When do CDS spreads lead? Rating events, private entities, and firm-specific information flows

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Journal of Financial Economics journal homepage: www.elsevier.com/locate/jfec

When do CDS spreads lead? Rating events, private entities, and firm-specific information flowsR Jongsub Lee a, Andy Naranjo b,∗, Guner Velioglu b a b

Seoul National University, SNU Business School, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, South Korea University of Florida, Warrington College of Business, 309C Stuzin Hall, Gainesville, FL 32611-7168, USA

a r t i c l e

i n f o

Article history: Received 21 April 2017 Revised 2 October 2017 Accepted 19 October 2017 Available online xxx JEL classification: D80 G14 G20 G32

a b s t r a c t We find that credit default swap (CDS) spreads contribute significantly to price discovery in financial markets when firm-specific credit information is prominent. Using 3,470 S&P rating notch and watch changes for US public and private entities from 2001–2013, we show that CDS prices contain unique firm credit risk information that is not captured by the prices of other related securities such as stocks and bonds of the same firm. Credit information unidirectionally flows from CDS to bonds, particularly for private entities whose stocks are not concurrently trading in markets. We further find that CDS returns significantly predict stock returns, particularly their idiosyncratic components. © 2018 Elsevier B.V. All rights reserved.

Keywords: CDS versus stocks and bonds Credit ratings Firm-specific credit information flow Lead-lag relations Private firms

1. Introduction The extent to which securities markets convey timely underlying credit risk information is central to the well-

R We thank Viral Acharya, Antje Berndt, Sudheer Chava, Christopher James, Timothy Johnson, Alexander Kurov, Mahendrarajah Nimalendran, Jiaping Qiu, David Rakowski, Jay Ritter, Marlis Schairer, William Schwert (the Editor), Marti Subrahmanyam, Semih Üslü, Fan Yu, and an anonymous referee for helpful comments and suggestions. This study benefited from the many insightful comments from discussants and participants at the 2017 Financial Management Association Annual Meeting (Boston) and the 5th International Conference on Credit Analysis and Risk Management (Basel). Jongsub Lee also acknowledges the support from the Institute of Management Research at Seoul National University and the Research Settlement Fund for the new faculty of Seoul National University. ∗ Corresponding author. E-mail addresses: [email protected] (J. Lee), [email protected]fl.edu (A. Naranjo), [email protected]fl.edu (G. Velioglu).

functioning of capital markets. Oftentimes, different securities markets reveal underlying firm credit information with varying degrees of information content, efficiency, and speed. A case in point is the credit default swap (CDS) market where there is considerable debate on the direction and speed of credit pricing discovery between CDS and other related securities of the same firm, including stocks and bonds. For example, Acharya and Johnson (2007), Berndt and Ostrovnaya (2007), Ni and Pan (2011), and Qiu and Yu (2012) show significant credit pricing information flows from CDS to stocks due, in part, to nonpublic information that informed banks have on borrowers through their lending relationships. In sharp contrast, Norden and Weber (2009), Marsh and Wagner (2012), and Hilscher, Pollet and Wilson (2015) show credit pricing information unequivocally flows from stocks to CDS, and not vice versa, due to a separating equilibrium where informed traders choose to trade only stocks for transaction cost

https://doi.org/10.1016/j.jfineco.2018.07.011 0304-405X/© 2018 Elsevier B.V. All rights reserved.

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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reasons.1 These mixed findings in the literature raise some important questions. In particular, does the CDS market convey any marginal credit pricing information, and what, if any, are the conditions under which CDS credit pricing information becomes relatively more timely and pivotal in price discovery? These questions are especially relevant in light of the increasing demands for credit risk transfer through (single name) corporate CDS and substantial CDS market growth in the last decade.2 Related to the information content in CDS markets, there is some recent evidence showing that CDS credit pricing information spills over to stocks and bonds, generating firm-specific credit information, particularly during negative credit events (Lee, Naranjo and Sirmans, 2014; Batta, Qiu and Yu, 2015; Chava, Ganduri and Ornthanalai, 2016; Feldhütter, Hotchkiss and Karakas¸ , 2016). However, despite this recent conditional evidence, there is a widespread view in the literature that stock returns predominantly lead CDS returns, making the findings in these recent studies puzzling. There is also an important methodological debate in the literature about drawing inferences from the CDS-stock predictive relation. Hilscher, Pollet and Wilson (2015) criticize the methodology used by Acharya and Johnson (2007) who use ex post measures of credit deterioration rather than ex ante measures based on ratings. Hilscher, Pollet and Wilson (2015) argue that Acharya and Johnson (2007)’s findings—faster information revelation in CDS than stocks for negative credit news—are confined to a small number of distressed firms that are classified using ex post measures of rising credit risk. Given this backdrop, we investigate the extent to which CDS spreads capture firm-specific credit risk information around important credit events relative to other related securities such as stocks and bonds. We use 3470 S&P rating notch and watch changes on 749 public and private firms from 2001 to 2013. Similar to Hilscher, Pollet and Wilson (2015), we also use panel vector autoregression (VAR) models on CDS-bond and CDS-stock return pairs. Importantly, our sample includes both public and private firms whose CDS and bond prices are not confounded by stock trading information and therefore provide a unique opportunity to sharply identify the lead-lag relations between CDS and bonds. Using these unique and comprehensive CDS prices and credit rating events, we investigate the ex-

1 The literature also provides mixed evidence on the credit pricing information flows between CDS and bonds; Longstaff, Mithal and Neis (2003) and Blanco, Brennan and Marsh (2005) conclude that the CDS market leads bond market, whereas Zhu (2006) argues that it is premature to conclude that the CDS market leads the cash bond market in price discovery. 2 See, for example, Financial Times, “Credit default swaps activity heats up: record $15.7bn in gross notional positions of single name CDS cleared by investors in January” (P. Stafford and J. Rennison, February 4, 2016. Available at https://www.ft.com/content/ c47dce8e- ca9f- 11e5- be0b- b7ece4e953a0). See also Bloomberg, “Creditdefault swaps are back as investor fear grows” (K. Linsell, February 12, 2016. Available at https://www.bloomberg.com/news/articles/2016- 02- 12/ credit- default- swaps- are- back- as- investors- look- for- panic- button).

tent to which the CDS market contributes to price discovery across related stock and bond markets.3 We find that CDS contribute significantly to price discovery when firm-specific credit information prevails. For a 180-day window around each S&P rating notch (or watch) change, we find that the CDS market contains unique, firmspecific credit risk information that is not captured by the prices of other related securities such as stocks and bonds of the same firm. Firm-specific credit information significantly flows from CDS to bonds, not vice versa. This finding is particularly evident for private firms whose stocks are not concurrently trading in markets. We further show that for public entities that undergo credit rating changes, CDS returns significantly predict future stock returns— particularly idiosyncratic returns. Such predictability of CDS returns on future stock returns exists in both pre- and post-credit rating event periods. The firm-specific information flow from CDS to stocks and bonds is stronger for CDS reference entities that have established strong lending relationships with primary CDS dealers who could generate endogenous hedging demand in the credit derivatives market. Additional nonparametric jump tests that detect company-specific news (Lee and Mykland, 2008) further confirm these conditional information dynamics in CDS spreads. Put together, our results highlight that when firmspecific credit information matters most, the CDS market becomes an important source of price discovery. Using S&P Capital IQ, we retrieve data on 3470 S&P credit rating notch and watch changes for 626 public and 166 private firms in the US. We collect their daily fiveyear CDS spread observations from the Markit Group. Bond transaction data and stock returns are from FINRA’s Trade Reporting and Compliance Engine (TRACE) and Center for Research in Security Prices (CRSP), respectively. Our sample period is from 2001–2013, which includes observations during significant economic downturns in the post-2007 financial crisis period—a period not covered by Norden and Weber (2009), Marsh and Wagner (2012), and Hilscher, Pollet and Wilson (2015). Our sample also includes credit rating changes and CDS spread information on private firms, which have not been investigated in the existing literature. Using this extended time series, as well as wider crosssection of CDS prices and credit rating events, we first show that CDS at-market spreads significantly anticipate upcoming rating and watch changes as early as 90 days prior to the announcement of the changes. The significant predictability of CDS spread changes on upcoming rating changes is evident for negative events such as downgrades and negative watches. For positive events, we find relatively weaker evidence of CDS market predictability on

3 None of the existing studies use data on CDS and rating events on private firms, together with significantly extended time series that cover a long time period following the 2007 financial crisis. For example, Hull, Predescu and White (2004) study public entities rated by Moody’s during early CDS market years such as 1998–2002. Norden and Weber (2004) study the CDS and stock reactions to rating events just for two years, 20 01–20 02. Galil and Soffer (2011) use a sample of rating events in the precrisis period from 2002 to 2006. Hilscher, Pollet and Wilson (2015) use daily and weekly CDS data for public firms in the precrisis period, 20 01–20 07.

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future rating changes. Our results are robust to controlling for clustered rating events that also occur during the same event window of a focal rating change and are also robust to controlling for concurrent stock market information, including stock returns and analyst forecasts. To more sharply identify the marginal information of CDS spreads on future rating changes, we use private company CDS data and conduct the following tests. First, we re-confirm that the CDS market significantly predicts upcoming rating changes (not the other way around), even in the absence of stock trading information. Moreover, we sharply identify that the CDS market predominantly leads the bond market in revealing underlying credit risk information. Applying the panel VAR model to our private company CDS and bonds subsample, we demonstrate that during 180 days around each credit rating event, the CDS market significantly leads the bond market, and not vice versa. Interestingly, the same panel VAR using an extended crosssection that also includes public firms shows a slightly marginal information flow from bonds to CDS, which indicates that earlier studies that find an equally important information flow between CDS and bonds (Zhu, 2006) could be biased due to confounding stock market information. We next examine potential sources of unique CDS market information on future rating changes. We show that CDS predictability is stronger when their reference entities have strong banking relationships with several key banks who also act as primary dealers in CDS markets.4 This is consistent with the results shown by Acharya and Johnson (2007)–informed lenders who generate endogenous trading liquidity in CDS markets can utilize their propriety information on underlying firms through their banking relationships. Such a close connection between banking relationships and price reactions is unique in the CDS market and not applicable to potential information dynamics in other related securities such as stocks and bonds of the same firms. We also explore stock-CDS cross-market price discovery using a panel VAR model similar to Hilscher, Pollet and Wilson (2015).5 We first replicate their key results; unconditionally, stock returns predominantly lead CDS returns. However, when systematic stock returns are effectively controlled for in the panel VAR, we find starkly different results; lagged CDS returns significantly predict future stock returns. We find important idiosyncratic information flows between CDS and stocks, particularly for (i) negative rating events, (ii) for reference entities that have strong banking relationships with primary dealers in the CDS market, and (iii) for CDS firms with high contract depths. For above median CDS depth firms, we find that during the [−30, −2] days prior to negative rating event announcements, a percentage increase in stock return results in a 10.1 basis points decrease in next day CDS

4 These are Bank of America, Barclays Bank, BNP Paribas, Citibank, Credit Suisse, Deutsche Bank, Goldman Sachs, HSBC, JPMorgan Chase, Lehman Brothers, Merrill Lynch, Morgan Stanley, Royal Bank of Scotland, and UBS. 5 See Abrigo and Love (2016) for a comprehensive discussion of this panel VAR approach.

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returns,6 whereas a percentage increase in CDS return leads to a 6.9 basis points decrease in next day stock return. For the post-event period [2,11], we also find similar evidence; a percentage increase in stock return leads to a 6.7 basis points decrease in CDS return, where a percentage point increase in CDS return leads to a 9.2 basis points decrease in stock returns. To collectively summarize and further test our pairwise market information flow findings, we next use all three markets jointly (stock-CDS-bond) in a three-way public market subsample VAR analysis. This integrated threemarket analysis clearly depicts that CDS continue to persistently contribute to price discovery for both stocks and bonds, but the role of bonds in price discovery is diminished once stock information is included. The latter result is consistent with our previous findings in the CDS-bond two-market pairwise analysis, where we find bonds of private firms do not have any predictive information on CDS spreads when stock trading information is absent. Finally, we turn our attention to a nonparametric jump test that detects company-specific news events (Lee and Mykland, 2008). We first find that firm-specific jumps are indeed more likely in CDS spreads around rating events, and the jumps associated with negative credit news significantly predict stock returns. All these results are consistent with our earlier findings from the panel VAR, supporting our conditional notion that CDS spreads reveal important firm-specific credit news around negative rating events. In summary, our results provide important new evidence on firm-specific credit risk information that spills over from CDS to other related securities, including stocks and bonds of the same firm. When firm-specific credit risk information prevails, the CDS market plays a significant role in price discovery. Conditionally, the CDS market provides important credit-related information that other related securities do not capture and convey as timely. Our paper makes four key contributions to the literature. First, we add novel evidence on private company CDS reactions to upcoming rating changes. Private firm CDS spreads significantly anticipate upcoming rating downgrades and negative watches despite the absence of concurrently trading stock price information. To the best of our knowledge, we are the first to study private company CDS reactions to upcoming rating changes, extending the existing findings on public firm CDS (Hull, Predescu and White, 2004; Norden and Weber, 2004, among others). Second, we sharply identify the lead-lag relation between CDS and bonds using our novel private company CDS data. We show that in the absence of stock market information, the CDS market predominantly leads the bond market in revealing firm-specific credit risk information, which provides strong support for the findings by Longstaff, Mithal and Neis (2003) and Blanco, Brennan and Marsh (2005). Our results also highlight the importance of controlling for concurrent stock trading information in drawing inferences on the lead-lag relation between CDS and bonds.

6

Here CDS return denotes the daily percentage change in CDS spread.

