Stylised Facts on Irish Corporate Balance Sheets by Rebecca Stuart1 ABSTRACT The health of the corporate sector is important for the health of the financial system insofar as non-financial corporates are an important group of borrowers from the domestic banking system. International literature suggests that profitability, liquidity and gearing are important indicators of the health of corporates. This paper documents these indicators for the Irish corporate sector. From a financial stability perspective the focus is on those companies most at risk of default — those with the lowest profitability, lowest liquidity or highest gearing — and in particular those firms in more than one of these categories. The firms most at risk of default are those with low liquidity, low profitability and high gearing. It is found that 2.7 per cent of the firms in the sample were in this cohort in 2004. An estimate of ‘debt at risk’ — the share of bank debt held by firms most at risk of default — shows that this most vulnerable cohort of firms held 1.4 per cent of total debt in 2004.

1. Introduction

2. Literature Review

The risk to financial stability from the non-financial

There are three relevant strands to the literature on corporate balance sheets. The first strand documents various indicators on corporate balance sheets, while the second strand uses these indicators to estimate the probability of failure. The third strand examines the banking sector’s exposure to those firms most at risk of default.

corporate sector usually focuses on the link between corporates and the banking sector. In particular, firms that default on their loans may pose a risk to financial stability. While banks expect a certain level of defaults, widespread default on loans could have a destabilising effect on the banking system. The ability to meet debt commitments is determined by the health of corporates. An assessment of firms’ health is therefore essential in any evaluation of financial stability. International literature suggests that measures of profitability,

liquidity

and

gearing

are

important

indicators of the health of corporates. This paper compiles these indicators for Ireland using micro-data from company balance sheets. The focus is on those companies most at risk of default — those with the lowest profitability, lowest liquidity or highest gearing — and in

Benito and Vlieghe (2000) use data on UK corporates’ balance sheets to develop profitability, liquidity and gearing indicators. These indicators are used to analyse the health of the corporate sector over the period 1974 to 1998. This involves the estimation of the distributions and persistence of the indicators to isolate those firms most at risk. Benito and Vlieghe also examine the coincidence of indicators, and the experience across time of those firms most at risk. Some of the analysis techniques of Benito and Vlieghe will be used in this paper.

particular those firms in more than one of these categories (e.g., low profitability and low liquidity). This paper proceeds as follows. In the next section, there is a brief overview of the literature in the area. Section 3 describes the data. There are two parts to the analysis. The first part, Section 4, analyses the distribution and persistence of the indicators. The second part, Section 5, applies the statistics developed in Section 4 to answer the financial stability question of what percentage of total debt is held by those firms with relatively greater risk of default. Section 6 concludes. 1

Literature on corporate liquidations illustrates the importance of these three indicators in measuring corporate sector health, as they are significant explanatory factors behind companies’ propensity to fail. There are two strands to the corporate liquidations literature. The first examines macro determinants (e.g., GDP, real interest rates and real wages) of company failure [see, for example, Vlieghe (2001)]. The second examines micro, or firm-level, determinants and is particularly relevant to the analysis of this paper. Lennox (1999) finds that profitability, liquidity, capital gearing, company size and industry sector are all significant

The author is an economist in the Monetary Policy & Financial Stability Department. The views expressed in this article are the personal responsibility of the author and are not necessarily those held by the CBFSAI or the ESCB. All remaining errors and omissions are the author’s. The author would like to thank her colleagues within the CBFSAI for invaluable assistance in completing this paper. Financial Stability Report 2006

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determinants of company failure. Lennox finds that increases in profitability and liquidity reduce the probability of failure, while increases in capital gearing increase the probability of failure. Geroski and Gregg (1997) find that the debt-to-assets ratio, employment, and certain profit measures are significant determinants of the liquidations rate. Bunn and Redwood (2003) find a negative relationship between profitability, interest cover and liquidity and corporate liquidations, while the debtto-assets ratio has a positive relationship with corporate liquidations. These indicators can also be used to estimate debt at risk. Debt at risk is an estimate of the expected value of defaulted loans [Benito, Whitley and Young (2001)]. It was used by Benito et al to assess the risks arising from banks’ exposures to the UK corporate and household sectors. Kearns (2004) used firm-level data and documented a slightly modified version of debt at risk for Irish companies. The key message from this work was that though corporate debt at risk increased somewhat in the early-2000s in comparison with the early- and mid1990s, the majority of corporate debt is concentrated in a small number of firms and any change in the financial condition of these firms has a disproportionately large impact on the debt at risk measure.

