Do Credit Default Swaps Prices

Reveal the Presence of Informed Trading?

Hao Cheng ∗ Kian Guan Lim †

June 2019

Keywords: Credit default swaps, Daily return disaggregation, Overreaction, Hedged invest-

ing, Hard-to-value stocks, Idiosyncratic volatility

JEL Classification: G14, G23

∗Research Associate (Ph.D.), GIC Singapore. Email address: hcheng@smu.edu.sg†OUB Chair Professor of Finance, Lee Kong Chian School of Business, Singapore Management University.

Corresponding author, Email: kgl@smu.edu.sg.We are grateful to Baeho Kim, Inmoo Lee, Lukas Schmid, Fu Fangjian, Tu Jun, Hu Jianfeng, Chen Ying,

and many other colleagues who have given us valuable feedbacks, including participants at the CAFM 201813th conference and the SMU 2018 quantitative finance workshop.

Do Credit Default Swaps Prices

Reveal the Presence of Informed Trading?

Abstract

This research investigates beyond the conundrum if Credit Default Swap (CDS) price

impacts on the associated stock return. By disaggregating daily stock return into day

(exchange trading hours), evening, and night returns, we find that the credit default

swap return of a stock with high credit spread and high idiosyncratic volatility (hard-to-

value) negatively impacts the following day stock return. But evening or after-market

hour return on the same stock would reverse due to overreaction. The overreaction

does not occur when in fact there will be a downgrade credit event the subsequent

day. For low idiosyncratic volatility stocks, however, the credit default swap return

mildly impacts the following day stock return positively due to risk hedging but with

similar reversion during the evening hours. Throughout, stock returns generally display

positive autocorrelations across the disaggregated sub-day intervals, in addition to the

CDS impacts. The test specifications do not rule out feedback effects from stock returns

to CDS returns. Aggregating returns across day and evening hours could potentially

show up no CDS impact when overreaction occurs. Similarly, regressions of stock returns

by pooling both high and low idiosyncratic volatility stocks could mask the CDS impact.

We find that limit-to-arbitrage such as stock illiquidity and short-sale constraint cannot

fully explain the predictive results on stock returns based on CDS return signals. Overall,

our empirical evidence shows that CDS reveal the value of private information and that

the informed traders may profit from trading in hard-to-value stocks with high credit

spread levels.

1 Introduction

The positive role of Credit default swap (CDS) remains ambivalent to financial economists,

policymakers, and market participants1. On one hand, CDS allows trading of default risk sep-

arately from other market risks and significantly improves the overall credit market liquidity2.

On the other hand, there is a widespread concern that the CDS market gives rise to insider

trading, market manipulations, and credit speculation. Acharya and Johnson (2007) used

1The CDS market is a derivatives market, which specializes in managing credit risk. The protection buyeror credit risk seller pays a periodic fee to the protection seller or credit risk buyer for a contingent paymentassociated with a reference entity’s credit events.

2The CDS market has grown tremendously over the last two decades and is increasingly a critical marketside by side the stock market.

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equity prices as proxies for public information and examined potential insider trading in the

CDS market relying on relative price discovery between equity and CDS market3. Acharya

and Johnson (2007), Berndt and Ostrovnaya (2007), Ni and Pan (2011), Qiu and Yu (2012),

Kryzanowski, Perrakis, and Zhong (2017), and Lee, Naranjo, and Velioglu (2017) found that

increasing CDS spreads lead to corresponding negative stock returns conditional on credit

events. However, Norden and Weber (2009), Hilscher, Pollet, and Wilson (2015), and Marsh

and Wagner (2016) showed that increasing stock returns strongly and unconditionally predict

opposite changes in CDS spreads. They found informational causality not to be the other

way around. There are thus mixed results in the literature regarding whether the credit de-

fault swap market could lead the stock market with new information flows. An alternative

perspective is whether the CDS market is informed relative to the equity market.

In this research work, we re-examine the information flow between stock and CDS markets

in two novel ways. Firstly, we refine the return measures by separating daily stock return into

3 components after the CDS market closes for day t− 1. The first component is market-close

hours from 16:00 hours to 24:00 hours on t − 1 itself - we call the return over this sub-day

interval the evening return. The second component is the night return before market-open for

day t from 00:00 hours to 09:30 hours (Eastern Time). This period is generally quiet and does

not feature strongly in economic terms. The third component is day return or market hour

return for day t from 09:30 hours to 16:00 hours. The returns are measured by taking the

logarithm of the price relatives. The day or market hours is the most interesting and critical

period where most trading activities take place. We measure another component which is

the market-close or evening return on the subsequent day t. This is also an important period

for two reasons - firstly, overreaction to CDS returns or news during day hours at t may see

3Price discovery is the process by which trading incorporates new information and market participants’expectations into asset prices. The early price discovery in one capital market followed by price impact ina related capital market is evidence of informed trading. We focus on single-name CDS market rather thanthe corporate bond market as the related capital market to the stock market because theoretical work byOehmke and Zawadowski (2015) indicates that single-name CDS is more efficient than underlying corporatebonds due to higher liquidity and scalable transactions. Indeed, empirical evidence shows that CDS dominatesunderlying corporate bonds because of higher informational efficiency of CDS markets. See Blanco, Brennan,and Marsh (2005). Furthermore, institutional investors such as mutual funds substitute corporate bonds byinvesting in CDS markets due to the latter’s liquidity advantage (Jiang and Zhu, 2016).

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reversion or correction in evening returns at t, and secondly, by the evening, where any big

news on possible credit events in the next day t + 1 may be leaked, informed traders could

use this evening period to alter their trading strategy.

Our primary focus on the short-term aspect of stock price reactions is motivated by Chor-

dia, Roll, and Subrahmanyam (2005) who found that stock price adjustments to new infor-

mation occurred within a day, suggesting that examining the subject of close-to-close stock

returns may not be optimal to study the information content of the CDS market. Secondly,

we use five-year CDS spread level as a key conditioning variable, amongst others, to study

the cross-market information flow because high CDS spread identifies its associated stocks

as those more keenly watched due to those firms facing higher credit and default risks. Any

news impacting those firms would be revealed more readily through CDS return (or percentage

change in CDS spread) when informed traders profit from the new information. CDS spread

has also been studied by Acharya and Johnson (2007) and Lee, Naranjo, and Velioglu (2017)

who found that CDS prices or spreads change faster, when there is adverse credit news, than

the related stock prices.

In our methods, we examine the responses of day, evening, and night stock returns to CDS

returns (or percentage spread changes) using (1) a panel regression approach, and (2) portfolio

sorting approach. In (2), portfolios are constructed using double sorts using CDS spreads

and then CDS returns, triple sorts using CDS spreads followed by idiosyncratic volatility

proxying for valuation uncertainty, and then CDS returns, and more layered sorts involving

trading volumes and turnovers, and so on. Robustness checks on the sorting as well as panel

regressions in different sub-periods are also performed and results are shown as supplementary

tables in the Appendix. Robustness check of the sorting using the separate CRSP data

is also performed to ensure consistent results. Our empirical results show that for stocks

with high credit spreads and high idiosyncratic volatilities (hard-to-value stocks), their CDS

returns strongly and negatively predict subsequent days stock returns during market hours,

but positively predicts subsequent day’s stock returns after the market closing in the form of

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a partial reversal due to overreaction during market hour trading. However, during days when

there is a CDS rating downgrade the following day, these stocks do not show any reversal,

possibly indicating the insiders could accurately predict the forthcoming credit event and thus

did not buy back CDS, resulting in no reversal and no overreaction for those days. For stocks

with low idiosyncratic volatility or higher return prediction, however, the CDS return mildly

impacts the following day stock return positively due reasonably to risk hedging, but showing

slight reversion during the evening hours. Throughout, stock returns generally display positive

autocorrelations across the disaggregated sub-day intervals, in addition to the CDS impacts.

Due to lower trading volumes and behaviorally slower activities, stock returns in evening

and night hours in general do not show significant reactions to CDS news in the preceding

day-closing. One interesting obervation arising out of these results is that previous literature

and studies that found much weaker daily stock responses to CDS price changes could be

that aggregating returns across day and evening hours with evening reversals would show a

much smaller return adjustment. Similarly, regressions of stock returns by pooling both high

and low idiosyncratic volatility stocks could mask the CDS impact due to the opposite stock

orders of inside speculators and hedgers. We also investigate to find that limit-to-arbitrage

such as stock illiquidity and short-sale constraint cannot fully explain the predictive results

on stock returns based on CDS return signals. Overall, our empirical evidence suggests that

CDS informed traders may profit from trading in hard-to-value stocks with high credit spread

levels.

We explore the potential mechanism of the unequivocal information flow from CDS market

news to stock markets as well as the interesting and varied patterns of effect on stock returns

over different day, evening, and night trading hours. Our informed-trading hypothesis suggests

that informed investors in CDS markets focus or concentrate on trading in opaque stocks

with high valuation uncertainty, associated with high idiosyncratic volatility, by using their

new information extracted from just lagged CDS prices. Because of the high information

asymmetry risk premium where the price impact of privately informed trades cannot be ex-ante

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fully diversified, and the relatively low execution costs in stocks, informed traders would trade

across to the stock market. These informed traders are akin to those described in Goldstein, Li,

and Yang (2014), including cross-market arbitrageurs who have better access to both the CDS

market and the stock market. The idea of focus on opaque or hard-to-value stocks relates

to the strand of literature suggesting that trading incentives of informed investors should

be more significant for opaque firms. See Seyhun(1986), Aboody, Hughes, and Liu (2005),

Kumar (2009), Ben-David, Glushkov, and Moussawi (2010), Wu (2018), and Chen, Kelly,

and Wu (2018). For example, high valuation uncertainty can amplify uninformed investors’

behavioral biases (Kumar (2009)), and thus relatively better-informed investors conditional

on CDS prices attempt to exploit those biases. Motivated by studies that showed that high

idiosyncratic volatility is associated with poor information environment of firms (Rajgopal

and Venkatachalam (2011) and Chen, Huang, and Jha (2012)), we use idiosyncratic volatility,

based on Fama and French (2015) five-factor model, to proxy firm’s valuation uncertainty.

By partitioning the high CDS spread group into both high and low idiosyncratic volatility

sub-groups, we find that the CDS predictability is clearly significant on stocks with high credit

spread as well as high valuation uncertainty. This result may superficially seem to replicate

Campbell et al. (2008) who found distress risk to adversely affect future montly stock returns.

But the literature since has moved on to consider shorter horizon information impacts that

we examine here than just pricing effects on a longer run. Moreover, we examine a distinction

between long-run default risk informed by general CDS percentage spread changes versus

short-run or imminent default risk when there is both presence of high credit spread level and

imminent news of a credit ratings downgrade.

We investigate the source of stock price overreaction to CDS news, after market-close the

next day, in more depth. If all informed traders know precisely what the credit private signal

is, the stock price will not undershoot or overshoot. Moreover, the stock price should react to

the credit news and the news should impact a permanent change and not a liquidity-induced

temporary price pressure, at least for the short duration of the day until any fresh credit

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signal, if any, arrives. Information of credit spread increases coupled with insider information

on how serious the news is, if it is coupled with an imminent credit rating downgrade, should

constitute a short-run (impacting in the short-run) default risk. This risk is more potent

than credit spread changes without hint on any subsequent credit rating downgrade - this

constitutes long-run default risk, that may lead to default only in the longer run. In the

latter, insiders may not know exactly the probability of default in a low risk environment and

may overreact just as in the theoretical construction of Veronesi (1999). Similarly, the well

cited study by Bondt and Thaler (1985) gave copious evidence on the prevalence of stock

overreaction as a behavioral anomaly. Based on this insight, we test stock price changes by

examining periods with both low and high short-run imminent credit event prediction. We

expect to find no stock returns overreactions and hence no reversals after market-close when

the imminent credit event prediction error is small or if the traders are fully informed about

the imminent credit event. We employ days on t + 1 where downgrade actually occurred as

indicators of imminent credit event at t. However, for low short-run default risk or when

there is no imminent credit event, i.e. a lower risk environment, then credit return news

may be biased toward higher anticipation of larger stock return responses, thus triggering

overreactions. The overreactions would lead to stock price reversals afterward.

The negative reaction of stock returns to CDS returns, however, concentrated on stocks

with high idiosyncratic volatilities which proxied for hard-to-value stocks or stocks with higher

uncertainties in valuation. For stocks with low idiosyncratic volatilities and thus returns

that are more predictable. For stock return predictability and its evidences, there are many

studies such as Cochrane (2008). Investors who believe they can predict stock returns would

buy (sell) the stock at market hours on day t, but hedge by buying (selling) CDS on day

t − 1. Such hedged investing occurs for low idiosyncratic volatility stocks and not in high

idiosyncratic volatility stocks with high credit spreads. Thus, the CDS impact on subsequent

day returns would be positive instead, due to the hedging in anticipation of stock purchase

or sale. The postive impact would be smaller, and would reverse in the evening returns due

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to overreaction. Such hedged investing behaviors are documented elsewhere as in Rodriguez

(2002) who showed that investors hedging demand is increasing in information environment.

This implies that hedged investors would like to participate in easy to value stocks or stocks

with low information uncertainty or low idiosyncratic volatility. Ratner and Chiu (2016) also

found that stock investors hedge stock market risks using CDS, particularly during economic

crisis.

Lastly, we also consider if limit-to-arbitrage hypothesis may be able to explain our results.

Limit-to-arbitrage could be due to stock trading illiquidity or short-sale constraint, and causes

non-integration between CDS and stock markets, thus possibly explaining the time gap before

stock returns catch up to CDS news. See Amihud (2002), Pastor and Stambaugh (2003), and

Achary and Pedersen (2005). However, we find that the predictive results are significant for

both high as well as low liquidity stock groups, suggesting that the stock illiquidity channel

is unlikely to explain our findings fully. Kapadia and Pu (2012) considered idiosyncratic

volatility as a measure of limit-to-arbitrage that causes stock and CDS market disintegration.

A possible concern is that idiosyncratic volatility may reflect stock illiquidity. However, we

find that trading activities for firms with high idiosyncratic volatility are two times higher

than those for firms with low idiosyncratic volatility. Moreover, we partition the stocks with

high idiosyncratic volatility into sub-groups with high as well as low trading activities, and

find that the return predictability is based on stocks with high trading activities.4 Besides

illiquidity, another plausible reason for CDS predictability may be driven by the arbitrage

asymmetry in the presence of stock short-sale constraint. Extracting short-sale volume data

from “Reg SHO” from 2005 to 2007, we find that the average short volume of high valuation

uncertainty group is much higher than that of low valuation uncertainty group. Further, using

residual institutional ownership as in Nagel (2005) to proxy for short-sale constraint, we find

that the predictive results appear in both high and low residual institutional ownership. Thus

the presence of short-sale constraint does not appear to limit the predictability of CDS price

4These additional results are available from the authors on request. They are not reported here to economizeon space.

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changes on stock returns. Overall, these results suggest that our findings cannot be fully

explained by limit-to-arbitrage arguments.

In summary, the contributions of this paper are multifold. This paper relates to the recent

debate on CDS market informativeness. As discussed earlier, the research literature showed

that CDS returns can predict stock returns on a daily basis only under circumstances when

credit events take place. We contribute to this literature by using a novel setting, which

decomposes daily stock returns into different sets of trading hours to provide new evidence

that stock markets reacts to CDS changes regardless if there is any credit event. However,

the CDS predictability has two directions. The typical negative impact on stock returns

occurs due to high valuation uncertainty especially with high credit spreads and high stock

liquidity. This characterization of the CDS news impact on stocks is consistent with the

recent theoretical paper by Collin-Dufresne and Fos (2016) where informed trading is very

much present. It is also consistent with theoretical works and empirical studies by Grossman

and Stiglitz (1980), Verrecchia (1982), Diamond (1985), Frankel and Li (2004), Aboody et

al. (2005), Kumar (2009), Chen, Kelly, and Wu (2018), and Wu (2018) on the presence of

information asymmetry across the markets. The milder postive impact on stocks with low

idiosyncratic volatility or higher return predictability can be attributed to hedging behavior

instead and not informed speculation in the former. The combined pool of both easy and

hard-to-value stocks in the same CDS impact analysis could mask the impact of CDS on the

hard-to-value stocks. We also find an interesting price reversal pattern excepts in days when

a credit event is imminent. Stock return reversals indicate overreaction in the first place.

This observation in CDS related stock price movements in different parts of the day add

another context to the literature of short-term return reversals by Lehmann (1990), Lo and

MacKinlay (1990), Jegadeesh (1990), Bremer and Sweeney (1991), and others. It also add the

CDS context to highly cited overreaction studies by Veronesi (1999) and Bondt and Thaler

(1985). Again we note that cumulating day and evening stock returns of a day would dilute

and lose the interesting patterns of trading behavior. Throughout, stock returns generally

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display positive autocorrelations across the disaggregated sub-day intervals, in addition to

the CDS impacts. The test specifications do not rule out feedback effects from stock returns

to CDS returns. Additionally we find that limit-to-arbitrage such as stock illiquidity and

short-sale constraint cannot fully explain the predictive results on stock returns based on

CDS return signals. Overall, our empirical evidence suggests that CDS informed traders may

profit from trading in hard-to-value stocks with high credit spread levels.

The rest of the paper is organized as follows. Section 2 presents the data and variables

used in our analyses. Section 3 studies the impact of CDS markets on stock markets during

different sub-day trading hours. Some baseline empirical results are reported. Peripheral

results to check for robustness are reported as supplementary tables in the Appendix. Section

4 investigates the sources of predictability by developing three hypotheses and then testing

them. Section 5 provides the conclusions.

2 Data and Variables Construction

We start by describing the data sample. We focus on U.S. firms with both stock return and

CDS spread data between January 2001 and March 2016 inclusive. The stock return data

from 2001 to 2013 are obtained from monthly Trade and Quote (TAQ), and the stock return

data from 2014 to 2016 are obtained from daily TAQ. The CDS price information is from

Markit Group. We also employ close-to-open, open-to-close, and close-to-close daily stock

returns from CRSP to replicate and check some results for robustness and comparisons with

past studies. We compute all returns based on transaction prices with associated trading

volumes. The transaction stock prices include dividends and are adjusted for share splits

before computing returns. We do not use quote-prices. For this reason, intraday CDS data

are not utilized as they do not contain transaction prices. By using stock transaction prices

instead of quoted bid and ask prices, we can determine actual economic trading benefits

as an outcome of informed trading. We notice that using for example close-to-open stock

returns (overnight) computed from CRSP sometimes generates large differences compared to

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using TAQ’s transaction price-based returns due to close-to-open price jumps from quotes

and sentimental retail order flow (without actual transactions) outside of the regular working

hours.

Stock Returns. We obtain stock return data from TAQ. We focus on common stocks.

Opening and closing prices are associated with the Stock Exchanges’ trading hours. First

and last trades on a day t are respectively defined as first trade after midnight of a day t− 1

on a day t, and last trade as that before midnight on day t. The opening price on a day

t is the first valid actual transaction price at or immediately after 09:30. The closing price

on a day t is the last valid transaction price on day t when the Stock Exchanges close at

16:00. The first day t trade price is the first transaction price, before 9:30 but no earlier

than 0:00, if there is a non-zero transaction volume attached to the price. Otherwise, we

code the first price before market-open as missing. The last day t trade price after market-

close is defined as the last transaction price with a non-zero trading volume after 16:00, but

no later than 24:00. Otherwise, we code it as missing. We compute daily close-to-close log

stock (transaction) price change as a proxy for daily stock return for a firm i on day t as

RCTCi,t = ln(Pi,16:00,t)− ln(Pi,16:00,t−1)

5 where Pi,c,t is the stock i’s price on day t at clock time

h:m, where h is hour and m is minute. Further, we look at three different parts of daily stock

returns.

We compute stock returns after market hours or evening stock returns RAMi,t as the log

difference between last trade at or prior to 24:00 and the earlier market-close price at 16:00

on a day t, if both transaction prices are available. Otherwise, the RAMi,t is coded as missing6.

5We notice that markets close earlier on holidays, when market closing time is adjusted from 16:00 to 13:00,13:15, or 13:30. We identify and adjust for these situations by replacing market closing time from 16:00 to13:00, 13:15, or 13:30 accordingly, all else remaining the same.