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Third, our paper reconciles the conflicting evidence on stock-CDS return predictability. Unconditionally, we confirm that stock returns predominantly predict CDS returns and not vice versa (Norden and Weber, 2009; Marsh and Wagner, 2012; Hilscher, Pollet and Wilson, 2015). For the average day, this evidence is consistent with market choice theory (Easley, O’Hara and Srinivas, 1998) and the sluggish response of CDS to systematic stock market information (Marsh and Wagner, 2012). However, when significant firm-specific information prevails during credit rating changes, we find important information flows between CDS and stocks. Such price discovery from CDS to stocks exists only if the aggregate stock market condition is effectively controlled for in the stock-CDS predictive regressions. For the remaining firm-specific credit risk information, we show that CDS are no longer just a sideshow to stocks, playing an important role in cross-market price discovery. Finally, our paper extends the literature on bank relationships and informed trading activities in CDS markets. We show that bank-driven informed trading is specific to CDS price discovery (Acharya and Johnson, 2007; Qiu and Yu, 2012) and is not applicable to other related securities of the same firm. We further show that specific types of banking relationships matter; that is, the marginal contribution of a firm’s banking relationship to CDS price discovery is stronger for firm’s with key CDS originating bank relationships. The remainder of the paper is organized as follows. Section 2 describes our data and variables. Section 3 presents our main results. Section 4 offers concluding remarks.

2. Data and methodology 2.1. Data 2.1.1. Events and CDS sample We use rating events on US public and private entities during 2001–2013 time period. We collect our key variables from various data sources. Our rating events data are obtained from S&P Capital IQ. The data include credit watches, outlooks, and rating notch changes for both public and private entities in the US. A rating outlook is a credit rating agency’s assessment about the long-term prospects of the company, with the term ranging from six months to two years. Credit watches focus on the shortterm credit changes regarding a company, and a company is put under special surveillance (“on watch”) by the rating agency if its credit situation is of concern.7 Credit watches are formal reviews about the company that are likely to result in a subsequent rating action, whether the action is an actual rating change, or at least, a confirmation of the existing grade. We exclude rating outlooks from our study

7 Negative watches caution the firm to exert recovery effort to avoid potential downgrades (Boot, Milbourn and Schmeits, 2006). See also https://www.standardandpoors.com/en_US/web/guest/article/-/view/ sourceId/504352 for more detailed definitions of S&P rating notches, outlooks, and watches.

since there is a high degree of uncertainty in their longterm credit opinions. Our CDS data are acquired from the Markit Group. The data consist of CDS spreads for different maturities and the number of distinct contributors (“CDS depth”) for each daily quote, in addition to contract type, base currency, and company related information. Markit calculates the daily composite CDS premiums from the quote information provided by market participants/data contributors, with the requirement of at least two distinct contributors to ensure the quality of their composite data. Markit further cleans the data by imposing criteria related to staleness of the quotes and outliers. We use the five-year CDS premiums on senior unsecured obligations in USD, as they are the most liquid trading CDS contracts. We require the contracts to have modified restructuring documentation clause prior to April 2009 (“CDS Big Bang”), and no restructuring clause afterward. Our final CDS sample consists of daily five-year premiums on senior unsecured obligations of USincorporated entities during the period from January 2, 2001, to May 7, 2014.8 To determine whether a firm was public at the time of a rating event, we follow a criteria similar to Gao, Harford and Li (2013) and Jostova, Nikolova, Philipov and Stahel (2013). Following Gao, Harford and Li (2013), we first check firm initial public offering (IPO) dates and going private transaction dates available from Capital IQ to verify the public-private status of each firm. We further examine the historical availability of company share prices in NYSE, Amex, and Nasdaq from CRSP. To account for the complete share price history of each firm, we carefully consider both header and historical entity CUSIPs at the time of the rating event, as well as the complete list of historical CUSIPs associated with the company to ensure an accurate and complete match. We further use the inactive (historical) equity listings data provided by Capital IQ, both to ensure the matches and to populate a few missing cases with share price information.9 After accounting for all the aforementioned conditions, private firm events do not have any stock price information during the entire event window, [−90, 90] days around the event. We match our CDS data to rating events using header and historical entity CUSIPs. We require events to have CDS quotes on 90% of the 180-day window around each event date. The resulting final sample consists of 626 public firms and 166 private firms with 3470 credit rating events and 624,993 daily CDS spread observations.10 Fig. 1 shows the distribution of our rating events throughout the

8 Although the sample period in this study is from 2001 to 2013, we utilize the CDS spreads from the early months of 2014 to capture the post-event reactions ([0, 90] days) of the events that occurred in the later months of 2013. 9 In the cases with sufficient changes to generate a new company record (notably, mergers and acquisitions), the permanent company identifier (PERMCO) assigned by CRSP changes, and the same entity gets a different CUSIP, identifier, and name (e.g., “Dollar General Corp” acquired in 2007 and became “Dollar General Corp New” with a new PERMCO; “Dole Food Inc” similarly became “Dole Food Inc New”). We use Capital IQ to track these firms and populate their share prices. 10 Among these firms, 43 firms change their public/private status during our sample period.

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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Fig. 1. Frequency of rating events by direction. This figure presents the frequency and direction of rating events on public and private US firms with CDS outstanding on their debt. The sample period is from January 2001 to December 2013. Panel A shows the distribution of 1316 rating downgrades and 1087 negative credit watches over the sample period. Panel B shows the distribution of 776 rating upgrades and 291 positive credit watches. The rating events data are provided by S&P Capital IQ.

whole sample period. Downgrades and negative watches are more frequent in 20 08 and 20 09 following the 20 07 financial crisis. Confounded events, such as watch changes that coincide with rating notch changes, are also prevalent, especially for downgrades. 2.1.2. Bond transactions We obtain daily bond pricing information from TRACE. The advantage of this database is that it is transaction based. The data covers 99% of all public transactions from February 7, 2005, onward. We follow the data cleaning procedure of Bessembinder, Kahle, Maxwell and Xu (2009) to eliminate canceled, commissioned, and corrected trades. We then obtain bond characteristics information (callable, convertible, fixed coupon, etc.) from Mergent Fixed Income Securities Database (FISD). Following Jostova, Nikolova, Philipov and Stahel (2013), we eliminate preferred shares, non-US dollar denominated bonds, bonds with unusual coupons, bonds with warrants, bonds that are mortgage or asset backed, bonds that are convertible or are part of unit deals (i.e., features that would result in differential pricing). We further require bonds to be senior unsecured to keep the bond yield spreads comparable to CDS spreads. Next, we calculate the equal-weighted average of bond yield spreads for each firm. We similarly match the

bond information for each firm to events by using both header and historical CUSIPs. 2.1.3. Banking relationships and firm financials We construct bank relationship variables following the methodology described by Bharath, Dahiya, Saunders and Srinivasan (2007). Utilizing syndicated loan data from the Loan Pricing Corporation (LPC) Dealscan, we match each loan issued to a firm to its lenders. From this matching, we can identify the lenders to a firm that undergo rating events in our sample. After combining all active loans at the beginning of each event window, we count the number of distinct lenders. We construct three different measures of banking relationships. First, we count the total number of relationships with both participants and lead banks. Second, as is common in the literature, we count the number of lead banks.11 We identify lead banks following the criteria in Cai, Saunders and Steffen (2011). Finally, we count the number of relationships with the “CDS originating” banks and their subsidiaries. CDS originating banks include Bank of America, Barclays Bank, BNP Paribas, Citibank,

11 Following Acharya and Johnson (2007), we count the relations at the parent level (i.e., relations for affiliated as well as predecessor companies).

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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Table 1 Descriptive statistics. This table presents the summary of underlying firm characteristics and their securities for the S&P rating events from January 2001 to December 2013. The overall sample includes 3470 rating events on 626 public and 166 private US-incorporated firms with 624,993 event-day observations in total. Statistics are computed for the observations around the event dates ([−90, 90]) at the firm-level first and then across firms. CDS premium is the daily five-year CDS premium in basis points. CDS depth is the daily number of distinct quote providers for each entity. Bond yield spread is the equal-weighted average yield spread of firm’s senior unsecured bonds, calculated using the daily bond trade observations obtained from TRACE. Number of analysts is the number of analysts that follow each firm prior to event, retrieved from the I/B/E/S database. Number of bank relations is the number of unique lenders in the LPC Dealscan database that had active loan relations with the underlying firm at the time of event. Number of lead bank relations is the number of unique lead lenders, and number of CDS banks is the number of unique ICE Trust members (i.e., CDS originating prominent banks and their subsidiaries). Credit rating is firm’s long-term credit rating provided by S&P, Moody’s, or Fitch, in availability order, where rating classes are converted to numerical scales from AAA (1) to D (22). Leverage is the ratio of firm’s total book debt to total assets, obtained from Compustat and Capital IQ for public and private firms, respectively. Total assets is the book value of firm’s assets in millions of dollars. Number of events is the number of events for a given firm throughout the sample period. Negative events represent the combined sample of rating downgrades and negative credit watches, and positive events represent rating upgrades and positive credit watches. Panel A presents the firm characteristics, and Panel B shows the correlation matrix of these variables. Panel A: Firm characteristics Public firms

CDS premium (bps.) CDS depth Bond yield spread (%) # of analysts # of bank relations # of lead bank relations # of CDS bank relations Credit rating Leverage Total assets ($ million) # of events # of negative events # of positive events # of distinct firms

Private firms

Mean

Median

St. dev.

Mean

Median

St. dev.

316.63 6.15 3.37 12.61 30.85 4.76 8.68 9.93 0.34 41,856

144.79 5.00 2.10 12.00 25.00 3.25 7.00 9.00 0.31 9508

591.35 4.01 4.50 8.41 29.05 5.38 6.16 3.57 0.20 149,506

451.25 4.80 6.30 – 26.78 5.04 8.46 10.60 0.56 58,214

198.98 4.00 2.84 – 22.00 4.00 7.00 9.00 0.57 11,701

907.63 3.23 11.06 – 18.83 3.98 5.68 4.36 0.33 174,256

4.62 3.20 1.42

3.00 2.00 1.00 626

3.86 3.23 1.67

3.49 2.42 1.07

2.00 2.00 1.00 166

3.32 2.82 1.38

Panel B: Correlation matrix

CDS premium CDS depth Bond yield s. # of analysts # of bank r. # of lead b. # of CDS b. Credit rating Leverage Ln(TA) # of events # of neg. e. # of pos. e.

CDS premium

CDS depth

Bond spread

# of analysts

# of bank r.

# of lead b.

# of CDS b.

Credit rating

1 −0.060 0.751 −0.147 0.095 0.061 0.036 0.499 0.364 −0.080 0.278 0.294 0.075

1 −0.090 0.232 0.197 0.149 0.250 −0.249 −0.095 0.339 0.248 0.307 −0.023

1 −0.198 0.016 0.038 −0.026 0.494 0.331 −0.071 0.144 0.182 −0.024

1 0.095 −0.003 0.160 −0.230 −0.331 0.276 0.070 0.041 0.083

1 0.634 0.794 0.065 0.156 0.280 0.178 0.165 0.092

1 0.659 −0.002 0.151 0.324 0.096 0.056 0.113

1 −0.043 0.116 0.375 0.143 0.124 0.092

1 0.429 −0.491 0.245 0.196 0.190

Credit Suisse, Deutsche Bank, Goldman Sachs, HSBC, JPMorgan Chase, Lehman Brothers, Merrill Lynch, Morgan Stanley, Royal Bank of Scotland, and UBS. We link the loan data to our rating events by using Chava and Roberts (2008) linking file. This linking file provides the mapping information from Dealscan facility identifiers to Compustat firm identifiers and therefore entity CUSIPs. Since the latest update of the linking file is August 31, 2012, our analysis of banking relations is based on a subsample of our rating events that occurred until that date. We obtain firm financials from Compustat for public firms and the same information from S&P Capital IQ for private firms. Finally, using the Institutional Brokers’ Estimate System (I/B/E/S) database, we count the number of analyst forecasts on a firm during 90 days that

Leverage

Ln(TA)

1 −0.196 0.088 0.099 0.010

1 0.071 0.065 0.037

# of events

# of neg. events

# of pos. events

1 0.907 0.564

1 0.164

1

precede each rating event. We include all forecasts, both on the short-term and the long-term performance of a company. Panel A of Table 1 provides the summary statistics of our underlying firm characteristics, security prices, banking relationships, and the types of rating events in our sample from January 2001 to December 2013. Panel B provides correlations among these key variables. 2.2. Methodology 2.2.1. Measuring CDS reactions Our methodology is consistent with Hull, Predescu and White (2004). We adjust daily changes in five-year CDS spreads using the same-day change in the credit

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benchmark portfolio to account for systematic trends. We calculate daily indices of CDS spreads for each rating class; for S&P, the rating classes are AAA, AA, A, BBB, BB, and B. We then use the median spread within each of the six rating categories to reduce outlier effects.12 The adjusted spread change in each day during the [−90, 90] event window is calculated as

Adjusted spread change(ASC )it = (Spreadit − Spreadit−1 ) − (Indexrt − Indexrt−1 ),

(1)

where Spreadit is the daily five-year maturity CDS spread of the firm i at date t, and Indexrt is the median spread for the rating class r at date t. Following Hull, Predescu and White (2004), rating class r is determined 90 days prior to each event date and is kept constant throughout the 180day event window, [−90, 90].13 We winsorize the adjusted spread changes at their 1st and 99th percentile values. Based on the adjusted spread changes (ASC), the cumulative adjusted spread change (CASC) for firm i from day t1 to t2 is calculated as follows:

CASCi,[t1 ,t2 ] =

t2 

ASCit .

the cumulative adjusted stock returns (CAR) of firm i for the time interval from t1 to t2 as follows:

CARi,[t1 ,t2 ] =

t2 

ARit .

(4)

t=t1

2.2.4. Confounded events During our sample period, approximately half the downgrades were signaled by a negative watch in the 90 days prior to each downgrade. These confounding events could bias us to find significant early reactions in CDS markets on upcoming rating downgrades. To be cautious on this possibility, we separately study CDS reactions to events that do not suffer from such confounders. We define an event as unconfounded if a rating notch change is not preceded by another rating notch or watch change during its [−90, 0] window, where we denote the announcement of the focal event by date zero. In cases in which a credit watch and a rating notch change occur on the same day, we only keep the notch change, classifying it as an unconfounded rating notch change.

