The perfect storm: What is the impact of Covid-19 on the Scottish hospitality industry? August 2020 - Scottish Tourism Alliance
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The perfect storm: What is the impact of Covid-19 on the Scottish hospitality industry? August 2020
Executive summary
One of the most significant victims of Covid-19 is the tourism
and hospitality sector badly affected by travel restrictions and
lockdown. There are concerns that many companies in this
sector will not be able to recover. This report provides insights
into expected default rates in the next twelve months for the
Scottish tourism and hospitality.
The analysis utilises Wiserfunding expertise in risk modelling
and applies its models to a sample of the Scottish tourism
and hospitality companies to estimate probability of default
(PD) at the company level under three scenarios: baseline,
mild downturn and severe downturn.
The main findings of our research are:
• The sample (and the Scottish tourism and hospitality particular, for large companies the proportion in
sector) is dominated by small and relatively young the highest PD band (over 30%) increases from 4.55%
firms. The latest financial statements from 2019 show under the baseline scenario to 27.27% in mild down-
a relatively healthy risk profile with a good profitability turn and to 68.18% under severe stress. For small
(ROA above 5% in 63% of the sample) and a generally companies that are riskier in normal circumstances,
low level of debt (debt to equity ratio lower than 1 in the PD levels also increase but with the magnitude
70% of the sample). which is less pronounced (the corresponding increase
is from 14.33% to 31.49% and 60.16%). This can be
• However, given the deterioration in the economy
attributed to the adaptability of smaller companies
and the impact of the lockdown on this sector,
that enjoy leaner structure and lower amount of
the average PD of these firms has more than
tangible assets and fixed costs. As such, they can
doubled from Dec 2019 to June 2020 and reaches
adjust faster to the challenging conditions.
15% even in the baseline scenario.
• As for the company age, younger businesses
• After stressing the financial inputs and macroeconomic
are more vulnerable compared to the more
variables to reflect the expectations of the next
established ones. The response to the Covid-19
months, we forecast the average level of default
shock is also determined by business fundamentals.
varying between 28% (mild stress) and 43%
More profitable companies are less likely to
(severe stress).
experience default, and the same applies to
• Firms of all sizes are seriously affected. Yet contrary the companies with moderate levels of debt.
to our expectations, medium and large companies The highest risk levels are exhibited by young
seem to be more sensitive to the shock caused companies with no profit and high levels of debt.
by Covid-19 as compared to small businesses. In
The analysis in this report has not addressed explicitly the effect of the government support, this
will be the subject of further research. However, given the high expected default rates, it confirms
that the current government efforts to support the sector (e.g. VAT discount) are going in the right
direction. However, we would recommend the support programs to be tailored on the company
size to maximise their impact. Business fundamentals should be taken into account too. Firms
that show the highest level of adaptability should be rewarded and offered additional support
to overcome the crisis, in order to increase the chances of success in the deployment of public
funds. Finally, the withdrawal of the current borrowing schemes should be carefully planned in
order not to create additional shocks to the companies with high leverage.