6We describe some institutional background on what happens after conventional trading hours. In theU.S., most of the trades in stock markets happen in NYSE and Nasdaq from 09:30 to 16:00 Eastern Time.However, investors can still trade after market hours. This is done by ECN (Instinet was the first ECNcreated in 1969), which is an alternative SEC-permitted trading system or network that allows trading outsidetraditional exchange after trading hours. Stocks and currencies are the most widely traded products in ECN.Before the 1990s, after-market hours trading were primarily dominated by institutional investors. After 1990s,ECNs attracted a much wider group including retail investors by giving clients full access and transparencyin the order books as well as imposing a much smaller transaction cost. In particular, most of the tradesin the after-hours session happen between 16:00 and 18:30 Eastern Time. To place an order on an ECN,

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We compute stock returns before market opens or night stock returns, RBMi,t , as the log

difference between opening market price at 09:30 on a day t and earlier first trade price

during 00:00 to 09:30 on a day t, if both first transaction price before the market opens and

the transaction price at the market open are available. Otherwise the RBMi,t is coded as missing.

We compute stock returns during market hours or day stock returns RMHi,t as the log difference

between a closing market price at 16:00 on a day t and the market-open price at 09:30 on a

day t, if both transaction prices are available. Otherwise the RMHi,t is coded as missing.

Our decomposition of daily stock returns ensures that there is no overlapping information

when we conduct the price discovery test between equity and CDS markets. Daily CDS

spreads are from Markit daily single-name CDS indicative transactions prices as of 15:30

Eastern Time, which is 30 minutes earlier than the stock market closing time, 16:00 Eastern

Time. But we shall ignore the small time difference in our analyses of more important pieces

of interactive market features.

CDS Spreads and Returns. We obtain daily CDS spread data for five-year CDSs on

senior, unsecured debt. We follow Hilscher, Pollet, and Wilson (2015) to restrict the CDS

sample by selecting those with modified restructuring default clause before the April 2009

CDS “big bang”, because this is the restructuring convention commonly used for U.S. firms.

We require no restructuring clause afterward, following Lee, Naranjo, and Velioglu (2017). We

compute the 5-year CDS return or credit protection return by the percentage change in credit

spread at a daily frequency. Positive credit return reflects the loss of the credit protection

seller. As noted by Hilscher, Pollet, and Wilson (2015), credit returns should equal to the

percentage change in the quoted CDS spread adjusted by the ratio of two annuity factors.

However, in practice, the percentage change of spread is an accurate proxy for CDS protection

individual investors must either have an account with a broker who can directly access ECN or they need tobe a subscriber. An execution occurs when the price of a buy order and the price of a sell order intersect onthe ECN. Nowadays, almost any investor can trade through an ECN, including retail investors, institutionalinvestors, market makers, and broker-dealers. Using month TAQ database, we find that the aggregate after-market (from 16:00 at t to 24:00 at t for all t) trading volume increases from January 1993 to December2013 based on the universe of all firms in the TAQ database. In particular, the aggregate (after-market hours)trading volume was about $5 trillion (approximately $3 million per day per stock) in 1993. It increased to $119trillion (approximately $62 million per day per stock) in 2009 and decreased to $96 trillion (approximately$52 million per day per stock) in 2013.

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return because the annuity ratio will always be close to 1. Thus, we use the percentage change

of CDS protection or CDS return in our empirical analyses. We apply a filter to remove stale

price observations, where prices are defined as stale when we observe the same price on at least

five consecutive days. In case of a stale price, we only consider the first of these observations

and classify subsequent observations as unavailable. Papers using percentage change in credit

spreads as a proxy for credit returns include Kapadia and Pu (2012), Hilscher, Pollet, and

Wilson (2015), and Lee, Naranjo, and Velioglu (2017).

Merge Stock/CDS Sample. Our strategy of merging the stock and CDS datasets

follows three steps. First, we link TAQ data to CRSP using TAQ-CRSP Link Table (tclink)

provided by WRDS. Second, we attach PERMNO (unique identifier) obtained from CRSP to

each reference entity with CDS using the first six digit CUSIP that is both available at CRSP

tapes and the “RED” reference entity file provided by Markit. Third, for those firms that

cannot be matched in the second step, we manually match across the two databases by using

long-legal names. As a result, there are 1186 unique firms with both stock returns and CDS

spreads.

Firm Characteristics. The firm characteristics include total daily trading volume, daily

trading volume turnover, stock market capitalization, idiosyncratic volatility, daily short vol-

ume turnover, residual institutional ownership, and downgrade events. We provide detailed

descriptions of these variables in the Appendix.

2.1 Summary Statistics

The total number of unique firms in our sample from 2001 to 2016 is 1186. There is, on

average, 721 unique firms per year. Table 1A reports the summary statistics of the variables

used in our analyses.

[Tables 1A and 1B about here]

There are 2,216,833 firm-day observations from 2001 to 2016. The sample average of after-

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market hours or evening returns RAM is about 0.00651% return on a daily basis, where before

market hours or night returns (RBM) and market hours or day returns (RMH) are negative at

about -0.00428% and -0.01014% respectively on a daily basis. In terms of volatility, (RBM)

and (RMH) are more volatile than RAM . Average daily trading volume during market hours

by aggregating across all stocks is about 15 times larger than the average daily trading volume

after the market closes from 16:00 till 24:00 and 119 times larger than average daily trading

volume before market opens during 00:00 to 09:30. The average 5-year CDS spread is about

179.212 bps. The CDS returns have higher volatility compared with stock returns.

Table 1B reports the correlation between day, evening, and night stock returns as well as

with CDS returns and stock returns computed based on close-to-close daily returns. We also

include an additional evening return on the following day, i.e. RAMt in addition to RAM

t−1 . We

calculate the various return correlations by averaging out individual stock return correlations

on the cross-section. In Table 1B, we confirm that the TAQ-based market hour stock returns

we constructed are closely related to CRSP-based open-to-close stock return with a correlation

of 99.9%. The correlations among CRSP-based overnight returns, RCTO,CRSP and the TAQ-

based overnight returns are quite low since they are measured using different time-stamps.

The correlation between RCTO,CRSPt and RBM

t is 31.2%. This is significantly higher than the

correlation between RCTO,CRSPt and RAM

r at only 1.7%. It suggests that RBMt may represent

a larger portion of variations in CRSP-based overnight returns. The correlation between RAMt

and RAMt−1 is close to zero at =0.79. Correlations between RMH and both RBM and RAM

are both negative, but small at -3.5% and -7.2% respectively. Thus uncconditionally across

different parts of the day, returns over each part do not appear to show strong correlations.

This could change once we introduce conditions on a CDS signal. Interestingly, the average

unconditional correlations between CDS return RCDSt−1 and RBM

t is almost zero, but is negative

for RMHt and postive for RAM

t .

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3 Baseline Empirical Results

Credit default swap (CDS) spreads or prices inform market participants through revealing

market expectations about stock default risk. Even without triggering credit or else default

events, CDS spreads still disclose useful information on long-term credit risk for the reference

entity. CDS percentage spread changes or returns on the other hand reveal signals or infor-

mation about changes in the distress risk, whether it is bad news or good news. Hilscher,

Pollet, and Wilson (2015) showed that stock returns strongly negatively predict CDS returns,

and not the other way around, suggesting the one-way information transmission from stock

markets to CDS markets. The suggested reason is that the relatively low transaction costs

of stocks facilitate informed investors to make gains from their private information first in

the stock market. This is consistent with the separating equilibrium hypothesis proposed by

Easley, O’Hara, and Srinivas (1998). We replicate the empirical results of Hilscher, Pollet,

and Wilson (2015) using daily close-to-close CRSP stocks returns and CDS returns - this is

reported as a supplementary Table A1 in our Appendix. We show that information indeed

unconditionally flows from stock returns to CDS returns and not vice versa using our sample.

The purpose of the above is firstly to ensure that our data is similar to that used in past stud-

ies and our results do not differ due to different sampling. Secondly, we note that the Hilscher

et al. study used daily returns, and our study introduced more information into the system

by using sub-day returns, As we had discussed, the CDS return impact on stock returns when

aggregated across a day and when aggregated across stocks with different idiosyncratic risks

may show up nothing much as it appears to be the case in this replication of their study.

The stock returns however appear to have significant negative impact on the following day

CDS returns. Similarly lagged CDS returns appear to have significant positive impact on the

following day CDS returns, or indications of positive autocorrelations.

[Table A1 about here]

14

Unlike the implication of unilateral information flow from stock to CDS market such as

in Hilscher, Pollet, and Wilson (2015), we focus on the information flow from CDS to stock

markets during shorter intervals within the day. As at this juncture, whether there exists

valuable private information flow from CDS to stock markets or not, unconditional on any

credit event, remains unclear in the literature. Uncovering and understanding such information

flow is essential because it would place rightful attention on news of distress risk that would

naturally be revealed first via CDS price signals. We show strong evidence that both levels

and changes of CDS spread can together predict subsequent period’s stock returns when

market hours and after-market hours stock returns are examined separately with respect to

the CDS price signals. We present the empirical evidence using a panel regression approach

and portfolio sorting approach.

3.1 Regression Results

We first employ regressions of dependent variables RAMt−1 , RBM

t , and RMHt separately on lagged

RCDSt−1 and lagged RMH

t−1 . To measure the predictive effect of CDS returns in stock returns

for firms that experience a high CDS spread, we employ indicator variables Hight−1 that

equals to one if the previous day’s five-year CDS spread is above its cross-sectional median,

and Lowt−1 that equals to one if the previous day’s five-year CDS spread is below its cross-

sectional median. High CDS spread level is singled out for attention as a conditional variable

in order to identify its associated stocks as those more keenly watched by CDS markets due

to these firms facing higher credit and default risks. The regression equation is specified as

RHRSi,t = β0 + β1Hight−1 ×RCDS

t−1 + β2Lowt−1 × CDSt−1 +RMHt−1 +Xi,t−1θ + εi,t (1)

where the dependent variable RHRSi,t representing stock return in hours periods includes RBM

i,t ,

RCTCi,t , RMH

i,t , and RAMi,t . Hight−1 and Lowt−1 are indicator variables that are equal to one

respectively if the previous day’s CDS spreads are above or below their cross-sectional medi-

15

ans.7 In addition, we include control variables for well-known determinants of stock returns,

such as idiosyncratic volatility computed using Fama and French 5-factor model with past

1-year rolling window, stock percentage effective spreads, market leverage, stock price mo-

mentum that is cumulative past 12-month returns excluding the most recent month, log stock

total trading volume, stock turnover, and log stock market capitalization. We also include

a firm-fixed effect to control for a potential omitted firm characteristics difference. Standard

errors are clustered by day. The standard errors in Table 2A are computed using adjust-

ments for heteroskedasticity and autocorrelation whereas in Table 2B, the adjustments are

via simulations using the bootstrap method that is more accurate for finite sample.

It is possible that the effect of CDS returns could be more definitive when they are consid-

ered under different categories of spread levels. There are existing literature that differentiate

the behavior of firms’ returns when their credit risks become too high. For instance, Avramov

et al., (2007) showed that firms with higher credit risks are associated with fewer analyst

coverages, higher forecast dispersions, and subject to higher revisions compared with those of

firms with lower credit risks. Acharya and Johnson (2007) and Lee, Naranjo, and Velioglu

(2017) found that CDS prices infuse information about adverse credit news faster than news

permeate via related stock prices. On the latter, it could also be that stocks with high credit

spreads tend to be less frequently traded, thus leading to a sluggish stock price adjustment

relative to associated credit derivatives (Kwan (1996)). Motivated by the existing literature,

we expect that a stronger CDS return or CDS price change signal could have higher predictive

power if we condition the changes on the CDS spread level. It allows us to use percentage

changes of CDS spread as a proxy for credit news arrival that is consistent with existing stud-

ies on relative efficiencies between stock and CDS markets that may be differentiated by the

7We use the cross-sectional median as a cutoff to ensure that there are enough stocks contained in each ofthe portfolios. In particular, in an unreported table of an average number of firms of each of quintile portfoliosin a single sorting strategy based on a CDS return, there are approximately 65 firms contained in each of theCDS return quintile for close-to-close stock return portfolio and market hours return portfolio with respectto the high CDS spreads and the low CDS spreads respectively. The average number of firms contained ineach quintile portfolio is smaller for before-market hours and after-market hours cases. For instance, thereare around 53 firms in the CDS return quintile for high CDS spreads and low CDS spreads. The averagenumber of firms reduces dramatically for the before-market hour return portfolio. There are only about 21firms contained in each of the quintile portfolio given the level of CDS spreads.

16

firms’ level of credit spread or risk.

[Tables 2A, 2B, and A3 about here]

When RAMi,t−1 is the dependent variable (for convenience we lump this together with the

others in Eq.(1) and ignore the slight deviation in time subscript for this case), all the ex-

planatory variables are lagged, including both RCDSt−1 and RMH

t−1 . The results in column (1) of

Tables 2A and 2B show that RCDSt−1 does not have clear significant impact on RAM

i,t−1 which is

the evening return following several hours immediately after the close of trading day t − 1

and the report of RCDSt−1 and RMH

t−1 . As shown in Column (1), conditional on high level CDS

spread, CDS returns of day t-1 has little impact on future stock returns after the stock mar-

kets close at the same day (t-1): β1=-0.0005 with a t-statistic of -0.88. As shown in Column

(2), RCDSt−1 × High shows little predictive power on RBM

t , suggesting that there is basically

no CDS signal effect for returns during before market-open during midnight hours 00:00 till

09:30. RMHt−1 on the other hand has a strong significant negative impact -0.0334 on RAM

i,t−1

which is not the typical positive autocorrelations in returns but indicates strong reversals.

In Column (3), conditional on high level CDS spread, the stock returns during market hours

strongly negatively react to RCDSt−1 . In particular, RCDS

t−1 × Hight−1 =-0.0110 with a t-stat =

-7.45. The economic magnitude is the highest among RAMt−1 , RBM

t , and RMHt . In Column (5),

RCDSt−1 ×Hight−1 positively predicts RAM

t with 0.0015 (t-stat = 3.04). Since we see that stock

returns at t (both close to close and its various market hours decomposition) negatively reacts

to credit news at t-1, a positive point estimate on stock returns after next day’s market close

indicates that stock markets overreact to CDS news at t− 1.

For low idiosyncratic volatility stocks, however, the credit default swap return mildly

impacts the following day stock return positively. Consistent with other studies such as

Rodriguez (2002) and Ratner and Chiu (2016), institutional investors who take positions in

stocks under more predictable envuronment (low idiosyncratic volatility) would take up prior

hedge positions in CDS to protect against adverse movements in the stock prices. This would

cause CDS returns and stock returns in the market hours afterward to move in the same

17

directions as the hedged investor either buy CDS and also stocks or sell CDS and also stocks.

In all regressions, the control variables basically have intuitively correct coefficient signs on

average or are not significant. For example, momentum increases next period market hours

stock returns, leverage and size cross-sectionally decreases stock returns.

However, when we split the sample into pre January 2009 and post December 2008 as in

Table A3, we do find post global financial crisis (GFC) evening returns for high credit spread

stocks to be sensitive and negatively reactive to just lagged CDS news while the pre-crisis

evening returns as with the overall sample evening returns are not. Similarly we find post

GFC night returns of high credit spread stocks to be negatively sensitive while overall sample

night returns are not. The next day market hours return RMHi,t of high credit spread stocks in

all cases - pre-GFC, post-HFC and overall - are significantly negatively impacted by the CDS

return news RCDSt−1 . This negative impact during day trading hours is the strongest amongst

the earlier evening and night post-GFC impact largely because we have seen in Table 1A that

the day market hours trading volumes and activities are far larger than the evening and low-

key night trading hours. Thus it is clear that CDS returns do have news effect on stock returns

over sub-day trading periods. On the other hand, we also observe the milder positive effect

of RCDSt−1 on the stock returns of different trading hours for stocks with low credit spreads.

Table 2B reports the t-statistics using bootstrapped standard errors. In particular, we

bootstrap Eq.(1) one thousand times and obtain the distribution of the t-statistic for testing

the null hypothesis that there is no return predictability. All else are similar to those in Table

2A. Table 2B confirms that our statistical inference is robust by sample small bias adjustment.

Overall, the analyses suggest that the stock market returns overreact negatively to the CDS

markets information during regular market hours and reverse after the market closes.

The regression specification in Eq.(1) tests the predictive impact of lagged CDS return on

returns of high credit spread stocks during the immediately following evening, night, and day

returns. Supposing RCDSt−1 was in turn affected by previous stock return RMH

t−2 . If we use for

example Hilscher et al. (2015) specification of stock impact on CDS return, then the lagged

18

CDS return we use in Eq.(1) would contain two components - one, old news incorporated in

RMHt−2 that still impacts RCDS

t−1 at a slower rate due to inertia and high transaction cost in the

CDS market, and two, news incorporated within CDS return itself that is not transmitted

from the stock market. As daily market-hours stock returns are known to correlate positively

(see Campbell, Grossman, and Wang (1993), and Anderson et al. (2012)) for reasons beyond

news, if we reduce Eq. (1) to just lagged market hours returns as predictive variables, we

would introduce unnecessary multi-collinearity and lose the economic investigation on direct

CDS impact. Thus, the key predictive variables of RCDSt−1 and RMH

t−1 are used, and this does

not rule out the exogenous (to the current system) impact of past stock returns on RCDSt−1 .

Most of the empirical studies focused on stock returns based on close-to-close, and there

are few studies working on the effect of prediction on more refined shorter period returns on,

before, or after market hours. We add the same set of control variables to regressions using

dependent variables of during, before-, and after-market hours stock returns for consistency.

Studies that investigated asset price behavior using overnight stock returns include Berkman,

Koch, Tuttle, and Zhang (2012) and Aboody, Even-Tov, and Lehavy (2018) who suggested

that overnight stock returns can serve as a measure of firm-specific investor sentiment. Boller-

slev, Li, and Todorov (2016) found that market betas associated with discontinuous intraday

as well as overnight returns contained significant risk premiums. Our night return results are

consistent with these somewhat related results in the literature and shows the importance of

considering sub-day returns.

3.2 Portfolio Tests

We conduct a double sorting strategy such that at the start of every trading day, we indepen-

dently split all stocks into high CDS spread group and low CDS spread group based on the

cross-sectional median8 of CDS spread levels observed on the previous trading day. Within

8We use the cross-sectional median as a cutoff to ensure that there are enough stocks contained in each ofthe portfolios. We also conduct additional robustness tests (unreported, but available from the authors) byshowing results of a similar double sorting portfolio test. In particular, we consider a top 80 percentile and abottom 20 percentile sort of cross-sectional lagged CDS spreads and portfolio tests are replicated with similar

19

each CDS spread group, we rank firms by five groups based on their CDS returns to form 5

portfolios. Specifically, at the start of every trading day, all stocks are sorted in ascending

order on the CDS return Rt−1CDS on the previous trading day. The ranked stocks are then

assigned to one of 5 quintile portfolios. The portfolio stocks are rebalanced every day based

on updated CDS signals. Besides the 5 quintile portfolios under CDS spread sort and the 5

quintile portfolios separately under CDS return sort, we also compute the H/L spread port-

folios for each of the above sorts. The H/L portfolio is constructed by going short the largest

quintile (Q5) stocks with high CDS prices or else high CDS returns, and long the smallest

quintile (Q1) stocks with low CDS prices or else low CDS returns. The portfolios are con-

structed based on either equal-weighted stocks or value-weighted stocks. Tables 3A and 3B

show the respective equal-weighted and value-weighted portfolio test results of daily profits.

These sorted portfolio results are, of course, nonlinear models instead of the linear regression

models in Tables 2A, 2B, in showing how investors could profit from nonlinear expectations

of stock returns conditioned on lagged information of control variables as well as the latest

CDS price signals.

