contracts referencing the same entity at different points in time provide a relevant means

to measure changes in credit risk.

The CDS is often regarded as insurance on the debt of the reference entity, such that the insured

party will be made whole following a credit event. As illustrated in the previous example, this

is not strictly true. The reference entity does not have to be party to or even aware of the CDS.

This means the party seeking payment does not have to claim damages as in an insurance contract.

Also, the CDS seller is not required to hold reserves, such that the seller may be unable to deliver

protection should a credit event arise. This counterparty risk is a central feature of the CDS market.

Another feature of CDSs is the ability to contract on any notional amount, even more than the debt

of the reference entity. This is because the contract is not tied to actual debt, but only references

the credit-worthiness of certain debts.

These various features allow for a wide range of speculation and the ability to short a companys

entire debt position. CDS spreads are also not limited to just bonds, but can reference any long-

term debt issued by a company and thus can be used to provide an approximate change in value of

a companys entire long-term debt position. As suggested by Veronesi and Zingales (2008), CDS

spreads thus allow for event studies on debt.

3 Hypothesis Development

A basic identity equates changing CDS spreads with changing debt values. Using the methodology

proposed by Veronesi and Zingales (2008), discounted CDS payments represent the cost of insuring

a corporate bond (B) against default. When that bond becomes more (less) risky, the CDS spread

will rise (drop) to reflect the new cost of insuring the bond. As a first approximation (ignoring

counterparty risk), the bond should thus have the same value as a government bond (G) of similar

maturity:

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B+PV(Insurance Cost)=G (1)

In the event of a default on debt, the CDS makes the insured party whole from the impaired

value of the bond. This impaired value is the amount the defaulted bondholder will recover from

the company, and the ratio of this value to the face value is referred to as the recovery rate. Given

an approximate recovery rate, the appropriately scaled CDS spread infers the expected probability

of default :

=CDS/10000

1 recovery rate(2)

The present value of the cost of insuring the bond (B) can thus be determined using an appro-

priate discount raterf

:

PV(Insurance Cost) =T

t=0

(1)t CDS10000 B1 + rf

t (3)where is the recovery rate previously defined, CDS is the spread represented in basis points

per annum, B is the bond face value, and T is the CDS maturity.

The implied cost of insuring a companys entire debt position can thus be determined from

spreads that reflect the market risk assessment for that company. Changes in this spread result in

real returns: an increase in the cost of insuring debt means the past buyer of a CDS can now sell a

new contract and lock in a positive profit. The rate of return on this investment from t= 0 to t= 1

is calculated as

R1 = PV(CDS1)

PV(CDS0)PV(CDS0) .(4)

An implicit compound rate of return for the investor can therefore be determined over any

interval

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CR =

T

t=1

(1 +Rt)

1 (5)

I use two different but related measures of debt market returns. For my first measure, I use equa-

tions 2-5 above, daily 5-year T-bill rates, and a 40% recovery rate (as recommended by Veronesi

and Zingales (2008) and typically used in the finance literature) to determine cumulative returns

over the event interval. This provides a measure of the change in credit risk (and implicitly debt

values) over relevant intervals. An advantage of this method is the ability to measure the change in

terms of both levels and percentages. A shortcoming of this method is the need to specify discount

and recovery rates. For my second measure, I focus on the percentage change in the CDS spread to

determine changes in credit risk and debt values. I then compound these daily changes to determine

cumulative returns. The CDS spread fundamentally reflects credit risk and changes in the spread

will be a function of both firm-specific and general market conditions. Although the two methods

are implemented quite differently, they are theoretically similar. I document that both methodolo-

gies produce similar results, and are only slightly different because of the need to specify recovery

and risk-free rates in the Veronesi and Zingales (2008) method.

I quantify relevant economic news using an earnings surprise variable, constructed as the differ-

ence between the median analysts forecasted earnings and realized earnings. To examine the role

of risk I group firms by low/high credit rating (above or below BBB-). I provide robustness checks

using other risk measures in section 6. Using these variables and the theoretical role of news in the

debt market, I define the following hypotheses, stated in the alternative:

H1: CDS spreads exhibit a positive response to a negative quarterly earnings surprise. The

magnitude of the response is different for high- and low-risk firms.

H2: CDS spreads exhibit a negative response to a positive quarterly earnings surprise. The

response is increasing in the level of risk.

I am specifically agnostic about the directional impact of risk in H1. As highlighted earlier, risk

may increase or decrease the impact of negative news for either firm type.

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My next hypothesis relates to the efficiency of the CDS market. Given the relative liquidity of

this market, I expect information to be reflected in price quickly with limited revision following the

announcement. Conversely, I am less confident about overall efficiency prior to the announcement

given the significant number of insiders in the debt market, as documented by Bushman et al.

(2009).

H3: The CDS market is efficient with respect to earnings announcements: the response

is isolated to the 3 days surrounding the announcement day for positive and negative

quarterly earnings surprises.