2
Contents
Executive Summary 2
Model Data Inputs and Scenarios 4
Results 6
• Risk Metrics Results Definitions 6
• Credit Risk Benchmarks by Region and Sector 7
• Overall Sample Results Distribution Comparisons across 3 Scenarios 9
• Sample Distribution Comparisons by Company Size 11
• Sample Distribution Comparisons by Age of Company 15
• Sample Distribution Comparisons by Company’s Profitability 19
• Sample Distribution Comparisons by Company’s Leverage 23
The speed of the economic recovery 27
• Most optimistic: The Z 28
• Still very optimistic: The V 28
• Somewhat pessimistic, and probably more likely: The U 29
• Another possible estimation: The W 29
• Most pessimistic: The L 30
Conclusions 31
Overview of risk modelling methodology 32
Appendix 34
Figures & tables 1. Historic data for turnover: 2001-2020, values are In THSD £ 5
2. SME Z-Score Risk Zone Mapping 6
3. UK Credit Risk Benchmark by region 2019-2020 7
4. Scotland Credit Risk Benchmark by sector 2019-2020 8
5. SME Z-Score Distributions under three scenarios (%) 9
6. Probability of Default Distributions under three scenarios 9
7. Bond Rating Equivalents (BRE) Distributions under three scenarios 10
8. Company Size Overview 11
9. SME Z-Score Distribution by company size – Baseline 11
10. SME Z-Score Distribution by company size – Mild Stress 12
11. SME Z-Score Distribution by company size – Severe Stress 12
12. Probability of Default Distribution by company size 13
13. BRE Distribution by company size 14
14. Company Age Overview 15
15. SME Z-Score Distribution by company age under 3 scenarios 16
16. PD Distribution by company age 17
17. BRE Distribution by company age 18
18. Company Profitability Overview 19
19. SME Z-Score Distribution by profitability (ROA) under 3 scenarios 20
20. PD Distribution by profitability level 21
21. BRE Distribution by profitability level 22
22. Company's Leverage Overview 23
23. SME Z-Score Distribution by leverage under 3 scenarios 24
24. PD Distribution by leverage level 25
25. BRE Distribution by leverage level 26
26. Z-Shaped Recovery 28
27. V-Shaped Recovery 28
28. U-Shaped Recovery 29
29. W-Shaped Recovery 29
30. L-Shaped Recovery 30
31. SME Z-Score Components 33
Table 1. Proposed Stress Factors for downturn scenarios 4
Table 2. Financial Ratios definitions 35
Table 3. Bond Rating Equivalent (BRE) Tier definition 36
3
Model Data Inputs and Scenarios
A sample of 5000 Scottish companies was selected The models estimate the Probability of Default (PD)
from tourism and hospitality industry sectors using financial ratios (see Table 1), non-financial
(SIC2007 codes = 55, 56, 79). This sample is used to variables and macroeconomic indicators. More
generate outcomes under three scenarios: information about risk modelling is given in the
next section.
1. Baseline scenario
Baseline scenario uses the values from the latest
2. Mild downturn
available year of financial statements submitted to
3. Severe downturn the Companies House (2018-2019) and corresponding
macroeconomic inputs. To model the two downturn
scenarios, the values for the financial ratios should
be ‘stressed’, i.e. adjusted to reflect the negative
effect of the pandemic.
Table 1 / Proposed stress factors for downturn scenarios
Stress table Numeric Examples £
Variables Stress Factors Baseline Mild Severe
(average of 5000
Mild Severe
companies in
the sample)
Financials Total Shareholder -60% -80% £801,695 £320,678 £160,339
Equity
Total Assets -30% -65% £2,308,293 £1,615,805 £807,9 03
Turnover -50% -91% £9 67,911 £483,956 £87,112
Short Term +50% +80% £554,102 £831,153 £997,384
Debt
Long Term Debt +40% +50% £810,429 £1,134,601 £1,215,644
Cash -40% -89% £137,131 £82,279 £15,084
Working Capital -35% -68% £9,158 £5,953 £2,931
Tangible Assets -20% -60% £1,535,132 £1,228,106 £614,053
Intangible -10% -25% £24,758 £22,282 £18,569
Assets
EBITDA -70% -96% £290,087 £87,026 £11,603
Retained -90% -99% £680,718 £68,072 £6,807
Earnings
Interest Expense +130% +260% £26,863 £61,785 £96,707
Macro GDP -6% -13%
Unemployment -7% -12%
Notes: The % reflects the change from the baseline scenario, e.g. if the
baseline Total Shareholder Equity is £100, then in mild scenario it drops by
60% or becomes £40, and in severe scenario it drops by 80% or becomes
£20. For Financial Ratios definitions, see Table 2 in Appendix.
