EMPRESA SELVA
1.4. MARCO CONCEPTUAL
Studies on the role of financial disclosure in credit markets can be divided into two groups. One group attempts to incorporate accounting information for pricing the CDS. The starting point of these studies is marked by numerous empirical models that utilize financial ratios to predict financial distress (see, for example, Altman, 1968; Beaver, 1966; Ohlson, 1980). Demirovic and Thomas (2007) investigate the relevance of accounting information for credit markets by considering credit rating as a proxy for credit risk.
More importantly, Das et al. (2009) are among the first to study the relevance of accounting
information in the CDS market. These authors follow the Moody’s private debt manual and construct 10 variables representing liquidity, sales growth, capital structure, size, trading activity, and profitability. Their results show that accounting-based models are comparable with market- based models and have a complementary role in CDS pricing. Batta (2011) provides more detail on the role of accounting information in CDS pricing. This author documents a decline in explanatory power of accounting information in explaining CDS variation when additional information, such as bond price, credit rating, and stock return, is included in the regression model. Accordingly, Batta claims that the role of this information in CDS pricing through related markets,
such as stock and bond markets, is more significant than the direct role. Moreover, Correia et al.
(2012) utilize market variables and accounting information, such as total assets, net income, and total liabilities, to construct variables to forecast corporate default. Using CDS and bond spreads as alternative variables for default risk, these authors provide evidence on the usefulness of accounting information in explaining credit spread.
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More recently, Demirovic et al. (2015) assess the usefulness of accounting information for
bond spreads. Adopting a panel data regression with fixed (firm/time) effect, these authors show that adding accounting variables improves the explanatory power of the market-based model. They confirm that a Merton-based measure of distance to default does not incorporate all credit-relevant information and that accounting data holds incremental information in conjunction with that information.
The second category of literature focuses on disclosure of separated accounting information. The aim of these studies is to investigate the reaction of the CDS market centered on the event day, and to test the informational efficiency of this market. For example, Greatrex (2009)
and Callen et al. (2009) analyse the reaction of the CDS market to earnings announcements.
Greatrex (2009) adopts event study methodology based on data from 2001 to 2006 and finds that the impact of earnings announcement on the CDS market is statistically and economically significant. This author claims that the market is inefficient based on this finding. Likewise, Callen
et al. (2009) observe a negative relation between CDS spread and earnings for reference entities with low credit rating and short tenor over the period 2002–2005. These authors also provide evidence showing inefficiency in the market.
Shivakumar, Urcan, Vasvari, and Zhang (2011) evaluate change in CDS price in response to release of management earnings forecasts and find that the market reacts significantly to forecast news even more strongly than actual earnings. These authors also document that this reaction is more severe during GFC.
Further, Zhang and Zhang (2013) examine the response of the CDS market in the US through an event study around earnings news during the period 2001–2005. These authors show an increase in CDS spread for negative announcements one month prior to the events and reveal
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that this reaction is stronger for speculative-grade firms. In contrast to previous studies, they show market efficiency.
All the papers mentioned above in this section provide evidence toward the value relevancy and incremental role of financial disclosure for the CDS market. The main research gap is that the reaction of the CDS market after release of periodic financial reports is unknown.
4.2.2 Hypothesis Development
According to the studies mentioned in the preceding subsection, we know that accounting information is value-relevant for the CDS market and contains incremental information for this market. Moreover, financial reports play a critical role in risk assessment of reference entities or counterparties and are useful for both sides of CDS contracts. CDS sellers may employ this information to determine whether counterparties are financially stable. On the other hand, CDS buyers can utilize financial reports to assess the default risk of reference entities and CDS sellers simultaneously, known as double risk. Moreover, we know that, in addition to the financial data detailed in periodic financial reports, specific sections of periodic financial reports, namely, “Risk Factors,” “MD&A” and “Quantitative and Qualitative Disclosures about Market Risk,” may hold credit-relevant information for the CDS market. All of these sections provide evidence about the value relevance of periodic financial reports for the CDS market. In this way, we construct our first hypothesis. We cannot predict the sign of the reactions because the sign is related to the content of the reports, and our main concern is to distinguish whether the CDS market reacts to the release of periodic financial reports.