(2)

t=t1

3. Main results

We calculate the standard errors of cumulative changes separately for each interval to test the null hypothesis–that there is no reaction during that interval. 2.2.2. Bond reactions As a benchmark comparison to CDS spread changes, we calculate the bond yield spread changes around each event. Since bond trades are more infrequent, we adjust the bond yields with interpolated maturity-matched treasury yields following Chen, Lesmond and Wei (2007). In our firm-specific price discovery tests, we also adjust the bond spreads with an equal-weighted index of the bonds in our overall sample. 2.2.3. Stock returns We use both raw stock returns and market-model adjusted stock returns. The market-model adjusted stock returns are calculated as follows:

ARit = Rit − αi − βi Rmt ,

7

(3)

where ARit and Rit are the adjusted and raw return of stock i on day t, respectively. Rmt is the NYSE/Amex/Nasdaq value-weighted holding period return on day t. Following Norden and Weber (2004), we estimate α i and β i using the daily closing stock and market returns from the lagged year (e.g., returns in 2005 are adjusted with the coefficients estimated from 2004). Similar to CDS, we calculate

12 When constructing these credit benchmark portfolios, if S&P ratings are unavailable, we use Moody’s or Fitch ratings to determine rating classes, using equivalent rating scales across the rating agencies. 13 To capture clean, unconfounded information in CDS and other related securities around a rating event, we use a 90-day estimation period before and after the event. This estimation period is also widely used in the CDS and rating events literature (e.g., Hull, Predescu and White, 2004; Norden and Weber, 2004, among many others).

3.1. CDS reaction to rating events CDS spreads and ratings are both indicators of firms’ credit quality. However, the contents and the speed of information that each measure reveals could vary due to the underlying incentives of credit rating agencies and CDS market participants. We examine whether CDS at-market spreads predict upcoming rating changes or vice versa. Our motivation in focusing on CDS reactions to rating events is to determine the extent to which firm-specific credit risk information originates from the CDS market prior to rating notch/watch changes due to informed traders in this market, and the extent to which such CDS-originated credit risk information subsequently spills over to other related securities prices. The results we report in this section will extend the existing findings in the literature to the economic downturns in the post-2007 financial crisis as well as the wider cross-section that includes private firms in addition to public firms. 3.1.1. Do CDS anticipate both positive and negative rating events? Panel A of Fig. 2 summarizes CDS spread reactions to upcoming rating changes. CDS react as early as 90 days prior to future rating changes, particularly for negative events including downgrades and negative watches. The CDS reaction curve is more convex for negative watches than downgrades, which suggests that negative watches are relatively more surprising to CDS market participants than downgrades. After each rating change announcement, CDS do not further react significantly, indicating that the CDS market predominantly leads rating changes. For both downgrades and negative watches, a relatively small daily jump is observed on the day of the event, and no significant changes occur afterwards.

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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J. Lee et al. / Journal of Financial Economics 000 (2018) 1–23

Fig. 2. CDS reactions to rating events. This figure presents the mean cumulative adjusted CDS spread changes (CASC) around the rating events. The sample consists of 3470 rating changes and credit watches by S&P throughout the period from January 2001 to December 2013. Panel A shows the reaction of CDS spreads to the unconditional sample of events. Panel B presents the CDS spread reactions to unconfounded sample where no other rating event occurred during the trade days [−90, 0] relative to the event day. CDS data are provided by Markit.

Table 2 summarizes this CDS reaction during 180day window around each event date (i.e, [−90, 90]). We separate the whole event window into three subintervals and report the cumulative change in adjusted CDS spreads (CASC) for each subinterval. The left panel presents

CDS reactions to negative events, whereas the right panel presents the results for positive events. In each panel, we further provide greater details on each subgroup CDS reaction. We partition our firms into several subgroups according to informational importance of the rating event on

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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J. Lee et al. / Journal of Financial Economics 000 (2018) 1–23

9

Table 2 CDS reactions to rating events. This table presents the cumulative changes in adjusted CDS spread around the rating events of public and private US firms from January 2001 to December 2013. The mean cumulative changes are reported in basis points. Investment grade and noninvestment grade subsamples represent the events of investment grade (IG) and noninvestment grade (NIG) firms, respectively. Change amount specifies the magnitude and the type of rating change. Unconfounded subsample shows the CDS reactions to unconfounded rating events where no other event occurred during the trade days [−90, 0]. T-statistics are reported in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗ indicate that the cumulative adjusted change is different from zero at the 10%, 5%, and 1% confidence levels. Negative events # of events

Positive events Time interval [−90, −2] [−1, 1]

Downgrade All

1316

Investment grade

896

Noninvestment grade

420

Change amount One notch

702

Multiple notches

614

IG to NIG

273

Unconfounded events … with no analyst coverage Negative watch All

705 192

1087

Investment grade

777

Noninvestment grade

310

Unconfounded events … with no analyst coverage

519 112

126.42∗ ∗ ∗ 15.37∗ ∗ ∗ (13.85) (8.26) 75.11∗ ∗ ∗ 11.82∗ ∗ ∗ (10.53) (6.46) 220.74∗ ∗ ∗ 21.89∗ ∗ ∗ (10.21) (5.40)

# of events

[−90, −2]

[2, 90] 16.78 (1.55) 6.05 (0.66) 36.83 (1.42)

51.01∗ ∗ ∗ (7.19) 197.41∗ ∗ ∗ (12.42) 135.37∗ ∗ ∗ (8.00) 89.37∗ ∗ ∗ (9.34) 130.83∗ ∗ ∗ (5.39)

6.94∗ ∗ ∗ 0.83 (4.21) (0.09) 23.27∗ ∗ ∗ 31.90∗ (7.21) (1.65) 19.60∗ ∗ ∗ −0.94 (5.12) (−0.04) 12.73∗ ∗ ∗ 22.76∗ (6.65) (1.91) 20.51∗ ∗ ∗ 1.02 (4.16) (0.04)

101.13∗ ∗ ∗ (11.12) 68.67∗ ∗ ∗ (9.20) 172.46∗ ∗ ∗ (7.33) 47.76∗ ∗ ∗ (5.69) 47.07∗ ∗ ∗ (2.74)

22.38∗ ∗ ∗ 18.09∗ (11.45) (1.83) 18.47∗ ∗ ∗ 1.77 (9.55) (0.19) 30.94∗ ∗ ∗ 55.18∗ ∗ (6.81) (2.27) 17.21∗ ∗ ∗ 20.40∗ ∗ (7.96) (2.27) 15.63∗ ∗ ∗ 15.87 (2.66) (0.92)

underlying credit risks (e.g., Investment grade versus Noninvestment grade, One notch versus Multiple notches, etc.).14 From the left panel, one can see that noninvestment grade firms receive greater CDS responses than investment grade firms. For example, in anticipation of downgrades to a noninvestment grade firm, the mean CASC over [−90, −2] days is 220.74 basis points (bps). This reaction is greater than the 75.11 bps for investment grade entities. The difference is almost 146 bps. These results confirm our prior that rating changes have greater impacts on relatively opaque entities. Multiple notch changes (Multiple notches), downgrades with more than a single notch change, also receive greater CDS responses than a single notch case (One notch). For multiple notch downgrades, CDS reaction continues in the post-event date. As shown in online Internet Appendix Table C.1 with more granular event windows, the positive reaction of 12.12 bps on [2, 10] days, together with the negative reaction of −15.97 bps on [11, 30] days, suggest that

14 We study the CDS reactions for the crisis and noncrisis subperiods in a separate analysis, and the results are presented in online Internet Appendix Fig. B.1. We find that the economic magnitude of CDS anticipation of upcoming downgrades and negative credit watches is greater during the crisis period.

Time interval

Upgrade All

776

Investment grade

444

Noninvestment grade

332

Change amount One notch

518

Multiple notches

258

NIG to IG

85

Unconfounded events … with no analyst coverage Positive watch All

574 157

291

Investment grade

130

Noninvestment grade

154

Unconfounded events … with no analyst coverage

172 40

[−1, 1]

[2, 90]

−14.57∗ ∗ −5.98∗ ∗ ∗ −1.46 (−2.41) (−5.31) (−0.24) −2.16 −1.42∗ ∗ ∗ 0.05 (−1.06) (−2.68) (0.03) −28.03∗ ∗ −10.97∗ ∗ ∗ −3.12 (−2.26) (−4.86) (−0.25) −10.74∗ ∗ −2.68∗ ∗ ∗ −0.87 (−2.28) (−2.88) (−0.20) −21.02 −11.58∗ ∗ ∗ −2.46 (−1.48) (−4.52) (−0.17) −19.10∗ −7.17∗ ∗ ∗ −3.00 (−1.94) (−3.87) (−0.45) −14.72∗ ∗ ∗ −4.29∗ ∗ ∗ 1.98 (−3.81) (−4.90) (0.32) −14.39 −5.75∗ ∗ ∗ 15.16 (−1.61) (−2.79) (1.61) −21.60∗ ∗ (−2.21) 12.24 (1.35) −53.94∗ ∗ ∗ (−3.24) −27.28∗ ∗ (−2.56) −54.50∗ (−1.66)

−17.12∗ ∗ ∗ (−6.16) −8.71∗ ∗ ∗ (−3.61) −25.03∗ ∗ ∗ (−5.21) −18.71∗ ∗ ∗ (−5.55) −10.64 (−1.43)

−4.41 (−0.74) −1.29 (−0.43) −7.54 (−0.66) −1.33 (−0.21) −10.51 (−0.88)

market overreacts and then reverts to an appropriate level of CDS premium for these multi-notch changes. As seen in IG to NIG results (in Table 2), CDS responses are greater for downgrades where a firm’s rating crosses the investmentto-junk grade borderline. At the bottom of the left panel of Table 2, we also show CDS reactions to negative watches (Negative watch). CDS again begin reacting 90 days prior to an upcoming negative watch. However, two differences are noteworthy. First, the event-day reaction is greater for negative watches (22.38 bps on [−1, 1]). This indicates that a negative watch announcement contains significant announcement effects. Second, there is a significant positive reaction of 18.09 bps over [2, 90], which is consistent with the negative watches effectively signaling potential downgrades that could follow. In the right panel of Table 2, we turn our attention to positive events. Confirming the findings in the existing studies, we also find weaker CDS reactions to positive rating events, both statistically and economically.15 The cumulative CDS spread change over days [−90, −2] is just

15 See also Hull, Predescu and White (2004), Norden and Weber (2004), Galil and Soffer (2011), among others.

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around −15 bps in anticipation to rating upgrades (see Upgrade), compared to 126 bps in the Downgrade cases. However, despite this weaker economic magnitude of CDS reactions to future upgrades, its statistical significance still remains over [−90, −2] day interval. In the same panel, we find similar CDS reactions to positive watches. For positive watches, we again find a significant announcement day reaction by −17.12 bps. Compared to the announcement day reaction of CDS by −5.98 bps to upgrades, the results suggest that positive watch changes provide important unexpected information about underlying firm credit risks to a greater extent than rating upgrades. 3.1.2. Do CDS react in the absence of credit watches? By intention and in effect, credit watches precede rating changes. Credit rating agencies act as both information providers and suppliers of rating grades that are used in various financial contracts and regulations (e.g., loan agreements, regulatory capital requirement for financial institutions, etc.). Consistent with these roles, credit rating agencies update ratings through cycles. Credit watches help agencies to achieve both timeliness and stability of their rating assignment: (i) watches provide timely signals to investors on important developments about a firm, and (ii) watches induce firms to take precautionary actions prior to actual rating changes.16 Given these credit rating agency practices, the CDS market’s anticipation to upcoming rating downgrades (upgrades) could be confounded by their reactions to negative (positive) watches that precede the rating changes. We repeat our analysis, eliminating these confounded cases. 47% of our downgrades are signaled by a negative credit watch in the preceding 90 days. In comparison, only 25% of our upgrades are preceded by a positive watch in the preceding 90 days. This larger proportion of confounded events for downgrades is consistent with the cautionary role of negative watches before actual rating downgrades. While it may not be a signal to an upcoming watch, a rating change could still contaminate CDS reactions when its post-event period [0, 90] overlaps with the pre-event period [−90, 0] of any subsequent watch. We also discard such cases. Panel B of Fig. 2 depicts CDS reactions to these unconfounded events. The magnitude of CDS spread changes are reduced in these clean events relative to their reactions in the full sample case (see Panel A of the same Fig. 2). For example, cumulative CDS reactions over [−90, +1] days to negative credit watches reduce from 123 bps to 65 bps. The details of these economic magnitudes are reported in the rows labeled Unconfounded events in Table 2. Although the economic magnitudes of CDS reactions to a negative event (left panel) decrease for each subinterval, their statistical significance remains significant at the 1% level. We find similar trends for unconfounded positive events (right panel). CDS market anticipation to future rating upgrades/positive watches remains significant, even after eliminating all confounded rating events. 16

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For example, negative credit watches allow firms time to recover after negative developments and avoid adverse consequences of downgrade (Chung, Frost and Kim, 2012).

3.1.3. Do CDS react in the absence of analyst coverage? As a further refinement to our unconfounded robustness tests, we further discard all events with any analyst forecast announcement in the pre-event window. We consider both short-term and long-term earnings forecasts as a potential source of confounding information from the stock market. This additional requirement reduces the number of our rating events by approximately 70% in each event type. Unconfounded events with no analysts coverage results in both panels of Table 2 present CDS reactions to these extra-refinement cases. Focusing first on downgrades in the left panel, we find a significant pre-event CDS reaction of approximately 131 bps. Similarly, we find a significant pre-event CDS reaction of approximately 47 bps for negative watches. Online Internet Appendix Table C.1 further shows that the CDS reaction curve to a rating downgrade becomes significantly more convex leading into the event; 61.59 bps is observed on [−30, −2] days out of a total of approximately 131 bps reaction in pre-event window [−90, −2]. Similarly, negative watches also receive relatively smaller early reactions in CDS spreads. The early stage CDS responses are, however, still significant both statistically and economically in both rating downgrades and negative watches. In the right panel of Table 2 where we focus on positive events, the early stage CDS reactions become relatively less statistically significant with no analyst coverage. However, despite this weakened statistical significance, the economic significance of the early stage CDS reactions still carries through for both positive event cases (i.e., rating upgrades and positive watches). 3.1.4. Additional unique CDS information: evidence from private firms A no arbitrage pricing relation between stocks and CDS through structural representations (Merton, 1974) implies that stock price information must be controlled to conclude that the CDS market predicts future rating and watch changes through its own market information. Simply controlling for concurrent stock trading information could be a way to address this concern on important stock market confounders.17 However, we go a step further and employ an alternative approach that uniquely and effectively addresses this issue. We use our novel data on private firm CDS. By definition, the sources of trading information in this private firm subsample are limited to bonds and CDS. Private firm CDS reactions to future rating changes are, therefore, unlikely to be explained by any direct responses to the stock market information such as stock returns and/or equity analyst forecasts. Fig. 3 depicts CDS reactions to private firm credit rating changes for both full events and the unconfounded event subsample. We summarize the economic and statistical significance of private firm CDS reactions in Table 3, with reactions to public firm events provided for compar17 For public firms, we indeed find that the CDS market significantly predicts upcoming rating events, even after we simply control for stock returns and the number of analysts in regressions. See online Internet Appendix Table B.2 for these results.