3. Data The sample of companies used in this analysis is taken from the FAME database of UK and Irish company accounts. The data cover the period 2000 to 2004. To identify those corporates that operate primarily in the Irish economy, the sample includes only firms with trading addresses in Ireland.2 The subsidiaries of some foreign multinational corporations were present in this initial sample. These companies were removed from the sample for a number of reasons. First, the financial health of these companies may not be primarily dependent on the Irish economy, but on their parent company’s health. Second, their links to the Irish financial system are unlikely to be significant as many of these subsidiaries might source funds from their parent. The final sample is an unbalanced panel of Irish companies. In each year, only companies with data for all the indicators are selected. The same companies are therefore not present in each year because either a company does not record information on all the indicators in all the years, or sometimes a company fails 2

during the period of analysis. As a result, the number of firms varies from 1,126 in 2003 to 915 in 2004. There is also a significant lag between companies filing accounts at year-end and the data becoming available electronically; therefore, the latest year is 2004.

4. Descriptive Analysis: Indicators of Corporate Health In this section the approach outlined by Benito and Vlieghe (2000) is used to examine two indicators from each of the areas of profitability, gearing and liquidity from three perspectives. First, the percentile distributions of these indicators over the period 2000 to 2004 will be estimated for each indicator individually. Second, we identify those firms performing worst according to each indicator — that is, those in the lower percentiles for liquidity and profitability and the highest percentiles for gearing. The experience of these firms is of particular interest because they are most at risk of default. Third, transition matrices are used to determine whether firms tend to remain in the same part of the distribution from one year to the next. 4.1 Profitability There is a wide distribution in profitability among firms and firms can move from being among the most profitable to being among the least profitable relatively quickly. The two profitability indicators used are return on capital and the profit margin. Return on capital is calculated as: Return on capital = profit (loss) before tax/(total assets-current liabilities). Chart 1 shows the distribution of the return on capital. The median firm (50th percentile) shows a slight increase over the period. Those firms with the lowest return on capital are of most interest as they are at a relatively higher risk of default ceteris paribus. There has been an overall increase in the lower percentiles over the period with those firms with the lowest return on capital converging towards the median, though the least profitable firms have remained loss-making (as shown by the 10th percentile line). Chart 2 shows the distribution of the second profitability indicator, the profit margin: Profit margin = profit (loss) before tax/turnover.

The alternative classification is firms that have a registered office address in Ireland. These are excluded as many may not necessarily carry out the majority of their business in Ireland.

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Chart 1: Percentiles of Distribution of Return on Capital

Chart 2: Percentiles of Distribution of Profit Margin 70

per cent

30

per cent

60

25

50

20

40

15 30

10 20

5 10

0

0

-5

-10 -20 2001

02

03

90th percentile

75th percentile

25th percentile

10th percentile

04

-10 2001

Median

Source: Bureau Van Dijk and author's calculations

02

03

90th percentile

75th percentile

25th percentile

10th percentile

04 Median

Source: Bureau Van Dijk and author's calculations

The profit margin indicator shows a similar trend to return on capital. There has been a slight increase in the median profit margin over the period. The lowest percentiles have also experienced an overall increase but despite this more than 10 per cent of firms experienced a negative profit margin (i.e., suffered losses) in 2004. A firm’s ranking in terms of profitability can change from year to year. However, the percentile methodology aggregates over the experience of individual firms, leaving open the question of whether the same firms are persistently the most or least profitable across time (i.e., they are in the same part of the distribution across time). To address this, the transition matrix (Table 1) shows the proportion of firms in each of five parts (quintiles) of the

distribution and whether they are in the same part of the distribution the following year. The aim of the transition matrix is to illustrate the persistence of companies’ profitability — the higher the proportion of companies remaining in the same quintile from one year to the next, the stronger the level of persistence. Persistence is important as firms in the bottom quintile of profitability may be there for one of two reasons: either they are experiencing temporarily low profitability or permanently low profitability. If the former is the case, persistence may be expected to be low, and the firm is unlikely to fail, as profitability will soon pick up. If the latter is the case, then persistence will be high and the firm is more likely to fail, as profitability is unlikely to pick up.