[Figure 1 about here]

Figure 1 shows the average returns of daily equal-weighted portfolios. At the start of

each trading day, all stocks are split into high CDS spread group and low CDS spread group

based on the cross-sectional median of the observed CDS spread levels on the previous trading

day. Using a double sort, within each spread group, the stocks are further divided into five

quintiles according to CDS return. The top 20% belong to the high CDS return group while

the bottom 20% belong to the low CDS return group. Panel A shows the average returns

over sampling period January 2001 through March 2016 of market hour returns of the long

position in stocks with low credit spread group (dashed line), and in the high credit spread

group (dotted line). The x-axis indicates the 5 CDS return quintiles where Q5 has the highest

CDS returns. The y-axis shows the equal-weighted portfolio return in %. Panel B shows the

results as what are shown in Tables 3A and 3B.

20

average of corresponding after-market hours returns. After conditioning on high CDS spread,

we indeed see a more significant predictive result on the stock returns. In particular, the

market hours stock returns are monotonically reducing and after-market hours stock returns

are monotonically increasing with respect to increasing CDS returns in the high CDS spread

group. In contrast, we do not see a clear pattern when CDS spread is low.

Further, we examine the H/L portfolio at daily frequency. In Tables 3A and 3B we examine

stock portfolio returns respectively based on equal-weighted and value-weighted portfolios

sorted by CDS returns but conditioned separately on high and low CDS spread levels. H/L is

the raw return of a zero-cost portfolio of going short stocks of the highest quintile CDS return

and going long stocks of the lowest CDS return quintile. These average returns are expressed

as %.

[Tables 3A and 3B about here]

In Column (3) of Table 3A as well as Table 3B, we find that RCDSt−1 negatively and signifi-

cantly predicts the next period’s stock returns during market hours given a high level of CDS

spread the previous period. The mean of equal-weighted H/L portfolio is 0.0646% or 6.46 bps

per day (t-stat = 6.498) and the mean of value-weighted H/L portfolio is 0.0382% or 3.82 bps

per day (t-stat = 2.985) when the CDS spread is high. In a sharp contrast, column (4) shows

that the means of both equal- and value-weighted H/L portfolios given a low level of CDS

spread are not significantly different from zeros.

Similarly, RCDSt−1 positively predicts in column (5) the next period’s after-market closing

returns RAMt . The mean of equal-weighted H/L portfolio is -0.0242% (t-stat =-7.912) and of

value-weighted H/L portfolio is -0.0186% or 1.86 bps per day (t-stat = -4.538) given high CDS

spread level but does not exhibit any predictive power given low CDS spread level (Column

(6) in both Table 3A and 3B).

Note that, Column (7) and (8) show that RCDSt−1 generates overall negative and strong

impact on next day’s stock market returns (RCTCt ) when CDS spread is high. In particular,

given a high CDS spread, the mean of equal-weighted H/L portfolio is 0.0588% or 5.88 bps

21

per day (t-stat = 5.370) and the mean of value-weighted H/L portfolio is 0.0356% or 3.56 bps

per day (t-stat = 2.419). There is no effect for low CDS spread cases. This suggests that CDS

markets have unconditionally negative effect on stock markets even though the next day’s

stock returns partially reverse.

Consistent with our prior findings for the overall sample, RCDSt−1 does not provide much

useful information to predict next day’s before-market open stock returns regardless of levels

of CDS spread, as shown in columns (1) and (2) for the equal-weighted cases (Table 3A) and

value-weighted cases (Table 3B).

We present further evidence to show that the economic magnitude of after market reversal

is also large. In particular, the monthly Sharpe Ratio of H/L of RAMt is about the same as

that of RMHt in terms of absolute values. For the case of equal-weighted portfolios, the Sharpe

Ratio of H/L of RAMt is 0.62 that is slightly larger than that of RMH

t of 0.49. It suggests that

the economic magnitude of the after market stock reversal is sizable. The results under value-

weighted cases of Table 3B are similar to the case of equal-weight of Table 3A. These Sharpe

ratios of 0.5 and above are comparable to many hedge fund and mutual fund’s performance

Sharpe ratios.

3.3 Robustness Tests

Table A2 also replicates the test in Table 3 but uses CRSP data to examine close-to-open

returns and open-to-close returns. The latter is approximately our TAQ market hours returns

and the former is the sum of our after-market returns and before market-open returns. The

results in Table A2 unsurprisingly yield similarly significant H/L portfolio returns for high

credit spread stocks during open-to-close or approximately during market hours in our TAQ

data results. However, other cases are largely insignificant. Overall, the empirical evidence

suggests that CDS returns, as a proxy for CDS news, can deliver useful information in pre-

dicting subsequent day’s stock returns and produce overreaction in firms with high levels of

CDS spread.

22

[Tables A2, A4, A5 about here]

Table A4, and A5 conduct sub-sample test by separating the sample into two halves pre-

GFC 2001-2008, and post-GFC 2009-2016 - in order to show that the key results of the

portfolio tests in Tables 3A and 3B are robust. As one can see from Tables A4 and A5, our

baseline predictive results remain robust in the both of the sub-period samples.

4 Information Mechanism and Empirical Tests

The findings in the last section allows us to construct a validated framework for understanding

how CDS market works in relation to the stock market since many stocks in U.S. markets

are entities to associated CDS contracts traded through Markit platform that is the major

platform for CDS trades. At 15:30 Eastern time, Markit end-of-day daily CDS price or spread

relative to the previous trading day’s spread provides credit news that could be short-run

imminent credit downgrade or default, or else a long-run distress risk signal. We term these

respectively as short-run default and long-run default risks. The percentage change in CDS

price or CDS return as a source of news could indicate bad news on stocks if CDS return is

positive or good news if CDS return is negative.

Institutional traders, including banks, that also arrange lending/borrowing deals with firms

would obtain private insider information on how material the CDS news are, as some CDS

returns could be due merely to liquidity trades or hedgers reloading or unloading positions.

Material news are concentrated in stocks with high credit spreads (lower credit ratings) and

that are hard-to-value, as characterized earlier. Based on bad credit returns at day t = 1.

evening and night stock returns may drop a little. Past midnight into 09:30 the next morning,

the period termed as before-market hours, stock prices do not move that much on average

when most traders are asleep or not keen to take decisive positions. It could also be a time to

soak in the day’s news. After 17 1/2 hours, by the time the market opens at 09:30 the next

day, informed traders start to aggressively build up short positions on the stocks given the

23

CDS return signals particularly on high credit spread and hard-to-value stocks. By the end

of the trading hours at 16:00, the stock returns would have reflected the impact as found in

our earlier empirical results. There would be overreaction followed by reversals in stock price

except in cases when actual imminent credit downgrades or default would happen.

Uninformed traders in the stock market could not read any revealed information in the

stock prices after 16:00 the next day as they could not be certain if a credit event is imminent

and that price reversal may not happen. If they had known a credit event would happen the

following day, they would be like the informed and would sell during day trading hours to

push stock prices down without seeing a rebounce afterward. They could then buy at the

permanent lower prices to profit. However, because the uninformed are not sure and it could

well be a case with no subsequent credit event. In such a case, the stock prices would rebounce

and the informed would have gotten ahead of them to buy back stocks. The uninformed would

then be left with a loss situation when they buy back stocks at a higher reversed price. Thus

in equilibrium, the uninformed are liquidity traders that pay the price for convenience and

the informed traders are rewarded for their private information.

This section explores plausible information mechanisms of the overreaction findings we

documented in Section 3. In particular, we ask (1) what is the source of the CDS predictabil-

ity? Also, (2) Why does the stock market overreact to CDS price/news? To address the first

question, we focus on an informed-trading hypothesis, suggesting that informed investors in

CDS markets trade in opaque stocks with high valuation uncertainty by using their private

information extracted from lagged CDS prices. As such, the CDS returns are more likely to

lead stock returns for opaque firms.

To address the second question, we posit that CDS prices carry two pieces of distress risk

information that are usually hard to tell apart: (1) long-run high distress risk that leads to

short-run negative stock returns but no imminent credit event, and (2) short-run imminent

danger of a credit event including downgrades and default. Case (2) happens more rarely.

Probabilities of long-run distress and short-run imminent credit event are defined as com-

24

plementary and sum to 1. Based on the last CDS percentage spread change or the CDS return,

the informed trader makes a bet about imminent credit event happening. If CDS return was

positive, the bet was about how likely a credit event would take place the next couple of days.

If CDS return was negative, the bet was about how less likely a credit event would take place

the next couple of days given a high credit spread level. Overreaction to CDS news is led by

the informed traders who do not have perfect information and could over-bet or overreact in

some situations, as evidenced by the works of Veronesi (1999) and Bondt and Thaler (1985).

4.1 Development of Hypotheses

4.1.1 Informed Trading - Source of the Predictability. The CDS market is sophis-

ticated and sees active participation by a set of primary dealers that are likely to be large

financial institutions including banks. This is because banks have better access to reference

entities’ credit status through established borrow/lending relationships with the reference en-

tities, and thus have an economic interest and the necessary experience to deal in CDS. This

could be a source of private information embedded in CDS changes. Kim et al. (2014) also

found that informed lenders to entities could trade on borrowers’ private information. Consis-

tent with this view, Acharya and Johnson (2007) found that CDS prices are more informative

for firms with a higher number of bank relations. Lee, Naranjo, and Velioglu (2017) found

that bank-related insider trading information seemed to be fully reflected in the CDS spread

reactions rather than related stock prices. Additionally, Siriwardane (2018) showed that in

2011 about 50% of total net CDS protection in the U.S. was sold by the top five dealers.

Informed trading should be more profitable for hard-to-value stocks. This is because

informed traders can benefit more by trading with uninformed traders who would find it

more difficult to infer information given the more challenging information environment. The

signal noises to uninformed investors is a crucial factor of informed investors’ trade incentives

(Seyhun (1986)). Theories of endogenous information acquisition suggest that the incentives to

acquire private information are inversely related to the informativeness of public information.

25

See Grossman and Stiglitz (1980), Verrecchia (1982), and Diamond (1985), among many other

studies. Empirically, Frankel and Li (2004) found that insiders prefer to trade in firms whose

financial statements are less value-relevant. Aboody et al. (2005) found that insider trading

activities are more pronounced for firms with poor earnings quality. Kumar (2009) found

that informed trading intensity is higher among hard-to-value stocks where retail investors

are more prone to behavioral biases. Wu (2018) found that insiders’ trading profits increase

sharply after an increase in information asymmetry. Chen, Kelly, and Wu (2018) found that

hedge funds trade more aggressively when information asymmetry increased.

If stocks have high transparency in terms of easy valuation, more accurate financial state-

ments, high earnings quality, and thus low information asymmetry, then CDS price signals

would not help in predicting stock return changes since both CDS and stock markets could

have processed any news simultaneously. However, for hard-to-value stocks where there is

information asymmetry, informed traders will be able to trade on stocks based on CDS price

changes which they would know contain information signals. It could also be that the initial

price movements of CDS markets reveal the trading activities of a small set of informed cross-

market arbitrageurs who have better access to both the CDS market and the larger stock

markets, and who could follow up by trading in the stock market, as found in Goldstein, Li,

and Yang (2014).

Hypothesis 1: CDS returns only lead stock returns when valuation uncertainty is

high.

4.1.2 Stock Market Overreaction to CDS Price Signals. CDS price changes carry

information about whether there would be short-run imminent default risk or else long-run

default risk. Based on the last CDS spread change or the CDS return, the informed trader

makes bet about imminent credit event happening. If CDS return was positive, the bet was

about how likely a credit event would take place the next couple of days. If CDS return

was negative, the bet was about how less likely a credit event would take place the next

26

couple of days given a high credit spread level. If all informed traders know precisely what

the credit private signal is, the stock price will not overshoot. However, informed insiders

have imperfect private information. Informed traders whether through overreaction in over-

estimating the probability of short-run credit downgrade or default and would over-sell more

stocks. In other words, they under-estimate long-run distress risk. When lagged CDS return

signal is negative, i.e. less credit risk, they under-estimate the probability of short-run credit

downgrade or default but over-estimate the complementary long-run distress risk. A testable

hypothesis is as follows.

Hypothesis 2: There is no stock return reversal for days with credit downgrade

following the CDS return signals.

Based on this hypothesis, we test stock price changes by examining periods with both low

and high short-run imminent credit event prediction error. We expect to find no stock returns

reversal after market-close whether the imminent credit event prediction error is small or large

if the traders are fully informed. We employ days on t+ 1 where downgrade actually occurred

as indicators of low imminent credit event prediction error at t. However, if traders are not

fully informed but overreact to positive CDS return indicating an imminent credit event, then

we would see stock returns fall and not reverse after market hours when an actual imminent

credit event occurs afterward, but stock price reversals when no imminent credit event actually

occurs. For negative CDS returns, if traders also overreact to having no imminent credit event

or adverse credit news that will follow, they would over-buy stocks so that there would also

be reversals when no credit events occur. Because incidences of negative CDS returns or low

CDS spread (below 120) followed by a credit downgrade is very rare, at 0.01% of the sample

points, these occurrences of subsequent stock price fall averagely do not affect the overall

after-market hours results to produce significant price reversals.

4.1.3 Limit-to-Arbitrage. If equity and CDS markets are fully integrated, then (1) con-

temporaneously price changes in one market should appear in the other market with consis-

tent signs (2) intertemporally, price changes in one market should not spillover to future price

27

changes in the other market. However, as shown by Kapadia and Pu (2012), in the presence

of market frictions and financial constraints, short-horizon intertemporal pricing discrepancies

can occur between the two markets. Thus, limit-to-arbitrage may have a large impact on our

results showing lagged CDS price changes impacting stock returns. To investigate this, we

consider two types of limit-to-arbitrage: trading liquidity in the stock market and short-sales

contraints in stock trading.

Lack of individual stock liquidity. The stock market reactions to CDS prices could

reflect the lack of liquidity of the related stocks. Theoretically, Grossman and Miller (1988),

Jegadeesh and Titman (1995), Amihud (2002), Pastor and Stambaugh (2003), Acharya and

Pedersen (2005), and many other studies suggested that an illiquid stock is supposed to have

a steeper demand curve compared with a liquid stock. Further, Brogaard, Li, and Xia (2017)

showed that the CDS spread can proxy for the steeper slope of the aggregate demand curve

of an individual stock. Thus stocks with adverse credit are on average less liquid. Suppose

credit news arrives that shifts the aggregate liquidity supply of the stocks the next period.

Bad credit news would increase supply while good news would decrease supply. The less liquid

or illiquid stock with a less elastic or steeper demand curve would see a larger change in stock

price than that of the more liquid stock. If stock demand is perfectly elastic, then there would

be no change in stock price. Furthermore, if supply should rever, then the less liquid stock will

also see a large price reversal compared to the more liquid stock. The above price reactions

are graphically shown in Figure A1 in the Appendix.

Hypothesis 3A: Stock price reactions are stronger for more illiquid stocks and

weaker for liquid stocks.

Short-sale constraint As shown in Figure 1, the quintile portfolios of high CDS spread

deliver, on average, negative returns during market hours, suggesting that stock markets are

more sensitive to bad credit news when the credit risks of the underlying debts are high.

One possible reason is that institutional investors face short-sale constraint and thus cannot

immediately short the stocks with bad credit news. This,in turn,delays the flow of information

28

into stock prices (Nagel (2005)). Therefore, the stock price reactions on lagged CDS price

changes may occur because of short-sale constraint.

Hypothesis 3B: Stock price reactions are stronger for stocks with greater short-

sale constraints and weaker for stocks with lesser short-sale constraints.

4.2 Empirical Findings

4.2.1 Test of Hypothesis 1: Informed trading and valuation uncertainty. To test

hypothesis 1, we further split the sample by high and low valuation uncertainty groups using

portfolio sorts. We consider idiosyncratic volatility (IVOL) as a proxy for the degree of

valuation uncertainty as existing literature showed that valuation uncertainty was more (less)

pronounced given a high (low) idiosyncratic volatility. For examples, Chen, Huang, and Jha

(2012) found that a high degree of the managerial discretionary earnings management activity

with idiosyncratic volatility has poorer information quality and less informativeness on stock

price; Rajgopal and Venkatachalam (2011) found that financial reporting quality is poorer

when idiosyncratic volatility is higher. We construct the idiosyncratic volatility for each stock

based on Fama and French (2015) five-factor model using daily returns data in a 1-year rolling

window framework.

The portfolio sorting approach is as follows. At the start of every trading day, all stocks

are independently split into groups of high/low CDS spread levels based on the cross-sectional

median of the CDS spread level observed on the previous trading day. Within each CDS

spread group, we further split stocks into high/low valuation uncertainty groups based on the

median of IVOL on the previous trading day. Then within each of the sub-groups, we sort

by CDS returns from the previous trading day into quintiles. Within each quintile portfolio

of stocks, the portfolio returns are computed using equal weights of individual stock returns.

The H/L portfolio return is computed by shorting stocks in the highest quintile and buying

stocks in the lowest quintile. Columns (1)-(4) report the result of RMHt . Columns (5)-(8)

report the result of RAMt .

29

Column (1) and (5) are portfolio returns under high CDS spreads and high IVOL, Column

(2) and (6) are portfolio returns under high CDS spreads and low IVOL, Column (3) and (7)

are portfolio returns under low CDS spreads and high IVOL, and Column (4) and (8) are

portfolio returns under low CDS spreads and low IVOL. We report these portfolio results in

Table 4. We show portfolio returns of individual quintile portfolios in Figure 2.9

[Figure 2 & Table 4 about here]

In Figure 2, among the stocks with high CDS spreads and high idiosyncratic volatilities, we

see that magnitudes of average market hours return in Panel A are monotonically decreasing

in CDS returns. This is reflected in Table 4 column (1) where the daily H/L portfolio return

is 0.1036% (t-stat = 6.504). This shows that for hard-to-value stocks (stocks with high IVOL)

with high credit spread levels (at higher credit risk), credit news or CDS return signals allow

informed investors to make profit via trading in the stock market.

On the other hand, the magnitudes of average after-market hours returns in Panel B of

Figure 2 are monotonic increasing in CDS returns. This is reflected in Table 4 column (5)

with a H/L portfolio return of -0.0333% per day (t-statistic: -6.012). These after-market

hours H/L portfolio negative returns are smaller in magnitudes compared to the positive H/L

portfolio returns during market hours, so there is evidence of stock price partially reversals

after market closes when CDS spread and also IVOL or valuation uncertainties are high. This

is consistent with hypothesis 1.

In stark contrast, in Figure 2 we do not observe clear patterns for low IVOL group stock

returns given high CDS spread. In Table 4, all H/L portfolios with low CDS spread or

those with high CDS spread but low IVOL have returns not significantly different from zeros.

Overall, our empirical results suggest that informed trading on stocks based on CDS price

change signals is higher when the underlying stocks have higher credit spreads and are hard-

to-value. This certainly serves to lend evidence to Hypothesis 1.

9We also perform portfolio aggregation using value weights, and find the results to be largely similar. Wedo not report these to economize on space.

30

One concern is that IVOL may capture limit-to-arbitrage as suggested by Kapadia and Pu

(2012). In other words, it may be limit-to-arbitrage that is facilitating the predictability result

and not valuation uncertainty. To rule out this possibility and to affirm valuation uncertainty

as in Hypothesis 1 to be a credible reason of the predictability, we further consider a latter

test on the impact of limit-to-arbitrage. The related hypotheses about limit-to-arbitrage’s

impact on stock price reactions are framed in Hypotheses 3A and 3B.

We check for economic significance of the portfolio results besides statistical significance

via daily Sharpe Ratio. The sample period covers January 2001 through March 2016. We

also compare the Sharpe ratios and skewness of the H/L portfolios with the equal-weighted

S&P 500 stock index returns (including dividends) as the benchmark. For the after-market

hours, we compute the Sharpe ratio by taking the absolute value of the average return. The

market hours Sharpe ratio is higher than the after-market hours Sharpe ratio by about 33%.

This is consistent with the results in Table 6 regarding the stronger profit of H/L in market-

hours trading. Moreover, the H/L portfolio Sharpe ratios far exceeds in performance versus

the S&P 500 index performance over the sampling period. The H/L portfolio performance

over stocks with low CDS spread or low IVOL, however, appear to be on par with the S&P

500 index performance. Besides, the H/L portfolio’s skewness is positive while that of the

S&P 500 index is negative over the same sampling period. The positive skewness together

with higher mean and smaller or same standard deviation clearly indicates H/L portfolios

as superior performers and to have contained profitable information for trading when CDS

spread is high and when there is valuation uncertainty.