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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Fig. 3. Rating events of private firms. This figure presents the cumulative changes in adjusted CDS spread for the rating events of US-incorporated private firms. The sample consists of 579 rating changes and credit watches by S&P throughout the period from January 2001 to December 2013. The mean cumulative changes are reported in basis points. Panel A presents the reactions for the overall sample of events on private firms. Panel B presents the reactions to unconfounded subsample of private firm events.

ison. We further present detailed CDS spread changes in each of eight subintervals of the full 180-day event window in our online Internet Appendix Table C.2. In Table 3, we first find that private firm CDS react as early as 90 days prior to future downgrades. CDS re-

action continues to be significant at the 1% level for the whole intervals from 90 days prior until the event day 0. Negative watches receive significant private firm CDS reactions as well. This result holds for both full events and the unconfounded event subsample. CDS reactions for our

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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J. Lee et al. / Journal of Financial Economics 000 (2018) 1–23 Table 3 Rating events of private and public firms. This table presents the cumulative changes in adjusted CDS spread around the rating events of private and public US firms. Unconfounded subsample shows the CDS reactions to unconfounded rating events where no other event occurred during the trade days [−90, 0]. The mean cumulative changes are reported in basis points. T-statistics are shown in parentheses. ∗ , ∗ ∗ , and ∗∗∗ indicate that the cumulative adjusted change is different from zero at the 10%, 5%, and 1% confidence levels, respectively. Private firm events # of events

Downgrade All Unconfounded Negative watch All Unconfounded Upgrade All Unconfounded Positive watch All Unconfounded

223 121

179 91

126 91

51 30

Public firm events

Time interval

# of events

[−90, −2]

[−1, 1]

[2, 90]

128.44∗ ∗ ∗ (5.81) 117.27∗ ∗ ∗ (4.36)

22.45∗ ∗ ∗ (4.27) 20.77∗ ∗ ∗ (3.37)

−47.78∗ (−1.90) −14.48 (−0.46)

1093

93.54∗ ∗ ∗ (4.38) 57.50∗ ∗ ∗ (2.80)

26.92∗ ∗ ∗ (4.12) 16.85∗ ∗ (2.38)

−26.47 (−1.17) 20.97 (1.00)

908

5.20 (0.46) 1.71 (0.20)

−7.44∗ ∗ (−2.13) −7.17∗ ∗ ∗ (−2.69)

5.51 (0.42) 9.48 (1.18)

2.40 (0.08) −31.30 (−1.30)

−17.52∗ ∗ ∗ (−2.84) −18.05∗ ∗ ∗ (−3.17)

1.96 (0.24) −5.50 (−0.47)

public firm sample are also significant for future down grades and negative watches, though the reactions are generally a bit smaller. Our private firm results confirm the CDS market’s unique information and also provide evidence suggesting that the observed CDS public firm reactions for downgrades and negative watches are driven by the CDS market’s unique information, not fully driven by concurrent stock trading information. Turning to our private firm positive events (upgrades and positive watches), we find insignificant CDS reactions prior to positive events—with the reaction only significant for three days around the event day (i.e., [−1, 1]). However, for public firm positive events, we find significant CDS reactions prior to the events as well as during the three-day event window. Together, the private and public firm results for positive events show the uniqueness of our private firm analysis in identifying CDS market information and the potential confounding effect of stock information on CDS reactions.

3.1.5. Could bonds lead the CDS reactions? For private firms, CDS participants could learn from the bond market. Since no stocks concurrently trade for private firms, this firm subsample provides an ideal opportunity to cleanly test which market among the two leads, either CDS or bonds. Despite the existing findings that CDS lead bonds in price discovery (Longstaff, Mithal and Neis, 2003; Blanco, Brennan and Marsh, 2005; Norden and Weber, 2009), such conclusions could be biased due to available stock market information as a potential confounder. CDS spreads are known to be more sensitively reacting to stock prices (Norden and Weber, 2009; Hilscher, Pollet and

584

428

650 483

240 142

Time interval [−90, −2]

[−1, 1]

[2, 90]

126.04∗ ∗ ∗ (12.58) 83.86∗ ∗ ∗ (8.26)

14.03∗ ∗ ∗ (7.10) 11.14∗ ∗ ∗ (5.75)

28.90∗ ∗ (2.42) 30.09∗ ∗ (2.34)

102.50∗ ∗ ∗ (10.23) 45.83∗ ∗ ∗ (4.98)

21.58∗ ∗ ∗ (10.84) 17.28∗ ∗ ∗ (7.90)

26.06∗ ∗ (2.39) 20.29∗ ∗ (2.04)

−18.11∗ ∗ ∗ (−2.65) −17.61∗ ∗ ∗ (−4.11)

−5.72∗ ∗ ∗ (−4.88) −3.79∗ ∗ ∗ (−4.13)

−2.70 (−0.40) 0.67 (0.09)

−26.35∗ ∗ (−2.58) −26.43∗ ∗ (−2.22)

−17.04∗ ∗ ∗ (−5.50) −18.85∗ ∗ ∗ (−4.82)

−5.68 (−0.82) −0.46 (−0.06)

Wilson, 2015), which could result in faster price discovery in the CDS market rather than bonds. To show that CDS anticipate upcoming rating changes independently of any such bond market information and to also test a clean lead-lag relation between CDS and bonds, we compare private firm CDS spread reactions to corresponding bond yield spread changes over our event windows from [−90, 90]. In particular, we estimate the following panel VAR model over our 180-day event windows:



SCDS it SitBond





 n   β β0,i,CDS k,C DS,C DS = + β0,i,Bond βk,Bond,CDS k=1  CDS    Sit−k εtCDS × + , Bond Sit−k εtBond

βk,CDS,Bond βk,Bond,Bond



(5)

where SCDS and SitBond are daily changes in CDS and it bond spread for firm-event i at date t, respectively.18 We choose the lag order (n) as three days and cluster the standard errors by date to adjust for cross-sectional correlation and heteroskedasticity (Hilscher, Pollet and Wilson, 2015). We estimate both the above baseline specification and an idiosyncratic (market-adjusted) specification for the overall sample of our events and contrast the results to those we obtain with our private firm-only event subsample. Since the bonds used in this test should be relatively more liquid with sufficient trading price records, our testing sample is likely to work against finding CDS playing a lead role over bonds in price discovery. It should be noted that the requirement of having consecutive daily bond trades reduces

18 Different than the event study, we do not use interpolated spreads in our daily lead-lag analyses.

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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J. Lee et al. / Journal of Financial Economics 000 (2018) 1–23 Table 4 Lead and lag relation of bond and CDS spreads. This table presents the lead-lag relation between CDS and bonds for the observations in event windows [−90, 90] during the years 2005– 2013. The estimates in the “All” column represent the overall sample of 1636 rating events with consecutive bond trades available. The “Private” column presents the estimates for the subsample of 217 private firm events with bond trades available. The“Baseline” panel shows lead-lag relations between daily changes in CDS and bond spreads, where CDS spread is the daily change in CDS spread, and Bond spread is the daily change in bond yield spread. The “Idiosyncratic (market adjusted)” panel presents estimation results with market-adjusted spread changes, where market index spreads are calculated as the equal-weighted average of CDS spreads for the CDS market and equally-weighted average of bond spreads for the bond market. We consistently use our full sample of firms in the construction of these indices. All variables are in percentages and winsorized at the 0.1% and 99.9% levels. All regressions include firm fixed effects. T-statistics from heteroskedasticity-robust standard errors clustered by dates are reported in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗ indicate significance at the 10%, 5%, and 1% levels, respectively. Baseline

CDS spread (t) All (1)

CDS spread

Private (2)

0.086∗ ∗ 0.126 (1.97) (1.33) ∗∗ 0.116 t − 2 0.076 (1.99) (1.37) t −3 0.045 0.088 (1.12) (1.27) Bond spread t − 1 0.025∗ 0.028 (1.68) (1.43) t −2 0.016 0.009 (1.02) (0.32) t −3 0.015 0.001 (1.08) (0.05) Observations 154,668 21,229 Idiosyncratic (market adjusted) CDS spread adj. (t)

CDS spread adj.

t −1

t −1 t −2 t −3

Bond spread adj. t − 1 t −2 t −3 Observations

Bond spread (t) All (1)

Private (2)

0.375∗ ∗ ∗ (6.28) 0.253∗ ∗ ∗ (4.68) 0.126∗ ∗ (2.28) −0.549∗ ∗ ∗ (−18.90) −0.300∗ ∗ ∗ (−8.93) −0.105∗ ∗ ∗ (−3.32) 154,668

0.570∗ ∗ ∗ (4.23) 0.340∗ ∗ ∗ (3.82) 0.138 (1.47) −0.575∗ ∗ ∗ (−9.20) −0.320∗ ∗ ∗ (−4.73) −0.178∗ ∗ ∗ (−3.07) 21,229

Bond spread adj. (t)

All (1)

Private (2)

All (1)

Private (2)

0.080∗ (1.83) 0.077∗ ∗ (1.99) 0.046 (1.15) 0.023 (1.59) 0.016 (1.01) 0.015 (1.05) 154,668

0.119 (1.25) 0.114 (1.33) 0.088 (1.27) 0.028 (1.41) 0.010 (0.36) 0.001 (0.04) 21,229

0.361∗ ∗ ∗ (6.08) 0.246∗ ∗ ∗ (4.56) 0.125∗ ∗ (2.24) −0.555∗ ∗ ∗ (−19.04) −0.307∗ ∗ ∗ (−9.14) −0.111∗ ∗ ∗ (−3.49) 154,668

0.551∗ ∗ ∗ (4.13) 0.328∗ ∗ ∗ (3.66) 0.132 (1.40) −0.576∗ ∗ ∗ (−9.19) −0.323∗ ∗ ∗ (−4.72) −0.180∗ ∗ ∗ (−3.08) 21,229

the number of distinct rating events used in this test; 53% for the overall sample and 63% for the private firm subsample. Table 4 presents our panel VAR estimates for both our baseline and idiosyncratic (market-adjusted) specifications. Market-adjustments are made using the equalweighted average credit spreads in each market. We first find that, when we use the full rating events for both public and private firms (see column All for CDS spread and Bond spread), CDS spread changes significantly lead bond spread changes (from lagged-one day to lagged-three days), although there is some marginal information flow

13

from lagged-one day bond spread changes to contemporaneous CDS spread changes (coefficient of 0.025 with a t-stat of 1.68). However, when we exclusively use our private firm subsample where no stock trading information is available, we find that the CDS market predominantly leads bond market in price discovery. This sharply identifies a unidirectional information flow from CDS to bonds when there is no confounding information from stock markets. The idiosyncratic results reported in the bottom portion of Table 4 further confirm the unidirectional information flow from CDS to bonds. Importantly, adjusting for market effects, the marginal information flow from bonds to CDS for the broader sample with public firms (All column) becomes insignificant.19 3.2. Sources of information in CDS spreads 3.2.1. Informed trading prior to downgrades What drives the significant CDS spread reaction before rating events? Any public information, such as information from preceding credit watches, concurrently trading stocks and analyst forecasts, and even bond yield spreads, cannot be the source of marginal CDS market information around rating changes. The CDS market operates with a small set of primary dealer banks. As suggested by Acharya and Johnson (2007), informed trading by these CDS originating banks who also establish strong lending relationships with CDS reference entities could be the source of unique CDS market information on underlying credit risks. We measure the number of banks that have relationships with CDS reference entities through active syndicated loan positions retrieved from LPC Dealscan data.20 We first test whether such banking relations are correlated with the CDS spread reactions during our 180-day event window by estimating:

C ASCi j = β0, j + β1, j BankRelat ionsi j + Controlsi j + εi j ,

(6)

where CASCij is the cumulative adjusted CDS spread change for firm-event i over time interval j. We focus on rat19 This indicates that CDS spreads are the leading indicators on the “firm-specific” credit risk information. In our online Internet Appendix D, we further provide additional robustness test results using an alternative VAR specification to highlight this key intuition. In this alternative VAR estimation, instead of market index-adjusting the original CDS and bond yield spread changes, we use the unadjusted CDS and bond yield spreads, while we explicitly control for the aggregate market index returns in the VAR. 20 The choice of bank relations as a proxy for the extent of informed trading could raise several concerns about the magnitude of CDS response. Bank relations could be correlated with other factors that could result in a stronger CDS anticipation. The number of bank relations could be increasing with the size of firm, since larger loans involve larger lending syndicates (Acharya and Johnson, 2007). Indeed, pairwise correlations in Table 1, Panel B show that the total bank relations, as well as the number of lead banks in syndicate, are positively correlated with firm size. Therefore, we include firm size in our specifications to control for scale effect. In addition, Qiu and Yu (2012) indicate that number of bank relations is a noisy measure since it does not adjust for the sale of loans or the possibility that loans are hedged with the credit protection to reduce the risk. We are unable to address these concerns due to data availability. However, such a possibility would only decrease a bank’s incentive to monitor the loan it syndicates. Therefore, even though such factors introduce measurement noise, such noise is likely to bias the results against finding a significant relation.