Table 1: Transition Matrix for One-Year Transitions between Quintiles of the Distribution of Return on Capital %

Year 2 Quintile 1

Quintile 2

Quintile 3

Quintile 4

Quintile 5

Quintile 1

42.2

26.1

8.0

11.7

12.0

Quintile 2 Quintile 3

12.6 4.5

37.2 21.0

23.6 33.1

16.0 28.8

10.6 12.6

Quintile 4 Quintile 5

4.8 6.5

9.2 9.8

18.8 12.4

45.3 35.0

21.9 36.3

Note: Rows sum to 100 per cent Source: Bureau Van Dijk and author’s calculations

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The principal diagonal in Table 1 (highlighted in bold) indicates the proportion of firms that remain in the same quintile in two consecutive years.3 For instance, 42.2 per cent of firms in quintile 1 in year 1 remain there in year 2. To the right (left) of the principal diagonal is that proportion of firms that are in a higher (lower) quintile in the second year of observation. For instance, 26.1 per cent of firms that are in quintile 1 in year 1 are in quintile 2 in year 2. Table 1 shows that persistence is clearly in evidence. In all quintiles less than half the firms remain in the same quintile from one year to the next. However, in general, a greater proportion of firms move to neighbouring quintiles than to quintiles further away implying that essentially the changes in profitability are not dramatic from one year to the next. This is particularly true for the least profitable firms (quintile 5) where only slightly fewer firms move to quintile 4 (35.0 per cent) than remain in quintile 5 (36.3 per cent) the following year. The transition matrix for the profit margin shows a slightly higher level of persistence in the top quintiles than for the return on capital (Table 2). In particular, over 50 per cent of firms remain in quintile 1 from one year to the next. However, of the remaining firms in quintile 1, almost 13 per cent are in quintile 5 the following year, an indication that profitability can drop quite rapidly. Profit margin shows a similar pattern to the return on capital in quintile 5 where 34.7 per cent of firms remain in quintile 5 the following year, and only a slightly smaller proportion, 32.2 per cent, move to quintile 4. Available evidence suggests that persistence for these profitability indicators is higher in the UK. Benito and Vlieghe (2000) constructed a transition matrix for return

on capital4 using data on quoted UK non-financial corporates over the period 1974 to 1998, and found that in all bar one quintile persistence was over 50 per cent. Persistence was highest in quintile 5 at 70.1 per cent. Further, for each quintile, less than 15 per cent of firms moved beyond the neighbouring quintile. For the Irish profitability indicators this is only true for return on capital in quintile 4.

4.2 Capital Gearing (Indebtedness) Capital and income gearing ratios are indicators of the levels and affordability of the debt held by a company. Capital gearing is a measure of the underlying indebtedness of the company that compares the borrowing made by a company with the finance contributed by the shareholders. In practice, this is calculated as:

Capital gearing = (short-term loans and overdrafts + long-term liabilities)/shareholders’ funds.

Though there was some movement in the median level of capital gearing over the period, the level in 2004 was similar to that in 2001 (Chart 3). By design, capital gearing is positive. This condition is reflected in the compression of the percentiles below the median. The lower capital gearing percentiles remain fairly constant from 2001 to 2004. The 25th percentile, for instance, remains between 21 and 24 per cent throughout the period. However, it is those firms that have high capital gearing, i.e., the upper percentiles, that are of particular

Table 2: Transition Matrix for One-Year Transitions between Quintiles of the Distribution of Profit Margin %

Quintile Quintile Quintile Quintile Quintile

Year 2

1 2 3 4 5

Quintile 1

Quintile 2

Quintile 3

Quintile 4

Quintile 5

50.3 17.5 5.1 6.6 11.0

17.2 40.7 15.4 8.7 9.4

8.3 14.6 34.7 18.6 12.7

11.6 14.9 32.1 43.2 32.2

12.6 12.3 12.7 22.9 34.7

Note: Rows sum to 100 per cent. Source: Bureau Van Dijk and author’s calculations.