4.2.2 Test of Hypothesis 2: Overreaction. Hypothesis 2 predicts that there is no

return reversal during after-market hours when a credit downgrade is imminent. We postulate

informed traders overreacting or over-estimating the probability of short-run credit downgrade

or default and would over-sell more stocks. When lagged CDS return signal is negative

indicating less credit risk, they under-estimate the probability of short-run credit downgrade

or default but over-estimate the complementary long-run distress risk. If all informed traders

31

know precisely how private signal looks like, the price will never overshoot and there would

not be overreaction implications.

To test Hypothesis 2, we run panel regressions of the stock returns during different trading

hours with stocks sorted by high/low IVOL. The explanatory variables include controls such

as lagged returns. The key explanatory variables include interactive variables combining

CDS price information such as spreads or returns with dummy variables indicating whether a

credit downgrade or default event follows the CDS price information the next day. Specifically,

Downgradei,t is a dummy variable that equals to 1 when a downgrade occurs at day t, and

is zero otherwise. NoDowngradei,t is a dummy variable that equals to 1 when there is no

downgrade at day t, and is zero otherwise. At t, we assume that there is perceived short-run

default risk of firm i if Downgradei,t+1 = 1. We assume the downgrade information following

closely the next day could have been somewhat perceived. Otherwise adverse changes in CDS

spread or return is construed as long-run default risk.

Tables 5A and 5B report the fixed-firm effect panel regression results for high valuation

uncertainty stocks in Panel A and low valuation uncertainty stocks in Panel B. The stocks

are divided into high or low valuation uncertainty group based on the median of IVOL on the

previous day. Portfolios are re-balanced every trading day. The panel regression across stocks

i and time t by days is specified as follows.

RHRSi,t = β0 + β1R

CDSi,t−1 ×Downgradei,t+1 + β2R

CDSi,t−1 ×NoDowngradei,t+1 +Xi,t−1θ + εi,t (2)

where Downgradei,t is a dummy variable that equals to 1 for downgrade or default that

occurred and zero otherwise. NoDowngradei,t is dummy variables that is equal to 1 when

downgrade or default does not occur imminently afterward. Xi,t−1 are control variables.

In the regression, we test whether the overreaction effect disappears when there is imminent

credit downgrade or default. In particular, we check whether β1 is indifferent from zero for

both market hours returns and also after-market hours returns.

32

[Tables 5A and 5B about here]

In Panel A of Table 5A, we find that point estimates β1 of RCDSi,t−1×Downgradei,t+1 is insignif-

icant for RAMt . These are seen in Column (5). This suggests that there is no stock return

reversal when a credit downgrade or default event is imminent. In other words, the signal errors

of short-run distress risk is low. In columns (3) & (4), estimates β1 of RCDSi,t−1×Downgradei,t+1

are significantly negative for market hours returns RMHt and close-to-close returns RCTC

t . Thus

when credit downgrades or defaults occur, informed traders did not overreact or overshoot by

excessive selling based on CDS price signals since there is no stock return reversal. There are

only about 0.01% of sample points where negative CDS return or smaller spread is followed

by a credit downgrade or default, so any reversal in this small portion of cases would not show

up on the β1 estimates for RAMt . The above validates Hypothesis 2.

On the other hand, when downgrades or defaults did not occur, the Panel A β2 estimates

for RMHt are significantly negative in columns (3). The magnitudes of the estimates are

smaller than those when downgrades or default occurs, indicating that whether a credit event

is imminent or not, higher CDS returns lead to decrease in stock prices, and vice-versa.

However, when downgrades or defaults did not occur, the Panel A β2 estimates for RAMt are

significantly positive in columns (5). This is strong evidence of overreaction in normal long-

run distress risk when there would be no credit downgrades or defaults. The overreaction

or overshooting is consistent also perhaps with overconfident traders who overestimate short-

run imminent downgrade or default risk when it would not occur, based on increased CDS

return or increased CDS spread. When credit events actually occur, then the over-betting

by overconfident traders would turn out to be just on target so that there would be no stock

price reversal.

When the CDS price signal is negative CDS return or decrease in CDS spread, the in-

formed traders would overestimate long-run distress risk and under-estimate short-run immi-

nent credit event. They would over-buy the stocks. Because of overreaction, there would still

be after-market hours stock price reversals when no imminent credit downgrades or defaults

33

occur. If credit events do occur, which form a very tiny fraction or 0.01% of the cases, β1 for

after-market returns would not be significantly different from zero, so there is on average no

stock price reversal.

The β1 and β2 coefficient estimates in Panel B when IVOL is low, are all insignificant.

This implies no reactions (and therefore no reversals) were present in stocks that have low

valuation uncertainty. Informed traders cannot profit from such stocks. In Table 5B, we

conduct bootstrapping standard errors to avoid small sample bias. We show that the results

in Table 5B are consistent with these reported in Table 5A.

4.2.3 Test of Hypothesis 3A: Illiquidity in Limit-to-arbitrage. One motivation to

test if limit-to-arbitrage could explain the overreaction in stock prices to CDS news is to

ascertain if indeed IVOL is a proxy not just for valuation uncertainty but also for limit-to-

arbitrage, as suggested by Kapadia and Pu (2012). If limit-to-arbitrage could not satisfactorily

explain the overreaction in stock prices to CDS news, then we could rule out the role of IVOL

as proxy for limit-to-arbitrage since our results so far have shown strongly that IVOL is key

in explaining significant overreaction in stock prices.

First, we examine whether the monotonic patterns of quintile portfolio returns in Figure

1 for both market hours returns and after-market hours returns, only in the case of lagged

high credit spread, is due to a lack of trading activities among those stocks. To do this,

we report equal-weighted total daily trading volumes and daily turnovers of the five stock

quintiles constructed in Figure 1, conditioned on high CDS spread and low CDS spread.

Note that to focus on the trading activities, we consider trading volume-based stock liquidity

measures rather than bid-ask spread-based liquidity measures because bid-ask spread may

contain information of valuation uncertainty (see, e.g., Glosten and Harris (1988) and Huang

and Stoll (1989)), which may overlap with the information asymmetry content in IVOL. The

results are shown in Figure 3.

[Figure 3 about here]

34

In Figure 3 Panel A, we compute equal-weighted average of total daily trading volume

for each of the CDS return quintiles given high and low CDS spread levels. Similarly, we

report the case of daily trading turnovers in Panel B. Two important results emerge from

the analyses. Firstly, both the trading volume and turnover of the high CDS spread group

are higher than those of the low CDS spread group. It indicates that significant amounts

of trading activities occurred for the high CDS spread group. Thus, we cannot accept that

stronger stock market reactions observed in a group of high CDS spreads are because these

stocks are less liquid. Secondly, the average trading volume and turnover exhibit a U-shaped

pattern from the quintile with the most negative CDS returns to quintile Q5 with the highest

CDS returns. This indicates that trading activities are more intense on stocks with either the

highest or the lowest CDS price changes, which is intuitive as material changes would more

likely imply content of news and not just liquidity trading effects in the CDS market.

Second, Kapadia and Pu (2012) showed that limit-to-arbitrage is a key factor to drive

stock and CDS markets non-integration. They considered IVOL as a measure of arbitrage

costs in stock markets. If so, the IVOL we used earlier may just proxy for limit-to-arbitrage,

not valuation uncertainty. At the start of each trading day, all stocks are split into high CDS

spread group and low CDS spread group based on the cross-sectional median of the observed

CDS spread levels on the previous trading day. Within each CDS spread group, the stocks are

further sorted using observed IVOL based on the cross-sectional median on the previous day.

Using a triple sort, within each IVOL group, the stocks are further divided into five quintiles

according to CDS return. We examine the magnitudes of trading activities of the different

quintiles under high/low IVOL and under different high/low credit spread levels. Figure 4

reports the results.

[Figure 4 about here]

In Figure 4, we show that the trading activities (total daily trading volume in Panel A and

daily turnover in Panel B) are much higher for high IVOL and high CDS spread sub-group

than the other sub-groups. Indeed, trading activities are the highest for high IVOL groups

35

regardless of CDS spreads. Thus, we reject that stronger stock market reactions observed in a

group of high idiosyncratic volatilities are because these stocks are less liquid and are subject

to limit-to-arbitrage.

Unlike Hypotheses 1 and 2, Hypothesis 3A is set up to allow a refutation of some existing

thinking about impact of liquidity on stock price reactions. In our context, clearly Hypothesis

3A is rejected. Lack of trading liquidity is not seen as the reason for overreaction in stock

prices after CDS market news. We also perform portfolio tests based on our high/low liquidity

metrics. The results are reported in Table 6.

[Table 6 about here]

At the start of every trading day, all stocks are split into groups of high/low CDS spread

level based on the cross-sectional median of CDS spread level observed on the previous day.

Within each CDS spread group, stocks are further divided high IVOL and low IVOL sub-

groups based a median of IVOL (that proxies for degree of information asymmetry) observed

on the previous day. In each of the 4 sub-groups, stocks are sorted in columns (1) & (3) and

(2) & (4) into High/Low trading volumes, in columns (5) & (7) and (6) & (8) into High/Low

trading turnovers, using their cross-sectional medians. After these triple sorts, within each

sub-group, stocks are sorted into five groups by lagged CDS return. These high and low CDS

return groups’ daily equal-weighted portfolios are shown for two measures of returns - the

market hours returns and the after-market hours returns.

Table 6 shows that the overreactions in stocks with greater trading activities are in fact

stronger than those in stocks with lesser trading activities. For example, for the case of high

total daily trading volume, the H/L portfolios of both market hours and after-market hours

stock returns are statistically significant with a high total daily trading volume (see Column

(1) and Column (3)). The results of daily turnover (Column (5) to (7)) and results in Panel B

are similar. There are no material difference between high and low liquidity group respect to

H/L returns. Overall, the results indicate that stock illiquidity cannot satisfactorily explain

our overreaction and reversal results.

36

4.2.4 Test of Hypothesis 3B: Short-sale constraint. We first examine whether the

short-sale activities of stock quintile portfolios given high CDS spreads are smaller than those

of low CDS spreads. We proxy short-sale activities using short trading turnover, which is

constructed by the ratio of the short trading volume to the number of shares outstanding for

the entire stock in a trading day. The short trading volume is from “Reg SHO”, which spans

from 2005 to 2007. Figure 5 reports the result.

[Figure 5 about here]

From Figure 5, we see that average short-sale turnovers along the quintile portfolios given

high CDS spreads in Panel A are about two times larger than those of low CDS spreads.

Moreover, the stock quintiles, based on sorting with CDS returns, evidence larger and intense

shorting activities at the two extremes of large and small CDS returns. By partitioning

into high and low IVOL sub-groups, we see that in Panel B, significant amounts of shorting

activities are concentrated on the high IVOL stocks rather than the low IVOL stocks. Hence

high IVOL stocks appear not to be inhibited by short-sale constraints. In other words, the

overreactions of high IVOL stocks especially those with high credit spreads are not due to

short-sales constraints.

We further investigate the role of short-sale constraint in a larger sample employing Nagel

(2005)’s residual institutional ownership (RIO) as a proxy for short-sale constraint. As noted

by Nagel, higher (lower) RIO indicates more (less) short-sale constraint because when there

are less institutional trading, there are less abilities to overcome short-sale restrictions. On

the contrary, more institutional trading implies abilities to overcome short-sale constraints.

We construct another portfolio test based on H/L trading day portfolio returns under a

triple sort. At the start of trading day, we split all stocks into high CDS spread level and

low CDS spread level based on their cross-sectional median. Within each CDS spread group,

we further split stocks by high/low short-sale constraint based on the median of residual

institutional ownership (RIO) on the previous day. Next, within each of the 4 sub-groups,

37

we again sort the stocks into 5 quintiles based on largest CDS returns in the 5th quintile

and smallest CDS return in the 1st quintile. We form zero-cost H/L portfolio by going short

the 5th quintile stocks and going long the 1st quintile stocks. The H/L portfolio returns are

measured as market hours returns as well as after-market hours returns, and are reported in

Table 7.

[Table 7 about here]

In Table 7, we see that the average returns of H/L portfolios for both market hours and

after-market hours are significant for high CDS spreads for both the high RIO and the low

RIO sub-groups. In terms of the market hours, for the high residual institutional ownership

sub-group with high CDS spreads, i.e. the stocks facing high short-sales constraint, the

H/L market hours average return is 0.0589% (H/L of Column (1)) which is smaller than the

0.0694% (H/L of Column (2)) in the sub-group facing lesser short-sales constraint with low

RIO. If strong stock reactions to CDS return signals are because of short-sale constraints, we

expect that the significant average returns of H/L should only occur in the high RIO stocks.

Similarly, after-market returns are significantly negative (see Column (3) and (4)), indicating

stock price reversals, and this occurs as well for both high and low RIO sub-groups. Overall,

we conclude that the stock market reactions to CDS price signals cannot be satisfactorily

explained by short-sales constraints and limit-to-arbitrage. We reject both Hypotheses 3A

and 3B.

5 Conclusions

Do CDS prices reveal the presence of informed trading on the stock market? This paper

establishes a strong and previously unexplored link between CDS spread, returns, and stock

returns during different sub-day trading hours of the daily trading period. We show that

lagged CDS spread changes or CDS returns negatively impact high credit spread and high

idiosyncratic volatility stock returns during market-open hours. Using panel regressions and

38

nonlinear portfolio sorting approaches, the impact is shown to be both statistically and eco-

nomically significant. We also find that after regular market hours or after stock exchange’s

market closing when trades still continue on electronic trading platforms, there is also a strong

and significant positive relationship between stocks returns and lagged CDS returns, indicat-

ing stock return reversals after market hours. Previous research have mixed or weak results

supporting any effect of lagged CDS spreads or lagged CDS returns on daily stock returns

measured on close-to-close basis. This could be due to the aggregation of both a negative

impact during market hours and a reversal after market hours.

To understand the source of return predictability, we explore an informed trading hypoth-

esis, an overreaction hypothesis, and limit-to-arbitrage hypotheses. We show that our results

are mainly contributing to the informed trading hypothesis and the overreaction hypothesis.

We provide an information mechanism framework that is consistent with the empirical find-

ings in our paper. Our characterization of the CDS news impact on stocks is consistent with

the theoretical paper by Collin-Dufresne and Fos (2016) where informed trading is very much

a centre-piece in understanding empirical results. We show that CDS predictability occurs

because of a high valuation uncertainty especially with high credit spreads and high stock

liquidity. Thus informed traders across CDS and stock markets concentrate on hard-to-value

and low credit rating stocks, and extract information rent or profit particularly during high

trading activities. Short-sale constraint is found not to be a satisfactory reason for overreac-

tion of stocks and in fact, more profitability seems to center around stocks with low residual

institutional ownership or stocks highly traded by financial institutions including banks. The

stock return reversal observations or patterns that consistently show up illuminates a strong

possibility of overreaction in information traders supported by our findings.

For low idiosyncratic volatility stocks, however, we also find that credit default swap

return mildly impacts the following day stock return positively, reflecting hedged investment

outcomes in the markets. Aggregating both high and low idiosyncratic volatility stocks would

also nullify the impact as the two types of stocks react differently to CDS signals, in different

39

directions.

We believe that our strong results indicating CDS price change impact on stock returns

are due to informed trading activities related to high valuation uncertainty and the presence

of information asymmetry across the CDS and stock markets. We anticipate more exciting

research ahead to discover more of the intriguing interactions of traders across these two

related markets, particularly when time-stamped data on CDS trades would become available

in the future. The microstructures of trades within fractions of a day are fascinating and our

decomposition of returns into three blocks within a trading day is an important first step to

understanding more deeply the information-based trading behaviours.

40

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45

Table 1A: Summary Statistics. This table reports the descriptive statistics of the main variablesused in this study covering 2001 to 2013. SCDS is 5-year CDS spread. RCDS is percentage change of 5-yearCDS spread. Four different stock returns are reported, comprising (1) stock returns computed based on close-to-close stock prices RCTC , (2) stock returns before market opens RBM , (3) stock returns during market hoursRMH , and (4) stock returns after market hours RAM . V olume is log Trading volume (in ’000’s). Lev is marketleverage. Rt−250:t−30 si stock price momentum. IV OL is idiosyncratic volatility computed based on the Famaand French 5-factor model using past 1-year rolling window. ES is stock percentage effective spreads. Size islog of stock capitalization (where capitalization is in $000’s). Turn is stock turnover that is volume dividedby total number of shares (in ’000’s). RIO is residual institutional ownership. Shortvolume is the aggregatedaily short volume from 2005 to 2007 (in ’000’s). The statistics include sample mean (Mean), sample standarddeviation (Stdv), minimum value Min, and maximum value Max. Ln stands for natural logarithm.

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

Variable No. of Obs Mean Stdv Min Max

SCDS 2,216,833 0.018 0.033 0.000 1.617

RCDS 2,216,833 0.00008 0.043 -4.790 3.288

RCTC 2,216,833 0.00019 0.041 -3.867 11.195

RBM 2,216,833 -0.00004 0.021 -3.259 13.090

RMH 2,216,833 -0.00010 0.023 -1.958 7.905

RAM 2,216,833 0.00007 0.008 -2.154 2.888

V olume(BM) 2,216,833 2.864 4.100 0.000 13.617

V olume(MH) 2,216,833 13.837 1.796 5.298 17.807

V olume(AM) 2,216,833 10.291 2.886 0.000 15.297

Lev 2,216,833 0.168 0.721 0.000 33.593

Rt−250:t−30 2,216,833 0.130 0.370 -3.976 5.818

IV OL 2,216,833 0.017 0.011 0.000 0.226

ES 2,216,833 0.003 0.056 -0.090 15.750

Size ($) 2,216,833 16.054 2.643 6.046 26.836

Turn 2,216,833 8.418 9.441 0.003 56.647

RIO 1,090,143 1.191 1.182 -8.343 9.303

ShortV olume 457,495 11.785 1.609 3.912 17.631

46

Table 1B: Correlation Table of Stock Returns Computed using Different MarketHours using TAQ and CRSP. This table reports correlations for stock returns during different

intervals of a day from 2001 to 2013. The various stock returns comprise (1) stock returns before market-open

RBMi,t , (2) stock returns during market hours RMH

i,t , (3) stock returns after market hours RAMi,t , (4) close-

to-close stock returns RCTC,TAQi,t computed by TAQ, (5) close-to-close stock returns RCTC,CRSP

i,t computed

by CRSP, (6) open-to-close stock returns ROTC,CRSPi,t computed by CRSP, (7) close-to-open stock returns

RCTO,CRSPi,t computed by CRSP, (8) stock returns after market hours RAM

i,t−1 measured at day t-1, and (9)

CDS price changes measures at day t-1. RBMi,t , RMH

i,t , and RAMi,t are computed using TAQ dataset. All units

are expressed in terms of percentages.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

(1) RBMt 100 -3.5 0.5 16.0 16.3 -3.4 31.2 -0.79 0.06

(2) RMHt 100 -7.2 73.4 77.1 99.9 -7.1 -1.20 -0.47

(3) RAMt 100 -4.8 -5.1 -7.3 1.7 1.50 1.23

(4) RCTC,TAQt 100 99.8 72.8 50.7 4.47 -0.22

(5) RCTC,CRSPt 100 76.6 53.4 4.33 -0.15

(6) ROTC,CRSPt 100 -7.2 -1.16 -0.46

(7)RCTO,CRSPt 100 4.33 -0.15

(8)RAMt−1 100 -0.28

(9)RCDSt−1 100

47

Table 2A: Regression of Stock Returns on 5-year CDS Returns. This table reportspredictive panel regressions of various stock returns (different intervals of a day) using lagged CDS return of5-year CDS spreads, lagged market return, CDS spread levels, and control variables. Sample size contains2,192,709 observations from Jan 2001 to March 2013. The various dependent variables are: (1) after markethours stock returns, RAM

t−1 from a previous day in Column (1), before market hours stock returns, RBMt in

Column (2), market hours stock returns, RMHt in Column (3), close-to-close stock returns, RCTC

t in Column(4), and after market hours stock returns, RAM

t in Column (5). Hight−1 and (Lowt−1) are indicator variablesthat equal to one if the previous day’s CDS spread is above (below) the cross-sectional median. In addition,we include control variables for well-known determinants of stock returns, such as idiosyncratic volatilitycomputed by Fama and French 5-factor model using past 1-year rolling window (IV OLt−1), stock percentageeffective spreads (ESt−1), market leverage (Levt−1), stock price momentum Rt−250:t−30, (log) total tradingvolume for each stock (V olumet−1), stock turnover (Turnt−1), and (log) stock market capitalization (Sizet−1).We include firm-fixed effect and cluster standard error by day. The standard errors are computed adjustedfor heteroskedasticity and autocorrelation (12 lags). t-statistics are reported in the brackets. ***,**, and *denote significance at 1%, 5%, and 10% level respectively.

