To examine the effects of credit ratings on capital structures and debt maturity structures, the construction of a credit quality coding scheme is done by using two coding methods. The first coding scheme is based purely on the actual credit ratings of firms, while the second coding scheme includes the non-rated firms as one of the categories of credit quality. In the second coding scheme, non-rated firms are classified as the firms with relatively inferior credit quality and having less access to debt markets. Rated firms are
Table 5.3
Distribution of Firm across the Sample Period (Rated Firms Only)
Panel A: Firm-years available and their weights in the sample
Years Firm-years Percent Year Firm-years Percent
1989 1 0.1% 2000 61 7.0% 1990 7 0.8% 2001 61 7.0% 1991 10 1.1% 2002 69 7.9% 1992 15 1.7% 2003 71 8.1% 1993 21 2.4% 2004 74 8.5% 1994 26 3.0% 2005 69 7.9% 1995 29 3.3% 2006 63 7.2% 1996 30 3.4% 2007 59 6.8% 1997 36 4.1% 2008 54 6.2% 1998 40 4.6% 2009 30 3.4% 1999 48 5.5% Total 874 100%
Panel B: Number of Firms with their available data
Years of Data No. of Firms Years of Data No. of Firms 1 2 11 5 2 11 12 3 3 6 13 4 4 8 14 2 5 5 15 2 6 5 16 1 7 9 17 2 8 10 18 4 9 7 19 4 10 14 Total 104
90 assigned a numerical code from 1 to 5 based on the broad rating category (see Subsection 5.2.1.2 for more details) and non rated firms are assigned a numerical code of ‘6’. The numerical coding is assigned according to the actual rating of the firms starting from ‘1’ to AA+, AA and AA- rated firms (the highest broad category available in the sample), numerical code of 2 to the second broad category of A+, A and A-, to ‘5’ to B+, B and B- rated firms (the lowest broad category available in the sample). This is followed by the last numerical code ‘6’ to non-rated or NR firms. The non-rated firms are considered to be of lower quality or firms with low creditworthiness due to various reasons. For example, there
might be a self-selection bias in the rating process (Adam et al., 2003). Prior to being rated,
the management of the rated firms may be aware and are confident that their firms would have a favourable outcome and thus they are more likely to get ratings from external rating agencies. In such cases, it can be assumed that firms whose management considers their credit quality to be superior are more likely to get rated. Stated differently, rated firms have better creditworthiness than non-rated firms.
Table 5.4
Industrial Classification of Rated Firms Total No. of Rated
Firms
Total Weight of Firms within the Sample (%)
Basic Materials 7 6.73 Consumer Goods 14 13.46 Consumer Services 30 28.85
Health Care 3 2.88
Industrials 18 17.31 Oil & Gas 4 3.85
Technology 2 1.92
Telecommunications 11 10.58
Utilities 15 14.42
Total 104 100
Apart from assigning the lowest code to the non-rated firms due to their creditworthiness, these firms may also face constrained debt capacity. For example, Lemmon and Zender (2010) argue that firms with credit ratings, whether investment or speculative grade, are not constrained by the debt capacity, while firms without a credit rating may face difficulties in raising the amount of leverage they require. This implies that firms that have speculative grade ratings are expected to be better-off in terms of debt capacity than their counterpart non-rated firms. For example, a number of studies, using ‘rating status’ as a proxy for debt capacity (e.g., Faulkender and Petersen, 2006; Judge and Mateus, 2009; Mittoo and Zhang, 2010), find that rated firms have higher leverage in comparison to non-
91 rated firms. Moreover, the rated firms have also an option to disclose or not their ratings to the public (Standard and Poor’s corporate rating criteria, 2008) and it is more likely that high rated firms disclose their ratings to the public more often. Given that the present sample contains only firms that have disclosed their ratings, these firms are expected to be of superior quality than remaining firms not included in the sample of rated firms. A similar procedure has also been followed by Stohs and Mauer (1996) who argue that non- rated firms have high liquidity risk but constrained access to long-term debt markets.
Although non-rated firms in the UK market may be considered as having constrained
access to public debt markets (the rating effect as suggested by the CR-MA hypothesis), it
can be argued that they may not be strictly classified as firms with poor credit quality such as in the case of the US market. Since possession of credit ratings is more prevalent in the US market, firms that do not possess credit ratings in the US market can be classified as firms having poor credit quality. However, in the UK market, the assumption about credit quality may not always hold, as several firms that do not acquire credit ratings cannot be strictly classified as low creditworthy firms. Nevertheless, in relative terms, rated firms can still be expected to have better access to public debt market than non-rated firms where public debt arguably have longer maturity than other sources of financing (e.g., bank loans). The results of the present study, as will be discussed further in Section 6.2.2 of Chapter 6 and Section 8.2.1 of Chapter 8, provide strong evidence for the validity of the assumption after controlling for several firm-level characteristics. It should also be noted that endogenous relationship in this case is also inevitable. Firms who do not want to access public debt market are not likely to have credit ratings. The results for non-rated firms should, therefore, be interpreted with caution.
To obtain a sample of non-rated firms, Datastream codes DEADUK 1-7 are used to extract the data for dead firms while Datastream code ‘FBRIT’ is used to extract data for all active firms. The data similar to rated firms extend over the period between 1989 and 2009. The initial sample contained 8,967 firms out of which 7,049/1,918 are dead/active firms (See Table 5.5 for sample selection steps). These firms are classified into 10 industries (Datastream code: ICBIN) to identify the financial firms. The classification reduces the number of firms to 6,013. A total of 1,320 firms with no industry classification available and 1,462 firms with missing accounting information during the sample period are also excluded from the sample.