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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ing downgrades, the events where CDS reactions are most significant in the pre-event window. The total number of bank relations (BankRelationsij ) in Eq. (6) is our key explanatory variable in this test. We measure various types of banking relations: (i) all types of syndicate lenders, including both lead banks and participants; (ii) only lead arrangers; and (iii) lead arrangers who are CDS market dealers. Control variables include cumulative market-adjusted stock returns over the same interval, number of stock analysts, an indicator for multiple rating notch changes, the natural logarithm of a firm’s market capitalization (Size), and year fixed effect dummies. Table 5, Panel A, presents the results. In the first two columns, we find that CDS reactions in anticipation to future rating changes are stronger for firms with a higher number of bank relations. This difference in CDS reaction across firms is more noticeable for the pre-event interval [−30, −2], rather than the event date interval, [−1, 1]. Moreover, CASC itself (i.e., Intercept) becomes statistically insignificant when the number of bank relationships is effectively controlled. This suggests that CDS market could anticipate future rating changes primarily due to the presence of informed lenders who are also active dealers of borrowers’ CDS contracts. This relation is significant only for downgrades, not for upgrades, indicating that these lenders’ hedging motives are closely related to the findings.21 We use the total number of bank relations in the first two columns of Panel A of Table 5. To further sharpen out the effects of informed CDS dealers who also establish strong lending relationships with CDS reference entities, in the remaining four columns of the same panel, we consider the following two alternative banking relation variables—the number of lead arrangers in active syndicates at the parent-bank level and the number of relations with large CDS originating banks and their subsidiaries. The marginal effect of having an additional relation with a lead or a CDS originating bank should result in greater CDS market reactions if unique information in this market indeed comes from the private information of the several key “CDS banks.” The third to sixth columns of Panel A of Table 5 show supporting evidence for lead banks, particularly lead “CDS banks” being an important information agent in the CDS market. Before downgrades, lead banks and lead CDS banks significantly explain CDS spread reactions that precede actual downgrades (i.e., CDS reactions during the subinterval [−30, −2]). The economic impacts of a marginal increase in the number of these specific types of lead banks are three to five times stronger than those of average banks that could simply participate in loan syndicates. Lead banks and CDS originating banks significantly contribute to the CDS market price discovery. 3.2.2. Is the bank-related private information revealed in other related securities’ prices as well? Is the private information that informed banks collect through their lending positions specific to the CDS mar-

21

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Online Internet Appendix Table B.3 presents the results for upgrades.

ket? Perhaps those informed banks could utilize their private information in other related securities’ markets with cheaper transaction costs. If true, CDS spreads could just react to other related securities’ prices through no arbitrage pricing relations. This would weaken the argument of significant information benefits in CDS spreads because their bank-related information advantage merely proxies for insider trading that occurs first in other securities’ markets, such as the stock market. For transaction cost reasons, informed traders could choose the stock market, rather than CDS, to profit from their private information. To alleviate these concerns, we test whether stock price reactions to rating downgrades could also be correlated with the number of bank relationships. Table 5, Panel B, compares the reaction in CDS and stock markets prior to downgrades. In the first column, one can see that even after controlling for the contemporaneous stock returns, the number of bank relationships still significantly explains CASC (bps.). The banking relationship effect is statistically significant at the 1% level. However, in column CAR stock (%), unlike in CDS, the number of bank relationships does not have any significant impact on stock returns when we effectively control for CDS spread reactions (CASC). The bank-related insider trading information seems to be fully reflected in the CDS spread reactions. We also find similar evidence for bond spread reactions and report the results in our online Internet Appendix Table B.4; after controlling for contemporaneous CDS market reactions, the number of bank relationships does not explain bond spread reactions. In summary, our findings in Table 5 suggest that bankrelated private information is unique to the CDS market. Such CDS market-specific information could help price discovery in the stock market through firm-specific information contents that the key CDS banks have on the reference entities’ credit quality. 3.3. Information flows from CDS to stocks In Table 4, we have shown that information significantly flows from CDS to bonds and not vice versa. In this section, we continue to study this lead-lag relation between CDS and stocks. We use the same panel VAR as in Hilscher, Pollet and Wilson (2015). The model is appropriate to capture the lead-lag relation between stock and CDS returns in a simultaneous framework (Engsted and Tanggaard, 2004; Norden and Weber, 2009):



RStock it RCDS it





 3   β β0,i,Stock k ,Stock ,Stock = + β0,i,CDS βk,CDS,Stock k=1  Stock    Rit−k εtStock × CDS + , Rit−k εtCDS

βk,Stock,CDS βk,CDS,CDS



(7)

where RStock is the daily stock return, and RCDS is the daily it it CDS spread change divided by lagged CDS spread, for firm i on day t.22

22 Markit records daily single name CDS quotes as of 15:30 EST/EDT, which is earlier than the stock market closing time, 16:00 EST/EDT. See Markit.com User Guide CDS & Bonds (February 2013) for more details on

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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Table 5 Bank relations. This table presents the determinants of CDS and stock response around rating downgrades of public US firms with available bank loan data from January 2001 to August 2012. The explanatory variable of interest is the number of bank relations. This is calculated by counting the number of related banks to firm via active loans at the time of event. CAR stock (%) is the cumulative market-model adjusted log return of the firm’s stock during the respective interval in percentages. Number of analysts is the number of distinct analysts that follow reference entity prior to the event. Indicator for multiple notch change equals one if the downgrade resulted in a multiple notch change in the firm’s rating, and zero otherwise. Size is the natural logarithm of firm’s market capitalization at the time of downgrade. CASC is the cumulative change in adjusted CDS spread over the interval in basis points. Panel A presents the impact of bank relationships on pre-event CDS response for different types of bank relationship measures. Lead bank relations is the distinct number of lead arrangers of loans for each firm. Lead CDS bank relations is the number of prominent CDS originating banks that served as lead arrangers in underlying firm’s active loans. Panel B presents the stock reactions after controlling for the information from CDS. T-statistics calculated from robust standard errors are provided in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗ denote statistical significance at the 10%, 5%, and 1% levels, respectively. Panel A: Measures of bank relations Dependent variable: Cumulative change in adjusted CDS spread (bps.) Interval: Intercept # of bank relations

[−30, −2]

[−1, 1]

[−30, −2]

[−1, 1]

[−30, −2]

[−1, 1]

43.101 (1.33) 0.602∗ ∗ ∗ (2.91)

12.866 (1.07) 0.156∗ ∗ (2.15)

48.543 (1.49)

14.148 (1.18)

46.478 (1.43)

13.792 (1.15)

2.202∗ ∗ (2.13)

0.349 (0.80) 1.001 (0.88) −1.775∗ ∗ ∗ (−6.08) −0.067 (−0.21) 10.832∗ ∗ ∗ (2.70) −1.343 (−0.76) Yes 803 0.131

# of lead banks

−3.284∗ ∗ ∗ (−7.09) −0.085 (−0.11) 56.837∗ ∗ ∗ (4.94) −6.530 (−1.55)

−1.759∗ ∗ ∗ (−6.13) −0.110 (−0.35) 9.855∗ ∗ (2.48) −1.396 (−0.83)

−3.321∗ ∗ ∗ (−7.17) 0.114 (0.15) 59.472∗ ∗ ∗ (5.14) −6.425 (−1.50)

−1.787∗ ∗ ∗ (−6.09) −0.058 (−0.18) 10.759∗ ∗ ∗ (2.67) −1.230 (−0.72)

5.101∗ ∗ (2.09) −3.327∗ ∗ ∗ (−7.16) 0.071 (0.09) 60.106∗ ∗ ∗ (5.18) −6.742 (−1.55)

Yes 815 0.223

Yes 803 0.141

Yes 815 0.209

Yes 803 0.130

Yes 815 0.209

# of lead CDS banks CAR stock (%) # of analysts I(Multiple notches) Size Year FE No. of events Adjusted R2

Panel B: Bank relations and stock reactions Dependent variable: Interval: Intercept # of bank relations CAR stock (%)

CASC (bps.)

CAR stock (%)

[−30, −2]

[−30, −2]

43.101 (1.33) 0.602∗ ∗ ∗ (2.91) −3.284∗ ∗ ∗ (−7.09)

−4.015 (−1.08) −0.003 (−0.20)

−0.085 (−0.11) 56.837∗ ∗ ∗ (4.94) −6.530 (−1.55)

−0.047∗ ∗ ∗ (−7.03) −0.039 (−0.41) 0.883 (0.65) 0.534 (1.18)

Yes 815 0.223

Yes 815 0.157

CASC (bps.) # of analysts I(Multiple notches) Size Year FE No. of events Adjusted R2

For the time period from 2001 to 2007, Hilscher, Pollet and Wilson (2015) find that information unidirectionally flows from stocks to CDS and not vice versa. In online Internet Appendix Table B.5, we replicate their results. In

the snap time information for each local close—Japan, Asia, London, Europe, and New York.

Panel B of online Internet Appendix Table B.5, one can see that our replication results in column (2) are nearly identical to those reported by Hilscher, Pollet and Wilson (2015); see column(1). In the same panel, we further extend their analysis to our full sample period (2001–2013) and confirm that unconditionally the CDS market is indeed a sideshow to the stock market as argued by Hilscher, Pollet and Wilson (2015).

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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However, Marsh and Wagner (2012) find that stocks lead CDS mainly through their systematic returns and that there is no significant lead role in idiosyncratic stock returns over idiosyncratic CDS returns. This suggests that the findings by Hilscher, Pollet and Wilson (2015) could be driven by sluggish CDS market responses to aggregate stock market news.23 When separated out from this systematic stock market influence, idiosyncratic CDS returns could significantly predict future idiosyncratic stock returns when firm-specific credit risk information prevails.24 Given these arguments, we revisit whether CDS returns, especially firm-specific CDS returns, exhibit significant predictability on future stock returns—again, their idiosyncratic components. We either control for the aggregate stock and CDS market conditions in our panel VAR specification, or we decompose stock and CDS returns to their systematic and idiosyncratic components and then run their lead-lag relation using their idiosyncratic components exclusively. Idiosyncratic returns are market-model adjusted returns for stocks and index-adjusted returns for CDS.25 Our results are reported in Table 6. In column (1) of the table, we first report the panel VAR results using raw CDS and stock returns, as in Hilscher, Pollet and Wilson (2015), but for our extended time series including observations in the postcrisis period. We first find that without market controls (i.e., baseline analysis), CDS returns are still a sideshow to stock returns in price discovery. In column (2) of Table 6, we employ alternative panel VAR specifications to control for aggregate market conditions. In particular, we directly control for the systematic components by including stock market and CDS market returns as exogenous controls to the baseline panel VAR estimation. We find that CDS returns significantly contribute to price discovery at the 1% significance level, once the aggregate market conditions are controlled. In column (3) of the same table, we estimate the panel VAR by using idiosyncratic stock and CDS returns to focus on the firm-specific variation. We again find strong support for the significant information flow from CDS to stocks through firm-specific information channels. Lagged one-day idiosyncratic CDS returns significantly predict idiosyncratic stock returns in the following day at the 1% significance level. In this full sample test, we cannot rule

23 It should be noted that Marsh and Wagner (2012) use proprietary CDS data from a single credit-oriented hedge fund. In contrast, Hilscher, Pollet and Wilson (2015) use Markit CDS data, which we also use in our analysis. 24 Online Internet Appendix Table B.6 presents the relation between aggregate stock and CDS market returns, where we find that the stock market significantly leads the CDS market. Consistent with Marsh and Wagner (2012), we find that the systematic lag in the CDS market overstates the contribution of the stock market to price discovery. As such, controlling for the market return component lowers the noise from the sluggishness of the CDS market, and enables us to isolate firm-specific variation in our analyses. 25 Our results are also robust to the use of alternative Markit CDS indices, CDX.NA.IG and CDX.NA.HY. These CDX series start from November 2004, and therefore, to avoid time series restrictions, we report the results using our in-sample (equal-weighted) CDS return index in our main text.

out significant information flow from stocks to CDS. However, our results show that in circumstances where “firmspecific” information becomes relatively more important, CDS may not be a complete sideshow to stocks. Conditionally, both markets could jointly contribute to cross-market price discovery. Overall, our results in Table 6 indicate that it is important to effectively control for market-wide news components when we investigate the lead-lag relation between CDS and stocks. The importance of the firm-specific CDS component in price discovery from our unconditional results of the overall sample provides motivation to explore circumstances where firm-specific information becomes prominent. Firmspecific credit events provide such a setting. We therefore center our analysis on rating notch and watch changes, since these rating events are triggered by firm-specific credit information. Table 7 presents our price discovery results around rating events. In each specification we use idiosyncratic stock and CDS returns to capture firm-specific information flows. Column (1) provides the results for the full sample. Column (2) shows the lead and lag relation for firms that had no S&P rating changes through 2001–2013, and column (3) shows the relation outside of the event windows. The results in columns (2) and (3) suggest that CDS’s role is insignificant or weak when there is no firmspecific credit information. In column (4), we test the relation during the rating event windows and indeed, find that CDS’s role is strongly related to the presence of firmspecific credit news. To further narrow the focus of our analysis and thereby sharpen the conditions under which we can highlight CDS’s role in price discovery, we turn to our event study setup. We showed earlier that CDS spread changes convey unique credit risk information on upcoming rating changes, which is distinct from the information from stocks and bonds. These findings are in line with the growing recent evidence on unique CDS market information content, particularly during important credit events. Feldhütter, Hotchkiss and Karakas¸ (2016) show that CDSbond basis diverges during credit deterioration times. Lee, Naranjo and Sirmans (2014) and Chava, Ganduri and Ornthanalai (2016) also show potential information benefits in the CDS market that could spill over to stocks and bonds of the same firm during rating changes. With this background, we test the lead-lag relation between CDS and stocks, while focusing on our 180-day event window around each rating change. We present these “conditional” panel VAR results in Table 8. Panel A of Table 8 presents the lead-lag relation between CDS and stocks around negative events. In the Baseline panel VAR (shown at the top of Panel A), we use raw stock and CDS returns, whereas in the Idiosyncratic returns (bottom of Panel A), we use firm-specific stock and CDS returns. We find that CDS returns significantly predict future stock returns, particularly in the Idiosyncratic returns specification. CDS return predictability on future stock returns is economically more significant in this idiosyncratic return specification than the baseline case. The CDS return predictability is also further extended beyond [2, 11], to including the [31, 60] window (see online Internet Appendix Table C.3, Panel A). However, such pre-

