As discussed in Section 5.1.1, two data sets are used in the study: a whole sample of rated and non-rated firms (42,872 firm-year observations) and a sample with rated firms only (874 firm-year observations). Both datasets require different outlier treatment procedures depending on the size of sample, as is discussed further below.
The initial sample contained 42,872 firm-year observations. As was discussed in the previous chapter, the sample selected for the study contains several outliers. These extreme values can be due to incorrect data entry, or might be correct values which are distant from the rest of the observations. Such observations may cause potential problems in the analyses by influencing the results. Further, it may increase the heteroskedasticity problem in the models and thus make the results biased (Gujarati, 2004, p.390). Since the control variables proposed in the study are mostly in the form of ratios, which have a tendency to be skewed, flat or dominated by issues of sample variance (Frecka and Hopwood, 1983), they are likely to decrease precision in the study irrespective of the fact that the present
study uses a large dataset.22
Various techniques can be employed to resolve the concern of extreme values in the data, including winsorising, trimming, and transformation into logarithmetic, squared or inverse form. In the literature of capital structure, studies do not generally use transformation of data and tend to use winsorising and trimming, depending on the sample size. It is argued that the issue of extreme observations may not be completely resolved by simple log or square transformation (Deakin, 1976; Frecka and Hopwood, 1983) and that elimination of the outliers is possibly a better solution (Frecka and Hopwood, 1983). Conversely,
22 Gujarati (2004) argues that when the data set is small, outliers’ inclusion and exclusion may have a more
123 Bollinger and Chandra (2005) and others argue that trimming or winsorising data may induce bias in the data.
Previous studies do not suggest any consistent procedure for the detection and treatment of outliers. However, two measures are relatively common in the literature: eliminating the outliers by winsorising at certain percentages, or deleting specific (or a certain percentage of) observations. Titman and Wessels (1988) and Barclay and Smith (1995) have trimmed the data at certain percentages, whereas others (Bevan and Danbolt, 2002; Johnson, 2003;
Aivazian et al., 2005; Frank and Goyal, 2009) have winsorized the variables at various
percentages. For the whole sample, data trimming is used, which appears to be superior compared to winsorising given that the study utilises a large sample size. Moreover, theoretical and empirical literature support the use of this method (Frecka and Hopwood,
1983; Titman and Wessels, 1988; Barclay and Smith, 1995; Booth et al., 2001; Baker and
Wulgler, 2002; Alti, 2006; Frank and Goyal, 2009). To maintain the integrity of data, most of the variables are trimmed at 0.50% at either or both sides of the distribution. This percentage would only remove the most extremely misrecorded data; using a higher percentage may induce bias in the sample. A summary of the method followed to identify outliers and treatment and the effect outlier treatment on the number of observations is presented in Table 5.8 and Table 5.9.
The first dataset contains 4,169 firms over an extended period of 22 years. Visual aids such as scattered plots and box plots and logical reasoning are used to assess the outlying observations. In the case of the debt ratio, outcomes of more than one and less than zero do not theoretically make sense, as the assets have to be financed by equity or debt or by a combination of both. The possible reason for this may be that firms are experiencing negative equity or there might be recording error in the database. Likewise, the total debt maturity ratio may be composed of either short-term debt or long-term debt, or both, and cannot exceed the total debt. Following previous studies (Baker and Wulgler, 2002; Alit,
2006; Aivazian et al., 2005) such firm-year observations are discarded from the sample.
For the MB ratio (MBR), the quality of firm (QUAL), the profitability ratio (PROF), the assets maturity ratio (AMAT) and the liquidity ratio (LIQD), 0.50% of both tails or either tail are discarded depending on the distribution of outlier, while for the fixed assets ratio (FAR), values above 1 and below 0 are discarded. Only the top three values are discarded which were found to be greater than one. After trimming the profitability ratio by 0.50%,
124 the dataset still contained values above +1 or below -1. Thus, all values above or below one are discarded. Berger and Ofek (1995) also truncate the values lying above or below 1 and -1 respectively.
The second dataset contains only 104 firms, with 874 firm-year observation. Relative to the first dataset containing rated and non-rated firms, this dataset is very small and contains few outliers. The size of the dataset does not allow trimming as this would seriously affect the sample size. In this case, therefore, winsorising is used for extreme values, using a 0.50% cut off where variables have extreme observations. The profitability (PROF) and the market to book ratio (MBR) are winsorised at both tails while the liquidity (LIQD) is winsorised at right tail. Similar to the procedure used for the combined sample of rated and non-rated firms, profitability is further truncated at ±1.