3

A quintile represents one-fifth of the distribution of the indicator. For instance, quintile 1, or the top quintile, contains firms in the 81st to 100th percentile range. Quintile 2 is those in the 61st to the 80th percentile, and so on. 4 Benito and Vlieghe estimate return on capital as: (earnings before interest and taxes)/capital. Though the calculation of this indicator is slightly different to that for the Irish profiabilty indicators, as it is used to compare persistence within quintiles of the profitability distribution rather than actual profitability levels, it is felt that the comparison is instructive. The same applies below when other indicators are compared with those calculated by Benito and Vlieghe. 148

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interest. In contrast with the lower percentiles, there is far more variation in the upper percentiles. The upper percentiles have experienced an overall reduction over the period, particularly in 2004.

Chart 3: Percentiles of Distribution of the Capital Gearing Ratio per cent

300

Persistence in capital gearing appears to be lower in Ireland than in the UK. In the UK, capital gearing persistence ranges from 47.5 per cent (quintile 3) to 71.5 per cent (quintile 5)5. Persistence in the highest quintile is 70.3 per cent in UK. However, in terms of movement beyond the neighbouring quintile, Ireland and the UK are relatively similar. On average 9.7 per cent of firms move beyond their neighbouring quintile in the UK, compared with 10.2 per cent in Ireland.

250

200

150

100

4.3 Interest Gearing (Repayment Burdens) Interest gearing is an indicator of the debt repayment burden of a company. It summarises information on the indebtedness and profitability of a company as well as the interest rates that a company faces. Interest gearing is calculated as:

50

0 2001

02

03

80th percentile

75th percentile

25th percentile

10th percentile

Interest gearing = (interest paid/profit (loss) before interest)

04 Median

Source: Bureau Van Dijk and author's calculations

The transition matrix in Table 3 shows the persistence in capital gearing. Of the most highly geared firms (quintile 1), over half (54 per cent) remain in that part of the distribution in the following year, while over 33.2 per cent are in quintile 2. Just under 13 per cent of the most highly geared firms, therefore, move into quintiles 3, 4 and 5.

Chart 4 shows that the median level of interest gearing fell over the period. Firms experiencing high interest gearing are at a relatively greater risk of default, as their repayments take up a large portion of their profits. However, firms making a loss are in a worse position. Interest gearing was set at 100 per cent for all firms experiencing a loss. As more than 10 per cent of firms experienced a loss in each year from 2000 to 2004, the 90th percentile of the distribution is at 100 per cent throughout the period.

Table 3: Transition Matrix for One-Year Transitions between Quintiles of the Distribution of Capital Gearing Ratio %

Quintile Quintile Quintile Quintile Quintile

Year 2

1 2 3 4 5

Quintile 1

Quintile 2

Quintile 3

Quintile 4

Quintile 5

54.0 13.5 2.5 2.0 1.6

33.2 47.2 15.7 4.3 5.2

6.1 27.9 46.6 10.1 6.3

4.7 8.0 30.1 56.1 37.7

2.0 3.4 5.1 27.5 49.2

Note: Rows sum to 100 per cent Source: Bureau Van Dijk and author’s calculations

5

Benito and Vlieghe (2000) used data on quoted UK non-financial corporates over the period 1974 to 1998 and estimated capital gearing as net debt/capital. Financial Stability Report 2006

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Chart 4: Percentiles of Distribution of Interest Gearing Ratio per cent 100

liquidity ratio. The liquidity ratio is a more conservative estimate of liquidity than the current ratio. The current ratio is measured as: Current ratio = current assets/current liabilities,

80

while the liquidity ratio subtracts stock and work in progress (w.i.p.) capital from current assets: Liquidity ratio = (current assets - stock - w.i.p. capital)/current liabilities.

60

40

20

0 2001

02

03

90th percentile

75th percentile

25th percentile

10th percentile

The distributions of the current and liquidity ratios show similar patterns (Charts 5 and 6). However, for each percentile, the somewhat lower values of the liquidity ratio attest to its more conservative nature. In 2004, for

Chart 5: Percentiles of Distribution of Current Ratio

04

ratio

4.0

Median 3.5

Source: Bureau Van Dijk and author's calculations

3.0 2.5

The transition matrix for interest gearing (Table 4) indicates persistence is relatively low for income gearing. In all quintiles, less than 50 per cent of firms remain in the same quintile two years in a row. Of the most highly geared firms 40.2 per cent remain in quintile 1 the following year, while 23.1 per cent of firms are in quintile 2. By comparison, in the UK, persistence is over 76 per cent6 in quintile 1. Overall, persistence is higher in the UK, at over 50 per cent for all quintiles.