4
Model Data Inputs and Scenarios
As for the mild downturn scenario, we have exam- variable between the peak and through points in the
ined the last 20 years of financial accounts for Scottish 2008 crisis as the initial estimates of mild downturn for
tourism and hospitality sectors. The worst changes were our current event.
observed during the Global Financial Crisis (GFC) Period
We then made further conservative adjustments following
(2007-2008), and post-GFC recessions followed by the
the feedback from the tourist industry experts, and
European debt crisis (2009-2011). The tourism industry
these are the values in Table 1.
also suffered the effects of swine flu in 2009-2010. We
took the observed percentage change of each financial
Figure 1 / Historic data for Turnover: 2001-2020, values are in THSD £
7000
6000
5000
4000
3000
2000
1000
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
* Further graphs of other financial indicators in 2001-2020 can be found in the Appendix.
For the severe downturn scenario, we initially used an The estimated drop for Accommodation & Food industry
additional 50% adjustment as compared to the 2008 in April 2020 is 85%, and this was used as the severe
crisis, as it is commonly acknowledged that the current stress factor for P&L inputs: Turnover, Cash, EBITDA,
COVID-19 event would bring about a more prolonged Retained earnings.
and severe impact on the economy and tourism industry.
Similar to the mild downturn scenario, these initial
We applied this adjustment to balance sheet financials.
estimates were adjusted following the expert advice,
As for Profit & Loss (P&L) variables, we took the recent
and the final estimates are given in Table 1.
values from the Scottish government estimates of GDP
(https://www.gov.scot/collections/economy-statistics/
#gdpmonthlyestimates).
5
Results
Risk Metrics Results Definitions
Our results are presented as SME Z-Score, Bond
Rating Equivalent (BRE) and Probability of Default (PD).
The SME Z-Score is a risk metric derived from
Wiserfunding’s proprietary risk models (which are
explained in the last section). The score goes between
0 and 1000 where the higher the score, the better the
risk profiles of the companies. Risk zones are provided
to help interpreting the number. These are presented
in Figure 2:
Figure 2 / SME Z-score Risk Zone Mapping
0 – 100 101 – 250 251 – 450 451 – 700 701 – 1000
Distress High Risk Medium Risk Low Risk Lowest Risk
Bond Rating Equivalent (BRE) is the transformation of
Z-Score in line with the metrics used by credit rating
agencies. It represents the credit worthiness of the
company with the following risk grades (from the best to
the worst): AAA, AA, A, BBB, BB, B, CCC, CC. For more
detailed definition, please refer to Appendix Table 3).
One of the most popular risk measures that is also
obtained from Z-Score is Probability of Default (PD).
It is a financial term describing the likelihood of default
over a particular time horizon. It provides an estimate of
the likelihood that a borrower will be unable to meet its
debt obligations. It ranges between 0 and 1 with higher
values corresponding to the higher risk of default. We
use this measure in reporting the results in Executive
Summary and Conclusions, whilst the results in this
section present all three measures for completeness.
6
Results
Credit Risk Benchmarks by Region and Sector
To better understand the relative impact of this pandemic
on the financial health of Scottish hospitality industry,
we compare the change of credit risk from December
2019 to June 2020 across the UK regions and industries,
respectively, as shown in Figures 3-4.
The following graphs (Figure 3) provide the comparison
of credit risk ratings for different UK regions. The overall
estimated Probability of Default (PD) for Scottish economy
is going up from 5.30% in December 2019 to 8.07% in
June 2020.
Figure 3 / UK Credit Risk Benchmark by Region 2019-2020
UK Credit Risk Benchmark by Region (Dec 2019) SME Z-Score PD
320 8%
309 7%
310
6.28%
5.80% 6%
300
5.30%
290 5%
SME Z-Score
4.01% 281
280 4% PD
272
270 3%
264
260 2%
250 1%
240 0%
England Scotland Wales Northern Irelands
Region
UK Credit Risk Benchmark by Region (June 2020) SME Z-Score PD
300 10%
285 9.28%
8.74%
250 239 8.07%
231 225 8%
200
SME Z-Score
6%
5.09%
150 PD
4%
100
2%
50
0 0%
England Scotland Wales Northern Irelands
Region
7
Results Credit Risk Benchmarks by Region and Sector
The following graphs (Figure 4) provide the comparison
of credit risk rating for different industries in Scotland.