For the analyses of potential and actual rating changes, the dependent variables (change in debt ratio (CDR) and Kisgen’s debt Ratio (KDR)) had a few observations greater than ±1 which do not make theoretical sense. Therefore, they are truncated to ±1. Debt issuance and reduction ratios had also a few observations above 1 which are truncated to 1. Lagged
change in the fixed assets ratio (∆FARt-1) and the market to book ratio (∆MBRt-1) do not
indicate any outlying observation, but lagged change in log of sales (∆LOSt-1), liquidity
(∆LIQDt-1) and profitability (∆PROFt-1) had a few extreme observations at both ends and
therefore they have been winsorised at 0.50%. A detailed analysis on the effects of outlier treatment is presented within each empirical chapter.
5.4. Conclusion
This chapter presented the research design and methodology of the present study. It began by discussing data sources, followed by a detailed discussion of the sampling procedures. The study utilises two types of data: Standard and Poor’s long-term issuers’ ratings and accounting data sourced from Datastream. The final sample consists of 4,169 firms over a period of 22 years (42,872 firm-years), from which 104 are rated firms and 4,065 are non- rated firms. The chapter also discussed various elements of the models used in the study. It explained in detail the methodological choices made for measurements of the proxies for the dependent, independent and control variables, along with the model specifications for the four main research questions of the study.
125
Table 5.8
Variables, Outlier Treatment and its Effects (Rated and Non-Rated Firms)
Variables Definition Outlier treatment Number of Observations Before and After Outlier Treatment Before After Difference
Panel A: Credit Rating and Capital Structure Analysis
TDTA total debt to total assets Values above +1 and below -1 are
discarded
42,872 42,346 436
LOS natural logarithm of sales do not suggest the presence of outliers
40,971 40,971 0
PROF earnings before interest, taxes and depreciation, to total assets
trimmed at 0.50% at each tail and used ±1
criteria
42,869 41,767 1,103
FAR fixed assets ratio (FAR) is the ratio of fixed assets to total assets
Values below zero and above one are
discarded
42,872 42,869 3
MBR book value of the assets minus the book value of the equity minus market value of equity divided by book value of assets
trimmed at 0.50% at each tail
42,851 42,423 428
LIQD current assets to total assets. trimmed at 0.50% at right tail
42,056 41,846 210
Panel B: Credit Rating and Debt Maturity Structure Analysis
DMR total long-term debt
(payable in more than one year) to total debt
do not suggest the presence of outliers
37,405 37,405 0
LOS natural logarithm of sales do not suggest the presence of outliers
40,989 40,989 0
QUAL difference between earnings before interest and taxation EBITt+1 and EBITt scaled by share price at time t
trimmed at 0.50% at each tail
38,024 37,644 380
AMAT total property, plant and equipment to total annual depreciation
trimmed at 0.50% at right tail
30244 30,093 151
MBR book value of the assets minus the book value of the equity plus market value of equity divided by book value of assets
trimmed at 0.50% at each tail
42,872 42,444 428
ETR total amount of tax
charged by total taxable income
winsorised at 0 and 1 42,345 42,345 0
126
Table 5.9
Variables, Outlier Treatment and its Effects (Rated Firms Sample)
Variables Definition Outlier treatment No. of
Observation Affected
Panel A: Credit Rating and Capital Structure Analysis
TDTA winsorised using ±1 criteria 9
LOS do not suggest the presence of outliers
0
PROF 0.50% of both tails and used ±1
criteria
17
FAR Values below zero and above one are winsorized to 0 and 1
respectively
3
MBR winsorised at 0.50% at each tail 8
LIQD winsorised at 0.50% at right tail 8
Panel B: Credit Rating and Debt Maturity Structure Analysis
DMR do not suggest the presence of outliers
0
LOS do not suggest the presence of outliers
0
QUAL winsorised at 0.50% at each tail 8
AMAT winsorised at 0.50% at each tail 8
MBR winsorised at 0.50% at each tail 8
ETR winsorised using 0 and 1criteria 19
Panel C: Credit Rating Changes and Capital Structure Analysis
CDR first difference in the total debt scaled by previous year’s total assets
winsorised using ±1 criteria 2
KDR (long term borrowings minus long-term debt reduction plus increase/decrease in short-term borrowings)-(net proceeds from sale/issue of common stock minus common/preferred
redeemed, retired, converted etc) scaled by previous year’s total assets
winsorised using ±1 criteria 3
DITA long-term borrowing by the total assets of previous year
winsorised using 0 and 1 criteria 3
DRTA long-term debt reduction by the total assets of previous year
winsorised using 0 and 1 criteria 2
EITA net proceeds from sale/issue of common stock by the total assets of previous year
