Impact of Readability on Corporate Bond Market

This paper investigates the impact of annual report readability on the corporate bond market. My findings indicate that in the US corporate bond market, firms with less readable annual reports tend to have higher credit spreads, higher credit spread volatilities, higher transaction costs, higher transaction costs volatility, smaller trade size, higher number of trades and higher number of trades volatility. This paper also provides the first answers to the question as to whether annual report readability matters to international market participants in the corporate bond market. My findings show evidence that in the EUR corporate bond market, firms with more readable annual reports are associated with lower credit spreads.


Introduction
Annual reports are very important information sources concerning publicly traded companies. Investors and analysts expend a lot of effort in the analysis of annual reports in order to obtain a true and complete view of the firms. Therefore, readability of annual reports is crucial for market participants. However, in the Wheat Report (1969), the Securities and Exchange Commission (SEC) complained about the complex language used in mandatory filings, and requested firms to improve the readability of their filings. Based on Arthur Levitt's remarks to the Securities Regulation Institute in 1998, the SEC adopted the plain English regulation to improve the readability of financial reports. Despite the efforts of the regulator, the size of annual reports has increased strongly over the past 20 years. 2 Furthermore, annual reports from different firms show very different levels of readability, which might be explained by the respective characteristics of the firms.
There is a growing amount of academic literature examining the relationship between annual report readability and equity market variables, e.g. the impact of readability on future performance and earnings persistence (Li 2008), firm investment efficiency (Biddle, Hilary, and Verdi 2009), analyst coverage and dispersion (Lehavy, Li and Merkley 2011), trading behavior of equity investors (Miller 2010/ Lawrence 2013, and return volatility and earnings forecast errors (Loughran and McDonald 2014). For debt markets, there is only a very limited amount of academic study available concerning the readability of annual reports. Most of the papers analyze the association between readability and pricing of debt securities (corporate bonds, bank loans and CDS).
This paper contributes to the existing literature in the following ways: First, I do not only analyze the relationship between readability and credit spread of corporate bonds, but I also provide the first evidence for the impact of annual report readability on spread volatility, transaction costs, transaction costs volatility, trading volume and trading volume volatility in corporate bond markets. I find that bonds issued by firms with less readable 10-k filings (i.e. larger Total Number of Words) are significantly associated with higher subsequent spreads, higher spread volatilities, higher transactions costs, higher transactions costs volatility, smaller trade size, higher number of trades and higher number of trades volatility.
Secondly, in the corporate bond market firms can issue corporate bonds in different currencies, which means that a group of firms that submit mandatory filings to the SEC also have corporate bonds in other currencies than USD. This allows me to conduct the first international examination regarding the impact of readability. The questions of interest are whether annual report readability matters to international market participants, and how it affects their evaluation of a firm's credit risk. I find that investors in EUR corporate bonds also consider annual report readability in their pricing logic.
Firms with less readable annual reports tend to have higher spreads.
Finally, I provide further evidence that the Fog Index is not an appropriate proxy for readability of financial filings with corporate bond data. I find that one element of the Fog Index (percentage of complex words) shows a significant negative impact on corporate bond spreads. This result is not unexpected and is in line with the findings of Loughran and McDonald (2014), who provide similar evidence for equity market data.
Electronic copy available at: https://ssrn.com/abstract=3412831 The paper proceeds as follows. In section 2, I summarize the relevant literature.
Section 3 defines the readability measures and describes the data used. Section 4 develops hypotheses and reports the empirical findings. Section 5 concludes the paper.

