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Credit default swap spreads and annual report readability

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Abstract

This paper investigates whether annual report readability matters to CDS market participants and how it affects their evaluation on a firm’s credit risk, as measured by CDS spreads. We find that the less readable the annual reports, the higher the CDS spreads. Furthermore, the impact of readability on CDS spreads is more concentrated on firms with high information asymmetry and with investment grade ratings. Our results suggest that investors take into account the readability in their view of the firms’ credit risk. Creditors appear to suffer higher cost on CDS protection of the debts if the underlying firms have less readable annual reports.

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Notes

  1. Hull et al. (2004) and Daniels and Jensen (2005) show that CDS spreads can incorporate debt-issuer related news and early signals of creditworthiness changes more quickly than credit ratings. In addition, Blanco et al. (2005) and Zhu (2006) demonstrate that CDS spreads have been shown to lead the bond market in terms of corporate credit risk and tend to be more responsive to changes in the credit conditions than bond yields. Also, Ericsson et al. (2009) point out that liquidity and tax treatment have less effect on CDS spreads than on corporate bonds since the CDS market has a higher level of standardization and better liquidity than the bond market.

  2. If lenders reduce the monitoring on borrowers after entering into CDS contracts, they may face reputation costs with CDS sellers if the firm performs poorly. In other words, CDS sellers attribute this negative outcome to reduced monitoring and will charge higher CDS spreads in subsequent transactions or even demonstrate less willingness to sell future contracts to lenders. However, if borrowers already have low credit ratings or high default risk, it is less likely for CDS sellers to attribute the bad performance of the borrowers to the reduced lender monitoring. Consequently, CDS-protected lenders are more likely to reduce monitoring of borrowers with low credit ratings.

  3. CDS is an insurance-type contract, which provides the buyer with protection against losses in the event that a bond issued by a corporation or sovereign entity defaults. In the event of default, the protection seller (CDS writer) pays for the financial loss the protection buyer (CDS holder) suffers. CDS contracts can be settled either in cash or in a physical settlement, and the default payment is designed to cover the losses a typical bondholder would experience in the event of default. In exchange for this insurance-type protection, the protection buyer makes periodic payments to the protection seller until the maturity of the contract, or until the default event occurs.

  4. At the country level, Lee et al. (2016) apply this structural model to measure sovereign credit risk.

  5. If lenders reduce the monitoring on borrowers after entering into CDS contracts, they may face reputation costs (charged higher CDS premiums in subsequent transactions) with CDS sellers if the firm performs poorly.

  6. It is possible that CDS sellers might take over the continuously monitoring role from the lenders. However, this is much less likely. According to Martin and Roychowdhury (2015), without contractual agreements between the CDS protection sellers and the underlying borrowers, the sellers’ ability of directly monitoring on the borrowers is rather limited. Nevertheless, we agree the implication of price-protection by CDS sellers might be far from obvious. Hence, we acknowledge it as an empirical question.

  7. CDS maturities range from a few months to 10 years or more. According to Moody’s report, over 85% of the trades in CDS contracts are the 5-year senior contracts. Thus, in this paper, we use only five-year CDS contracts (the most liquid one in the market in our analysis).

  8. Our financial reports are annual base, while the CDS spread is daily base. We need to link them together and thus use yearly average CDS spreads as our dependent variable.

  9. Market Jump Risk is proxied by the slope of implied volatility smile for S&P 500 options (Cremers et al. 2008) and is positively associated with the corporate credit spreads. The slope of implied volatility for S&P 500 index option is estimated as the difference between implied volatility of at the money puts and calls. It is directly downloaded from Bloomberg.

  10. The mean CDS spread is estimated using the CDS spread subsequent to the annual report fiscal year. In other words, the readability of the annual report of the previous fiscal year is linked to the average CDS spread in the next 12 months. The same principle applies to volatility.

  11. In addition, we include credit spreads on bond indices (AAA and Baa) as a macroeconomic variable in our regressions. However, we notice the credit spread has a relative higher correlation with the other four macro level variables, which raised the potential issue of multicollinearity. Therefore, we would note to interpret this finding with caution. When controlling for credit spread, we found that most of our results are still holding, especially the Table Space measurement. However, Fog index become only significant for one-sided t test.