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Annual and quarterly financial reports have some differences. Annual reports are more comprehensive in comparison to quarterly reports and are more detailed. Knutson (1992) states that annual reports are recognized as one of the most essential reports for analysts. Moreover, in contrast to quarterly reports, annual reports are audited documents. Consequently, quarterly reports may be perceived as less reliable than annual reports, and investors pay more attention to the release of annual reports to update their beliefs. On the other hand, annual reports are summaries of previous quarterly information, and may be considered less interesting to investors who have already analysed previous quarterly information. Therefore, the CDS market may react differently to the release of 10-Qs and 10-Ks. Hence, our second hypothesis is as follows:
Hypothesis 4.2: The CDS market reacts differently to the release of quarterly versus annual financial reports.
Several theoretical studies (see, for example, Epstein and Schneider, 2008; Lang, 1991; Veronesi, 1999) claim that market reaction to release of information depends upon information uncertainty. Applying this argument to the CDS market, we predict that release of financial reports is probably more informative during GFC with greater information uncertainty. Moreover,
Shivakumar et al. (2011) show that CDS reaction to some accounting information was stronger
during GFC. As a result, motivated by these findings, this study provides results for CDS market behaviour toward the release of accounting information during GFC and non-GFC periods. Therefore, our third hypothesis is as follows:
Hypothesis 4.3: The US CDS market reacts differently in GFC and non-GFC situations regarding the release of periodic financial reports.
Numerous studies examine corporate disclosure practices in several countries and attempt to relate the extent of financial disclosure to firm characteristics, such as size, industry, and profits
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(see, for example, Ahmed and Courtis, 1999; Cooke, 1989, 1992). The findings from these studies suggest that there are systematic differences in corporate financial reporting by corporate characteristic. Large firms are much more in the public eye and these firms are followed by more analysts (Lang and Lundholm, 1993) compared to small firms. Large firms are under pressure from investors, their agents, and other users to engage in acceptable levels of disclosure. Moreover, Firth (1979) suggests that information propagation is a costly exercise and such expenses are likely more affordable for large firms. Meek, Roberts, and Gray (1995) find that large firms generally disclose more information than small firms due to lower information production costs, or lower costs of competitive disadvantage associated with their disclosures. Furthermore, agency theory suggests that large firms have higher agency costs, therefore, the probability of disclosing more information
is higher for these types of firm.31
These findings imply that large firms provide more information to the public in comparison to small and medium-size firms. There are, therefore, size effects and we hypothesize that there is a greater reaction by the CDS market for large firms compared to small- and medium-size firms.
Apart from firm size-based reactions to release of periodic financial reports, there is also evidence suggesting heterogeneity of industries from financial exposure (see, for example, Cooke, 1992; Stanga, 1976). Demirovic and Thomas (2007), for instance, demonstrate that the incremental informativeness of accounting information differs not only by firm size but also by industry. Meek
et al. (1995) solidifies this message by highlighting that proprietary costs, such as competitive disadvantage and political costs, vary across industries. Competitive disadvantage and political costs, it is argued, may change the incentive of firms to disclose financial information. For
31 Moreover, a survey by Buzby (1974) confirms that many items of information that financial analysts believe to be important are not being adequately disclosed by small and medium firms.
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example, because of the nature of financial firms’ activities, they are likely to be more sensitive to disclosure than other firms. Furthermore, Choi and Hiramatsu (1987) discuss how accounting in certain industries is highly regulated. The utility and financial industries are known to be highly regulated. A priori, we expect a significant response from the utility and financial sectors after release of periodic financial reports compared to other sectors. Accordingly, our next hypothesis is as follows:
Hypothesis 4.4: Reactions of the CDS market to release of financial reports vary across sectors and size groups.
4.3 RESEARCH DESIGN 4.3.1 Variable Selection
Several studies in the CDS literature attempt to find the determinants of CDS spread based on
reduced-form and structural variables. Galil et al. (2014) propose a parsimonious model to explain
CDS spread changes; three of our control variables are based on the findings of this study. Galil et
al. (2014) consider several groups of variables as contributing factors for CDS spread, and finally
specify three variables that outperform the other considered variables in explaining CDS spread variation. Based on availability of data, we include three of these variables in our model, namely, stock return, change in stock return volatility, and change in spot rate (5-year treasury constant maturity rate). Moreover, we control for the effect of earnings announcements and credit ratings. The list of included variables is presented in Table 4.1, and a discussion of the selected variables follows below.