Given the different payoff functions of equity and debt, their inverse relation need not be constant

across firm type and earnings. In particular, positive earnings surprises for low-risk firms, given

their asymmetric payoffs and limited impact on CDS pricing, should result in less negative cor-

relation between CDS and equity markets. For negative earnings surprises, both equity and CDS

spreads are hypothesized to react in opposite directions, leading to a greater degree of negative

correlation.

H4: CDS and equity returns are inversely related; the relation is greater for firms experi-

encing a negative earnings surprise and for firms with high risk.

4 Data

My dataset consists of 214 five-year single name CDS spreads for 212 distinct non-financial US

firms. I download these spreads directly from Bloomberg and dates range from January 2002

to August 2007. The 5-year maturities are the most liquid among the single-name swaps avail-

able through Bloomberg, and thus the focus of this paper. For a given date and reference firm,

Bloomberg reports a composite CDS spread computed by a proprietary algorithm. This spread is

derived from multiple investment banks and brokers quotes. Bloomberg does not post a spread

when an insufficient number of quotes is submitted. To be included in the sample I require a firm

to have at least 40 reported spreads over the event window, which eliminates 45 otherwise potential

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firms from my analysis. The data consist of 3,054 sixty-day firm event-window observations with

a mean number of event-trading days of 54.6 (min=40, max=59, Q1=52, Q2=57, Q3=58). Thus

of the approximate 60 available quarterly trading days, this dataset represents a significant number

of firms with relatively liquid CDS spreads. This is in stark contrast to debt studies, where Easton

et al. (2009) document that the mean (median) number of days on which a bond trades in a year

is 6.78 (4). The market for CDSs therefore permits meaningful analysis on the timing with which

information is reflected in spreads.

To adjust for market trends I construct an equally-weighted index (rebalanced each month) of

the most liquid credit derivatives (defined as those that trade on each day of the month). Given that

my sample consists of large firms, equally-weighting should not be an issue. Further, CDSs are not

valued on a per share basis, which makes defining value weights problematic. Using this index, I

adjust infrequent trades with a more liquid measure of the market movement for the same relative

time period.

Descriptive statistics are presented in Table 1. The average firm size (as measure by market

value of equity) is quite large, consistent with CDSs targeting large mature firms as the reference

entity. Also consistent with the large size, these firms were on average profitable (mean quarterly

ROA of 1.2%); 74.9% of the firms beat their median quarterly analyst forecast, and 64.8% saw

an increase in quarterly earnings before extraordinary items compared with the previous year. The

mean 3-day and 6-day abnormal CDS returns are close to zero, but importantly smaller than the

raw return and exhibit greater variation in the tails.

As a result of rebalancing, my index has a changing number of firms across months, but is

constant within the same month. This index results in a slight loss of observations because I cannot

compute an abnormal return for the first trading day of each month in the sample. For each monthlyindex I require a minimum of five credit derivatives. My shortened data window results from a

limited number of CDSs in Bloomberg that trade each day of the month prior to 2002 and beginning

in late 2007. The remaining credit derivatives are then matched into COMPUSTAT and CRSP to

identify relevant debt levels, leverage ratios, credit ratings, quarterly earnings announcement days,

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and equity prices.

5 Results

I begin with a general analysis of CDS returns over the event window. Figure 1 depicts the cumu-

lative returns to investing in my sample of CDSs and the equities of the same entities. I compare

both methods for calculating CDS returns (CDS-PV using the Veronesi & Zingales (2009) and

CDS-Percentage using a simple percentage change). Figure 1 confirms the adjusted returns are

approximately mean zero over the event interval. In contrast, the raw CDS cumulative return is

strongly biased upwards over the event window. Because both methods for computing adjusted

CDS returns yield similar results, the rest of the paper focuses on the percentage change method.

I partition my sample by analyst forecast surprise and display the results graphically in Figure

2. Negative news implies an increase in credit risk (and a resulting loss for debt holders) of ap-

proximately 4%, while positive news reduces credit risk for the entire sample of firms by nearly

2%. The response is essentially isolated around the earnings announcement, suggesting the CDS

market is efficient with respect to accounting information.

Figure 3 splits the sample by the earnings surprise and firm-level risk variables. I define firms

with a credit score above/below BBB- as low/high risk. In this and all remaining figures I use the

compounded percentage change in CDS spreads. My (untabulated) results are unchanged when I

use the present value technique proposed by Veronesi and Zingales (2008). Figure 3 documents a

larger and earlier reduction in credit risk due to positive news for high-risk firms, while positive

news results in only a marginal reduction in credit risk for low-risk firms. This is consistent with

debt values being more sensitive to good news when the firm-level risk is high. Interestingly,

low-risk firms continue to realize a significant increase in credit risk from negative-news events.

High-risk firms have a slightly diminished increase relative to low-risk firms; however, the small

sample of high-risk firms with negative news and a CDS spread results in low power for this test.