  12. When controlling for year fixed effect by removing our macro level information, using table space measurement as an example, we observe the following patterns. The highest readability reports show up in year 2009. From year 2005–2009, financial reports become less readable until 2008 and then its readability dramatically increases in year 2009.

  13. To be specific, for borrowers already with low credit rating or higher credit risk, it is less likely for CDS sellers to attribute the bad performance of the borrowers to reduced lender monitoring. As a result, when riskier borrowers are involved, CDS-protected lenders face lower reputation effects and thereby are more likely to reduce monitoring on riskier borrowers. There will be less demand for more readable annual reports from lenders especially for firms with speculative grade rating.

  14. The firms with a credit rating of BB+ or lower by Standard & Poor’s are classified as speculative grade, while those with a credit rating of BBB− or higher by Standard & Poor are classified as investment grade.

  15. Due to the special business models and regulatory requirements of the utility and financial services industries, we remove firms in those sectors to address the potential issue that our results are driven by regulation or other factors.

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Authors and Affiliations

  1. School of Management, Xi’an Jiaotong University, Xi’an, China

    Nan Hu

  2. Howe School of Technology Management, Stevens Institute of Technology, Hoboken, NJ, USA

    Nan Hu

  3. College of Business, University of Wisconsin, Eau Claire, WI, USA

    Ling Liu

  4. College of Business Administration, California State University, Long Beach, CA, USA

    Lu Zhu

Authors
  1. Nan Hu
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  2. Ling Liu
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  3. Lu Zhu
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Corresponding author

Correspondence to Lu Zhu.

Additional information

The authors thank the comments by Pepa Kraft, Hsin-Hui Chiu, Gaiyan Zhang and seminar participants at 2016 Midwest Finance Association Conference and 2016 Eastern Finance Association Conference.

Appendices

Appendix 1: construction of the measure for readability—Fog Index and Table Space

We follow current literature by using PERL to perform content analysis on a firm’s 10-K filings. For example, Li (2008) uses PERL to extract MD&A text from the 10-K filings of US firms and uses PERL to measure the Fog Index. Leone et al. (2007) use PERL to analyze text on the use of IPO proceeds in IPO prospectuses. In this study, we use PERL to download 10-K filings from the SEC Edgar website and, then, calculate the two readability proxies. The details of the steps are listed below.

Step 1:

Downloading the Electronic 10-K Filings

We download all the 10-K filings (annual reports) for the period 2005-2011 from the SEC Edgar ftp website by using PERL. We also download the company index files from SEC Edgar for mapping purposes. Each company’s index file contains its name, central index key (i.e., CIK), report type, report URL, and so on. We then used the CIK code to merge the COMPUSTAT file with the SEC Edgar filing.

Step 2:

Measuring Readability

Based on the 10-K files downloaded, we calculate the two measures of readability by applying the formulas below:

$$Fog\;Index\; = \; \left( {mean\;of\;words\;per\;sentence \; + \;percent\;of\;complex\;words} \right) \times 0.4;$$
$$Table\;Space\; = \;the\;number\;of\;table\;lines\;in\;the\;10 - K\;filing/the\;number\;of\;lines\;in\;the\;10{ - }K\;filing.$$

In order to find out the total number of table lines in a 10-K filing, we follow the steps below:

  • Determine the format of a 10-K filing. If a 10-K filing we downloaded is html formatted, then the ‘function for html format’ is applied. Otherwise, the ‘function for text format’ is used.

  • Function for html format—The html language uses the tag of <table> to indicate the beginning and </table> to indicate the end of the tables. Then, we count the number of lines (i.e., table lines) that start with <table> and end with </table>;

  • Function for text format—we count all the lines with more than 50% of non-alphabetic characters (e.g., white spaces or numbers) as potential table lines (Li 2008).

  • Next, only five or more potential table lines that are connected together are counted as table lines.

  • Finally, the ‘table space intensity’ is defined as the number of table lines in a 10-K filing scaled by the total number of lines in that 10-K filing.

Appendix 2

See Table 12.

Table 12 The determinant model of report readability- First-stage regression results
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Hu, N., Liu, L. & Zhu, L. Credit default swap spreads and annual report readability. Rev Quant Finan Acc 50, 591–621 (2018). https://doi.org/10.1007/s11156-017-0639-8

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