(1) (2) (3) (4) (5)RAM

t−1 RBMt RMH

t RCTCt RAM

t

RCDSt−1 ×Hight−1 -0.0005 -0.0008 -0.0110*** -0.0123*** 0.0015***

(-0.88) (-0.97) (-7.45) (-7.22) (3.04)RCDS

t−1 × Lowt−1 0.0007* 0.0007 0.0038** 0.0050* -0.0005**(1.84) (1.54) (2.08) (1.89) (-2.14)

RMHt−1 -0.0334*** 0.0082*** 0.0061**

(-16.78) (3.74) (2.35)RMH

t -0.0333***(-16.89)

RCTCt−1 -0.0088***

(-6.11)IV OLt−1 0.0145*** 0.0014 -0.0167*** -0.0001 0.0153***

(10.08) (0.62) (-4.14) (-0.03) (10.48)ESt−1 0.0002 0.0013* 0.0003 0.0018** 0.0001**

(0.64) (1.73) (1.05) (2.07) (2.05)Levt−1 -0.0000 -0.0000 -0.0004*** -0.0004*** 0.0000

(-0.41) (-0.08) (-4.60) (-3.73) (0.23)Rt−250:t−30 -0.0003*** -0.0000 0.0004*** 0.0001 -0.0003***

(-8.45) (-0.02) (4.39) (1.24) (-8.61)V olumet−1 0.0001*** -0.0001*** -0.0002*** -0.0002*** 0.0001***

(11.74) (-5.47) (-8.91) (-7.17) (6.05)Turnt−1 0.0000*** -0.0000 -0.0000 -0.0000 0.0000*

(3.02) (-0.04) (-1.63) (-0.71) (1.90)Sizet−1 -0.0000*** 0.0000 -0.0000*** -0.0001*** -0.0000***

(-4.54) (0.77) (-6.01) (-5.09) (-4.28)Intercept -0.0014*** 0.0014*** 0.0041*** 0.0041*** -0.0007***

(-11.95) (4.56) (11.90) (8.78) (-6.14)

Firm FE Yes Yes Yes Yes YesCluster SE Yes Yes Yes Yes YesR-sq 0.012 0.001 0.003 0.002 0.012adj. R-sq 0.012 0.000 0.003 0.001 0.012

48

Table 2B: Regression of Stock Returns on 5-year CDS Returns using BoostrapStandard Errors. This table reports predictive panel regressions of various stock returns (differentintervals of a day) using lagged CDS return of 5-year CDS spreads, lagged market return, CDS spread levels,and control variables. Sample size contains 2,192,709 observations from Jan 2001 to March 2013. The variousdependent variables are: (1) after market hours stock returns, RAM

t−1 from a previous day in Column (1),before market hours stock returns, RBM

t in Column (2), market hours stock returns, RMHt in Column (3),

close-to-close stock returns, RCTCt in Column (4), and after market hours stock returns, RAM

t in Column (5).Hight−1 and (Lowt−1) are indicator variables that equal to one if the previous day’s CDS spread is above(below) the cross-sectional median. In addition, we include control variables for well-known determinants ofstock returns, such as idiosyncratic volatility computed by Fama and French 5-factor model using past 1-yearrolling window (IV OLt−1), stock percentage effective spreads (ESt−1), market leverage (Levt−1), stock pricemomentum Rt−250:t−30, (log) total trading volume for each stock (V olumet−1), stock turnover (Turnt−1),and (log) stock market capitalization (Sizet−1). We include firm-fixed effect and cluster standard error byday. The standard errors are computed using bootstrap. t-statistics are reported in the brackets. ***,**, and* denote significance at 1%, 5%, and 10% level respectively.

(1) (2) (3) (4) (5)RAM

t−1 RBMt RMH

t RCTCt RAM

t

RCDSt−1 ×Hight−1 -0.0005 -0.0008 -0.0110*** -0.0123*** 0.0014***

(-0.96) (-0.95) (-6.80) (-5.30) (2.97)RCDS

t−1 × Lowt−1 0.0007 0.0007* 0.0038** 0.0050** -0.0005*(1.02) (1.96) (2.01) (2.06) (-1.93)

RMHt−1 -0.0334*** 0.0082*** 0.0061*

(-19.68) (4.75) (1.75)RMH

t -0.0333***(-13.39)

RCTCt−1 -0.0088***

(-5.67)IV OLt−1 0.0145*** 0.0014 -0.0167*** -0.0001 0.0151***

(6.12) (0.55) (-2.63) (-0.02) (5.33)ESt−1 0.0002 0.0013 0.0003 0.0018 0.0001

(0.07) (1.20) (0.13) (0.59) (0.17)Levt−1 -0.0000 -0.0000 -0.0004*** -0.0004** 0.0000

(-0.40) (-0.10) (-4.88) (-2.34) (0.14)Rt−250:t−30 -0.0003*** -0.0000 0.0004*** 0.0001 -0.0003***

(-7.45) (-0.02) (3.01) (1.11) (-5.51)V olumet−1 0.0001*** -0.0001*** -0.0002*** -0.0002*** 0.0001***

(6.61) (-5.49) (-7.65) (-5.34) (3.73)Turnt−1 0.0000 -0.0000 -0.0000 -0.0000 0.0000

(0.22) (-0.02) (-0.21) (-0.18) (0.50)Sizet−1 -0.0000** 0.0000 -0.0000*** -0.0001*** -0.0000***

(-1.98) (0.71) (-4.37) (-5.08) (-3.75)Intercept -0.0014*** 0.0014*** 0.0041*** 0.0041*** -0.0007***

(-6.76) (4.35) (9.28) (6.22) (-3.75)

Firm FE Yes Yes Yes Yes YesCluster SE Yes Yes Yes Yes YesR-sq 0.012 0.001 0.003 0.002 0.012adj. R-sq 0.012 0.000 0.003 0.001 0.012

49

Table 2C: Regression of Stock Returns on 5-year CDS Returns: CDS spreadand IVOL. This table reports predictive panel regressions of various stock returns (different intervals of aday) using lagged CDS return of 5-year CDS spreads, lagged market return, CDS spread levels, and controlvariables. Sample size contains 2,192,709 observations from Jan 2001 to March 2013. The various dependentvariables are: (1) after market hours stock returns, RAM

t−1 from a previous day in Column (1), before markethours stock returns, RBM

t in Column (2), market hours stock returns, RMHt in Column (3), close-to-close

stock returns, RCTCt in Column (4), and after market hours stock returns, RAM

t in Column (5). Hight−1and (Lowt−1) are indicator variables that equal to one if the previous day’s CDS spread is above (below) thecross-sectional median. HIt−1 and LIt−1 are indicator variables equal to one if the previous day’s idiosyncraticvolatility is above (below) the cross-sectional median. In addition, we include control variables for well-knowndeterminants of stock returns, such as idiosyncratic volatility computed by Fama and French 5-factor modelusing past 1-year rolling window (IV OLt−1), stock percentage effective spreads (ESt−1), market leverage(Levt−1), stock price momentum Rt−250:t−30, (log) total trading volume for each stock (V olumet−1), stockturnover (Turnt−1), and (log) stock market capitalization (Sizet−1). We include firm-fixed effect and clusterstandard error by day. The standard errors are computed adjusted for heteroskedasticity and autocorrelation(12 lags). t-statistics are reported in the brackets. ***,**, and * denote significance at 1%, 5%, and 10% levelrespectively.

(1) (2) (3) (4) (5)RAM

t−1 RBMt RMH

t RCTCt RAM

t

RCDSt−1 ×Hight−1 ×HIt−1 -0.0041*** -0.0011 -0.0176*** -0.0198*** 0.0026***

(-5.87) (-0.91) (-8.92) (-8.62) (3.96)RCDS

t−1 ×Hight−1 × LIt−1 -0.0002 -0.0003 0.0050*** 0.0058*** -0.0013***(-0.85) (-0.36) (2.91) (3.17) (-2.73)

RCDSt−1 × Lowt−1 × LIt−1 -0.0006* 0.0010* 0.0045*** 0.0060*** -0.0008***

(-1.82) (1.74) (5.65) (6.06) (-3.26)RCDS

t−1 × Lowt−1 ×HIt−1 -0.0010** 0.0000 0.0022 0.0026 0.0002(-2.43) (0.05) (1.35) (1.42) (0.41)

RMHt−1 -0.0337*** 0.0082*** 0.0058**

(-16.84) (3.74) (2.26)RMH

t -0.0090***(-6.22)

RC2Ct−1 -0.0333***

(-16.89)IV OLt−1 0.0142*** 0.0014 -0.0168*** -0.0003 0.0152***

(9.91) (0.61) (-4.17) (-0.06) (10.51)ESt−1 0.0002 0.0013* 0.0003 0.0018** 0.0001**

(0.65) (1.73) (1.04) (2.07) (2.03)Levt−1 -0.0000 -0.0000 -0.0004*** -0.0004*** 0.0000

(-0.42) (-0.08) (-4.61) (-3.74) (0.17)Rt−250:t−30 -0.0003*** -0.0000 0.0004*** 0.0001 -0.0003***

(-8.51) (-0.03) (4.36) (1.21) (-8.59)V olumet−1 0.0001*** -0.0001*** -0.0002*** -0.0002*** 0.0001***

(11.89) (-5.47) (-8.96) (-7.21) (6.01)Turnt−1 0.0000*** -0.0000 -0.0000 -0.0000 0.0000*

(3.01) (-0.04) (-1.63) (-0.71) (1.89)Sizet−1 -0.0000*** 0.0000 -0.0000*** -0.0000*** -0.0000***

(-4.53) (0.78) (-5.98) (-5.06) (-4.19)Intercept -0.0014*** 0.0014*** 0.0041*** 0.0041*** -0.0007***

(-12.08) (4.56) (11.94) (8.81) (-6.27)

Firm FE Yes Yes Yes Yes YesCluster SE Yes Yes Yes Yes YesR-sq 0.012 0.001 0.003 0.002 0.012adj. R-sq 0.012 0.000 0.003 0.001 0.012

50

Table 2D: Regression of Stock Returns on 5-year CDS Returns using BoostrapStandard Errors: CDS spread and IVOL. This table reports predictive panel regressions ofvarious stock returns (different intervals of a day) using lagged CDS return of 5-year CDS spreads, laggedmarket return, CDS spread levels, and control variables. Sample size contains 2,192,709 observations fromJan 2001 to March 2013. The various dependent variables are: (1) after market hours stock returns, RAM

t−1from a previous day in Column (1), before market hours stock returns, RBM

t in Column (2), market hoursstock returns, RMH

t in Column (3), close-to-close stock returns, RCTCt in Column (4), and after market

hours stock returns, RAMt in Column (5). Hight−1 and (Lowt−1) are indicator variables that equal to one if

the previous day’s CDS spread is above (below) the cross-sectional median. HIt−1 and LIt−1 are indicatorvariables equal to one if the previous day’s idiosyncratic volatility is above (below) the cross-sectional median.In addition, we include control variables for well-known determinants of stock returns, such as idiosyncraticvolatility computed by Fama and French 5-factor model using past 1-year rolling window (IV OLt−1), stockpercentage effective spreads (ESt−1), market leverage (Levt−1), stock price momentum Rt−250:t−30, (log) totaltrading volume for each stock (V olumet−1), stock turnover (Turnt−1), and (log) stock market capitalization(Sizet−1). We include firm-fixed effect and cluster standard error by day. The standard errors are computedusing bootstrap. t-statistics are reported in the brackets. ***,**, and * denote significance at 1%, 5%, and10% level respectively.

(1) (2) (3) (4) (5)RAM

t−1 RBMt RMH

t RCTCt RAM

t

RCDSt−1 ×Hight−1 ×HIt−1 -0.0041*** -0.0011 -0.0176*** -0.0198*** 0.0026***

(-6.60) (-0.82) (-9.79) (-8.44) (3.27)RCDS

t−1 ×Hight−1 × LIt−1 -0.0002 -0.0003 0.0050** 0.0058*** -0.0013***(-0.93) (-0.36) (2.54) (3.48) (-2.69)

RCDSt−1 × Lowt−1 × LIt−1 -0.0006* 0.0010** 0.0045*** 0.0060*** -0.0008***

(-1.81) (2.22) (9.22) (5.96) (-4.74)RCDS

t−1 × Lowt−1 ×HIt−1 -0.0010** 0.0000 0.0022 0.0026* 0.0002(-2.48) (0.05) (1.29) (1.78) (0.34)

RMHt−1 -0.0337*** 0.0082*** 0.0058*

(-16.43) (2.70) (1.84)RMH

t -0.0090***(-6.23)

RC2Ct−1 -0.0333***

(-13.24)IV OLt−1 0.0142*** 0.0014 -0.0168** -0.0003 0.0152***

(5.87) (0.73) (-2.21) (-0.06) (7.30)ESt−1 0.0002 0.0013 0.0003 0.0018 0.0001

(0.05) (1.14) (0.46) (0.56) (0.13)Levt−1 -0.0000 -0.0000 -0.0004*** -0.0004*** 0.0000

(-0.37) (-0.09) (-2.89) (-3.11) (0.14)Rt−250:t−30 -0.0003*** -0.0000 0.0004*** 0.0001 -0.0003***

(-4.99) (-0.03) (3.06) (1.05) (-6.58)V olumet−1 0.0001*** -0.0001*** -0.0002*** -0.0002*** 0.0001***

(6.84) (-5.54) (-5.56) (-5.18) (3.17)Turnt−1 0.0000 -0.0000 -0.0000 -0.0000 0.0000

(0.31) (-0.02) (-0.63) (-0.10) (0.25)Sizet−1 -0.0000 0.0000 -0.0000*** -0.0000*** -0.0000**

(-1.26) (0.72) (-5.35) (-3.73) (-2.09)Intercept -0.0014*** 0.0014*** 0.0041*** 0.0041*** -0.0007***

(-9.83) (4.44) (8.58) (6.93) (-3.84)

Firm FE Yes Yes Yes Yes YesCluster SE Yes Yes Yes Yes YesR-sq 0.012 0.001 0.003 0.002 0.012adj. R-sq 0.012 0.000 0.003 0.001 0.012

51

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inti

le(Q

5)

CD

Sre

turn

an

dgoin

glo

ng

stock

sof

the

low

est

CD

Sre

turn

qu

inti

le(Q

1).

Equ

al-w

eighte

dre

turn

sare

rep

ort

ed.

Th

ese

aver

age

retu

rns

are

exp

ress

edas

%.

Colu

mn

s(1

)an

d(2

)re

port

the

H/L

bef

ore

mar

ket-

open

stock

retu

rn(R

BM

t)

giv

enh

igh

(Colu

mn

(1))

an

dlo

wC

DS

spre

ad

(Colu

mn

(2))

resp

ecti

vel

y.C

olu

mn

s(3

)an

d(4

)re

port

the

H/L

du

rin

gm

arket

hou

rsst

ock

retu

rn(R

MH

t)

giv

enh

igh

(Colu

mn

(3))

an

dlo

w(C

olu

mn

(4))

CD

Ssp

read

resp

ecti

vely

.C

olu

mn

s(5

)an

d(6

)re

port

the

H/L

afte

r-m

arke

th

ours

stock

retu

rn(R

AM

t)

giv

enh

igh

an

dlo

wC

DS

spre

ad

resp

ecti

vely

.C

olu

mn

s(7

)an

d(8

)re

port

the

H/L

close

-to-c

lose

stock

retu

rn(R

CTC

t)

give

nH

igh

and

Low

CD

Ssp

read

resp

ecti

vel

y.T

he

stock

retu

rns

are

con

stru

cted

by

tran

sact

ion

pri

ces

from

TA

Q(u

sin

gm

onth

lyT

AQ

for

Jan

uar

y20

01to

Dec

emb

er20

13an

dd

aily

TA

Qfo

rJanu

ary

2014

toM

arc

h2016.)

.T

he

sam

ple

per

iod

cove

rsJanu

ary

2001

thro

ugh

Marc

h2016.

We

calc

ula

tem

onth

lyS

har

pe

Rat

io.

Inp

arti

cula

r,w

ecu

mu

late

dail

yre

turn

sin

tom

onth

lyre

turn

san

dco

mp

ute

the

Sh

arp

eR

ati

ob

ase

don

sam

ple

mea

nan

dsa

mp

lest

and

ard

dev

iati

onof

the

mon

thly

retu

rns

seri

es.

Both

Month

lyS

harp

eR

ati

oan

dN

ewey

an

dW

est

(nw

)t-

stati

stic

sad

just

edfo

r12

lags

are

rep

orte

din

the

bra

cket

s.**

*,**

,an

d*

den

ote

sign

ifica

nce

at

1%

,5%

,an

d10%

leve

lre

spec

tive

ly.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

RBM

RM

HR

AM

RCTC

Hig

hSCDS

Low

SCDS

Hig

hSCDS

Low

SCDS

Hig

hSCDS

Low

SCDS

Hig

hSCDS

Low

SCDS

Q1R

CDS

0.00

02-0

.000

5-0

.0136

0.0

269*

-0.0

042*

0.0

024

-0.0

175

0.0

287

nw

t-st

at(0

.046

)(-

0.04

0)(-

0.5

49)

(1.8

02)

(-1.7

43)

(1.2

64)

(-0.7

09)

(1.4

54)

Q2R

CDS

-0.0

033

0.00

51-0

.0287

0.0

216

0.0

044

0.0

024

-0.0

275

0.0

291

nw

t-st

at(-

0.40

4)(0

.409

)(-

1.1

41)

(1.4

49)

(1.5

28)

(1.2

52)

(-1.0

43)

(1.5

15)

Q3R

CDS

0.00

060.

0060

-0.0

214

0.0

219

0.0

066**

0.0

031

-0.0

142

0.0

310

nw

t-st

at(0

.083

)(0

.435

)(-

0.9

01)

(1.4

72)

(2.4

88)

(1.5

68)

(-0.5

75)

(1.5

54)

Q4R

CDS

0.00

32-0

.009

4**

-0.0

355

0.0

302**

0.0

147***

0.0

019

-0.0

176

0.0

227

nw

t-st

at(0

.354

)(-

2.30

4)(-

1.4

23)

(2.1

30)

(3.8

98)

(0.8

22)

(-0.6

83)

(1.5

84)

Q5R

CDS

-0.0

180.

0001

-0.0

782***

0.0

214

0.0

2***

0.0

027

-0.0

763***

0.0

242

nw

t-st

at(-

1.65

5)(0

.009

)(-

2.7

80)

(1.4

75)

(5.3

57)

(1.1

62)

(-2.7

22)

(1.3

50)

H/L

0.01

820.

0006

0.0

646***

-0.0

055

-0.0

242***

-0.0

003

0.0

588***

0.0

045

nw

t-st

at(1

.209

)(0

.114

)(6

.498)

(-1.0

43)

(-7.9

12)

(-0.1

45)

(5.3

70)

(0.5

79)

Sh

arp

eR

atio

(0.2

41)

(-0.

009)

(0.4

89)

(0.0

85)

(-0.6

20)

(-0.0

11)

(0.4

02)

(0.0

44)

52

Tab

le3B

:V

alu

e-w

eig

hte

dSto

ckP

ort

foli

os

Sort

ed

by

CD

SR

etu

rns

Condit

ion

ed

on

Hig

h/L

ow

CD

SS

pre

ad

Levels

.T

his

tab

lere

por

tsva

lue-

wei

ghte

dH

/Lp

ort

foli

otr

ad

ing

day

retu

rns

un

der

ad

oub

leso

rt.