Table 2 analyzes the statistical validity of the Figure 3 cumulative abnormal returns (CARs).

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CARs for negative-news low-risk firms result in a significant increase in credit risk, while the

negative-news high-risk sample CARs are indistinguishable from zero. This is not surprising given

the small sample size of these firms. CARs for positive-news low-risk firms are different from zero

with low statistical probability, as expected. The decrease in credit risk due to positive news for

high-risk firms is not only large, but also significant.

Table 3 tests for a difference in means across the partitions in my sample. Of note, I find a sig-

nificant difference across the earnings surprise variable (T-Low Risk and T-High Risk), indicating

that negative-news high-risk CARs are significantly different from positive-news high-risk CARs

following the earnings announcement, and similarly for positive news. However, holding the earn-

ings surprise constant I find the difference between high- and low-risk CARs to be insignificantly

different for the negative-news sample (t-Negative News). This again may flow from the low sam-

ple size of high-risk firms. I do find a statistically significant difference between means of high-

and low-risk CARs for the positive news sample following the event (T-Positive News), suggesting

that the credit risk of high-risk firms does respond differently than that of low-risk firms to positive

news.

I next investigate the efficiency of the CDS market. Figures 2 and 3 both suggest that, with

the exception of the positive-news high-risk group, the credit risk response is isolated to a three-

window. In order to substantiate this, I estimate the amount of the news released immediately

prior to and following the earnings announcement relative to the total news associated with the

announcement. I implement this as the mean CAR from day -30 to -3 and from day -30 to +3

relative to the mean CAR from day -30 to +15. I select the later grouping based on the figures,

which suggest the returns level off by day 15. Table 4 presents these ratios for both the CDS and

equity CARs. With the exception of the positive-news high-risk group, less news is reflected inCDS spreads before the announcement relative to the equity market. Also, while the equity groups

over-respond immediately after the earnings announcement, the CDS groups reflect most of the

total news by day 3. This finding suggests that the majority of the total credit-risk news contained

in an earnings announcement is reflected in CDS spreads in a timely manner.

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Given that credit risk significantly responds to negative news and when the firm is risky, I fur-

ther investigate the relationship between CDS and equity returns by examining cross-correlations.

Figure 4 shows the abnormal cumulative equity returns (adjusted by size-matched deciles) for the

same firms and constraints as in Figure 3. Table 5 presents cross-correlations between CDS and

equity returns across a variety of sample partitions and event-time periods. Using all firms in the

sample, I note a statistically negative correlation for all time intervals. When I partition this pop-

ulation by earnings surprise, the correlation for the negative-news sample is several magnitudes

greater than the positive-news sample. This is reflective of the nature of the CDS markets limited

response to good news. Risk further amplifies the negative correlation. Although the negative-

news high-risk sample is not statistically defined, for positive news the high-risk correlation is two

to ten times greater than the low-risk correlation. These results are consistent with a functional

relationship between the CDS and equity markets that is increasing in negative news and firm risk.

6 Robustness Checks

One stark feature of these figures is the lack of drift prior to the actual announcement. With the ex-

ception of the high-risk positive-news sample, most of the price movement happens directly around

the announcement. To determine whether this is a function of my earnings surprise variable, Figure

5 uses seasonally adjusted quarterly earnings. The intuition is that quarterly earnings higher/lower

than the previous year represent positive/negative signals to debt holders. While the asymmetric

response to positive and negative news for low-risk firms is robust to this specification, there is a

pronounced drift leading up to the earnings announcement for high-risk firms. High-risk firms now

appear more highly scrutinized, with information relating to future performance reflected signifi-

cantly prior to the actual announcement.

To examine the role of risk I use additional risk measures: alternative credit rating groupings,

market leverage, and CDS spread level. Figures 6 and 7 present alternative groupings of credit

ratings. I group around the risk cutoff (BBB- to BBB+) in Figure 7 and by customized groups (A,

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BBB, BB) in Figure 6. Similar to Figure 3, negative news results in a significant increase in credit

risk of various magnitudes for all credit-level groupings. The largest reaction is for firms rated

BBB- (Figure 7), consistent with credit risk being most sensitive on this risk boundary. Figure 6

suggests that even the credit risk of firms with relatively higher credit ratings (and thus lower risk)

still significantly responds to negative news, resulting in a decrease in the market value of debt for

these companies. Regarding positive news, all credit rankings indicate a decrease in credit risk that

is increasing in firm-level risk. The credit risk of firms with high credit scores (A and BBB) only

slightly decreases, while the credit risk of low-rated high-risk firms (BB) responds significantly

and early. Similar evidence is presented in Figure 7 for positive news. Together, Figures 6 and 7

provide further evidence on the asymmetric credit risk response to negative and positive news for

low-risk firms.