Readability literature in the equity market
There are a large number of empirical papers that analyze the impact of annual report readability on equity markets. Li (2008) provides the first large-sample evidence on determinants of the readability of 10-k filings, and the relationship between 10-k readability and future performance and earnings persistence. Li (2008) uses two readability measures, Fog Index and Total Number of Words. His findings are in line with the motivation behind the plain English disclosure regulation of the SEC.
Companies may be opportunistically choosing the readability of the annual report to hide adverse information from investors. Li (2008) finds that companies with lower earnings tend to publicize more complicated annual reports, and companies with more complicated annual reports show a lower persistence of earnings when they are profitable. Biddle et al. (2009) examine the impact of readability on a firm's investment efficiency by using the Fox Index. They find evidence that the quality of financial reporting positively influences the capital investment efficiency. Lehavy et al. (2011) investigate the relationship between readability, analyst coverage and dispersion by using the Fog Index. They find that firms with less readable annual reports tend to have higher analyst coverage, greater analyst dispersion, lower accuracy, and greater overall uncertainty in analyst earnings forecasts.
Electronic copy available at: https://ssrn.com/abstract=3412831 Miller (2010) and Lawrence (2013) focus on the relationship between readability and trading behavior of equity investors. Miller (2010) finds that firms with less readable annual reports tend to be less traded by equity investors. This is caused by a decrease of trading activity by small investors (trades less than or equal to $5,000 volume). Lawrence (2013) uses discount brokerage data of individual small investors, and finds that individual investors invest more money in firms with more readable annual reports. Loughran and McDonald (2014) show that the traditional readability measure (Fog Index) is not a suitable readability measure for annual financial reports, and they suggest applying the "10-K file size" as a measure of readability. They argue that the element "complex words" of the Fog Index is the main issue of this measure, since there are a large number of multi-syllable words in business context which are easy for investors and analysts to understand. Therefore, the usage of multi-syllable words does not necessarily increase the complexity of financial documents. Loughran and McDonald (2014) find that the Fog Index does not show significant impact on unexpected earnings and analyst dispersion. The suggested measure "10-K file size" has a significant positive impact on return volatility, earnings forecast errors and earnings forecast dispersion.

Readability literature in the debt market
There is also some literature on the impact of annual report readability on debt markets (bank loans, corporate bonds and CDS). Ertugrul et al. (2017) analyze bank loans for the time period between 1995 and 2013, and find that firms with less annual report readability and higher percentage of uncertain words in their annual reports tend to have higher loan spreads.
Electronic copy available at: https://ssrn.com/abstract=3412831 Bonsall and Miller (2017) examine 3,659 initial bond ratings and bond offering credit spreads between 1994 and 2014. Their evidence suggests that issuers with poorer readability tend to issue bonds with worse initial ratings, and with larger bond rating disparities amongst different rating agencies. Furthermore, the bond offering credit spreads are higher for issuers with less annual report readability. Hu et al. (2018) investigate the CDS market for the time period 2005-2011. They show that firms with less readable annual reports are associated with higher CDS spreads.
The impact of readability is more pronounced for firms with high information asymmetry (e.g. firms with high growth or with high R&D expenditures) and with investment grade ratings.

Readability measures
In this paper I apply the two most common readability measures: Fog Index and Total Number of Words. The Fog Index is developed by Gunning (1952) and is very widely used in various academic fields. The value of Fog Index can be interpreted as number of education years needed to understand the text after the initial reading. The definition of Fog Index is as following:

= . × (Number of words per sentence + Percent complex words)
The two elements of the Fog Index are average number of words per sentence in the entire text, and percentage of complex words (words more than 2 syllables) of all words.
The idea behind the definition is that a text with longer sentences and more complex words is associated with less readability.
The second measure is Total Number of Words, which is defined as

Hypotheses
In this study I analyze the impact of the readability of annual reports on the subsequent corporate bond spreads, corporate bond transaction costs and trading behavior of corporate bonds. Li (2008) finds that firms attempt to hide adverse information from investors by increasing the complexity of their annual reports. More complex annual reports require more efforts and time by investors to possess the information. It is also more difficult for investors to evaluate and interpret the information of annual reports (Bloomfield 4 The EUR universe makes up only 11% of the entire senior, unsecured, bullet investment grade EUR-denominated corporate bonds in Merrill Lynch Global Corporate Index. The reason is that I have only 10-k filings for publicly traded firms in US. Annual reports for firms with EUR-denominated bonds and without 10-k filings are not available.