At

the

start

of

an

inve

stm

ent

day

,w

esp

lit

all

stock

sin

toh

igh

CD

Ssp

read

leve

lan

dlo

wC

DS

spre

adle

vel

base

don

thei

rcr

oss

-sec

tion

al

(acr

oss

all

firm

son

pre

vio

us

day

)m

edia

n.

Wit

hin

each

CD

Ssp

read

grou

p,

we

ran

kfi

rms

by

qu

inti

les

bas

edon

thei

rC

DS

retu

rns

or

per

cent

spre

ad

chan

ge,

an

dfo

rm5

por

tfoli

os.

Th

ep

ort

foli

os

are

reb

ala

nce

dev

ery

day

acco

rdin

gly.

H/L

isth

era

wre

turn

ofa

zero

-cos

tp

ort

foli

oof

goin

gsh

ort

stock

sof

the

hig

hes

tqu

inti

le(Q

5)

CD

Sre

turn

an

dgoin

glo

ng

stock

sof

the

low

est

CD

Sre

turn

qu

inti

le(Q

1).

Equ

al-w

eigh

ted

retu

rns

are

rep

ort

ed.

Th

ese

aver

age

retu

rns

are

exp

ress

edas

%.

Colu

mn

s(1

)an

d(2

)re

port

the

H/L

bef

ore

mar

ket

-op

enst

ock

retu

rn(R

BM

t)

give

nh

igh

(Colu

mn

(1))

an

dlo

wC

DS

spre

ad

(Colu

mn

(2))

resp

ecti

vel

y.C

olu

mns

(3)

an

d(4

)re

port

the

H/L

du

rin

gm

arke

th

ours

stock

retu

rn(R

MH

t)

give

nh

igh

(Colu

mn

(3))

an

dlo

w(C

olu

mn

(4))

CD

Ssp

read

resp

ecti

vely

.C

olu

mn

s(5

)an

d(6

)re

port

the

H/L

afte

r-m

arke

th

ours

stock

retu

rn(R

AM

t)

giv

enh

igh

an

dlo

wC

DS

spre

ad

resp

ecti

vely

.C

olu

mn

s(7

)an

d(8

)re

port

the

H/L

close

-to-c

lose

stock

retu

rn(R

CTC

t)

give

nH

igh

and

Low

CD

Ssp

read

resp

ecti

vel

y.T

he

stock

retu

rns

are

con

stru

cted

by

tran

sact

ion

pri

ces

from

TA

Q(u

sin

gm

onth

lyT

AQ

for

Jan

uar

y20

01to

Dec

emb

er20

13an

dd

aily

TA

Qfo

rJanu

ary

2014

toM

arc

h2016.)

.T

he

sam

ple

per

iod

cove

rsJanu

ary

2001

thro

ugh

Marc

h2016.

We

calc

ula

tem

onth

lyS

har

pe

Rat

io.

Inp

arti

cula

r,w

ecu

mu

late

dail

yre

turn

sin

tom

onth

lyre

turn

san

dco

mp

ute

the

Sh

arp

eR

ati

ob

ase

don

sam

ple

mea

nan

dsa

mp

lest

and

ard

dev

iati

onof

the

mon

thly

retu

rns

seri

es.

Both

Month

lyS

harp

eR

ati

oan

dN

ewey

an

dW

est

(nw

)t-

stati

stic

sad

just

edfo

r12

lags

are

rep

orte

din

the

bra

cket

s.**

*,**

,an

d*

den

ote

sign

ifica

nce

at

1%

,5%

,an

d10%

leve

lre

spec

tive

ly.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

RBM

RM

HR

AM

RCTC

Hig

hSCDS

Low

SCDS

Hig

hSCDS

Low

SCDS

Hig

hSCDS

Low

SCDS

Hig

hSCDS

Low

SCDS

Q1R

CDS

0.00

71-0

.002

50.0

137

0.0

220

-0.0

072**

0.0

033

0.0

136

0.0

229

nw

t-st

at(0

.752

)(-

0.10

4)(0

.647)

(1.4

61)

(-2.0

03)

(1.0

12)

(0.5

77)

(0.8

01)

Q2R

CDS

-0.0

037

-0.0

074

-0.0

073

0.0

216

0.0

018

0.0

013

-0.0

093

0.0

155

nw

t-st

at(-

0.49

2)(-

0.81

1)(-

0.3

59)

(1.5

20)

(0.4

81)

(0.3

62)

(-0.4

23)

(0.9

27)

Q3R

CDS

0.00

48-0

.004

10.0

113

0.0

151

0.0

013

0.0

044*

0.0

175

0.0

155

nw

t-st

at(0

.591

)(-

0.28

2)(0

.556)

(1.1

03)

(0.3

79)

(1.7

09)

(0.7

58)

(0.7

84)

Q4R

CDS

0.00

69-0

.013

3*0.0

029

0.0

225*

0.0

102***

0.0

012

0.0

201

0.0

103

nw

t-st

at(0

.519

)(-

1.76

1)(0

.138)

(1.6

68)

(2.9

42)

(0.3

67)

(0.8

23)

(0.6

84)

Q5R

CDS

-0.0

089

-0.0

016

-0.0

245

0.0

028

0.0

114***

0.0

053*

-0.0

220

0.0

064

nw

t-st

at(-

0.81

0)(-

0.09

0)(-

1.0

72)

(0.1

91)

(2.9

18)

(1.6

89)

(-0.8

68)

(0.2

78)

H/L

0.01

6*-0

.000

90.0

382***

0.0

192

-0.0

186***

-0.0

020

0.0

356**

0.0

165

nw

t-st

at(1

.948

)(-

0.72

1)(2

.985)

(0.8

57)

(-4.5

38)

(-1.4

36)

(2.4

19)

(0.3

94)

Sh

arp

eR

atio

(0.1

39)

(0.0

53)

(0.2

31)

(0.0

68)

(-0.3

36)

(-0.2

62)

(0.1

90)

(0.0

32)

53

Tab

le4:

Sto

ckP

ort

folios

Sort

ed

by

CD

SR

etu

rns

Condit

ioned

on

Hig

h/L

ow

CD

SSp

read

Level

and

Hig

h/L

ow

IVO

L(V

alu

ati

on

Un

cert

ain

ty).

Th

ista

ble

rep

ort

sH

/L

trad

ing

day

port

foli

ore

turn

su

nd

era

trip

leso

rt.

At

the

start

of

each

day

,w

esp

lit

all

stock

sin

toh

igh

CD

Ssp

read

level

and

low

CD

Ssp

read

leve

lb

ase

don

thei

rcr

oss

-sec

tion

al

(acr

oss

all

firm

son

pre

vio

us

day

)m

edia

n.

Wit

hin

each

CD

Ssp

read

grou

p,

we

furt

her

spli

tst

ock

sin

toh

igh

orlo

wva

luati

on

un

cert

ain

tygro

up

base

don

the

med

ian

of

IVO

Lon

the

pre

vio

us

day

.N

ext,

wit

hin

each

ofth

e4

grou

ps,

we

agai

nso

rtth

est

ock

sin

to5

qu

inti

les

base

don

larg

est

CD

Sre

turn

sin

the

5th

qu

inti

lean

dsm

all

est

CD

Sre

turn

inth

e1st

qu

inti

le.

We

form

zero

-cos

tH

/Lp

ortf

olio

by

goin

gsh

ort

the

5th

qu

inti

lest

ock

san

dgoin

glo

ng

the

1st

qu

inti

lest

ock

s.E

qu

al-

wei

ghte

dre

turn

sare

rep

ort

ed.

Th

eav

erag

ere

turn

sar

eex

pre

ssed

in%

.C

olu

mn

s(1

)an

d(2

)re

port

the

H/L

mark

ethou

rsst

ock

retu

rnof

hig

hC

DS

spre

ad

(SCDS

)giv

enh

igh

IVO

l(C

olu

mn

(1))

and

low

IVO

L(C

olu

mn

(2))

resp

ecti

vely

.C

olu

mn

s(3

)an

d(4

)re

port

the

H/L

mark

eth

ou

rsst

ock

retu

rnof

low

CD

Ssp

read

(SCDS

)gi

ven

hig

hIV

Ol

(Col

um

n(3

))an

dlo

wIV

OL

(Colu

mn

(4))

resp

ecti

vely

.C

olu

mn

s(5

)an

d(6

)re

port

the

H/L

aft

erm

ark

eth

ou

rsst

ock

retu

rnof

hig

hC

DS

spre

ad(S

CDS

)gi

ven

hig

hIV

Ol

(Col

um

n(5

))an

dlo

wIV

OL

(Colu

mn

(6))

resp

ecti

vely

.C

olu

mns

(7)

an

d(8

)re

port

the

H/L

aft

erm

ark

eth

ou

rsst

ock

retu

rnof

low

CD

Ssp

read

(SCDS

)gi

ven

hig

hIV

Ol

(Colu

mn

(7))

and

low

IVO

L(C

olu

mn

(8))

resp

ecti

vely

.T

he

stock

retu

rns

are

con

stru

cted

by

tran

sact

ion

pri

ces

from

TA

Q(u

sin

gm

onth

lyT

AQ

for

Janu

ary

2001

toD

ecem

ber

2013

an

dd

ail

yT

AQ

for

Janu

ary

2014

toM

arc

h2016.)

.T

he

sam

ple

per

iod

cove

rsJan

uar

y20

01th

rou

ghM

arch

2016

.W

eca

lcu

late

month

lyS

harp

eR

ati

o.

Inp

art

icu

lar,

we

cum

ula

ted

ail

yre

turn

sin

tom

onth

lyre

turn

san

dco

mp

ute

the

Sh

arp

eR

atio

bas

edon

sam

ple

mea

nan

dsa

mp

lest

an

dard

dev

iati

on

of

the

month

lyre

turn

sse

ries

.B

oth

Month

lyS

harp

eR

ati

oan

dN

ewey

and

Wes

tt-

stat

isti

csad

just

edfo

r12

lags

are

rep

ort

edin

the

bra

cket

s.***,*

*,

an

d*

den

ote

sign

ifica

nce

at

1%

,5%

,an

d10%

leve

lre

spec

tive

ly.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

RM

Ht

RAM

t

Hig

hSCDS

Low

SCDS

Hig

hSCDS

Low

SCDS

Hig

hIVOL

LowIVOL

Hig

hIVOL

LowIVOL

Hig

hIVOL

LowIVOL

Hig

hIVOL

LowIVOL

Q1R

CDS

-0.0

481

0.00

860.0

261

0.0

266**

0.0

045

0.0

029

0.0

039*

0.0

005

nw

t-st

at(-

1.46

1)(0

.473

)(1

.494)

(2.0

27)

(1.3

04)

(0.4

61)

(1.7

67)

(0.2

54)

Q2R

CDS

-0.0

639*

*0.

0168

0.0

240

0.0

234*

0.0

119***

0.0

139

0.0

048**

0.0

002

nw

t-st

at(-

2.04

2)(0

.865

)(1

.349)

(1.7

66)

(2.6

56)

(0.6

63)

(2.0

74)

(0.0

88)

Q3R

CDS

-0.0

69**

0.01

060.0

136

0.0

232*

0.0

147***

-0.0

013

0.0

069***

-0.0

001

nw

t-st

at(-

2.12

2)(0

.582

)(0

.765)

(1.7

73)

(3.6

24)

(-0.4

02)

(2.5

93)

(-0.0

30)

Q4R

CDS

-0.0

619*

0.00

250.0

309*

0.0

298**

0.0

284***

-0.0

002

0.0

071***

-0.0

030

nw

t-st

at(-

1.88

6)(0

.133

)(1

.797)

(2.3

68)

(4.0

63)

(-0.0

31)

(2.6

96)

(-1.0

94)

Q5R

CDS

-0.1

516*

**-0

.003

70.0

241

0.0

211

0.0

378***

-0.0

005

0.0

038

0.0

023

nw

t-st

at(-

4.07

4)(-

0.18

2)(1

.428)

(1.5

96)

(6.0

83)

(-0.0

44)

(1.0

84)

(1.1

52)

H/L

0.10

36**

*0.

0123

0.0

020

0.0

055

-0.0

333***

0.0

034

0.0

001

-0.0

018

nw

t-st

at(6

.504

)(1

.332

)(0

.246)

(1.0

09)

(-6.0

12)

(0.5

72)

(0.0

30)

(-1.0

34)

Sh

arp

eR

atio

(0.5

01)

(0.1

05)

(0.0

20)

(0.0

74)

(-0.4

57)

(-0.4

86)

(0.0

02)

(-0.0

82)

54

Tab

le5A

:P

an

el

Regre

ssio

nof

Sto

ckR

etu

rns

Condit

ioned

on

Imm

inent

Cre

dit

Dow

ngra

de

or

Defa

ult

Ris

k.

Th

ista

ble

rep

orts

pan

elre

gres

sion

resu

lts

for

CD

Ssp

read

stock

s(P

an

elA

)an

dlo

wC

DS

spre

ad

stock

s(P

an

elB

).S

tock

sare

div

ided

into

hig

hor

low

CD

Ssp

read

grou

ps

bas

edon

the

med

ian

ofC

DS

spre

ad

on

the

pre

vio

us

day

.P

ort

foli

os

are

re-b

ala

nce

dev

ery

trad

ing

day

.C

olu

mn

s(1

)to

(5)

den

ote

the

diff

eren

tp

anel

regr

essi

ons

usi

ng

diff

eren

td

epen

den

tsu

b-d

ayre

turn

vari

ab

les.

Th

eke

yex

pla

nato

ryva

riab

les

are

show

non

the

colu

mn

s.Downgrade i

,t+1

isa

du

mm

yva

riab

leth

ateq

ual

sto

1w

hen

ad

own

gra

de

occ

urs

at

day

t+

1,

an

dis

zero

oth

erw

ise.NoD

owngrade i

,t+1

isa

du

mm

yva

riab

leth

ateq

ual

sto

1w

hen

ther

eis

no

dow

ngra

de

at

day

t,an

dis

zero

oth

erw

ise.

Att,

we

ass

um

eth

at

ther

eis

per

ceiv

edsh

ort

-ru

nd

efau

ltri

skof

firm

iif

CD

Ssp

read

isal

read

yh

igh

andDowngrade i

,t+1

=1.

We

ass

um

eth

ed

own

gra

de

info

rmati

on

foll

owin

gcl

ose

lyth

en

ext

day

cou

ldh

ave

bee

nso

mew

hat

per

ceiv

ed.

Oth

erw

ise

adve

rse

chan

ges

inC

DS

retu

rnis

con

stru

edas

lon

g-r

un

def

au

ltri

sk.

Th

eco

ntr

ol

vari

ab

les

are

sim

ilar

toth

ose

inT

ab

le2A

and

2B.

We

incl

ud

efi

rmfi

xed

effec

tan

dcl

ust

erst

an

dard

erro

rby

day

.T

he

stan

dard

erro

rsare

com

pu

ted

aft

erad

just

ing

for

het

erosk

edast

icit

yan

dau

toco

rrel

atio

n(1

2la

gs).

t-st

atis

tics

are

rep

orte

din

the

bra

cket

s.***,*

*,

and

*d

enote

sign

ifica

nce

at

1%

,5%

,an

d10%

level

resp

ecti

vely

.

PanelA:High

CDS

Spread

(1)

(2)

(3)

(4)

(5)

RAM

t−1

RBM

tR

MH

tR

C2C

tR

AM

t

RCD

St−

1×

Downgrade t

+1

-0.000

-0.017*-0.033**

-0.041

0.004

(-0.01)

(-1.66)

(-2.08)

(-1.42)

(0.74)

RCD

St−

1×

NoDowngrade t

+1

-0.001

-0.001

-0.005*-0.007**

0.001**

(-1.04)

(-1.05)

(-1.80)

(-2.09)

(2.42)

RM

Ht−

1-0.030***

0.009**

0.014

(-10.38)

(2.35)

(1.26)

RM

Ht

-0.030***

(-10.31)

RC2C

t−1

-0.003

(-0.43)

IVOLt−

10.018***

0.001

-0.014

0.048

0.018***

(5.91)

(0.17)

(-0.51)

(1.45)

(5.90)

ESt−

10.000

0.001

0.000

0.001

0.000

(0.49)

(1.19)

(0.87)

(0.53)

(1.17)

Lev

t−1

-0.000

-0.000

-0.000*-0.000**

-0.000

(-0.90)

(-0.90)

(-1.91)

(-2.11)

(-0.70)

Rt−

250:t−30

-0.000***

-0.000

0.001

0.000-0.000***

(-4.59)

(-0.26)

(0.93)

(0.08)

(-4.63)

Volume t

−1

0.000***

0.000-0.000**

0.000

0.000**

(3.11)

(0.75)

(-2.37)

(1.28)

(2.51)

Turnt−

10.000***

-0.000

-0.000*

-0.000

0.000*

(3.15)

(-0.28)

(-1.66)

(-1.46)

(1.81)

Size t

−1

-0.000

-0.000

-0.000

-0.000

-0.000

(-0.09)

(-0.01)

(-0.95)

(-1.64)

(-0.14)

Intercep

t-0.000***

-0.000

0.000

-0.001-0.000***

(-4.41)

(-0.64)

(0.01)

(-1.46)

(-4.66)

Firm

FE

Yes

Yes

Yes

Yes

Yes

Cluster

SE

Yes

Yes

Yes

Yes

Yes

N1082748108274810827481082748

1082748

R-sq

0.011

0.001

0.005

0.001

0.011

adj.

R-sq

0.010

0.000

0.004

0.000

0.010

PanelB:Low

CDS

Spread

(1)

(2)

(3)

(4)

(5)

RAM

t−1

RBM

tR

MH

tR

C2C

tR

AM

t

RCD

St−

1×

Downgrade t

+1

0.006

0.004

0.004

0.003

-0.002

(1.11)

(1.37)

(0.47)

(0.49)

(-1.58)

RCD

St−

1×

NoDowngrade t

+1

0.001

0.000

0.002

0.003

-0.000

(1.57)

(0.65)

(0.80)

(0.93)

(-1.06)

RM

Ht−

1-0.045***

0.006**

-0.023

(-12.28)

(2.12)

(-1.58)

RM

Ht

-0.045***

(-12.53)

RC2C

t−1

-0.010

(-1.41)

IVOLt−

10.001

-0.013

0.004

-0.049

0.000

(0.17)

(-1.05)

(0.09)

(-0.78)

(0.08)

ESt−

1-0.000

0.001

-0.000

-0.001

-0.000

(-0.82)

(1.16)

(-0.07)

(-0.92)

(-0.68)

Lev

t−1

-0.000

0.000

-0.000*

-0.000

-0.000

(-1.08)

(0.57)

(-1.95)

(-0.47)

(-0.24)

Rt−

250:t−30

-0.000

0.000

-0.001

-0.000

-0.000

(-0.87)

(0.72)

(-1.37)

(-0.09)

(-0.69)

Volume t

−1

0.000**

-0.000

-0.000

-0.000

0.000

(2.26)

(-0.72)

(-0.64)

(-0.31)

(0.77)

Turnt−

10.000

-0.000

0.000

-0.000

-0.000

(0.04)

(-1.10)

(1.08)

(-1.13)

(-0.65)

Size t

−1

-0.000

-0.000

-0.000

-0.000

-0.000

(-1.59)

(-0.33)

(-1.00)

(-1.41)

(-1.31)

Intercep

t0.000

0.000

0.000

0.001

0.000

(0.16)

(0.68)

(1.07)

(1.26)

(0.19)

Firm

FE

Yes

Yes

Yes

Yes

Yes

Cluster

SE

Yes

Yes

Yes

Yes

Yes

N1082597108259710825971082597

1082597

R-sq

0.020

0.001

0.003

0.001

0.020

adj.

R-sq

0.019

-0.000

0.002

0.000

0.019

55

Tab

le5B

:P

an

el

Regre

ssio

nof

Sto

ckR

etu

rns

Condit

ioned

on

Imm

inent

Cre

dit

Dow

ngra

de

or

Defa

ult

Ris

ku

sing

Boots

rap

ped

Sta

ndard

Err

ors

.T

his

tab

lere

port

sp

anel

regre

ssio

nre

sult

sfo

rC

DS

spre

adst

ock

s(P

an

elA

)an

dlo

wC

DS

spre

ad

stock

s(P

anel

B).