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17

Table 6 Systematic components. This table presents the lead and lag relations between daily stock returns and CDS returns for the sample of public firms (n = 897 ) from January 2001 to December 2013. In this analysis, we include both event and non-event observations (full sample). Column (1) shows the baseline results with ordinary stock and CDS returns. Column (2) shows the lead-lag relation between ordinary stock and CDS returns, controlling for stock and CDS market indices as exogenous aggregate market controls. Stock return is the daily holding period return, and CDS return is the daily change in CDS spread, both in percentages. The stock market control is the value-weighted NYSE/Amex/Nasdaq return obtained from CRSP, whereas the CDS market control is the equal-weighted CDS return of all firms in our sample. Column (3) presents the lead-lag relation between idiosyncratic stock and CDS returns. Idiosyncratic returns are market-model adjusted returns for stocks and index-adjusted returns for CDS. Given that these idiosyncratic returns are already market adjusted, this specification does not include additional market controls. CDS and stock returns are winsorized at the 0.1% and 99.9% levels. All regressions include firm fixed effects. T-statistics from heteroskedasticity-robust standard errors clustered by dates are reported in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗ indicate significance at the 10%, 5%, and 1% levels, respectively. Stock return (t)

Stock return

t −1 t −2 t −3

CDS return

t −1 t −2 t −3

Observations

CDS return (t)

Baseline (1)

Baseline with mkt. controls (2)

Idiosyncratic (3)

Baseline (1)

Baseline with mkt. controls (2)

Idiosyncratic (3)

−0.013 (−1.07) −0.014 (−0.96) 0.001 (0.05)

−0.0 0 0 (−0.04) −0.005 (−0.87) −0.014∗ ∗ (−2.15)

−0.002 (−0.45) −0.004 (−0.86) −0.0 0 0 (−0.05)

−0.131∗ ∗ ∗ (−21.51) −0.066∗ ∗ ∗ (−8.97) −0.040∗ ∗ ∗ (−5.86)

−0.073∗ ∗ ∗ (−23.70) −0.040∗ ∗ ∗ (−12.40) −0.029∗ ∗ ∗ (−10.69)

−0.065∗ ∗ ∗ (−23.94) −0.037∗ ∗ ∗ (−14.71) −0.029∗ ∗ ∗ (−12.52)

−0.003 (−0.91) −0.001 (−0.26) 0.006 (1.41)

−0.005∗ ∗ ∗ (−5.19) −0.001 (−1.02) 0.0 0 0 (0.41)

−0.005∗ ∗ ∗ (−5.07) −0.001 (−0.56) 0.001 (0.82)

0.004 (0.71) 0.027∗ ∗ ∗ (5.57) 0.005 (1.13)

−0.032∗ ∗ ∗ (−8.30) 0.008∗ ∗ (2.46) −0.007∗ ∗ (−2.32)

−0.031∗ ∗ ∗ (−8.14) 0.008∗ ∗ (2.58) −0.007∗ ∗ (−2.26)

1,447,322

1,447,322

1,447,322

1,447,322

1,447,322

1,447,322

Table 7 Significance of rating events. This table presents the lead and lag relations between daily idiosyncratic stock and CDS returns for subsamples. Column (1) provides results for our full sample that includes both rating event and nonevent observations. Column (2) presents the results for the subsample of firms with no S&P rating changes from 2001 to 2013. In columns (3) and (4), the full sample is partitioned into firm days outside of rating event windows ([−90, 90]) and firm days inside of rating event windows, respectively. All estimations are based on idiosyncratic stock and CDS returns, where idiosyncratic returns are market-model adjusted returns for stocks and index-adjusted returns for CDS. The stock market index is the value-weighted NYSE/Amex/Nasdaq return obtained from CRSP, whereas CDS market index is the equal-weighted CDS return of all firms in our sample. CDS and stock returns are winsorized at the 0.1% and 99.9% levels. All regressions include firm fixed effects. T-statistics from heteroskedasticity-robust standard errors clustered by dates are reported in parentheses. ∗ ∗∗ , , and ∗ ∗ ∗ indicate significance at the 10%, 5%, and 1% levels, respectively. Idiosyncratic returns Stock return (t) Firms with no Full sample rating changes (1) (2) Stock return

t −1 t −2 t −3

CDS return

t −1 t −2 t −3

Observations

CDS return (t)

Outside event windows (3)

Inside event windows (4)

Full sample (1)

Firms with no rating changes (2)

Outside event windows (3)

Inside event windows (4)

−0.002 (-0.45) −0.004 (−0.86) −0.0 0 0 (−0.05)

−0.008 (−1.42) −0.001 (−0.30) 0.003 (0.69)

−0.011∗ ∗ (−2.30) −0.003 (−0.70) 0.0 0 0 (0.12)

0.014∗ (1.79) −0.006 (−1.03) −0.002 (−0.24)

−0.065∗ ∗ ∗ (−23.94) −0.037∗ ∗ ∗ (−14.71) −0.029∗ ∗ ∗ (−12.52)

−0.033∗ ∗ ∗ (−8.33) −0.027∗ ∗ ∗ (−7.69) -0.022∗ ∗ ∗ (−6.59)

−0.050∗ ∗ ∗ (−18.12) −0.031∗ ∗ ∗ (−12.11) −0.028∗ ∗ ∗ (−11.21)

−0.089∗ ∗ ∗ (−17.75) −0.043∗ ∗ ∗ (−9.45) −0.028∗ ∗ ∗ (−6.54)

−0.005∗ ∗ ∗ (−5.07) −0.001 (−0.56) 0.001 (0.82)

0.0 0 0 (0.04) −0.001 (−1.28) 0.0 0 0 (0.24)

−0.001∗ (−1.67) 0.0 0 0 (0.29) 0.0 0 0 (0.52)

−0.013∗ ∗ ∗ (−6.00) −0.002 (−1.06) 0.002 (0.86)

−0.031∗ ∗ ∗ (−8.14) 0.008∗ ∗ (2.58) −0.007∗ ∗ (−2.26)

−0.070∗ ∗ ∗ (−13.91) −0.012∗ ∗ ∗ (−2.76) −0.018∗ ∗ ∗ (−4.75)

−0.043∗ ∗ ∗ (−10.88) 0.001 (0.16) −0.010∗ ∗ ∗ (−3.30)

0.004 (0.64) 0.028∗ ∗ ∗ (5.63) 0.001 (0.14)

1,447,322

476,369

1,149,725

297,597

1,447,322

476,369

1,149,725

297,597

dictability is less noticeable for positive events. As reported in Panel B of Table 8, where we focus on positive rating events, we find significantly weaker predictability of CDS returns on future stock returns, particularly during the pre-event period. Overall, our results in both panels of Table 8 are consistent with greater incentives of informed lenders in the CDS market to exploit private in-

formation on “negative” credit news that is more available around important rating events (Acharya and Johnson, 2007; Lee, Naranjo and Sirmans, 2014; Chava, Ganduri and Ornthanalai, 2016). This asymmetry in CDS predictability on future stock returns is also consistent with the investor clientele effects asserted by Marsh and Wagner (2012).

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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Table 8 Stock and CDS price revelation around rating events. This table presents the lead and lag relations between daily stock returns and CDS returns during 180-day windows around the rating events of public firms. Rating downgrades and negative credit watches are combined into “Negative events” sample, and rating upgrades and positive credit watches are combined into “Positive events” sample. Panels A and B show the lead-lag relations around 1762 negative events and 775 positive events where both stock and CDS return information are available, respectively. Time intervals in the column headers show the VAR estimation period relative to the announcement date. “Baseline” panel shows the relation between ordinary stock and CDS returns. In “Idiosyncratic returns” panel, we repeat the estimations with idiosyncratic returns, where idiosyncratic returns are market-model adjusted returns for stocks and index-adjusted returns for CDS. All regressions include firm fixed effects. T-statistics from heteroskedasticity-robust standard errors clustered by dates are reported in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗ indicate significance at the 10%, 5%, and 1% levels, respectively. Panel A: Negative events (n = 1,762 ) Stock return (t)

Baseline Stock return

[−90, −31]

[−30, −2]

[−1, 1]

[2, 11]

[12, 90]

[−90, −31]

[−30, −2]

[−1, 1]

[2, 11]

[12, 90]

t −1

0.026 (1.31) −0.023 (−1.02) 0.007 (0.36)

0.024 (1.10) −0.005 (−0.18) 0.009 (0.41)

0.098∗ ∗ ∗ (3.40) 0.041 (1.03) 0.013 (0.31)

0.058∗ (1.65) −0.020 (−0.71) −0.022 (−0.81)

0.029 (1.47) −0.012 (−0.64) 0.006 (0.30)

−0.161∗ ∗ ∗ (−11.74) −0.044∗ ∗ ∗ (−3.20) −0.021 (−1.52)

−0.160∗ ∗ ∗ (−10.40) −0.050∗ ∗ ∗ (−3.67) −0.035∗ ∗ (−2.54)

−0.184∗ ∗ ∗ (−5.55) −0.031 (−0.89) −0.036 (−1.00)

−0.130∗ ∗ ∗ (−5.39) −0.052∗ ∗ ∗ (−2.85) −0.017 (−0.99)

−0.127∗ ∗ ∗ (−10.40) −0.037∗ ∗ ∗ (−3.45) −0.021∗ ∗ (−2.22)

−0.021∗ ∗ (−2.05) 0.003 (0.25) 0.023∗ (1.93) 98,388

−0.032∗ ∗ ∗ (−2.99) −0.008 (−0.81) 0.009 (0.63) 49,909

−0.047∗ ∗ ∗ (−3.11) 0.039∗ (1.77) −0.005 (−0.26) 5058

−0.055∗ ∗ ∗ (−2.92) 0.004 (0.35) 0.017 (1.25) 16,712

−0.012 (−1.09) −0.005 (−0.48) 0.002 (0.23) 128,302

0.057∗ ∗ ∗ (4.10) 0.029∗ ∗ (2.44) 0.015 (1.06) 98,388

0.070∗ ∗ ∗ (3.63) 0.034∗ ∗ (2.24) 0.023 (1.43) 49,909

0.161∗ ∗ ∗ (7.02) 0.068∗ ∗ (2.46) 0.027 (0.85) 5058

0.065∗ ∗ ∗ (2.63) 0.003 (0.16) 0.010 (0.58) 16,712

0.003 (0.23) 0.051∗ ∗ ∗ (3.80) 0.009 (0.84) 128,302

0.022 (1.45) −0.015 (−0.85) 0.001 (0.12)

0.028 (1.36) −0.010 (−0.65) −0.010 (−0.56)

0.148∗ ∗ ∗ (3.60) −0.007 (−0.14) 0.017 (0.40)

0.059 (1.53) −0.007 (−0.23) −0.014 (−0.39)

0.025 (1.47) −0.005 (−0.40) −0.006 (−0.49)

−0.110∗ ∗ ∗ (−9.36) −0.035∗ ∗ ∗ (−3.16) −0.015 (−1.55)

−0.126∗ ∗ ∗ (−6.51) −0.032∗ ∗ (−2.22) −0.022∗ ∗ (−2.03)

−0.142∗ ∗ ∗ (−4.19) −0.025 (−0.80) −0.031 (−0.99)

−0.087∗ ∗ ∗ (−3.41) −0.033 (−1.45) −0.018 (−0.83)

−0.082∗ ∗ ∗ (−6.16) −0.024∗ ∗ (−2.17) −0.008 (−0.99)

−0.029∗ ∗ ∗ (−3.58) 0.003 (0.38) 0.011 (1.26) 98,388

−0.046∗ ∗ ∗ (−4.69) −0.017∗ (−1.89) 0.010 (1.06) 49,909

−0.058∗ ∗ ∗ (−3.26) −0.003 (−0.12) −0.009 (−0.41) 5058

−0.058∗ ∗ ∗ (−3.06) −0.006 (−0.43) 0.016 (1.18) 16,712

−0.015 (−1.48) −0.008 (−1.05) −0.002 (−0.35) 128,302

0.035∗ ∗ (2.56) 0.021∗ (1.90) 0.008 (0.61) 98,388

0.046∗ ∗ (2.27) 0.026∗ (1.68) 0.015 (0.95) 49,909

0.143∗ ∗ ∗ (6.27) 0.061∗ ∗ (2.28) 0.004 (0.11) 5058

0.048∗ (1.96) 0.001 (0.05) 0.009 (0.55) 16,712

−0.023 (−1.63) 0.043∗ ∗ ∗ (3.22) 0.003 (0.26) 128,302

t −2 t −3 CDS return

t −1 t −2 t −3

Observations Idiosyncratic returns Stock return t −1 t −2 t −3 CDS return

CDS return (t)

Interval:

t −1 t −2 t −3

Observations

Panel B: Positive events (n = 775 ) Stock return (t)

Baseline Stock return

[−90, −31]

[−30, −2]

[−1, 1]

[2, 11]

[12, 90]

[−90, −31]

[−30, −2]

[−1, 1]

[2, 11]

[12, 90]

t −1

0.015 (0.95) −0.003 (−0.18) 0.001 (0.08)

0.054∗ ∗ ∗ (2.67) 0.002 (0.07) 0.010 (0.56)

0.017 (0.59) −0.037 (−0.92) −0.061 (−0.95)

0.023 (0.97) −0.005 (−0.26) −0.011 (−0.67)

−0.003 (−0.18) −0.037 (−1.55) −0.008 (−0.40)

−0.121∗ ∗ ∗ (−7.72) −0.058∗ ∗ ∗ (−3.30) −0.043∗ ∗ (−2.55)

−0.123∗ ∗ ∗ (−7.94) −0.036∗ ∗ (−2.51) −0.038∗ ∗ (−2.36)

−0.131∗ ∗ ∗ (−2.58) −0.014 (−0.28) −0.100∗ (−1.67)

−0.128∗ ∗ ∗ (−5.52) −0.039∗ (−1.77) −0.003 (−0.17)

−0.124∗ ∗ ∗ (−11.65) −0.048∗ ∗ ∗ (−3.98) −0.029∗ ∗ ∗ (−3.11)

0.001 (0.07) −0.011 (−1.52) 0.003 (0.26) 43,368

0.0 0 0 (0.03) −0.0 0 0 (−0.06) −0.0 0 0 (−0.06) 22,064

−0.004 (−0.21) 0.006 (0.29) −0.040∗ (−1.65) 2237

−0.004 (−0.42) −0.005 (−0.53) 0.013∗ (1.96) 7432

−0.006 (−1.10) −0.001 (−0.15) 0.003 (0.44) 57,042

−0.043∗ ∗ ∗ (−2.60) 0.026∗ (1.69) 0.004 (0.26) 43,368

0.016 (0.61) 0.019 (0.99) −0.007 (−0.33) 22,064

0.105∗ ∗ (2.48) 0.059 (0.91) −0.086∗ (−1.68) 2237

0.031 (1.52) 0.019 (0.84) −0.005 (−0.26) 7432

0.021 (1.51) 0.037∗ ∗ ∗ (3.00) 0.004 (0.33) 57,042

0.012 (0.85) −0.004 (−0.32) 0.010 (0.72)