Because credit ratings are sticky I also calculate market leverage as the ratio of long-term debt

to the sum of total liabilities and market equity and then rank each firm-quarter into 3 groups (high,

medium, and low). I measure changes in relative leverage rankings between seasonal quarters and

identify two groups: those that increase (decrease) their leverage rank or remain in the highest (low-

est) group. This changes specification is an attempt to identify firms with worsening/ improving

risk profiles independent of a credit rating.

Figures 8 and 9 look at leverage rankings and persistence in leverage rankings as alternatives to

credit ratings. In Figure 8 firms are ranked by their quarter-end market leverage, and then placed

into one of two buckets in Figure 9 if their leverage rank is persistently high/low compared with

the previous years season-matched quarter. Of note, the highly levered firms result in the greatest

increase in credit risk for negative news, yet do not amplify the reduction in credit risk due to

positive news. For positive news the low-leverage firms and those reducing leverage experience thegreatest reduction in credit risk (CDS spread). Thus, these results do not confirm the asymmetric

response to negative/positive news for low-risk firms. This conflicting evidence may be due in part

to the use of market leverage.

For the last measure of risk, I rank firms into three groups based on the CDS spread closest

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to the end of the quarter. CDS spreads contain information about credit risk independent of a

changes specification. Firms with a high spread are likely to have higher credit risk, and vice

versa for low-risk firms. I attempt to capture this pricing information in Figure 10. In Figure 10,

negative news results in consistent increases in credit risk across all CDS rankings and positive

news results in a corresponding reduction in credit risk. The figure suggests there is a large credit-

risk response for the negative-news firms with the highest tercile of CDS prices, but a muddled and

less distinguishable impact for the rest of the sample.

7 Conclusion

I quantify the timing and magnitude of a credit-risk response to quarterly earnings announcement

surprises. In contrast to studies that exploit bond prices, I use more liquid CDS spreads as a direct

measure of credit risk. By examining the interaction of both signed surprises and firm-level risk,

I document an asymmetric response to positive and negative news for low-risk firms. In contrast

to low-risk firms, high-risk firms realize a significant reduction in credit risk following a positive

earnings surprise. I also provide empirical evidence of a greater response to negative news for low-

risk firms. This evidence builds on the findings of Easton et al. (2009), DeFond and Zhang (2008)

regarding an asymmetric response and of Callen et al. (2009) regarding the role of earnings in the

CDS market.

I find that the earnings surprise is priced into the CDS spread quickly (within approximately

3 days). However, different models of earnings surprise yield contrasting results for information

leakage and the early incorporation of earnings information. Results are generally consistent with a

variety of credit-risk groupings and alternative risk measures. I argue that the inherent risky nature

of the CDS spread warrants investigating abnormal changes (adjusted by a market measure) rather

than raw changes as in Callen et al. (2009).

I also contrast the CDS market with the equity market and provide evidence that the natural

negative correlation between the two is increasing in negative news and firm-level risk. The results

17


18/34

suggest that failure to measure changing debt values is essential at an enterprise level whenever

there is negative information or the firm-level risk is high.

An impediment to capital markets research is the inability to directly observe market values of

debt. Credit default swaps provide a means to evaluate changes in debt market values and evaluate

enterprise-level effects. This study advances our understanding of the nature of the debt market

and the asymmetric response to accounting information and the nature of the relationship between

market values of debt and equity.

References

[] Acharya, V., Johnson, T., 2007, Insider trading in credit derivatives. Journal of Financial

Economics 84, 110141.

[] Blanco, R., Brennan, S., Marsh, I., 2005, An empirical analysis of the dynamic relation be-

tween investment-grade bonds and credit default swaps. Journal of Finance 60, 22552281.

[] Bushman, R., Smith, A., Wittenberg-Moerman, R., 2009, Price discovery and dissemination

of private information by loan syndicate participants. working paper .

[] Callen, J., Livnat, J., Segal, D., 2009, The impact of earnings on the pricing of credit default

swaps. The Accounting Review 84.

[] DeFond, M., Zhang, J., 2008, The information content of earnings surprises in the corporate

bond market. working paper .

[] Easton, P., Monahan, S., Vasvari, F., 2009, Initial evidence on the role of accounting earnings

in the bond market. Journal of Accounting Research 47, 721 766.

[] Hotchkiss, E., Ronen, T., 2002, The informational efficiency of the corporate bond market:

An intraday analysis. The Review of Financial Studies 15, 13251354.

18


19/34

[] Longstaff, F., Mithal, S., Neis, E., 2005, Corporate yield spreads: Default risk of liquidity?

new evidence from the credit default swap market. The Journal of Finance 60, 22132253.

[] Morgan, J., Group, T. R., 1999, The J.P. Morgan Guide to Credit Derivatives.

[] Norden, L., Weber, M., 2004, The comovement of credit default swap, bond and stock mar-

kets: An empirical analysis. working paper .

[] Veronesi, P., Zingales, L., 2008, Paulsons gift. working paper .