Sto

cks

are

div

ided

into

hig

hor

low

CD

Ssp

read

gro

up

sb

ase

don

the

med

ian

of

CD

Ssp

read

on

the

pre

vio

us

day

.P

ort

foli

os

are

re-b

ala

nce

dev

ery

trad

ing

day

.C

olu

mn

s(1

)to

(5)

den

ote

the

diff

eren

tp

an

elre

gre

ssio

ns

usi

ng

diff

eren

td

epen

den

tsu

b-d

ayre

turn

vari

ab

les.

Th

eke

yex

pla

nato

ryva

riab

les

are

show

non

the

colu

mn

s.Downgrade i

,t+1

isa

du

mm

yva

riab

leth

at

equ

als

to1

wh

ena

dow

ngra

de

occ

urs

at

dayt+

1,

an

dis

zero

oth

erw

ise.

NoD

owngrade i

,t+1

isa

du

mm

yva

riab

leth

ateq

uals

to1

wh

enth

ere

isn

od

own

gra

de

at

day

t,an

dis

zero

oth

erw

ise.

Att,

we

ass

um

eth

at

ther

eis

per

ceiv

edsh

ort-

run

def

ault

risk

offi

rmi

ifC

DS

spre

ad

isalr

ead

yh

igh

an

dDowngrade i

,t+1

=1.

We

ass

um

eth

ed

own

gra

de

info

rmati

on

foll

owin

gcl

osel

yth

en

ext

day

cou

ldh

ave

bee

nso

mew

hat

per

ceiv

ed.

Oth

erw

ise

ad

vers

ech

an

ges

inC

DS

retu

rnis

con

stru

edas

lon

g-r

un

def

au

ltri

sk.

Th

eco

ntr

ol

vari

able

sar

esi

mil

arto

thos

ein

Tab

le2A

and

2B.

We

incl

ud

efi

rmfi

xed

effec

tan

dcl

ust

erst

an

dard

erro

rby

day

.T

he

stan

dard

erro

rsare

com

pu

ted

usi

ng

boot

stra

p.

t-st

atis

tics

are

rep

orte

din

the

bra

cket

s.***,*

*,

an

d*

den

ote

sign

ifica

nce

at

1%

,5%

,an

d10%

leve

lre

spec

tivel

y.

PanelA:High

CDS

Spread

(1)

(2)

(3)

(4)

(5)

RAM

t−1

RBM

tR

MH

tR

C2C

tR

AM

t

RCD

St−

1×

Downgrade t

+1

-0.000

-0.017

-0.033**

-0.041

0.004

(-0.01)

(-1.53)

(-2.31)

(-1.20)

(0.55)

RCD

St−

1×

NoDowngrade t

+1

-0.001

-0.001*-0.005***-0.007***

0.001***

(-0.96)

(-1.65)

(-6.72)

(-4.36)

(4.52)

RM

Ht−

1-0.030***

0.009***

0.014***

(-10.45)

(3.56)

(3.75)

RM

Ht

-0.030***

(-11.03)

RC2C

t−1

-0.003

(-1.04)

IVOLt−

10.018***

0.001

-0.014

0.048***

0.018***

(6.37)

(0.26)

(-1.61)

(6.37)

(4.92)

ESt−

10.000

0.001

0.000

0.001

0.000

(0.02)

(1.32)

(0.03)

(0.03)

(0.03)

Lev

t−1

-0.000-0.000***-0.000***-0.000***

-0.000

(-1.33)

(-2.68)

(-2.62)

(-3.77)

(-0.57)

Rt−

250:t−30

-0.000***

-0.000

0.001***

0.000-0.000***

(-5.33)

(-0.50)

(4.36)

(0.55)

(-6.98)

Volume t

−1

0.000**

0.000

-0.000*

0.000

0.000

(2.47)

(0.92)

(-1.67)

(1.17)

(1.08)

Turnt−

10.000

-0.000

-0.000

-0.000

0.000

(0.19)

(-0.06)

(-0.43)

(-0.09)

(0.19)

Size t

−1

-0.000

-0.000

-0.000*-0.000***

-0.000

(-0.04)

(-0.02)

(-1.90)

(-4.01)

(-0.16)

Intercep

t-0.000***

-0.000

0.000-0.001***-0.000***

(-4.46)

(-1.15)

(0.03)

(-7.78)

(-4.36)

Firm

FE

Yes

Yes

Yes

Yes

Yes

Cluster

SE

Yes

Yes

Yes

Yes

Yes

N1082744

1082744

1082744

1082744

1082744

R-sq

0.011

0.001

0.005

0.001

0.011

adj.

R-sq

0.010

0.000

0.004

0.000

0.010

PanelB:Low

CDS

Spread

(1)

(2)

(3)

(4)

(5)

RAM

t−1

RBM

tR

MH

tR

C2C

tR

AM

t

RCD

St−

1×

Downgrade t

+1

0.006

0.004

0.004

0.003

-0.002

(0.69)

(1.48)

(0.34)

(0.77)

(-1.51)

RCD

St−

1×

NoDowngrade t

+1

0.001

0.000

0.002

0.003

-0.000*

(1.18)

(0.86)

(0.95)

(1.22)

(-1.68)

RM

Ht−

1-0.045***

0.006*

-0.023*

(-14.95)

(1.83)

(-1.90)

RM

Ht

-0.045***

(-14.06)

RC2C

t−1

-0.010

(-0.91)

IVOLt−

10.001-0.013***

0.004-0.050***

0.000

(0.55)

(-3.49)

(1.13)

(-4.33)

(0.23)

ESt−

1-0.000

0.001

-0.000

-0.001

-0.000

(-0.45)

(1.52)

(-0.09)

(-0.30)

(-0.45)

Lev

t−1

-0.000

0.000-0.000***

-0.000

-0.000

(-0.93)

(1.04)

(-3.40)

(-0.82)

(-0.29)

Rt−

250:t−30

-0.000**

0.000**-0.001***

-0.000

-0.000**

(-2.44)

(2.44)

(-9.07)

(-0.75)

(-2.46)

Volume t

−1

0.000*

-0.000

-0.000**

-0.000

0.000

(1.95)

(-1.17)

(-2.36)

(-1.00)

(0.81)

Turnt−

10.000

-0.000

0.000

-0.000

-0.000

(0.00)

(-0.21)

(0.40)

(-0.26)

(-0.09)

Size t

−1

-0.000**

-0.000-0.000***-0.000***

-0.000**

(-2.33)

(-1.12)

(-4.09)

(-5.34)

(-1.98)

Intercep

t0.000

0.000**

0.000***

0.001***

0.000

(0.55)

(2.34)

(8.20)

(6.75)

(0.57)

Firm

FE

Yes

Yes

Yes

Yes

Yes

Cluster

SE

Yes

Yes

Yes

Yes

Yes

N1082601

1082601

1082601

1082601

1082601

R-sq

0.020

0.001

0.003

0.001

0.020

adj.

R-sq

0.019

-0.000

0.002

0.000

0.019

56

Tab

le6:

Sto

ckP

ort

folios

Sort

ed

by

CD

SR

etu

rns

Condit

oned

on

Hig

h/L

ow

Tra

din

gV

olu

mes

and

Hig

h/L

ow

Tra

din

gT

urn

overs

giv

en

Hig

hC

DS

Spre

ad

Level

and

Hig

hIV

OL

(where

Volu

me

and

Tu

rnover

pro

xy

for

Liq

uid

ity).

Th

ista

ble

rep

orts

H/L

trad

ing

day

port

foli

ore

turn

su

nder

ad

ou

ble

sort

but

rest

rict

ing

tost

ock

sw

ith

hig

hC

DS

spre

ad

an

dh

igh

IVO

L.

At

the

star

tof

each

day

,w

esp

lit

all

stock

sin

toh

igh

CD

Ssp

read

leve

lan

dlo

wC

DS

spre

ad

level

base

don

thei

rcr

oss

-sec

tion

al

(acr

oss

all

firm

son

pre

vio

us

day

)m

edia

n.

Wit

hin

each

CD

Ssp

read

gro

up

,w

efu

rth

ersp

lit

stock

sin

toh

igh

or

low

valu

ati

on

un

cert

ain

tygro

up

base

don

the

med

ian

ofIV

OL

onth

ep

revio

us

day

.N

ext,

wit

hin

the

gro

up

wit

hh

igh

CD

Ssp

read

an

dhig

hIV

OL

,w

eagai

nso

rtth

est

ock

sin

toh

igh

trad

ing

volu

me

an

dlo

wtr

adin

gvo

lum

ere

spec

tive

ly.

Th

est

ock

sar

eth

enso

rted

into

five

qu

inti

legro

up

sb

ase

don

lagged

CD

Sre

turn

s.C

olu

mn

s(1

)an

d(2

)re

port

the

equ

al-w

eigh

tedR

MH

tre

turn

sof

the

qu

inti

lep

ortf

oli

os,

wh

ile

colu

mn

s(3

)an

d(4

)re

port

the

equ

al-

wei

ghte

dR

AM

t−1

retu

rns

of

the

qu

inti

lep

ort

foli

os.

Col

um

ns

(5)

to(8

)fo

llow

thos

eof

colu

mn

s(1

)to

(4)

exce

pt

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22)

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57

Table 7: Residual Institutional Ownership, CDS price, and Stock Markets Re-action. This table reports H/L trading day portfolio returns under a triple sort. At the start oftrading day, we split all stocks into high CDS spread level and low CDS spread level based on theircross-sectional (across all firms on that day) median. Within each CDS spread group, we furthersplit stocks by high/low short-sale constraint based on the median of residual institutional owner-ship (RIO) on the previous day. Next, within each of the 4 sub-groups, we again sort the stocksinto 5 quintiles based on largest CDS returns in the 5th quintile and smallest CDS return in the1st quintile. We form zero-cost H/L portfolio by going short the 5th quintile stocks and going longthe 1st quintile stocks. The H/L portfolio returns are measured as market hours returns as wellas after-market hours returns. The sample period covers January 2001 through March 2016. Wecalculate monthly Sharpe Ratio. In particular, we cumulate daily returns into monthly returns andcompute the Sharpe Ratio based on sample mean and sample standard deviation of the monthlyreturns series. Both Monthly Sharpe Ratio and Newey and West t-statistics adjusted for 12 lags arereported in the brackets. ***,**, and * denote significance at 1%, 5%, and 10% level respectively.

(1) (2) (3) (4)

RMH RAM

High RIO Low RIO High RIO Low RIO

Q1 RCDS -0.0143 -0.0148 -0.0035 -0.0029

nw t-stat (-0.531) (-0.603) (-1.183) (-0.928)

Q2 RCDS -0.0390 -0.0236 -0.0003 0.007**

nw t-stat (-1.428) (-0.971) (-0.072) (2.298)

Q3 RCDS -0.0288 -0.0113 0.0065** 0.0065*

nw t-stat (-1.123) (-0.482) (2.000) (1.912)

Q4 RCDS -0.0407 -0.0327 0.0125*** 0.0172***

nw t-stat (-1.503) (-1.354) (3.013) (3.519)

Q5 RCDS -0.0732** -0.0841*** 0.0171*** 0.0218***

nw t-stat (-2.462) (-3.036) (4.015) (4.873)

H/L 0.0589*** 0.0694*** -0.0206*** -0.0247***

nw t-stat (4.733) (5.359) (-5.269) (-5.872)

Sharpe Ratio (0.331) (0.391) (-0.431) (-0.439)

58

Figure 1 shows the average returns of daily equal-weighted portfolios. At the start of each trading day, all

stocks are split into high CDS spread group and low CDS spread group based on the cross-sectional median of

the observed CDS spread levels on the previous trading day. Using a double sort, within each spread group,

the stocks are further divided into five quintiles according to CDS return. The top 20% belong to the high

CDS return group while the bottom 20% belong to the low CDS return group. Panel A shows the average

returns over sampling period January 2001 through March 2016 of market hour returns of long position in

the stocks in the high credit spread group (dashed line), and in the low credit spread group (dotted line).

The x-axis indicates the 5 CDS return quintiles where Q5 has the highest CDS returns. The y-axis shows the

equal-weighted portfolio return in %. Panel B shows the average of corresponding after-market hours returns.

59

Figure 2 shows the average returns of daily equal-weighted portfolios. At the start of each trading day,

all stocks are split into high CDS spread group and low CDS spread group based on the cross-sectional

median of the observed CDS spread levels on the previous trading day. Only results for the high CDS spread

(spd) group are shown. Within this group, the stocks are further sorted using observed IVOL based on the

cross-sectional median on the previous day. Using a triple sort, within each IVOL group, the stocks are

further divided into five quintiles according to CDS return. The top 20% belong to the high CDS return

group while the bottom 20% belong to the low CDS return group. Panel A shows the average returns over

sampling period January 2001 through March 2016 of market hour returns of long position in high IVOL

(solid line) and in low IVOL group (dashed line). The x-axis indicates the 5 CDS return quintiles where Q5

has the highest CDS return. The y-axis shows the equal-weighted portfolio return in %. Panel B shows the

average of corresponding after-market hours returns.

60

Figure 3 At the start of each trading day, all stocks are split into high CDS spread group and low CDS

spread group based on the cross-sectional median of the observed CDS spread levels on the previous trading

day. Using a double sort, within each spread group, the stocks are further divided into five quintiles according

to CDS return. The top 20% belong to the high CDS return group while the bottom 20% belong to the low

CDS return group. The average daily trading volumes of the different quintile portfolios are shown in Panel

A and daily turnovers are reported in Panel B.

61

Figure 4 At the start of each trading day, all stocks are split into high CDS spread group and low CDS

spread group based on the cross-sectional median of the observed CDS spread levels on the previous trading

day. Within each CDS spread group, the stocks are further sorted using observed IVOL based on the cross-

sectional median on the previous day. Using a triple sort, within each IVOL group, the stocks are further

divided into five quintiles according to CDS return. The top 20% belong to the high CDS return group while

the bottom 20% belong to the low CDS return group. Panel A shows the average trading volumes of equal-

weighted quintile portfolio at a daily frequency for (1) high IVOL and high CDS spread, (2) low IVOL and

high CDS spread, (3) high IVOL and low CDS spread, and (4) low IVOL and low CDS spread. Panel B shows

the average daily turnover.

62

Figure 5 shows the average short volume turnover of equal-weighted quintile portfolios at a daily frequency

from 2005 to 2007. Panel A shows the average short volume turnover of high/low CDS spread groups. Panel

B shows the average short volume turnover of high/low IVOL groups given high CDS spread.

63

Appendix

Construction of Firm Characteristics

• Trading volume turnover. It is a ratio between total trading volume (during regular

trading hours) and number of shares outstanding.

• Stock market capitalization. The stock market capitalization is the sum over all stocks

of their product of end-of-day share price and number of shares outstanding, obtained

from the Center for Research in Security Prices (CRSP).

• Volatility-based measure. The idiosyncratic volatility is computed on a daily 1-year

rolling window using Fama and French 5 factor model. Specifically, we project daily

excess stock returns on Fama and French (2015) five-factor model and extract the sample

standard deviation of residuals. The stock volatility is the realized volatility computed

by a sample standard deviation of intra-day (log) transaction price changes.

• Short volume. The number of shares with short contracts outstanding for the entire

sample of stocks in a day. The sample is from “RHO” file from 2005 to 2007.

• Residual Institutional Ownership. The residual of institutional ownership is constructed

by running the Fama and MacBeth (1973) regression: logit(INSTi,t) = c+β1Log(Size)i,t+

β2Log(Size)2i,t + ei,t, where logit(INSTi,t) = logINST i,t

1−INST i,t. We run the regression at the

beginning of each quarter and apply the fit to the rest of the days in the following quar-

ter in order to derive the residuals. The idea is to isolate the IO effect from the size

effect suggested by Nagel (2005). The INST is from 13F filings.

• Downgrade events. The rating events data are obtained from S&P Capital IQ. There

were 1068 downgrade events from January 2001 to March 2016.

• Percentage Effective Spread. The percentage effective spread (ES) for day t is computed

by ESi,t = 1Ni,t

∑Ni,t

j=12Di,j(Pi,j−Mi,j)

Mi,j, where i indicates firm, j indicates second, and N

is the total number of trades of stock i on day t. Given day t, Pi,j is transaction price

for firm i at second j. Mi,j is the bid-ask mid price between bidding quote and asking

64

quote for each second. Di,j is buy or sell indicator. In particular, Di,j = 1 if there is a

buy, Di,j = −1 if there is a sell. The buy/sell indicator and merging between quote and

trade database is using the Lee and Ready (1991) algorithm.

• Market leverage. Following Ericsson, Jacobs, and Oviedo (2009), the market leverage ra-

tio (Lev) is computed as Lev = BookV alueofDebt+BookV alueofPreferredEquityMarketV alueofEquity+BookV alueofDebt+BookV alueofPreferredEquity

where the market value of equity is computed as shares outstanding (SHROUT) multi-

plied by stock prices (PRC) from CRSP. The book value of debt and the book value of

preferred equity are obtained from COMPUSTAT. The book value of debt is the sum

of debt in current liability (DLCQ) and long-term debt (DLTTQ). Preferred stocks are

labeled as PSTKQ in COMPUSTAT.

• Price momentum. The stock price momentum (MOM) is the accumulated past 12-month

stock returns using daily stock returns. In constructing the price momentum signal we

skip the most recent month to avoid short-term reversal.

65

A Graphical Illustration of Hypothesis 3A

As shown in Figure A1, suppose that the total net supply of stocks (assumed to be perfectly inelastic) at dayt is affected by credit news on day t-1 by 10 units during market hours. The illiquid stock (with steeper slopein the demand curve) should see a greater price drop than the liquid stock with a more gentle slope. Giventhe 10 units excess supply due to the adverse news, price impact on the illiquid stock is |P (t− 1)− P1(t)|and for the liquid stock it is |P (t− 1)− P2(t)| where |P (t− 1)− P1(t)| > |P (t− 1)− P2(t)|. If net excesssupply should revert from 10 to only 5 units, after market hours, then the illiquid stock price would reversemore than the liquid stock price.

0 10 20 30 40 50 60

Hypothetical Quality

0

5

10

15

20

25

30

Hyp

othe

tical

Sto

ck P

rice

Price Reversal Given Different Liquidity

Demand Curve of liquid StockDemand Curve of illiquid Stock

Price reversal given illiquid stock ifovershoot during market hours

P(t-1)

P1(t): New price along theilliquid demand curve

Price reversal given liquid stock ifovershoot during market hours

P2(t): New price along theliquid demand curve

Net supply at t

suppose adverse CDS news increasesstock supply by 10 units

Figure A1 provides a graphical illustration of larger impact of CDS news on stock prices ofilliquid stocks.

66

Table A1: Replication of Hilscher, Pollet, and Wilson (2015) Panel VAR result.This table presents our replication of Hilscher et al. (2015), and an extension of their findings from 2001 to2013 based on a Panel fixed-effect regression model. RStock

i,t is the daily close-to-close stock returns and RCDSi,t

is the daily percentage change of CDS spread or CDS return, for firm i on day t. In t-k, k indicates the numberof lags. Following Hilscher et al. (2015), we apply 3 lags. Panel A reports the VAR results for firms withrating of A or above. Panel B reports results for firms with rating of BBB. Panel C reports results for firmswith rating below or equal to BB. Hilscher et al. (2015)’s results are shown in column (1), 2001 to 2007, forpurpose of comparison. Our replication results based on our data are reported in column (2) based on thesame sample period of 2001-2007. Column (3) is the extension using data from 2001 to 2013. All regressionsinclude firm fixed-effects. t-statistics from heteroscedasticity-robust standard errors clustered by dates arereported in parentheses. *, and ** indicate significance at the 5% and 1% levels, respectively.