winsorised using 0 and 1 criteria 0
ERTA common/preferred redeemed, retired, converted etc by the total assets of previous year
winsorised using 0 and 1 criteria 0
∆LOSt-1 lag of first difference in the log of sales winsorised at 0.50% at each tail 8
∆PROFt-1 lag of first difference in the profitability winsorised at 0.50% at each tail 8
∆FARt-1 lag of first difference in the fixed assets
ratio
do not suggest the presence of outliers
0
∆MBRt-1 lag of first difference in the market to
book ratio
do not suggest the presence of outliers
0
∆LIQDt-1 lag of first difference in the liquidity
ratio
127
To test the implications of the credit rating – capital structure hypothesis (CR-CS), credit
ratings are incorporated into previously tested models for capital structure and debt maturity structure. Specifically, to test the relationship between levels of credit ratings and leverages, quadratic regression models are proposed in order to capture the hypothesised non-linearity between the two variables. As the quadratic regression models are used for the first time to analyse the impact of credit ratings on capital structure, they are likely to extend the limited view of the relationship between the two variables. This will ensure correct inferences about the capital structure determinants and will provide a comprehensive depiction of actual capital structure decision making behaviour. These models are tested in Chapter 6.
The chapter also presented the methodology followed to test the implications of the credit
rating – capital structure hypothesis for the relationship between credit rating changes and the capital structure decisions of firms. Despite an extension of Kisgen’s (2006 and 2009) studies to a different market, the proxies of rating changes are incorporated in models which have been specifically tested in the UK market. However, control variables in the model are measured as lagged change variables due to the nature of the dependent variables. For consistency with Kisgen (2006, 2009), similar models are also tested to ensure direct comparison with US studies. These models are tested in Chapter 7.
Finally, the chapter also presented a detailed discussion of the measurement of variables and the model proposed to test Diamond’s 1991 liquidity risk hypothesis. The proposed relationship between credit ratings and the debt maturity structures of firms are modelled using a quadratic regression model, which is likely to capture the non-linear relationship hypothesised between the two variables. Chapter 8 provides empirical results for the model proposed.
128
Chapter 6
Credit Ratings and Capital Structure
6. Introduction
The overall objective of this chapter is to present and discuss a detailed empirical analysis of the potential impact of credit ratings on the capital structures of UK firms. Since early 2000, there has been a growing theoretical and empirical literature on the importance of credit ratings for the financial structure of the firms. Specifically, after Graham and Harvey’s (2001) survey study, highlighting the relevance of credit ratings for capital structure determination, a shift can be noted towards exploring the influence of credit ratings on capital structures and their components. However, so far studies are largely concentrated on the US market and provide limited insight into the relationship and its applicability to other markets, which are also actively using credit ratings. In this regard, the hypotheses presented in Chapter 3 extend the previous studies investigating the credit rating – capital structure relationship. Unlike previous studies, which implicitly or explicitly postulate a linear relationship between credit ratings and capital structures, the present study predicts a non-monotonous relationship between the two variables, as discussed in Section 3.3.1. This chapter specifically provides detailed univariate and multivariate analyses to examine the hypothesised relationship between credit ratings and the capital structures of UK firms.
The present chapter is divided into four main sections. Section 6.1 provides a detailed description of the selected sample to analyse the impact of credit ratings on the amount of leverage in firms’ capital structures. It also discusses in detail the potential issues concerning the estimation techniques and limitation of the methodologies with the possible remedies to address those concerns. Section 6.2 presents a multivariate analysis of the impact of credit ratings on overall capital structures to affirm or negate the non-linearity hypothesis for credit ratings and capital structures. Section 6.3 presents and discusses the robustness and sensitivity checks against the alternative coding schemes and estimation techniques. Particular emphasis will be placed on addressing the potential reverse causation of credit rating in the models. Section 6.4 concludes the chapter.
129