2002)
, which leads to a higher information risk and higher uncertainty in the forecast of future cash flows and default risk of the firms. Therefore, corporate bond investors require higher compensation for the greater information risk and uncertainty. Huang and Yu (2010) and Korteweg and Polson (2010) add the information uncertainty in the structural model framework. They show that information uncertainty impacts the bond pricing, and therefore the corporate bond spread. Accordingly, I hypothesize the following: H1: firms with less readable annual reports are associated with higher corporate bond spreads. This relationship holds for USD investors as well as EUR investors. Guo et al. (2017) examine the relationship between uncertainty and liquidity of corporate bond market. They find that information uncertainty influences the liquidity of the corporate bond market. Corporate bonds with greater information uncertainty are associated with lower trading volume, and higher bid/ask spreads. They argue that in case of high information uncertainty, corporate bond investors are not confident with their corporate bond valuation. This reduces willingness to trade, and therefore lowers the trading volume. Additionally, information uncertainty influences the corporate bond dealers. Bond dealers are not willing to make a market for firms with high information uncertainty, due to the high probability of pricing errors. Therefore, I test the following hypothesis: H2: firms with less readable annual reports tend to have lower liquidity, which means lower trading volume, higher transaction costs, and smaller trade size. Lehavy et al. (2011) analyze the relationship between the readability of annual reports and analyst earnings forecasts. They argue that complex annual reports lead to disagreement or ambiguity among analysts. Accordingly, they find that firms with less readable annual reports are associated with greater analyst dispersion, lower accuracy, and greater overall uncertainty in analyst earnings forecasts. For the corporate bond market, Bonsall and Miller (2017) find that firms with less readable annual reports are associated with higher probability of split ratings (Moody's and S&P) on the same issuance and exhibit a greater difference between the ratings of the two rating agencies.
This disagreement among equity analysts and bond rating analysts can cause -or intensify -the disagreement among investors and dealers concerning pricing. These arguments lead to the following hypothesis: H3: firms with less readable annual reports are associated with higher credit spread volatility, higher volatility of transaction costs, and higher volatility of trading behavior (trading volume / number of trades).

Multivariate Results for USD Universe
This section reports the empirical results of the impact of readability on Option Adjusted Spreads (OAS), transaction costs, trading volume, number of trades, and trade size and volatility of variables above. Table III shows the results of multivariate regressions for the impact of readability on spreads in the USD universe.

Impact of readability on OAS
Insert Table III about here For this research question, the following equation is used: Electronic copy available at: https://ssrn.com/abstract=3412831 The dependent variable is the OAS of a corporate bond at the end of each month.