Equity return t CDS return tHilscher Replication Extended Hilscher Replication Extended

et al. (2015) 2001-2007 2001-2013 et al. (2015) 2001-2007 2001-2013(1) (2) (3) (1) (2) (3)

Panel A: Rating A & AboveEquity return, RStock t-1 -0.023 -0.022 -0.045** -0.158** -0.155** -0.170**

(-1.87) (-1.93) (-4.51) -(12.78) (-6.86) (-11.34)t-2 -0.013 -0.012 -0.017 -0.105** -0.109** -0.063**

(-1.17) (-1.42) (-0.96) (-8.09) (-8.12) (-8.97)t-3 -0.007 -0.007 -0.003 -0.077** -0.085** -0.030**

(-0.62) (-1.04) (-0.19) (-6.19) (-9.72) (-3.43)CDS return, RCDS t-1 0.000 -0.001 -0.002 -0.020** -0.026** 0.038

(0.20) (-0.55) (-0.60) -(3.00) (-3.42) (1.27)t-2 0.000 0.000 0.002 0.019** 0.013** 0.049**

(0.20) (0.04) (0.70) (3.49) (2.97) (5.17)t-3 0.002 0.002 0.010** 0.001 -0.007 0.012*

(0.88) (1.30) (3.43) (0.25) (-1.11) (2.07)Number of observations 261750 239257 494519 261252 239257 494519

Panel B: Rating BBBEquity return, RStock t-1 -0.010 -0.011 -0.027** -0.127** -0.139** -0.127**

(-1.07) (-1.48) (-3.50) (-15.64) (-10.09) (-19.44)t-2 -0.013 -0.011 -0.014 -0.083** -0.091** -0.052**

(-1.32) (-1.41) (-1.32) (-10.24) (-9.16) (-8.46)t-3 0.002 0.000 -0.000 -0.068** -0.068** -0.028**

(0.20) (0.09) (-0.04) (-7.68) (-5.25) (-4.85)CDS return, RCDS t-1 -0.001 -0.001 -0.002 0.011 0.015 0.079**

(-0.47) (-0.69) (-0.68) (1.53) (0.75) (3.12)t-2 0.000 -0.001 -0.001 0.027** 0.024** 0.059**

(0.12) (-1.11) (-0.30) (5.44) (4.06) (8.82)t-3 0.002 0.001 0.008* 0.016** 0.002 0.022**

(0.08) (1.44) (2.46) (2.94) (0.74) (5.22)Number of observations 325722 359806 772066 325028 359806 772066

Panel C: Rating BB & BelowEquity return, RStock t-1 0.009 0.005 0.004 -0.109** -0.138** -0.111**

(1.04) (1.07) (1.33) (-14.66) (-10.33) (-22.16)t-2 -0.008 -0.010 -0.008 -0.067** -0.089** -0.054**

(-0.91) (-1.46) (-1.50) (-10.44) (-8.44) (-9.94)t-3 -0.004 -0.000 -0.002 -0.046** -0.063** -0.031**

(-0.48) (-0.06) (-0.29) (-6.33) (-6.65) (-5.45)CDS return, RCDS t-1 -0.004 -0.001 -0.006* -0.056** -0.051* 0.045

(-1.16) (-1.03) (-2.35) (-6.46) (-2.10) (1.79)t-2 -0.005 -0.001 -0.005* 0.006 -0.008 0.048**

(-1.72) (-1.33) (-1.98) (0.88) (-0.61) (6.26)t-3 -0.002 -0.000 0.005 -0.003 0.002 0.020**

(-1.67) (-0.90) (1.64) (-0.44) (1.12) (5.10)Number of observations 162911 466444 786064 162318 466444 786064

67

Table A2: Stock Portfolios sorted by CDS Returns Conditioned on High/LowCDS Level using CRSP. This table reports trading day H/L portfolio returns under a doublesorting strategy. A double sort is conducted such that at the start of every trading day or week ormonth, all stocks are split into high CDS levels and low CDS levels based on their cross-sectionaltop 20 percentile and bottom 20 percentile observed on the previous day or week or month. Withineach CDS level group, we further rank firms by quintiles based on their CDS returns from t-2 to t-1and form 5 portfolios. The portfolios are rebalanced every day. H/L is the raw return of a zero-costportfolio of going short stocks of the highest quintile CDS return and going long stocks of the lowestCDS return quintile. Equal-weighted returns are reported in Panel A, and value-weighted returnsare reported in Panel B. The average daily returns are expressed as %. Columns (1) and (2) reportthe H/L strategy of close-to-open stock returns (RCTO

t ) given high/low CDS spreads. Columns (3)and (4) report the H/L strategy of open-to-close returns (ROTC

t ) given high/low CDS spreads. Thestock returns are constructed by price data from CRSP. The sample period covers January 2001through March 2016. Newey and West t-statistics adjusted for 12 lags are reported in the brackets.***,**, and * denote significance at 1%, 5%, and 10% level respectively.

(1) (2) (3) (4)

HighSCDSt−1 LowSCDS

t−1 HighSCDSt−1 LowSCDS

t−1

RCTOt RCTO

t ROTCt ROTC

t

Panel A: Equal-weight

H/L RCDSt−1 0.006 -0.009* 0.057*** 0.006

(0.8) (-1.7) (5.4) (1.0)

Panel B: Value-weight

H/L RCDSt−1 0.024 -0.006 0.042*** 0.004

(1.1) (-1.1) (3.0) (0.7)

68

Table A3: Sub-sample regression results. This table reports predictive panel regressions ofvarious stock returns (different sub-day market hours) using 5-year CDS return at a daily frequency for twosub-sample period: (1) 2001 to 2009 in Panel A and (2) 2009 to 2016 in Panel B. The sample period coversJanuary 2001 through March 2016. The regression equation is specified as RHRS

i,t = β0 +β1Hight−1×RCDSt−1 +

β2RCDSt−1 + RMH

t + Xi,t−1θ + εi,t where the various dependent variables are: (1) after market hours stockreturns, RAM

t−1 from a previous day in Column (1), before market hours stock returns, RBMt in Column (2),

market hours stock returns, RMHt in Column (3), close-to-close stock returns, RCTC

t in Column (4), and aftermarket hours stock returns, RAM

t in Column (5). Hight−1 is a indicator variable that equals to one if theprevious day’s CDS spread is above its cross-sectional median. In addition, we include control variables forwell-known determinants of stock returns, such as idiosyncratic volatility computed by Fama and French 5-factor model using past 1-year rolling window (IV OLt−1), stock percentage effective spreads (ESt−1), marketleverage (Levt−1), stock price momentum Rt−250:t−30, (log) total trading volume for each stock (V olumet−1),stock turnover (Turnt−1), and (log) stock market capitalization (Sizet−1). We include firm-fixed effect andcluster standard error by day. We do not report the control and market return coefficient estimates here toeconomize on space as they do not show any significant variation from similar previous tables. The standarderrors are computed adjusted for heteroskedasticity and autocorrelation (12 lags). t-statistics are reported inthe brackets. ***,**, and * denote significance at 1%, 5%, and 10% level respectively. (The coefficients ofcontrol variables do not show significant difference from those reported earlier in Tables 2 and are not shownhere.)

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

Panel A: Before 01/01/2009RAM

t−1 RBMt RMH

t RCTCt RAM

t

RCDSt−1 ×Hight−1 -0.0008 -0.0008 -0.0053*** -0.0073*** 0.0010**

(-1.35) (-0.66) (-2.75) (-3.79) (2.22)Intercept -0.0002 -0.0006 0.0154*** 0.0145*** 0.0006

(-0.11) (-0.76) (3.81) (3.05) (0.99)

Firm FE Yes Yes Yes Yes YesCluster SE Yes Yes Yes Yes YesControls Yes Yes Yes Yes YesN 1252684 1252684 1252684 1252684 1252684R-sq 0.011 0.002 0.005 0.004 0.011adj. R-sq 0.010 0.001 0.004 0.003 0.010

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

Panel B: After 01/01/2009RAM

t−1 RBMt RMH

t RCTCt RAM

t

RCDSt−1 ×Hight−1 -0.0023*** -0.0023** -0.0073*** -0.0112*** 0.0009**

(-2.95) (-1.97) (-2.70) (-3.59) (2.30)Intercept -0.0019*** 0.0070 0.0052 0.0102 -0.0009***

(-3.99) (1.19) (1.39) (1.48) (-12.12)

Firm FE Yes Yes Yes Yes YesCluster SE Yes Yes Yes Yes YesControls Yes Yes Yes Yes YesN 940025 940025 940025 940025 940025R-sq 0.018 0.001 0.004 0.002 0.002adj. R-sq 0.017 0.000 0.004 0.001 0.002

69

Tab

leA

4:

Sto

ckP

ort

folios

Sort

ed

by

CD

SR

etu

rns

Condit

ioned

on

Hig

h/L

ow

CD

SS

pre

ad

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weig

hte

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ple

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re01/01/2009.

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ista

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star

tof

anin

vest

men

td

ay,

we

spli

tall

stock

sin

toh

igh

CD

Ssp

read

leve

lan

dlo

wC

DS

spre

ad

level

base

don

thei

rcr

oss-

sect

ion

al(a

cros

sal

lfi

rms

onp

revio

us

day

)m

edia

n.

Wit

hin

each

CD

Ssp

read

gro

up

,w

era

nk

firm

sby

qu

inti

les

base

don

thei

rC

DS

retu

rns

orp

erce

nt

spre

adch

ange

,an

dfo

rm5

por

tfol

ios.

Th

ep

ort

foli

os

are

reb

ala

nce

dev

ery

day

acc

ord

ingly

.H

/L

isth

era

wre

turn

of

aze

ro-c

ost

port

foli

oof

goin

gsh

ort

stock

sof

the

hig

hes

tqu

inti

le(Q

5)C

DS

retu

rnan

dgoin

glo

ng

stock

sof

the

low

est

CD

Sre

turn

qu

inti

le(Q

1).

Equ

al-

wei

ghte

dre

turn

sar

ere

por

ted

.T

hes

eav

erag

ere

turn

sar

eex

pre

ssed

as

%.

Colu

mn

s(1

)an

d(2

)re

port

the

H/L

bef

ore

mark

et-o

pen

stock

retu

rn(R

BM

t)

giv

enh

igh

(Col

um

n(1

))an

dlo

wC

DS

spre

ad(C

olu

mn

(2))

resp

ecti

vel

y.C

olu

mn

s(3

)an

d(4

)re

port

the

H/L

du

rin

gm

ark

eth

ou

rsst

ock

retu

rn(R

MH

t)

giv

enh

igh

(Col

um

n(3

))an

dlo

w(C

olu

mn

(4))

CD

Ssp

read

resp

ecti

vely

.C

olu

mn

s(5

)an

d(6

)re

port

the

H/L

aft

er-m

ark

eth

ou

rsst

ock

retu

rn(R

AM

t)

giv

enh

igh

and

low

CD

Ssp

read

resp

ecti

vely

.C

olu

mn

s(7

)an

d(8

)re

port

the

H/L

close

-to-c

lose

stock

retu

rn(R

CTC

t)

giv

enH

igh

an

dL

owC

DS

spre

ad

resp

ecti

vely

.T

he

stock

retu

rns

are

con

stru

cted

by

tran

sact

ion

pri

ces

from

TA

Q(u

sin

gm

onth

lyT

AQ

).T

he

sam

ple

per

iod

cove

rsJanu

ary

2001

thro

ugh

Dec

emb

er20

08.

We

calc

ula

tem

onth

lyS

har

pe

Rat

io.

Inpart

icu

lar,

we

cum

ula

ted

ail

yre

turn

sin

tom

onth

lyre

turn

san

dco

mp

ute

the

Sh

arp

eR

ati

ob

ase

don

sam

ple

mea

nan

dsa

mp

lest

and

ard

dev

iati

onof

the

month

lyre

turn

sse

ries

.B

oth

Month

lyS

harp

eR

ati

oan

dN

ewey

an

dW

est

t-st

ati

stic

sad

just

edfo

r12

lags

are

rep

orte

din

the

bra

cket

s.**

*,**

,an

d*

den

ote

sign

ifica

nce

at

1%

,5%

,an

d10%

leve

lre

spec

tivel

y.

Before

01/01/2009:

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

RBM

RM

HR

AM

RCTC

Hig

hSCDS

Low

SCDS

Hig

hSCDS

Low

SCDS

Hig

hSCDS

Low

SCDS

Hig

hSCDS

Low

SCDS

Low

-0.0

12**

-0.0

11***

-0.0

260

0.0

190

-0.0

030

0.0

04*

-0.0

410

0.0

120

nw

t-st

at(-

2.32

9)(-

3.4

23)

(-0.7

80)

(0.8

81)

(-0.9

68)

(1.7

97)

(-1.2

68)

(0.5

47)

Q2

-0.0

16**

*-0

.01***

-0.0

580

0.0

130

0.0

07*

0.0

06**

-0.0

67*

0.0

090

nw

t-st

at(-

4.13

3)(-

3.8

78)

(-1.6

43)

(0.6

16)

(1.7

75)

(2.1

75)

(-1.8

88)

(0.4

17)

Q3

-0.0

11**

*-0

.007***

-0.0

480

0.0

140

0.0

08**

0.0

05*

-0.0

510

0.0

120

nw

t-st

at(-

2.79

2)(-

2.7

86)

(-1.4

40)

(0.6

41)

(2.4

40)

(1.8

65)

(-1.5

24)

(0.5

60)

Q4

-0.0

1***

-0.0

11***

-0.0

550

0.0

260

0.0

19***

0.0

040

-0.0

450

0.0

190

nw

t-st

at(-

2.72

3)(-

3.9

90)

(-1.5

54)

(1.3

36)

(3.2

95)

(1.1

98)

(-1.3

10)

(0.9

96)

Hig

h-0

.019

***

-0.0

040

-0.0

98**

0.0

120

0.0

18***

0.0

040

-0.0

98**

0.0

110

nw

t-st

at(-

3.35

4)(-

0.6

52)

(-2.4

76)

(0.5

69)

(3.6

31)

(1.0

12)

(-2.5

12)

(0.4

93)

H/L

0.00

70-0

.0070

0.0

71***

0.0

070

-0.0

21***

0.0

010

0.0

57***

0.0

010

nw

t-st

at(0

.894

)(-

0.9

84)

(5.0

68)

(0.8

98)

(-4.8

53)

(0.2

76)

(3.8

07)

(0.0

68)

Sh

arp

eR

atio

(0.1

00)

(-0.0

97)

(0.5

53)

(0.1

13)

(-0.5

02)

(0.0

31)

(0.4

11)

(0.0

16)

70

Tab

leA

5:

Sto

ckP

ort

folios

Sort

ed

by

CD

SR

etu

rns

Condit

ioned

on

Hig

h/L

ow

CD

SS

pre

ad

Levels

,E

qu

al-

weig

hte

d,

Sub-s

am

ple

aft

er

01/01/2009.

Th

ista

ble

rep

ort

seq

ual-

wei

ghte

dH

/L

port

foli

otr

ad

ing

day

retu

rns

un

der

ad

ou

ble

sort

base

don

bef

ore

01/0

1/20

09sa

mp

le.

At

the

star

tof

anin

vest

men

td

ay,

we

spli

tall

stock

sin

toh

igh

CD

Ssp

read

leve

lan

dlo

wC

DS

spre

ad

level

base

don

thei

rcr

oss-

sect

ion

al(a

cros

sal

lfi

rms

onp

revio

us

day

)m

edia

n.

Wit

hin

each

CD

Ssp

read

gro

up

,w

era

nk

firm

sby

qu

inti

les

base

don

thei

rC

DS

retu

rns

orp

erce

nt

spre

adch

ange

,an

dfo

rm5

por

tfol

ios.

Th

ep

ort

foli

os

are

reb

ala

nce

dev

ery

day

acc

ord

ingly

.H

/L

isth

era

wre

turn

of

aze

ro-c

ost

port

foli

oof

goin

gsh

ort

stock

sof

the

hig

hes

tqu

inti

le(Q

5)C

DS

retu

rnan

dgoin

glo

ng

stock

sof

the

low

est

CD

Sre

turn

qu

inti

le(Q

1).

Equ

al-

wei

ghte

dre

turn

sar

ere

por

ted

.T

hes

eav

erag

ere

turn

sar

eex

pre

ssed

as

%.

Colu

mn

s(1

)an

d(2

)re

port

the

H/L

bef

ore

mark

et-o

pen

stock

retu

rn(R

BM

t)

giv

enh

igh

(Col

um

n(1

))an

dlo

wC

DS

spre

ad(C

olu

mn

(2))

resp

ecti

vel

y.C

olu

mn

s(3

)an

d(4

)re

port

the

H/L

du

rin

gm

ark

eth

ou

rsst

ock

retu

rn(R

MH

t)

giv

enh

igh

(Col

um

n(3

))an

dlo

w(C

olu

mn

(4))

CD

Ssp

read

resp

ecti

vely

.C

olu

mn

s(5

)an

d(6

)re

port

the

H/L

aft

er-m

ark

eth

ou

rsst

ock

retu

rn(R

AM

t)

giv

enh

igh

and

low

CD

Ssp

read

resp

ecti

vely

.C

olu

mn

s(7

)an

d(8

)re

port

the

H/L

close

-to-c

lose

stock

retu

rn(R

CTC

t)

giv

enH

igh

an

dL

owC

DS

spre

ad

resp

ecti

vely

.T

he

stock

retu

rns

are

con

stru

cted

by

tran

sact

ion

pri

ces

from

TA

Q(u

sin

gm

onth

lyan

dd

ail

yT

AQ

).T

he

sam

ple

per

iod

cove

rsJanu

ary

2001

thro

ugh

Dec

emb

er20

08.

We

calc

ula

tem

onth

lyS

har

pe

Rati

o.

Inp

art

icu

lar,

we

cum

ula

ted

ail

yre

turn

sin

tom

onth

lyre

turn

san

dco

mp

ute

the

Sh

arp

eR

ati

ob

ased

onsa

mp

lem

ean

and

sam

ple

stan

dar

dd

evia

tion

of

the

month

lyre

turn

sse

ries

.B

oth

Month

lyS

harp

eR

ati

oan

dN

ewey

an

dW

est

t-st

ati

stic

sad

just

edfo

r12

lags

are

rep

orte

din

the

bra

cket

s.***,*

*,

an

d*

den

ote

sign

ifica

nce

at

1%

,5%

,an

d10%

level

resp

ecti

vely

.

After01/01/2009:

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

RBM

RM

HR

AM

RCTC

Hig

hSCDS

Low

SCDS

Hig

hSCDS

Low

SCDS

Hig

hSCDS

Low

SCDS

Hig

hSCDS

Low

SCDS

Low

0.01

400.0

130

0.0

010

0.0

37*

-0.0

060

0.0

000

0.0

090

0.0

500

nw

t-st

at(1

.531

)(0

.454)

(0.0

18)

(1.7

76)

(-1.4

87)

(-0.0

57)

(0.2

38)

(1.4

21)

Q2

0.01

300.0

240

0.0

050

0.0

320

0.0

020

-0.0

010

0.0

190

0.0

540

nw

t-st

at(0

.726

)(0

.866)

(0.1

30)

(1.5

54)

(0.3

60)

(-0.5

01)

(0.4

75)

(1.6

06)

Q3

0.01

600.0

220

0.0

070

0.0

340

0.0

040

0.0

010

0.0

260

0.0

570

nw

t-st

at(1

.025

)(0

.707)

(0.2

01)

(1.6

44)

(0.8

31)

(0.4

04)

(0.7

17)

(1.5

70)

Q4

0.01

80-0

.0080

-0.0

080

0.0

36*

0.0

1**

0.0

000

0.0

190

0.0

270

nw

t-st

at(0

.958

)(-

0.9

97)

(-0.2

48)

(1.7

25)

(2.1

87)

(-0.1

31)

(0.5

08)

(1.2

55)

Hig

h-0

.016

*0.0

060

-0.0

620

0.0

330

0.0

22***

0.0

020

-0.0

560

0.0

410

nw

t-st

at(-

1.91

3)(0

.277)

(-1.5

40)

(1.6

10)

(3.9

52)

(0.6

62)

(-1.3

92)

(1.3

89)

H/L

0.03

***

0.0

070

0.0

63***

0.0

040

-0.0

28***

-0.0

020

0.0

65***

0.0

090

nw

t-st

at(2

.589

)(0

.835)

(4.3

27)

(0.5

87)

(-6.4

35)

(-0.9

11)

(3.9

14)

(0.8

11)

Sh

arp

eR

atio

(0.2

01)

(0.0

98)

(0.4

20)

(0.0

47)

(-0.7

97)

(-0.1

09)

(0.3

92)

(0.0

84)

71