0.080∗ ∗ (2.09) 0.001 (0.06) 0.018 (0.76)

0.034 (1.29) −0.029 (−0.56) −0.013 (−0.15)

0.022 (1.19) 0.007 (0.65) −0.003 (−0.29)

0.005 (0.44) −0.017 (−1.36) −0.018 (−1.60)

−0.036∗ (−1.85) −0.027 (−1.38) −0.028∗ (−1.80)

−0.058∗ ∗ ∗ (−3.00) −0.017 (−1.23) −0.035∗ ∗ (−2.26)

−0.085∗ ∗ (−2.44) 0.011 (0.21) −0.070 (−1.28)

−0.053∗ ∗ (−2.05) −0.012 (−0.76) 0.015 (1.02)

−0.057∗ ∗ ∗ (−5.44) −0.020∗ ∗ (−2.01) −0.017∗ ∗ (−1.98)

−0.003 (−0.36) −0.010∗ (−1.75) 0.006 (0.63) 43,368

−0.006 (−0.68) −0.003 (−0.35) 0.001 (0.18) 22,064

0.017 (0.76) 0.005 (0.22) −0.047∗ ∗ (−2.06) 2237

−0.007 (−1.00) −0.006 (−1.06) 0.003 (0.54) 7432

−0.012∗ ∗ ∗ (−3.52) 0.003 (0.69) −0.004 (−1.14) 57,042

−0.077∗ ∗ ∗ (−4.59) 0.007 (0.46) −0.008 (−0.51) 43,368

−0.015 (−0.60) −0.001 (−0.05) −0.022 (−0.94) 22,064

0.095∗ ∗ (2.25) 0.065 (1.01) −0.076 (−1.44) 2237

0.007 (0.37) 0.008 (0.40) 0.0 0 0 (0.00) 7432

−0.012 (−0.90) 0.018 (1.59) −0.001 (−0.04) 57,042

t −2 t −3 CDS return

t −1 t −2 t −3

Observations Idiosyncratic returns Stock return t −1 t −2 t −3 CDS return

t −1 t −2 t −3

Observations

CDS return (t)

Interval:

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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3.3.1. Hedging and liquidity motives In Table 9, we further extend our conditional leadlag relation tests to several important conditions that are more closely related to hedging and liquidity-motivated CDS trades. We use our larger pairwise market sample of CDS-stock market relations in these tests. In Panel A of Table 9, we estimate our panel VAR models separately for the following three groups: (i) [−30, −2] days prior to each event as a benchmark; (ii) [−30, −2] days prior to each event for firms with above median bank relations (Acharya and Johnson, 2007); and (iii) [−30, −2] days prior to each event for firms with above median CDS depths (Qiu and Yu, 2012). In column High bank rel., we consider the lead-lag relation during the [−30, −2] days prior to an event date for firms with a greater than median number of banking relations. In this High bank rel. subsample, it is noticeable that both CDS and stock returns are equally significant in predicting each other during the negative rating events. The information flow from CDS to stocks in this subsample is greater than the flow in the baseline column (1) by a net of 17% (=0.054/0.046−1). Similarly, when we estimate the lead-lag relation for the firms with more liquid CDS proxied by above median CDS contract depth in column (3) (High depth), we find even stronger information flow from CDS to stocks. There is a net of 50% (=0.069/0.046−1) increase in the information flow from CDS to stocks when compared to the effect reported in baseline column (1). These results are consistent with Qiu and Yu (2012) who show that CDS depth is positively related to the level of informed trading in the CDS market. In Panel B of Table 9, we similarly test the role of hedging and liquidity motives after the announcement day. We estimate our panel VAR over [2, 11] days following each rating change announcement. This post-event analysis is important to establish CDS return predictability on future stock returns without being subject to the methodological drawbacks criticized by Hilscher, Pollet and Wilson (2015). We are not conditioning on any future credit deterioration; rising credit risk in this test is cleanly defined ex ante. The results confirm that CDS returns significantly predict future stock returns, both economically and statistically, particularly around negative rating events. When CDS reference entities have strong banking relationships and the CDS contracts exhibit greater hedging demands proxied by high contract depths, we find relatively stronger CDS return predictability on future stock returns. For example, during the post-event period days [2, 11], for High depth CDS, a percentage increase in one-day lagged stock returns results in 6.7 bps decrease in future CDS returns (t-statistic of −2.17), whereas a percentage increase in one-day lagged CDS returns results in 9.2 bps decrease on future stock returns (t-statistic of −2.70). However, as reported in both panels of Table 9, we do not find a significant information lead by CDS on future stock returns for positive rating events. Overall, our results in both panels of Table 9 jointly suggest that informed traders choose to participate in both CDS and stock markets during important negative rating events. These informed traders in CDS market are relatively inactive when rating events are positive. Such asymmetry indicates clien-

19

tele effects in CDS market price discovery processes, as asserted by Marsh and Wagner (2012). 3.4. Lead and lag relations among stock, CDS, and bond Having shown the pairwise return relations between CDS and bonds, as well as between CDS and stocks, a natural and important question is what are the overall price discovery relations among stock, CDS, and bond returns once we include them simultaneously in the threeway VAR analysis.26 Following the return notions used in Hilscher, Pollet and Wilson (2015), we define CDS and bond returns as the daily changes in CDS spreads and bond yield spreads (all in percentages), respectively. We present the overall lead-lag price discovery relations among the three securities in Table 10. We find that CDS continue to persistently contribute to price discovery for both stocks and bonds. Consistent with our prior pairwise CDS-bond market results, CDS returns continue to predict bond returns, and, importantly, the role of bonds in CDS price discovery in this public company analysis diminishes once stock information is included. Moreover, consistent with our prior pairwise market results, CDS’s role is more economically significant around rating events (see inside event windows columns). Overall, we find that CDS continue to provide unique pricing information for both bonds and stocks, even after concurrently controlling for the reference entity’s stock and bond pricing information. 3.5. Detecting firm-specific news: jumps in security returns Given the importance of firm-specific news underlying cross-market price discovery processes, we explore the properties of security returns associated with them. Lee and Mykland (2008) indicate that jumps in individual stock returns occur with firm-specific news, whereas jumps in index returns are primarily related to macroeconomic news. Following this intuition, we use Lee and Mykland (2008)’s test on idiosyncratic CDS and stock returns to detect jumps associated with firm-specific news. Denote by Rj (i) the logarithmic idiosyncratic security return of firm j on day ti and by nj the number of observed prices of that security for firm j over the sample period. The instantaneous volatility for a given security is then es-

26 It is important to note that this three-way stock-CDS-bond VAR analysis limits our sample to only public firms by construction. In addition, with the three-way analysis, there is also a significant decrease in both the number of observations and number of distinct firms, since the data for each firm across all three markets must be available simultaneously to conduct the analysis. When we move from the two-way stock-CDS tests to three-way stock-CDS-bond tests, the number of observations decrease from 1,447,322 with the stock-CDS tests (Table 7) to 305,347 with the stock-CDS-bond tests (Table 10). For the inside event windows subsample, we find a similar magnitude reduction in our sample size; 297,597 firm days in Table 7 becomes 79,553 firm days in Table 10. This sample reduction is mainly driven by infrequent trading in bond markets, where a majority of firms do not trade on an average day. For these sample limitation reasons, we conduct our main analyses using pairwise CDS-stock returns without including bonds. Our two-way CDS-stock analysis also enables us to more directly compare and build on the related pairwise market research (e.g., Hilscher, Pollet and Wilson, 2015).

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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J. Lee et al. / Journal of Financial Economics 000 (2018) 1–23

Table 9 Hedging and liquidity motives. This table presents the lead and lag relations between idiosyncratic stock and CDS returns around rating events conditioned on firms with high bank relations and firms with high CDS liquidity. We partition public firm events in Table 8 into high bank relations (high CDS liquidity) subsample if the underlying firm has above median number of bank relations (median CDS depth). Panel A presents the lead-lag relations prior to announcement, during days [−30, −2], and Panel B shows the lead-lag relations after the announcement, during days [2, 11]. In Panel A, column (1) shows overall sample results for the interval [−30, −2]. “High bank rel.” shows the lead-lag relation for the firms with above median bank relationships within the same estimation period [−30, −2]. “High depth” shows the lead-lag relation for the firms with above median CDS depth within the same estimation period [−30, −2]. Similarly, Panel B repeats the same analysis for the interval [2, 11]. Stock and CDS returns are winsorized at the 0.1% and the 99.9% levels. All regressions include firm fixed effects. T-statistics from heteroskedasticity-robust standard errors clustered by dates are reported in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗ indicate significance at the 10%, 5%, and 1% levels, respectively. Panel A: Pre-event predictability Idiosyncratic returns Stock return (t)

Negative events (n = 1762) Stock return t −1 t −2 t −3 CDS return

t −1 t −2 t −3

Observations Positive events (n = 775) Stock return t −1 t −2 t −3 CDS return

t −1 t −2 t −3

Observations

CDS return (t)

[−30, −2] (1)

High bank rel. (2)

High depth (3)

[−30, −2] (1)

High bank rel. (2)

High depth (3)

0.028 (1.36) −0.010 (−0.66) −0.010 (−0.56)

0.024 (0.88) 0.010 (0.49) −0.014 (−0.59)

0.009 (0.37) −0.034 (−1.61) −0.012 (−0.51)

−0.126∗ ∗ ∗ (−6.51) −0.031∗ ∗ (−2.21) −0.022∗ ∗ (−2.04)

−0.149∗ ∗ ∗ (−5.55) −0.029 (−1.36) −0.018 (−1.10)

−0.101∗ ∗ ∗ (−4.40) −0.006 (−0.38) 0.003 (0.28)

−0.046∗ ∗ ∗ (−4.69) −0.017∗ (−1.89) 0.010 (1.06)

−0.054∗ ∗ ∗ (−3.86) −0.018 (−1.29) 0.014 (1.00)

−0.069∗ ∗ ∗ (−4.99) −0.008 (−0.52) 0.020 (1.30)

0.046∗ ∗ (2.27) 0.026∗ (1.67) 0.015 (0.95)

0.067∗ ∗ (2.32) 0.033 (1.49) 0.039 (1.63)

0.239∗ ∗ ∗ (7.06) 0.045∗ ∗ ∗ (2.74) −0.006 (−0.41)

49,909

24,721

25,759

49,909

24,721

25,759

∗∗

∗∗∗

∗

0.079 (2.09) 0.001 (0.06) 0.018 (0.76)

0.084 (1.44) 0.029 (1.09) 0.015 (0.71)

0.108 (1.56) −0.049 (−1.45) 0.027 (0.59)

−0.058 (−3.00) −0.017 (−1.24) −0.035∗ ∗ (−2.25)

−0.051 (−1.93) −0.001 (−0.07) −0.040∗ (−1.79)

−0.026 (−0.90) −0.027 (−1.32) −0.041∗ (−1.75)

−0.006 (−0.68) −0.003 (−0.35) 0.001 (0.17)

−0.002 (−0.18) −0.007 (−0.68) 0.004 (0.47)

−0.029 (−1.54) −0.008 (−0.32) 0.009 (0.61)

−0.015 (−0.58) −0.001 (−0.04) −0.022 (−0.94)

−0.043 (−1.18) −0.007 (−0.26) −0.026 (−0.80)

−0.161∗ ∗ ∗ (5.02) −0.005 (−0.23) −0.019 (−0.98)

49,909

24,721

25,759

49,909

24,721

25,759

Panel B: Post-event predictability Idiosyncratic returns Stock return (t)

Negative events (n = 1762) Stock return t −1 t −2 t −3 CDS return

t −1 t −2 t −3

Observations Positive events (n = 775) Stock return t −1 t −2 t −3 CDS return

t −1 t −2 t −3

Observations

CDS return (t)

[2, 11] (1)

High bank rel. (2)

High depth (3)

[2, 11] (1)

High bank rel. (2)

High depth (3)

0.059 (1.53) −0.007 (−0.23) −0.014 (−0.38)

0.077∗ (1.88) 0.004 (0.09) 0.002 (0.04)

0.055 (1.16) 0.006 (0.16) −0.029 (−0.78)

−0.087∗ ∗ ∗ (−3.41) −0.033 (−1.46) −0.018 (−0.83)

−0.106∗ ∗ ∗ (−2.88) −0.029 (−0.86) −0.018 (−0.48)

−0.067∗ ∗ (−2.17) 0.023 (0.72) −0.003 (−0.11)

−0.058∗ ∗ ∗ (−3.07) −0.006 (−0.44) 0.016 (1.18)

−0.066∗ ∗ (−2.39) 0.001 (0.04) 0.026 (1.16)

−0.092∗ ∗ ∗ (−2.70) −0.013 (−0.80) 0.017 (0.91)

0.048∗ (1.96) 0.001 (0.05) 0.009 (0.54)

0.042 (1.26) −0.011 (−0.43) −0.004 (−0.15)

0.207∗ ∗ ∗ (7.04) −0.004 (−0.18) 0.011 (0.64)

16,712

8269

8665

16,712

8269

8665

0.023 (1.27) 0.009 (0.85) −0.002 (−0.20)

0.043∗ (1.68) 0.007 (0.47) 0.002 (0.12)

0.021 (0.79) 0.020 (1.21) 0.021 (1.43)

−0.054∗ ∗ (−2.12) −0.012 (−0.82) 0.015 (1.04)

−0.050 (−1.39) 0.007 (0.37) 0.025 (1.20)

−0.054 (−1.48) 0.023 (0.82) 0.039 (1.46)

−0.006 (−0.94) −0.005 (−0.93) 0.003 (0.55)

−0.011 (−1.20) −0.009 (−1.07) 0.005 (0.72)

0.007 (0.66) −0.014∗ (−1.95) 0.009 (1.12)

0.008 (0.41) 0.009 (0.43) 0.0 0 0 (0.01)

0.001 (0.03) 0.027 (1.00) −0.032 (−1.02)