92 As most of the explanatory and control variables are scaled by total assets, any firm that reports ‘zero’ total assets or where the data are not available for total assets had to be excluded from the sample. Out of the remaining 4,551 firms, 53 firms are deleted on this basis. Firms with no information available on total debt are also excluded although firms reporting ‘zero’ total debt remains part of the sample as they convey meaningful information. From the remaining sample, 285 firms were identified which had similar data but reappear with different codes. Such firms may distort the analysis by their double or triple inclusion and may underestimate the effects of explanatory or control variables, specifically for rated firms.
Table 5.5
Sample Selection Procedure
Dead Active Sub-Total Total
Initial Sample 7,049 1,918 8,967 8,967 (-)Firms with no industry classification available 1,319 1 1,320
Financial Firms 1,147 487 1,634 Firms with no accounting data available 1,462 Firms with no total assets information
available
53 Firms with no total debt information available 21 Firms included more than one time 285 Firms Rated by FITCH 8 Firms Rated by Moody’s 3
Rated firms with previous names appearing as
separate firm 12 (4,798)
Final sample containing firms rated
by Standard and Poor’s 2,868 1,301 4,169 4,169
(-) Firms rated by Standard and Poor’s 47 57 104 104
Non-rated Firms 2822 1,244 4,065 4,065
Firm-Year Observations 42,872
To ensure that the sample only comprises of non-rated firms, firms rated by the other two major rating agencies Moody’s and FITCH, are also eliminated from the sample. The
credit rating data of the two rating agencies is extracted from their official databases.17 As
discussed earlier, these three credit rating agencies (Standard and Poor’s, Moody’s and FITCH) have the largest share of rating business in the UK and it is believed that the chances of error due to inclusion of rated firm by other small agencies is minimal.
FITCH only reports the current issuer rating data on their website and no historical information is available. There are potential chances of error that firms, which were
17
93 historically rated by FITCH, are not identifiable in the current database and are therefore not removed from the non-rated sample. However, after matching FITCH currently rated firms with Standard and Poor’s and Moody’s datasets, it is noted that most of the FITCH rated firms have also Moody’s and/or Standard and Poor’s ratings. This is likely to reduce any chances of erroneous inclusion of rated firms in the non-rated sample as those firms become part of the Moody’s or Standard and Poor’s sample set.
A total of 131 firms are rated by FITCH as on September 2009 out of which 48 firms have accounting data available from Datastream. FITCH ratings information contains details about the CUSIP, Ticker’s, SIC code, GICS, NAICS of firms. Each firm is individually matched with Datastream firms ID’s. Eight firms were identified as having ratings from FITCH but not Standard and Poor’s and Moody's and are excluded from the sample. Data for firms rated by Moody’s are also collected from their official website. A similar procedure is followed to eliminate rated firms from the sample. Three firms were identified which do not have ratings from Standard and Poor’s or FITCH and are also removed.
Out of the remaining 4,181 firms, twelve firms were identified from FITCH, Moody’s or Standard and Poor’s databases with a different previous name. It is difficult to identify when they received a rating status or whether the rating status was before or after the change of their name. Therefore, these firms are excluded from the sample to ensure that the sample for ‘NR’ or code ‘6’ only contains firms not rated by Standard and Poor’s,
Moody’s or FITCH. The final sample consists of 4,169firms whichincludes the 104 firms
rated by Standard and Poor’s. The total number of non-rated firms in the sample is 4,065. The final sample of 4,196 firms, without the exclusion of the 104 Standard and Poor’s firms, is dominated by the industrial sector followed by consumer services sector (see Table 5.6). The utility sector only carries 2.3% weight (lowest) in the total sample, the inclusion of which is not expected to change the results significantly. To minimise any potential impact of the industry-level heterogeneity, industry dummies are also incorporated in the model (discussed in detail in Subsection 5.2.1.3).
The final dataset is an unbalanced panel dataset containing 42,872 firm-years observations. It should be noted that the sampling procedures followed in this study are more reliable than a majority of prior studies, as there are no minimum criteria for the firms to be selected in the sample. For example, Ozkan (2001) only included firms with at least 5 years of data available, De Miguel and Pindado (2001) for at least 6 consecutive years, and
94 Titman and Wessels (1988) and Auerbach (1985) amongst others, also excluded firms which did not have continuous data within the sample period. Prior studies, therefore, bias their sample towards large capitalisation firms. For this reason, the sample used in this study is large compared to that employed by previous studies on capital structure and debt maturity for UK firms (Ozkan, 2000, 2001 and 2002; Bevan and Danbolt, 2002 and 2004; Rajan and Zingales, 1995). This study, therefore, aims to provide results for a wider market capitalization of firms.
Table 5.6
Industry Classification of Sample Firms Dead Activ e Total % Weight Basic Materials 284 155 439 10.53 Consumer Goods 376 100 476 11.42 Consumer Services 670 239 909 21.80 Health Care 194 97 291 6.98 Industrials 728 374 1102 26.43 Oil & Gas 116 126 242 5.80 Technology 341 159 500 11.99 Telecommunications 27 87 114 2.73 Utilities 71 25 96 2.30
Total 2807 1362 4169 100