19


20/34

Table1.DescriptiveStatisticsVariable N Mean StdDev 5thPtcl 25thPctl Median

MktValueofEquity($Mil.) 3054 26,139.47 39,116.55 2,362.69 6,829.30 13,512.41

LongTermDebtLeverage 3054 0.188 0.114 0.045 0.100 0.169

ROA 3051 0.012 0.022 -0.007 0.006 0.011

ForecastError 2669 0.025 0.130 -0.090 0.000 0.020

ConcensusForecastIndicator 2348 0.749 0.434 0 0 1

SeasonallyAdjustedEarningsIndicator 3029 0.648 0.478 0 0 1

CDSAbnormal3dayWindowReturn 3041 0.0005 0.0821 -0.1045 -0.0345 -0.0030

CDSRaw3dayWindowReturn 3041 0.0012 0.0917 -0.1061 -0.0370 -0.0031

CDSAbnormal6DayWindowReturn 3053 0.0005 0.1188 -0.1520 -0.0535 -0.0063

Thistablepresentsquarterlydescriptivestatisticsforthesampleof212nonfinancialUSfirms.Longtermdebtleverageisc

debttothesumofmarketvalueofequityandbookvalueofdebt.ROAisincomebeforeextraordinaryitemsdividedbytota

differenceofactualEPSandthemedianconsensusforecast.ConsensusForecastIndicatoris1ifEPSisabovethemedianfo

SeasonallyAdjustedEarningsIndicatoris1ifearningsbeforeextraordinaryitemsarehigherthanlastyearsseasonallymatc

abnormalreturnsareadjustedbyanindexofthemostliquidCDScontracts,whilerawreturnsarethesimplepercentagech


21/34

Table2EventTimeReturnsaround

Announcements

Trading

Day CAARs tstat N CAARs tstat N CAARs tstat N CAARs tstat N

30 0.331% 1.46 457 -0.358% -0.59 73 -0.246% -1.56 1433 -0.165% -0.42 159

29 0.149% 0.62 485 -0.453% -0.84 78 -0.185% -1.24 1526 -0.324% -0.76 171

28 0.075% 0.26 494 -0.281% -0.57 82 0.002% 0.01 1554 -0.554% -0.99 172

27 -0.202% -0.65 495 -0.345% -0.55 82 0.177% 0.65 1560 -0.554% -0.88 17426 -0.525% -1.67 496 -0.814% -1.26 83 -0.238% -0.87 1564 -0.369% -0.64 174

25 -0.601% -1.71 497 1.537% 0.59 83 -0.289% -0.98 1565 -0.124% -0.20 174

24 -0.624% -1.66 497 -0.471% -0.52 83 -0.286% -0.88 1566 -0.361% -0.56 174

23 -0.663% -1.69 501 -0.567% -0.63 83 -0.127% -0.30 1568 -0.466% -0.67 174

22 -0.370% -0.87 502 -0.747% -0.84 84 -0.093% -0.22 1570 -0.762% -1.14 174

21 -0.457% -0.93 503 -0.861% -1.03 84 -0.488% -1.42 1570 -0.858% -1.20 174

20 -0.434% -0.85 503 -0.781% -0.84 84 -0.303% -0.73 1571 -1.078% -1.44 174

19 -0.281% -0.50 503 -0.847% -0.89 84 0.033% 0.07 1572 -1.717% -1.88 174

18 -0.269% -0.43 504 -0.540% -0.51 85 -0.172% -0.35 1575 -1.910% -2.15 174

17 0.153% 0.23 504 -0.941% -1.02 85 -0.007% -0.01 1575 -1.606% -1.40 175

16 0.208% 0.30 504 -0.876% -0.89 85 -0.021% -0.04 1575 -1.930% -1.59 175

15 0.349% 0.47 504 -0.351% -0.35 85 -0.164% -0.32 1577 -1.567% -1.35 176

14 0.222% 0.30 504 -0.479% -0.47 85 -0.125% -0.24 1578 -1.823% -1.53 176

13 -0.116% -0.16 504 -0.826% -0.77 85 -0.003% 0.00 1578 -2.112% -1.76 176

12 -0.058% -0.08 504 -0.519% -0.49 85 -0.074% -0.14 1579 -2.373% -1.98 176

11 -0.315% -0.43 504 -0.459% -0.41 85 -0.114% -0.21 1579 -2.630% -2.14 17610 -0.219% -0.28 504 -0.472% -0.39 85 -0.153% -0.28 1579 -2.812% -2.14 176