0.028 (0.76) 0.064∗ (1.65) 0.033 (1.53)

7432

3923

3242

7432

3923

3242

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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21

Table 10 Lead and lag relation among stock, CDS, and bond returns. This table presents the lead and lag relations among idiosyncratic stock, CDS, and bond returns. The “Full sample” column shows the results for the overall sample of observations, and the “Inside event windows” column shows the relation for the observations inside event windows ([−90, 90]). Idiosyncratic stock return is the daily market-model adjusted stock return. Idiosyncratic CDS return is the daily market-adjusted return in CDS spread. Idiosyncratic bond spread return is the daily market-adjusted return in bond spread. Market return for stocks is the value-weighted NYSE/Amex/Nasdaq return obtained from CRSP. CDS market returns and bond market returns are, respectively, equal-weighted CDS returns and equal-weighted bond spread returns of all firms in our sample. All variables are winsorized at the 0.1% and the 99.9% levels. All regressions include firm fixed effects. T-statistics from heteroskedasticity-robust standard errors clustered by dates are reported in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗ indicate significance at the 10%, 5%, and 1% levels, respectively. Idiosyncratic returns Stock return (t)

t −1

Stock return

t −2 t −3 t −1

CDS return

t −2 t −3 t −1

Bond spread return

t −2 t −3 Observations

CDS return (t)

Full sample (1)

Inside event windows (2)

Full sample (1)

Inside event windows (2)

Full sample (1)

Inside event windows (2)

0.010 (1.36) −0.003 (−0.51) −0.007 (−1.03)

0.022∗ ∗ (2.04) 0.002 (0.16) −0.014 (−1.26)

−0.086∗ ∗ ∗ (−15.32) −0.026∗ ∗ ∗ (−4.83) −0.034∗ ∗ ∗ (−7.10)

−0.106∗ ∗ ∗ (−11.18) −0.024∗ ∗ ∗ (−2.76) −0.033∗ ∗ ∗ (−4.18)

−0.115∗ ∗ ∗ (−5.57) −0.046∗ ∗ (−2.15) 0.008 (0.38)

−0.173∗ ∗ ∗ (−6.08) −0.009 (−0.33) −0.029 (−1.03)

−0.012∗ ∗ ∗ (−4.48) −0.002 (−0.88) 0.002 (0.79)

−0.026∗ ∗ ∗ (−4.43) −0.004 (−0.65) 0.005 (0.86)

0.067∗ ∗ ∗ (9.36) 0.045∗ ∗ ∗ (6.75) 0.002 (0.35)

0.094∗ ∗ ∗ (8.39) 0.056∗ ∗ ∗ (5.57) −0.018∗ (−1.82)

0.106∗ ∗ ∗ (5.57) 0.034∗ (1.66) 0.018 (0.86)

0.167∗ ∗ ∗ (5.59) 0.043 (1.37) 0.005 (0.17)

0.0 0 0 (0.81) 0.0 0 0∗ ∗ (2.11) −0.0 0 0 (−0.11)

−0.0 0 0 (−0.62) 0.0 0 0 (0.10) 0.0 0 0 (0.19)

0.0 0 0 (0.57) −0.0 0 0 (−0.44) −0.0 0 0 (−0.16)

0.0 0 0 (0.78) −0.0 0 0 (−1.02) 0.0 0 0 (0.18)

−0.299∗ ∗ ∗ (−45.64) −0.071∗ ∗ ∗ (−10.69) 0.001 (0.22)

−0.290∗ ∗ ∗ (−22.19) −0.065∗ ∗ ∗ (−4.21) 0.040∗ ∗ ∗ (2.72)

305,347

79,553

305,347

79,553

305,347

79,553

timated as follows:

σj (ti )2 ≡

1 K−2

i−1 

|R j (m )||R j (m − 1 )|,

(8)

m=i−K+2

where K is the rolling window size. For daily returns, we follow the recommendation of Lee and Mykland (2008) and set K equal to 16. The jump test statistic Lj (i) for firm j on day i is then:

L j (i ) ≡

R j (i ) . σj (ti )2

(9)

Lee and Mykland (2008) suggest the following rejection criteria for the null hypothesis that there is no jump:

|L j (i )| − Cn j Sn j

> 4.6001, (2 log n j )1/2

(10) log π +log (log n j )

, Sn j = c(2 log1n )1/2 , j c ≈ 0.7979, and 4.6001 is the 1% threshold for the rejection region according to Gumbel distribution. If this condition is met, we reject the null hypothesis and claim that there is a jump on day ti . We repeat this procedure with idiosyncratic CDS and stock returns to detect firm-specific jumps in CDS spreads and stock prices, respectively. Panel A of Table 11 presents the likelihood of jumps in idiosyncratic stock and CDS returns. We find that jumps are more likely in CDS returns than stock returns. For the baseline jump detection (K = 16), we find that probability of observing a jump in a non-event day is 2.66% for CDS returns and 0.84% for stock returns. Increasing the where Cn j =

c

−

Bond spread return (t)

2c (2 log n j )1/2

rolling window size (K) introduces diffusion noise, thereby increasing instantaneous volatility and reducing the likelihood of detecting jumps in salient periods. Consistent with the increased likelihood of firm-specific information revelation, we find that jumps are more likely around rating events, despite the potential underestimation due to greater volatility during event windows.27 In Panel B of Table 11, we examine whether firmspecific CDS jumps have any information content. Column (1) shows that a positive CDS jump (i.e., a large CDS spread increase and thus, bad news) leads to a −4.3 bps return in the next day stock return. In columns (2) and (3), where we use alternative estimations with market controls and using idiosyncratic returns, respectively, we find similar results. In Panel C of Table 11, where we further contrast the effects of positive CDS jumps (bad news) to those of negative CDS jumps (good news), we find that CDS jumps predict future stock returns primarily during negative credit events. Overall, we find that CDS jumps are followed by a significant stock return response, consistent with the conjecture that CDS jumps convey firm-specific credit risk information.

27 In untabulated results, we alternatively consider parametric jumpdiffusion models (Nimalendran, 1994, among others) to characterize security returns. We find that the arrival rate, as well as the size of jumps, are significantly greater for CDS returns in comparison to stock returns. We confirm that jumps are more likely around rating events.

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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J. Lee et al. / Journal of Financial Economics 000 (2018) 1–23

Table 11 Jumps in security returns. This table presents the jump likelihood of security returns (Panel A) and shows the lead-lag relations between stock returns and CDS jumps (Panels B and C). In this analysis, we use idiosyncratic stock and CDS returns. We follow Lee and Mykland (2008) to detect jumps in security returns for the overall sample of public firms (n = 897 ) from January 2001 to December 2013. Panel A summarizes the jump probability of idiosyncratic CDS and stock returns on event and nonevent days. Rolling window shows the number of days in the estimation window we used to detect jumps. Panel B presents the lead and lag relations between stock returns and idiosyncratic CDS jumps, where jumps are detected relative to the instantaneous volatility estimated over a rolling window of 16 days. CDS jump takes the value of 1 if a positive jump is observed in the idiosyncratic CDS return on that day, −1 if a negative jump is observed, and 0 otherwise. Panel C presents the lead-lag relations between idiosyncratic stock returns, and CDS jumps where the CDS jumps are partitioned according to their information content (good versus bad news). In column (1) of Panel C, CDS jump takes the value of 1 if a positive jump (bad news) is observed in the idiosyncratic CDS return on that day, and 0 otherwise. In column (2), CDS jump takes the value of −1 if a negative jump (good news) is observed in the idiosyncratic CDS return on that day, and 0 otherwise. Idiosyncratic returns are defined as in Table 6. All VAR estimations include firm fixed effects. T-statistics from heteroskedasticity-robust standard errors clustered by dates are reported in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗ indicate significance at the 10%, 5%, and 1% levels, respectively. Panel A: Probability of jumps Jumps in stock returns Rolling window (days): Event window Nonevent window Difference Rolling window (days): Event window Nonevent window Difference

K = 16

K = 90

K = 250

N

0.94% 0.84% 0.10%∗ ∗ ∗

0.55% 0.45% 0.11%∗ ∗ ∗ Jumps in CDS returns

0.56% 0.39% 0.17%∗ ∗ ∗

297,597 1,149,725

K = 16

K = 90

K = 250

N

2.72% 2.66% 0.06%∗

1.51% 1.38% 0.13%∗ ∗ ∗

1.20% 0.99% 0.21%∗ ∗ ∗

297,597 1,149,725

Panel B: Stock response to CDS jumps Stock return (t)

Stock return

t −1 t −2 t −3

CDS jump

t −1 t −2 t −3

Observations

CDS jump (t)

Baseline (1)

Baseline with mkt. controls (2)

Idiosyncratic (3)

Baseline (1)

Baseline with mkt. controls (2)

Idiosyncratic (3)

−0.013 (−1.00) −0.014 (−0.92) 0.0 0 0 (0.00)

0.0 0 0 (0.04) −0.005 (−0.80) −0.013∗ ∗ (−2.12)

−0.002 (−0.35) −0.003 (−0.78) −0.0 0 0 (−0.03)

−0.001∗ ∗ ∗ (−15.21) −0.001∗ ∗ ∗ (−7.83) −0.001∗ ∗ ∗ (−9.23)

−0.001∗ ∗ ∗ (−13.29) −0.001∗ ∗ ∗ (−8.73) −0.0 0 0∗ ∗ ∗ (−7.03)

−0.001∗ ∗ ∗ (−13.28) −0.001∗ ∗ ∗ (−9.03) −0.001∗ ∗ ∗ (−7.24)

−0.043∗ (−1.86) 0.019 (0.63) −0.033 (−1.34)

−0.044∗ ∗ (−2.10) 0.015 (0.52) −0.047∗ ∗ (−2.16)

−0.031∗ ∗ (−2.16) −0.009 (−0.71) −0.004 (−0.27)

−0.025∗ ∗ ∗ (−7.80) −0.013∗ ∗ ∗ (−4.70) −0.008∗ ∗ ∗ (−7.01)

−0.025∗ ∗ ∗ (−7.95) −0.013∗ ∗ ∗ (−4.91) −0.009∗ ∗ ∗ (−7.41)

−0.024∗ ∗ ∗ (−7.76) −0.012∗ ∗ ∗ (−4.68) −0.008∗ ∗ ∗ (−6.97)

1,447,322

1,447,322

1,447,322

1,447,322

1,447,322

1,447,322

Panel C: Direction of CDS jumps and stock response Idiosyncratic returns Stock return (t)

Stock return

t −1 t −2 t −3

CDS jump

t −1 t −2 t −3

Observations

CDS jump (t)

Positive jumps (1)

Negative jumps (2)

Positive jumps (1)

Negative jumps (2)

−0.002 (−0.35) −0.003 (−0.78) −0.0 0 0 (−0.03)

−0.002 (−0.34) −0.003 (−0.77) −0.0 0 0 (−0.02)

−0.001∗ ∗ ∗ (−10.90) −0.0 0 0∗ ∗ ∗ (−6.60) −0.0 0 0∗ ∗ ∗ (−5.14)

−0.0 0 0∗ ∗ ∗ (−5.46) −0.0 0 0∗ ∗ ∗ (−4.01) −0.0 0 0∗ ∗ ∗ (−2.91)

−0.054∗ ∗ (−2.42) −0.015 (−0.74) −0.019 (−0.94)

−0.005 (−0.30) −0.004 (−0.25) 0.019 (1.09)

0.040∗ ∗ ∗ (11.30) 0.004∗ ∗ ∗ (3.36) 0.004∗ ∗ ∗ (3.85)

0.028∗ ∗ ∗ (14.46) 0.009∗ ∗ (2.25) 0.005∗ ∗ ∗ (4.68)

1,447,322

1,447,322

1,447,322

1,447,322

Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011

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4. Conclusion

References

We show that CDS spreads contain unique information that is not captured by other related securities whose prices are linked to common firm fundamentals. Using a broad sample of public and private US firms from 2001 to 2013, we find that CDS spreads significantly react in advance of future rating events—even in the absence of other related securities’ market information, such as information from concurrently trading stock prices of the same firm. This unique CDS market information on future rating changes is explained by the bank-related informed trading that creates endogenous liquidity provision in the CDS market. Related to the CDS market’s efficiency in anticipating and processing relevant firm credit risk information, we find that CDS markets contribute significantly to price discovery when firm-specific credit information is prominent. CDS strictly lead bonds, whereas CDS jointly contribute to price discovery with stocks. When firm credit risk matters most, the CDS market is a main show in price discovery. We also highlight that the existing evidence of an overarching predictability from stocks to CDS (Norden and Weber, 2009; Marsh and Wagner, 2012; Hilscher, Pollet and Wilson, 2015) is mainly driven by a sluggish CDS response to aggregate stock market news. Importantly we show that when aggregate market conditions are effectively controlled for, and therefore, firm-specific information is sharply identified in stock and CDS returns, CDS returns uniquely contribute to price discovery above and beyond the information contribution from stock returns. Our additional nonparametric jump test that detects firmspecific credit news also confirms these conditional information dynamics in CDS spreads. Using our novel private company CDS data, we also provide evidence on the strict lead role played by CDS over bonds in revealing firmspecific credit risk information. In this study, we highlight different information dynamics across related securities that are driven by different types of information that each security mainly covers in its payoff structure (e.g., an upside payoff focus from stocks versus a downside payoff focus from bonds and CDS). Different market conditions magnify the wedge between the various payoff focus across these related securities. The CDS cross-section is also differentially targeted by different clienteles whose trading motives depend on the availability of their private information. Such private information plays a critical role in creating endogenous trading liquidity that eventually helps explain CDS market own as well as CDS-stock/CDS-bond cross-market price discovery. Our strong conditional evidence, that the CDS market is informationally important during firm-specific downturns and for the cross-section that is connected to key CDS dealer banks who generate endogenous CDS liquidity, reconciles the puzzling findings in the recent literature on the extent to which the CDS market has an information advantage, even conditionally, over other related securities. We show that the CDS market plays an especially important role in price discovery when firm-specific credit risk information prevails.

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Please cite this article as: J. Lee et al., When do CDS spreads lead? Rating events, private entities, and firm-specific information flows, Journal of Financial Economics (2018), https://doi.org/10.1016/j.jfineco.2018.07.011