116 4.3.1.1 Stock Return (SR)
Merton (1974) argues that the increase in a firm’s market value of equity decreases the probability of default. In this study, we use stock return as an indicator of firm value, because an increase in firm return can decrease the CDS spread, theoretically. Numerous studies empirically confirm this reverse relationship (see, for example, Aunon-Nerin, Cossin, and Hricko, 2002; Blanco, Brennan,
and Marsh, 2005; Galil et al., 2014); thus, we expect a negative sign for this variable in our results
as well.
4.3.1.2 Stock return volatility (∆𝑽𝑶𝑳)
Based on option pricing theory, firm debt is considered a short position of a put option on the firm’s assets accompanied by a risk-free loan. Therefore, higher uncertainty on the market value of firm assets increases the probability of default, and consequently an increase in the CDS spread is expected. Prior research provides empirical results on a positive relationship between equity
volatility and CDS spread (see, for example, Abid and Naifar, 2006; Aunon-Nerin et al., 2002;
Blanco et al., 2005; Ericsson, Jacobs, and Oviedo, 2004). To calculate volatility, we use a
methodology like Galil et al. (2014), Campbell and Taksler (2002), and; Ericsson, Jacobs, and
Oviedo (2009), and compute the annualized variance of each stock’s return based on its preceding 250 trading days. We predict a positive relation between this variable and change in CDS return.
4.3.1.3 Changes in spot rate (∆𝑺𝒑𝒐𝒕)
We follow Galil et al. (2014) and include a variable that measures change in spot rate to control
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and this causes a surge in future value of cash flows. Consequently, the probability of default is reduced and a reduction in CDS spread is expected. Consistent with 5-year tenor CDS contracts, we add the variable for 5-year maturity spot rate to our model. We predict a negative association between change in spot rate and CDS return.
Table 4.1: Definition of variables
This table lists the name and description of all variables that we consider in our model. In third column, we provide the description for each of variable and in the last column we show how we create the variables. The expected sign for each variable is provided in the last column.
Variable Description Variable Measurement
Expected Sign (Significant: Yes or No?) Spread variable ∆𝐶𝐷𝑆, Change in CDS spread for 196 firms of S&P 500 index
∆ 𝐶𝐷𝑆, =
log( 𝐶𝐷𝑆𝑖,𝑡+𝑇/ 𝐶𝐷𝑆𝑖,𝑡) ∗ 100
Firm-specific variables
𝑆𝑅, Daily stock return
𝑆𝑅, = log( 𝑆𝑅𝑖,𝑡/ 𝑆𝑅𝑖,𝑡−1) ∗ 100 - (yes) ∆𝑉𝑜𝑙𝑖,𝑡 Equal-weighted 250 days variance of individual stock return 𝑉𝑜𝑙, = 𝑆𝑇𝐷[𝑆𝑅 , − 𝑚𝑎𝑟𝑘𝑒𝑡 (𝑆&𝑃 𝑖𝑛𝑑𝑒𝑥) 𝑟𝑒𝑡𝑢𝑟𝑛, + (yes) Market factors ∆𝑆𝑝𝑜𝑡𝑖,𝑡 Change in 5-year
Treasury bill rate ∆𝑆𝑝𝑜𝑡, = 𝑆𝑝𝑜𝑡, − 𝑆𝑝𝑜𝑡,
- (yes) Dummy variable for
controlling the effect of prior earnings
announcement
𝐸𝐴i𝑡
1 if credit rating of firm i was announced in the day 𝑡, and 0 otherwise
? (yes)
Dummy variable for controlling the effect of
prior credit announcement 𝐶𝑅i𝑡
1 if quarterly earnings of firm i
was announced in the day 𝑡, and 0
otherwise
? (yes)
Dummy variable for the impact of annual financial
reports 𝐴𝑅i𝑡
1 if annual financial reports of firm i
release on day 𝑡, and 0 otherwise
? (yes)
Dummy variable for the impact of quarterly financial reports
𝑄𝑅i𝑡
1 if quarterly financial reports of firm i release on day
𝑡, and 0 otherwise
? (yes)
118 4.3.1.4 Effect of earnings announcement (ER)
Many studies confirm that earnings announcements contain information relevant to the stock market. Along this line, some studies in the CDS literature empirically show that earnings
announcements convey information for the CDS market as well (see, for example, Callen et al.,
2009; Greatrex, 2009; Jenkins et al., 2016; Zhang and Zhang, 2013). Therefore, we control for the
effect of earnings announcement by considering an indicator variable, and we expect this variable to be significant in our model. We cannot predict the expected sign because the earnings news may contain good news or bad news for investors based on dispersion in analyst earnings forecasts.