9 -0.289% -0.36 504 -0.606% -0.50 85 -0.291% -0.53 1579 -2.903% -2.21 176

8 -0.215% -0.26 504 -0.997% -0.80 85 -0.020% -0.04 1579 -3.121% -2.40 176

7 -0.206% -0.23 504 -0.325% -0.25 85 0.185% 0.30 1579 -2.982% -2.27 176

6 -0.050% -0.05 504 -0.527% -0.39 85 0.083% 0.14 1579 -2.953% -2.18 176

5 0.240% 0.25 504 -1.101% -0.74 85 0.062% 0.10 1579 -2.846% -2.06 176

4 0.372% 0.40 504 -0.810% -0.53 85 0.085% 0.13 1579 -2.818% -2.01 176

3 0.576% 0.60 504 -0.867% -0.54 85 0.134% 0.21 1579 -3.247% -2.28 176

2 0.864% 0.89 504 -0.102% -0.06 85 0.228% 0.35 1579 -3.146% -2.24 176

1 0.749% 0.74 504 0.211% 0.10 85 0.456% 0.68 1579 -3.055% -2.16 176

0 1.765% 1.61 504 0.918% 0.40 85 -0.137% -0.21 1579 -3.789% -2.62 176

1 2.833% 2.45 504 2.788% 0.85 85 -0.497% -0.75 1579 -4.904% -3.35 176

2 3.266% 2.78 504 2.699% 0.80 85 -0.470% -0.70 1579 -5.367% -3.64 176

3 2.947% 2.45 504 2.794% 0.82 85 -0.767% -1.15 1579 -5.292% -3.52 176

4 3.261% 2.62 504 2.903% 0.84 85 -0.792% -1.17 1579 -5.720% -3.76 176

5 3.545% 2.78 504 3.045% 0.86 85 -0.893% -1.32 1579 -5.981% -3.91 176

6 3.998% 3.05 504 3.085% 0.88 85 -0.894% -1.30 1579 -6.184% -4.00 176

7 3.907% 2.98 504 3.300% 0.93 85 -0.876% -1.26 1579 -6.425% -4.10 176

8 3.817% 2.99 504 2.914% 0.82 85 -1.150% -1.67 1579 -6.808% -4.27 176

9 3.854% 3.02 504 2.409% 0.66 85 -1.228% -1.78 1579 -6.918% -4.30 176

10 3.918% 3.05 504 2.457% 0.69 85 -1.008% -1.42 1579 -7.065% -4.39 176

11 4.057% 3.07 504 2.502% 0.65 85 -0.857% -1.16 1579 -7.055% -4.36 176

12 3.846% 2.91 504 2.420% 0.64 85 -0.834% -1.11 1579 -7.521% -4.53 176

13 3.924% 2.97 504 2.519% 0.67 85 -1.142% -1.54 1579 -7.659% -4.49 176

14 3.864% 2.89 504 2.382% 0.63 85 -1.058% -1.42 1579 -7.300% -4.10 176

15 4.098% 3.03 504 2.463% 0.64 85 -1.197% -1.62 1579 -7.363% -4.04 176

16 4.026% 2.95 504 1.902% 0.49 85 -1.267% -1.71 1579 -7.126% -3.73 176

17 4.320% 3.15 504 1.572% 0.41 85 -1.326% -1.81 1579 -7.610% -4.00 176

18 4.111% 2.99 504 1.650% 0.43 85 -1.280% -1.69 1579 -7.525% -3.97 176

19 4.054% 2.91 504 0.770% 0.20 85 -1.199% -1.53 1579 -7.419% -3.92 176

20 3.954% 2.88 504 0.466% 0.12 85 -1.181% -1.45 1579 -7.391% -3.87 176

21 3.987% 2.89 504 0.656% 0.17 85 -1.281% -1.56 1579 -7.224% -3.81 176

22 3.958% 2.92 504 0.284% 0.08 85 -1.045% -1.22 1579 -6.997% -3.62 17623 3.844% 2.84 504 0.562% 0.15 85 -1.347% -1.62 1579 -7.505% -3.87 176

24 3.755% 2.75 504 0.648% 0.17 85 -1.358% -1.63 1579 -7.021% -3.50 176

25 3.647% 2.70 504 1.397% 0.37 85 -1.202% -1.43 1579 -6.103% -2.81 176

26 3.483% 2.60 504 1.114% 0.29 85 -1.119% -1.29 1579 -7.153% -3.60 176

27 3.603% 2.72 504 1.117% 0.29 85 -1.198% -1.37 1579 -7.969% -4.04 176

28 3.826% 2.83 504 1.456% 0.38 85 -1.517% -1.75 1579 -8.322% -4.18 176

29 3.742% 2.75 504 1.497% 0.39 85 -1.631% -1.85 1579 -8.272% -4.13 176

30 3.623% 2.65 504 1.165% 0.30 85 -1.575% -1.79 1579 -8.444% -4.21 176

PositiveNewsLowRisk PositiveNewsHighRiskNegativeNewsLowRisk NegativeNewsHighRisk


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Table3TTestforSignificanceofMeans

TestfortheSignificancebetweenportfoliosgroupedbyearningsannouncementorfirmlevelrisk.TNegative

News/TPositiveNewstestthestatisticaldifferencebetweenlow andhighriskfirms(above/belowBBB).TLow