2.0 1.5 1.0 0.5 0.0 2001

02

4.4 Liquidity A high liquidity ratio can insulate a firm to some extent against adverse shocks to cash flow. The two liquidity indicators examined are the current ratio and the

03

90th percentile

75th percentile

25th percentile

10th percentile

04 Median

Source: Bureau Van Dijk and author's calculations

Table 4: Transition Matrix for One-Year Transitions between Quintiles of the Distribution of Interest Gearing Ratio %

Quintile Quintile Quintile Quintile Quintile

Year 2

1 2 3 4 5

Quintile 1

Quintile 2

Quintile 3

Quintile 4

Quintile 5

40.2 25.0 12.6 14.2 10.0

23.1 40.7 23.7 6.3 2.3

11.3 26.3 35.0 12.7 3.8

17.0 6.1 24.2 45.8 40.0

8.4 1.9 4.5 21.0 43.9

Note: Rows sum to 100 per cent Source: Bureau Van Dijk and author’s calculations

6

Benito and Vlieghe (2000) used data on quoted UK non-financial corporates over the period 1974 to 1998, and estimated interest gearing as: interest paid/(earnings before tax + interest received).

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Chart 6: Percentiles of Distribution of Liquidity Ratio 3.5

ratio

3.0

2.5

2.0

1.5

1.0

0.5

0.0 2001

02

03

90th percentile 25th percentile

75th percentile

04

to both an increase in the 90th percentile and a fall in the 10th percentile. Both ratios therefore show an overall reduction in the level of liquidity among the least liquid firms between 2001 and 2004 (i.e., those in the 10th percentile). The transition matrix (Table 5) for the liquidity ratio shows that there is a relatively high level of persistence, particularly among the most liquid firms. Seventy two per cent of firms in quintile 1 remain there the following year. Persistence is somewhat weaker among the other quintiles. Of the least liquid firms (quintile 5), 45.9 per cent remain in the bottom quintile the following year, while 38.8 per cent move into quintile 4. Even where persistence is least strong in quintile 3, less than 10 per cent of firms move beyond the neighbouring quintiles in the following year.

Median

10th percentile

Source: Bureau Van Dijk and author's calculations

example, the median liquidity ratio is 1.0, compared to a median of 1.3 for the current ratio. The range of the distributions varies somewhat also. In 2004 the interdecile range7 for the current ratio was 2.9 compared with 2.6 for the liquidity ratio. Both distributions exhibit a slight broadening in range over the period. This is due

The current ratio transition matrix (Table 6) shows a slightly lower level of persistence in the top quintile, though with almost 67 per cent of firms remaining in quintile 1 in two consecutive years persistence remains high. In contrast, persistence is marginally higher for the least liquid firms compared to the liquidity ratio; 49.2 per cent of firms remain in the bottom quintile two years in a row. Of the remaining firms in quintile 5, 36.8 per cent are in quintile 4 the following year, while only 14 per cent are in quintiles 1, 2 or 3.

Table 5: Transition Matrix for One-Year Transitions between Quintiles of the Distribution of Liquidity Ratio %

Quintile Quintile Quintile Quintile Quintile

Year 2

1 2 3 4 5

Quintile 1

Quintile 2

Quintile 3

Quintile 4

Quintile 5

72.0 23.5 5.5 4.3 5.5

15.0 48.4 28.5 4.4 4.2

5.5 17.9 39.9 19.2 5.6

4.7 7.9 21.9 51.6 38.8

2.8 2.3 4.2 18.5 45.9

Note: Rows sum to 100 per cent Source: Bureau Van Dijk and author’s calculations Table 6: Transition Matrix for One-Year Transitions between Quintiles of the Distribution of Current Ratio

%

Quintile Quintile Quintile Quintile Quintile

Year 2

1 2 3 4 5

Quintile 1

Quintile 2

Quintile 3

Quintile 4

Quintile 5

66.7 28.2 3.4 2.6 4.1

22.8 42.0 29.5 5.9 4.8

4.0 21.8 42.5 19.5 5.1

4.3 5.5 19.4 50.1 36.8

2.2 2.5 5.2 21.9 49.2

Note: Rows sum to 100 per cent Source: Bureau Van Dijk and author’s calculations 7