Readability measures are the natural logarithm of Total Number of Words and the Fog
Index. The readability measures of certain 10-k filings are matched to the 12 months following publication of the filings. 5 The control variables are based on prior literature regarding determinants of corporate bond spreads. Merton's (1974) structural model shows that asset volatility (historical volatility of the ultimate parent companies' stocks is used as proxy for asset volatility) and leverage ratio (Debt / Enterprise Value) are very important spread determinants. I also include profitability (Ebitda / Total Assets) and size (natural logarithm of the market capitalization) of the ultimate parent companies. Two bond characteristics -rating score and modified duration -are included. Rating score varies between 1 and 10 and represents rating AAA to BBB-, respectively. The higher the rating score, the worse the rating category. AAA (BBB-) rating has a score of 1 (10). All regressions include an intercept, month fixed effect and industry sector fixed effect. Standard errors are adjusted for month and industry clusters. 10-k filings with less than 3,000 words are excluded.
In the baseline regression (1)  interpret. Therefore, the readability of annual reports can be considered as a proxy for information risk and uncertainty about the future performance and cash flows. The more readable the annual reports, the less information asymmetry and less information risk. Investors require higher compensation (higher credit spread) for the higher information risk.
The Fog Index in regression (3) also shows a significantly positive effect on the credit spread, but less significant than Log (Number of Words). In regression (4) and (5) I regress OAS on two elements of the Fog Index separately. The average sentence length shows significantly positive impact on credit spread. However, the percentage of complex words has a significantly negative estimate, which means 10-k filings with a higher percentage of complex words tend to have lower credit spreads. This is not consistent with the hypothesis 1. This is in line with the findings by Loughran and McDonald (2014); namely that the Fog Index is not an appropriate readability/complexity measure for business filings. The main reason is the component "percentage of complex words". Complex words are defined in the Fog Index context as words with more than two syllables. Loughran and McDonald (2014) argue that in annual reports there are many complex words which are easy to understand and do not make the text less readable, e.g. words like company, financial, agreement, management. Based on the definition of the Fog Index, usage of these words leads to a high Fog Index value, which indicates less readable text. Therefore, the component "percent of complex words" might lead to a wrong assessment of business text readability. In the empirical analysis of Loughran and McDonald (2014) they find that the Fog Index has the expected significant and positive impact on subsequent volatility.
Electronic copy available at: https://ssrn.com/abstract=3412831 However, the component "percent complex words" shows an insignificantly negative impact on stock price volatility. Li (2008) finds that complexity of a company's business is positively related to the readability measures. Therefore, I include the Herfindahl Index in term of the sales in different business segments. The maximum of the Herfindahl Index is 1 which means the company has only one business segment. The smaller the Herfindahl Index, the more complex is the company's business. The results in regression (6) show the same evidence for the Total Number of Words, even after controlling for the complexity of the business. The worse readability (higher number of words) of the 10-k filings, the higher the credit spread. Investors require more compensation for greater information risk. The Herfindahl Index has a significant negative estimate, which means firms with more complex business structures are associated with higher credit spreads. The Fog Index in regression (7) does not show significant impact after controlling for the complexity of the business (T-value=1.42). Regression (9) shows again the problem with the "percent complex words"; namely, that "percent complex words" has a significantly negative impact on spreads. Based on this finding, the Fog Index is not an appropriate proxy of readability for corporate bond characteristics. Therefore, I use only Total Number of Words as the proxy of readability in the following analyses. Insert Table IV about here The following equation is used. analyst coverage and dispersion in the equity market. They find that firms with less readable annual reports are associated with greater analyst dispersion. Bonsall and Miller (2017) find that bonds with less readable annual reports tend to receive split ratings and exhibit higher difference between Moody's and S&P ratings. This disagreement among equity analysts and bond rating analysts can cause or intensify disagreement among investors and dealers concerning pricing. This is in line with the findings of spread volatility; namely, that firms with less readable annual reports are associated with higher information risk and higher dispersion, which leads to higher spread volatility in the time period following publication of the filings.

Impact of readability on OAS Volatility
The OAS volatility in the previous month has a significant positive impact on spread volatility. Bonds with worse ratings have higher spread volatility.
Insert Table V about here According to Hong and Warga (2000) and Chakravarty and Sarkar (2003), the price spread for a certain month is calculated as the average daily price spread in that month.
The daily price spread is calculated as 100 * , / , − 1 .  Table V shows that the readability measure Total Number of Words has a significantly positive impact on the price spread of corporate bonds in the month following publication of 10-k filings, which means bonds with poorer readable 10-k filings have higher transaction costs. This is evidence that corporate bond dealers also consider Electronic copy available at: https://ssrn.com/abstract=3412831 readability of the annual reports in their decisions as to whether and/or how to make a market for the bonds of certain issuers. Corporate bond dealers prefer bonds with better readability of annual reports (firms with less information risk and less uncertainty about the future performance and cash flows) and therefore lower price spreads of their bonds.  (3) (2), which means bonds with less readable filings tend to have higher number of trades volatility in the month following publication of the 10-k filings. There is no evidence of significant impact of readability on trading volume volatility. This observation is consistent with the previous finding; namely that the readability of annual reports has no significant impact on subsequent trading volume.