Risk/THighRisktestthestatisticaldifferencebetweennegativeandpositivenewswithintheriskgroup.Trading

Day

TNegative

News

TPositive

News

TLow

Risk

THigh

Risk

30 1.06 -0.19 2.09 -0.27

29 1.02 0.31 1.18 -0.19

28 0.62 0.92 0.19 0.37

27 0.20 1.06 -0.92 0.23

26 0.40 0.20 -0.69 -0.51

25 -0.81 -0.24 -0.68 0.62

24 -0.16 0.10 -0.68 -0.10

23 -0.10 0.42 -0.93 -0.09

22 0.38 0.84 -0.46 0.01

21 0.42 0.47 0.05 0.00

20 0.33 0.91 -0.20 0.25

19 0.51 1.70 -0.42 0.66

18 0.22 1.72 -0.12 0.99

17 0.97 1.28 0.19 0.45

16 0.90 1.46 0.27 0.67

15 0.56 1.11 0.57 0.79

14 0.56 1.31 0.38 0.86

13 0.55 1.61 -0.13 0.8012 0.36 1.76 0.02 1.16

11 0.11 1.87 -0.22 1.30

10 0.18 1.87 -0.07 1.32

9 0.22 1.84 0.00 1.28

8 0.52 2.18 -0.19 1.18

7 0.08 2.18 -0.36 1.44

6 0.29 2.05 -0.12 1.27

5 0.76 1.92 0.16 0.86

4 0.66 1.89 0.26 0.97

3 0.78 2.17 0.38 1.11

2 0.47 2.19 0.55 1.34

1 0.23 2.24 0.24 1.29

0 0.33 2.29 1.48 1.74

1 0.01 2.74 2.50 2.15

2 0.16 3.02 2.76 2.18

3 0.04 2.75 2.70 2.16

4 0.10 2.96 2.86 2.29

5 0.13 3.04 3.07 2.34

6 0.24 3.13 3.30 2.42

7 0.16 3.24 3.22 2.50

8 0.24 3.26 3.43 2.49

9 0.37 3.25 3.50 2.33

10 0.38 3.44 3.36 2.43

11 0.38 3.48 3.24 2.28

12 0.35 3.67 3.08 2.40

13 0.35 3.50 3.34 2.46

14 0.37 3.24 3.21 2.33

15 0.40 3.13 3.43 2.30

16 0.51 2.86 3.41 2.07

17 0.68 3.08 3.63 2.16

18 0.60 3.06 3.43 2.14

19 0.80 3.04 3.29 1.91

20 0.85 2.99 3.22 1.8321 0.83 2.87 3.28 1.87

22 0.92 2.82 3.12 1.72

23 0.82 2.92 3.26 1.91

24 0.76 2.61 3.19 1.78

25 0.56 2.10 3.05 1.71

26 0.58 2.78 2.89 1.91

27 0.62 3.14 3.02 2.12

28 0.58 3.13 3.33 2.27

29 0.55 3.03 3.31 2.26

30 0.60 3.14 3.20 2.22


23/34

Table4TimingandEfficiencyMeasures

ThistablepresentsratiosoftheamountofinformationreflectedintheCDSmarket3daysbeforeand

aftertheearningsannouncementrelativetothetotalamountreflected15daysafterthe

announcement.

CARt3/CARt+15 CARt+3/CARt+15

NegativeNews 11% 76%

PositiveNews 10% 67%

NegativeNewsLowRisk 14% 72%

NegativeNewsHighRisk -35% 113%

PositiveNewsLowRisk -11% 64%

PositiveNewsHighRisk 44% 72%

CARt3/CARt+15 CARt+3/CARt+15

NegativeNews 35% 105%

PositiveNews 35% 110%

NegativeNewsLowRisk 33% 103%

NegativeNewsHighRisk 47% 112%

PositiveNewsLowRisk 33% 111%

PositiveNewsHighRisk 37% 105%

PanelA:CDSCARratiossplitbycreditratingandearningssurprise

PanelB:Equity

CAR

ratios

split

by

credit

rating

and

earnings

surprise


24/34

Table5CDSandEquityCorrelationCoefficients

ThistablepresentscorrelationsofequityandCDScumulativeabnormalreturnsovervariousintervals

relativetoanearningsannouncement.Earningssurprisedefinedasdifferencebetweenmediananalyst

EPSconsensusforecastandactualearnings.Firmssplitintolow/highriskgroupsbycreditscore

(above/belowBBB).

CDSandEquity

Correlation AllFirms

Negative

News

Positive

news

NegativeNews

LowRisk

NegativeNews

HighRisk

PositiveNews

LowRisk

PositiveNews

HighRisk

Rho [10,10] 0.11844 0.10549 0.09285 0.12163 0.04518 0.06443 0.30156

pvalue


25/34

Figure160DayCDSandEquityCumulativeAbnormalReturns(CARs)CenteredonQuarterlyEarnings

Announcement.CDSPercentagecompoundsasimplepercentagechangeintheCDSspread,

whileCDSPVusestheVeronesi&Zingales(2008)method.Bothreturnsareadjustedbya

monthlyrebalancedindexofthemostliquidCDSspreads. Equityisthesizeadjustedcumulative

equityreturnforthesamefirms.RawCDSPercentageistheunadjustedcompounded

percentagechangeintheCDSspread.