Measured as the difference between the 10th and 90th percentiles of the distribution. Financial Stability Report 2006

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5. Financial Stability Implication — Debt at Risk The risk to financial stability from the non-financial corporate sector usually focuses on the link between corporates and the banking sector. The above analysis of the ‘at risk’ cohorts allows a rough estimate of ‘debt at risk’ to be calculated for the sample, where debt at risk could be interpreted as the share of bank debt held by firms with the relatively highest risk of default. The concept of debt at risk has been defined by Benito, Whitley and Young (2001) as ‘the sum of all loans outstanding, weighted by the probability that each borrower will default within that period, but not including any estimate of loss-given-default’. Benito et al estimated a precise probability of failure for each firm by first estimating a firm-level model of failure with firm-level and macroeconomic explanatory factors. It is not possible to apply this approach in an Irish context because of a lack of data on firms that have failed. We use an alternative approach, similar to that used by Kearns (2004), which measures the share of bank debt held by those firms with a coincidence of negative indicators. Specifically, we calculate a measure of debt at risk that estimates the share of bank loans held by those firms experiencing a coincidence of low profitability, low liquidity and high capital gearing. The first step in applying this estimate of debt at risk is to identify those firms experiencing a coincidence of indicators — those in the bottom quintiles of the return

on capital ratio and current ratio and the top quintile of capital gearing. For simplicity, these three sets of firms are referred to as the ‘at risk’ cohorts. The second step is to calculate the percentage of total debt held by these firms. 5.1 Coincidence of Indicators When firms experience a coincidence of indicators, whereby they are present in two or more of the ‘at risk’ cohorts, their probability of default or failure is likely to be higher. The Venn diagram in Chart 7 is a simple and effective way to illustrate the first step in calculating debt at risk: the percentage of firms experiencing a coincidence of indicators in a particular year; here 2004. Each circle in the diagram shows the proportion of the overall sample in each of the ‘at risk’ cohorts: high gearing, low profitability or low liquidity. Overlaps between circles show the proportion of firms experiencing a coincidence of the relevant indicators. The larger the proportion of firms in these overlaps, the greater the number of firms with a coincidence of negative financial indicators. Overall, almost 40 per cent of firms are in at least one of the ‘at risk’ cohorts (each ‘at risk’ cohort contains one quintile, or 20 per cent, of the sample). Therefore 60 per cent of firms are not in either the low profitability, low liquidity or high gearing categories. A majority of firms in both the low liquidity and high gearing cohorts registers in one of the other ‘at risk’ cohorts. For instance, 8.4 per cent of firms experience high gearing only, while

Chart 7: Coincidence of Financial Health Indicators - 2004

Low liquidity

Low profitability

4.0% 10.3%

7.4%

2.7% 6.0%

3.0%

8.4%

60.3% High capital gearing

Source: Bureau Van Dijk and author’s calculations

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Chart 8: Percentage of Total Debt held by Firms in the ‘at risk’ Cohorts - 2004

Low Liquidity

Low Profitability

0.7% (4.0%)

3.6% (10.3%)

5.3% (7.4%)

1.4% (2.7%) 4.2% (6.0%)

25.1% (3.0%)

9.6% (8.4%) 50.1% (60.3%) High Gearing

Source: Bureau Van Dijk and author’s calculations

11.7 per cent (3.0 per cent plus 2.7 per cent plus 6.0 per cent) register a coincidence of high gearing with at least one other ‘at risk’ cohort. In fact 5.7 per cent (3.0 per cent plus 2.7 per cent) of firms experience a coincidence of high gearing and low profitability, and 8.7 per cent (2.7 per cent plus 6.0 per cent) experience a coincidence of high gearing and low liquidity. Overall, 2.7 per cent of firms experience a coincidence of all three indicators.

per cent of the total debt held. However, a narrower measure using only the 2.7 per cent of firms with a coincidence of all three indicators shows debt at risk amounts to 1.4 per cent of total debt. The time series of this narrower measure shows that there has been a downward trend in debt at risk over the period 2001 to 2004 (Chart 9).