4.3.1.5 Effect of credit rating announcement (CR)
Hull et al. (2004) and Norden and Weber (2004) are among the first studies to examine the
reaction of the CDS market to credit rating announcements. These authors show that credit ratings
contain information relevant to the CDS market. Moreover, Aunon-Nerin et al. (2002) investigate
the determinants of CDS spread and verify that credit rating is the single most important source of information on credit risk. Further, Micu, Remolona, and Wooldridge (2006) note that credit rating announcements hold relevant pricing information for the CDS market. As a result, we control for this effect in our model via an indicator variable, and we predict this variable to be strongly significant in our results. The sign of this variable is not clear and depends on the downgrading or
upgrading in credit rating, which can be negative or positive.32
32 We do not differentiate between downgrade and upgrade events since credit rating announcement is not our main interest.
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4.3.1.6 Variables related to periodic financial reports (QR, AR)
The focus throughout this study is to examine the reaction of the CDS market after release of periodic financial reports. We add two indicator variables to recognize the effect of quarterly reports and annual reports for the CDS market. We predict these variables to be significant, but the expected signs are not clear because the direction of change in the CDS spread depends on the content of reports.
4.3.2 Data
We obtain the release dates of annual and quarterly reports from the DataStream Professional database. Note that, in addition to the date of a periodic report’s filing, we also consider the dates of amended report releases, because the contents of these reports may contain useful information for market participants as well.
The data related to CDS spread for modified structuring type are extracted from Bloomberg, and the S&P 500 Index constituents are from DataStream. Data are daily for the period May 7, 2004 to April 30, 2015. We focus on the CDS price with 5-year tenor because it is the most
liquid type in the market.33 Of the 500 constituents of the S&P 500 Index, only 196 stocks with
corresponding CDS spread have sufficient time series data. As a result, we exclude the remaining stocks and our final data set consists of 196 firms. This sample has 505,037 observations. Moreover, the corresponding equity prices for available CDS data, dates of announcement for earnings, and credit ratings are acquired from DataStream, Bloomberg, and Compustat, respectively. A list of data sources is presented in Table 4.2.
33 We employ the same data set used by Narayan et al. (2014). However, we update this data set to cover the April 30, 2015 period. The data set used by Narayan et al. (2014) contains 212 firms; after updating, we were forced to remove 16 firms because they did not have sufficient data series.
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After obtaining the required data, we winsorize them at 1% and 99% to remove the effect of outliers. Following this, we divide our data sample into sectors based on the GICS. In addition, we split our sample into three size groups based on firm market capitalization. Specifically, our approach is to sort the data by the contemporaneous daily market capitalization and then consider two breakpoints to create three equal groups (low, medium, and large) of rims. Our sample covers approximately 53% of the US market. Moreover, the sector-based panels indicate that the consumer staples, financial, and industrial sectors comprise more than 50% of our data sample.
Our initial data set contains 2,375, 6,312, and 6,311 observations for release dates of annual reports, quarterly reports, and earnings announcements, respectively. In 47 annual reports, and 2,044 quarterly reports, filing of reports and earnings disclosures occurred concurrently. According to the literature, the CDS market reacts to earnings announcements on the release day. Therefore, regarding controlling for contamination, we remove these coincident dates from our sample. In addition to earnings announcements, we consider the effect of coincident credit rating announcements. For this purpose, we eliminate 497 (280 for quarterly and 217 for annual reports) concurrent releases of periodic reports and announcements of earnings or credit ratings. The number of observations for each type of event in our data sample is provided in Table B.1.
Table 4.2: Data sources
This table provides the list of required data and the corresponded databases that used to obtain data.
Data Type Used Databases
CDS price DataStream-Bloomberg
Equity price DataStream
SEC filing dates DataStream Professional
Earnings announcement dates Bloomberg
5-year treasury rate FRED