RawCDSPercentage

EquityCDSPercentage


26/34

Figure260DayCDSCumulativeAbnormalReturns(CARs)CenteredonQuarterlyEarnings

Announcement.Firmsarepartitionedbythesignoftheearningssurprise(calculatedasthe

differencebetweenthemediananalystsconsensusforecastandactualearnings).CARsare

calculatedusingthepercentagechangeintheCDSspreadandareadjustedbyamonthlyrebalancedindexofthemostliquidCDSspreads.

NegativeNews

PositiveNews


27/34

Figure360DayCDSCARsCenteredonQuarterlyEarningsAnnouncement.Earningssurprisedefinedas

differencebetweenmediananalystEPSconsensusforecastandactualearnings.Firmssplitinto

low/high risk groups by credit score (above/below BBB). CDS Return calculated as simple

percentagechangeinspreadandadjustedformonthlyindexofmostliquidspreads(definedas

anavailablepriceoneachtradingdayofthemonth).Samplesizeindicatednexttoeachgroup.

PositiveNewsHighRisk

PositiveNewsLowRisk

NegativeNewsHighRisk

NegativeNewsLowrisk


28/34

Figure460DayEquityCARscenteredonQuarterlyEarningsAnnouncement.Earningssurprisedefined

asdifferencebetweenmediananalystEPSconsensus forecastandactualearnings.Firmssplit

intolow/highriskgroupsbycreditscore(above/belowBBB).Samplesizeindicatednexttoeach

group.


NegativeNewsLow Risk


Positive News Low Risk


29/34


Seasonally Adjusted Quarterly Earnings. Firms split into low/high risk groups by credit score

(above/belowBBB).CDSReturncalculatedassimplepercentagechangeinspreadandadjusted

for

monthly

index

of

most

liquid

(defined

as

an

available

price

on

each

trading

day

of

the

month)CDSspreads.Samplesizeindicatednexttoeachgroup.


PositiveNewsLowRisk

NegativeNewsLowrisk



30/34


difference between median analyst EPS consensus forecast and actual earnings. Risk level

definedbygroupedcreditratings.CDSReturncalculatedassimplepercentagechangeinspread

andadjustedformonthlyindexofmostliquid(definedasanavailablepriceoneachtradingdayofthemonth)CDSspreads.Samplesizeindicatednexttoeachgroup.

NegativeNewsBBB

NegativeNewsBB

NegativeNewsA

PositiveNewsA

PositiveNewsBB

PositiveNewsBBB


31/34



defined by credit ratings around BBB. CDS Return calculated as simple percentage change in

spread and adjusted for monthly index of most liquid (defined as an available price on eachtradingdayofthemonth)CDSspreads.Samplesizeindicatednexttoeachgroup.

NegativeNewsBBB

NegativeNewsBBB

NegativeNewsBBB+

PositiveNewsBBB

PositiveNewsBBB

PositiveNewsBBB+


32/34



defined by quarterly ranked tercile of market debt leverage. CDS Return calculated as simple

percentage

change

in

spread

and

adjusted

for

monthly

index

of

most

liquid

(defined

as

an

availablepriceoneach tradingdayof themonth)CDSspreads.Samplesize indicatednext to

eachgroup.

NegativeNewsMidLev

NegativeNewsHighLev

NegativeNewsLowLev

PositiveNewsHighLev

PositiveNewsLowLev

PositiveNewsMidLev


33/34



defined by quarterly ranked tercile of market debt leverage and then grouped by those that

increase/decrease rankingorpersistinthehighest/lowestrankingoverconsecutiveseasonallymatchedquarters.CDSReturncalculatedassimplepercentagechange inspreadandadjusted

for monthly index of most liquid (defined as an available price on each trading day of the

month)CDSspreads.Samplesizeindicatednexttoeachgroup.

NegativeNewsHighLev

NegativeNewsLowLev

PositiveNewsLowLev

PositiveNewsHighLev


34/34



definedbyquarterlyranked(terciles)CDSlevelattheendofthequarter.CDSReturncalculated

assimplepercentagechangeinspreadandadjustedformonthlyindexofmostliquid(definedas

anavailablepriceoneachtradingdayofthemonth)CDSspreads.Samplesizeindicatednextto

eachgroup.

NegativeNewsHighCDS

NegativeNewsLowCDS

NegativeNewsMidCDS

PositiveNewsLowCDS

PositiveNewsHighCDS

PositiveNewsMidCDS

Documents

Madsen Summer Paper 2009