Chart 9: Corporate Debt at Risk per cent

5.2 Debt at Risk The second step in estimating debt at risk is to calculate the percentage of total debt held by those firms that are in more than one of the ‘at risk’ cohorts. The Venn diagram in Chart 8 shows the proportion of total debt held by those firms in each of the ‘at risk’ cohorts. In brackets beneath these proportions are the figures from Chart 7 showing the proportion of the firms in each of the ‘at risk’ cohorts. As noted above, almost 40 per cent of firms are in at least one of the ‘at risk’ cohorts. This 40 per cent of companies accounts for almost 50 per cent of the debt held by all firms. As would be expected, firms with high gearing account for a large proportion of total debt. In total, highly geared firms hold over 40 per cent of total debt and almost 81 per cent of debt held by all firms in the ‘at risk’ cohorts.

2.5

2.0

1.5

1.0

0.5

0.0 2001

02

03

04

Source: Bureau Van Dijk and author's calculations

Debt at risk may be calculated to include debt held by those firms experiencing a coincidence of at least two indicators and almost 16 per cent of firms meet this criterion. By this measure, debt at risk amounts to 31.4 Financial Stability Report 2006

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6. Summary The international literature suggests that profitability, liquidity and gearing are important indicators of the health of non-financial corporates. This paper documents these indicators for the Irish non-financial corporate sector. Data for a sample of Irish companies over the period 2000 to 2004 were taken from the FAME database of UK and Irish company accounts. The subsidiaries of foreign multi-national corporations were removed from the sample as the health of these companies is not completely dependent on the Irish economy and their direct links to the Irish financial system are unlikely to be significant. The number of firms varies from a high of 1,126 in 2003 to a low of 915 in 2004, as in each year only companies with data for all the indicators used were selected. The latest year for which data are available is 2004. There were two parts to the analysis. In the first part, the descriptive analysis, the indicators of profitability, gearing and liquidity were introduced and analysed from three perspectives. First, the distributions of two indicators each of profitability, gearing and liquidity were plotted. Second, the analysis focused on that part of the distribution containing the firms most at risk of default — the lower percentiles of liquidity and profitability, and the upper percentiles of the two gearing indicators. Third, transition matrices were used to examine the persistence of these indicators. It was found that persistence was generally higher for the capital gearing and liquidity ratios than for the interest gearing and profitability ratios. The second part of the analysis applied these statistics to answer the financial stability question of whether a lot of debt is held by those firms with relatively greater risk of default. For this the companies at relatively greater risk of default were identified as those in the lowest quintile of profitability and liquidity and the highest quintile of gearing — the ‘at risk’ cohorts. As the risk of default tends to be higher where there is a coincidence of indicators,

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firms in more than one ‘at risk’ cohort were of particular interest. A measure of debt at risk was calculated by estimating the share of total debt held by firms in more than one ‘at risk’ cohort. Overall, it was found that the 16 per cent of firms experiencing a coincidence of at least two negative indicators accounted for 31.4 per cent of the total debt held. However, a narrower measure of the 2.7 per cent of firms experiencing a coincidence of all three indicators showed that this most vulnerable cohort of firms held 1.4 per cent of total debt.

References Benito, A., and G. Vlieghe, (2000), ‘‘Stylised Facts on UK Corporate Financial Health: Evidence from MicroData’’, Financial Stability Review, June, 83-93, Bank of England. Benito, A., J. Whitley and G. Young, (2001), ‘‘Analysing Corporate and Household Sector Balance Sheets’’, Financial Stability Review, December, 160-174, Bank of England. Bunn, P., and V. Redwood, (2003), ‘‘Company Accounts Based Modelling of Business Failures and the Implications for Financial Stability’’, Working Paper No. 210, Bank of England. Geroski, P., and P. Gregg, (1997), Coping with recession: UK company performance in adversity, Cambridge University Press. Kearns, A., (2004), ‘‘Are Irish Households and Corporates Over-Indebted and Does it Matter?’’, Barrington Lecture 2003/04. Lennox, C., (1999), ‘‘Identifying Failing Companies: A ReEvaluation of the Logit, Probit and DA Approaches’’, Journal of Economics and Business, 51(4), 347-364. Vlieghe, G. W., (2001), ‘‘Indicators of Fragility in the UK Corporate Sector’’, Working Paper, Bank of England.