Impact of readability on OAS
Some of the companies which submit 10-k filings to the SEC also issue non-USD denominated bonds. In the non-USD universe there are bonds denominated in EUR, GBP, JPY, AUD and CAD which are relevant for investors in the respective currency areas. EUR-denominated bonds are the second-largest universe in the BofA Merrill Insert Table III about here Regressions (2) and (4) of Panel B show that Total Number of Words has a statistically highly significant positive impact on spread, with or without control of the complexity of business (Herfindahl Index). This is the first evidence for international investors. Based on this result, EUR investors take into account the readability of the annual report in the pricing of the firms' credit risk. Firms with less readable annual reports tend to have higher credit spreads. This result is consistent with the result for USD investors; namely that investors consider the readability of annual reports as a proxy for information risk of the firms. Firms with less readable annual reports tend to have higher information risk and therefore higher corporate bond spreads. The Fog Index in regressions (3) and (5) are not statistically significant. This is consistent with the previous finding for the USD universe and the finding of Loughran and McDonald (2014); namely that Fog Index is not an appropriate readability measure for financial filings.

Impact of readability on OAS Volatility
Panel B of Table IV reports the results for the EUR universe in terms of the impact on spread volatility 8 . The following equation is used.
In the EUR universe the readability of annual reports has a positive but not significant (T-Value=1.19) impact on excess spread volatility in the subsequent month. One reason could be that there are only a small number of observations. There are only 1,182 observations for the EUR universe.

Conclusion
In this paper, I show that on the US corporate bond market, firms with less readable annual reports tend to have higher credit spreads, higher credit spread volatilities, higher transaction costs, higher transaction costs volatility, higher number of trades, smaller trade size and higher number of trades volatilities in the month following publication of the 10-k filings. This paper also provides the first answers to the question as to whether international market participants take readability of the annual report into account when pricing a firm's credit risk. Firms with less readable annual reports tend to have higher credit spreads. This result is consistent with the result for USD investors.

Graphic I: Trace data statistics
This graph shows the percentage of trading volume from small trades (trades with volume less than $100,000) to total trading volume in each month.       The dependent variable in each regression is trading volume, number of trades and average size of each trade, in the month following publication of the 10-k filing. Log (Number of Words) is the natural logarithm of the total number of words in each filing. Definitions of control variables can be found in Appendix A. All regressions include an intercept, month fixed effect and industry sector fixed effect. Tstatistics are in parentheses, with standard errors clustered by month and industry.  The dependent variable in each regression is trading volume daily volatility and number of trades daily volatility, in the month following publication of the 10-k filing. Log(Number of Words) is the natural logarithm of the total number of words in each filing. Definitions of control variables can be found in Appendix A. All regressions include an intercept, month fixed effect and industry sector fixed effect. Tstatistics are in parentheses, with standard errors clustered by month and industry. Herfindahl index based on revenues in different industry business segments. Herfindahl index = sum of squares of percentage of revenue of individual industry segment in total revenue ExOASdailyVol(t-1,t) OAS volatility in excess of market OAS volatility based on daily OASs in time period t-1 to t Log (Days Since Issue Date) Natural logarithm of number of days since issue date of corporate bonds

Log (Market Value)
Natural logarithm of market value of corporate bonds Price Spreadt Average daily price spread of corporate bonds in month t.
Daily price spread is defined as 100*(mean(BuyPrice)/mean(SellPrice)-1) Price_Spread_STDt Standard deviation of daily price spreads of corporate bonds in month t.

TradingVolumet
Total trading volume of corporate bonds in month t NoTradest Total number of trades of corporate bonds in month t AvgTradeSizet Total trading volume / total number of trades of corporate bonds in month t VolumeDailyVolt Standard deviation of daily trading volume of corporate bonds in month t NoTradesDailyVolt Standard deviation of daily number of trades of corporate bonds in month t