The overall estimated Probability of Default (PD) for
Scottish ‘Leisure’ sector almost doubled from 7.37%
in December 2019 to 15.15% in June 2020, given the
deterioration in the economy and the impact of the
lockdown on this sector.
Figure 4 / Scotland Credit Risk Benchmark by Sector 2019-2020
Scotland Credit Risk Benchmark by Sector (Dec 2019) SME Z-Score PD
350 10%
9.37%
289 301
300
7.37% 261 8%
248
250
224 6.48%
SME Z-Score
6%
200
4.89% PD
4.34%
150
4%
100
2%
50
0 0%
Services CRE/Construction Leisure Retail/Wholesale Manufacturing
Sector
Scotland Credit Risk Benchmark by Sector (June 2020) SME Z-Score PD
300 20%
9.37% 301
18%
289 261
250
7.37% 16%
14%
200
248
SME Z-Score
12%
224
150 10% PD
4.89% 6.48%
8%
100 4.34% 6%
4%
50
2%
0 0%
Services CRE/Construction Leisure Retail/Wholesale Manufacturing
Sector
8
Results
Overall Sample Results Distribution Comparisons
across 3 Scenarios
Figure 5 shows the SME Z-Score distributions under the
three scenarios, where the average scores are 263, 165,
93 under baseline, mild stress and severe stress scenarios,
respectively. To bring these estimates into perspective,
they can be compared to results reported in Figures 3-4,
that show the risk distributions for different UK regions
and for different sectors in Scotland.
Figure 5 / SME Z-Score Distributions under three scenarios (%)
Base Mild Severe
% of companies
SME Z-Score
Figure 6 indicates the changes of the companies’ PD
and distribution in the assumed downturn conditions.
In baseline condition, most companies in the hospitality
industry show an average 15% PD, increasing to 25%
and 43% after applying mild and severe stresses.
Figure 6 / Probability of Default Distributions under three scenarios
Base Mild Severe
% of companies
PD
9
Results Overall Sample Results Distribution Comparisons across 3 Scenarios
The below graph illustrates the impact on companies’
Bond Rating Equivalents (BRE) under the stress scenarios.
As shown in Figure 7, it indicates the rating deterioration
from the baseline scenario to the assumed downturn
conditions. In the baseline condition, companies’ BRE
is fairly well diversified varying from investment grades
to non-investment grades. With a greater magnitude
of economic stress applied, the sample’s BRE worsen
compared to the baseline with a concentration in the
lowest rating grades in the severe stress scenario.
Figure 7 / Bond Rating Equivalents (BRE) Distributions under three scenarios
BRE
CC- CC CC+ CCC- CCC CCC+ B- B B+ BB- BB BB+ BBB- BBB BBB+
36.50%
40%
% of companies within certain scenario
35%
30%
23.00%
22.00%
25%
17.50%
16.00%
20%
13.90%
13.50%
12.60%
12.20%
11.50%
10.60%
15%
10.30%
10.10%
9.90%
9.50%
9.50%
9.20%
8.10%
8.10%
6.90%
10%
6.50%
5.50%
3.60%
3.30%
2.70%
2.40%
1.90%
5%
1.10%
0.90%
0.70%
0.40%
0.10%
0%
Baseline Scenario Mild Stress Scenario Severe Stress Scenario
10Results
Sample Distribution Comparisons
by Company Size
Figure 8 – Figure 13 illustrate the results distribution by Figure 8 / Company Size Overview
company size under the three scenarios. To begin with,
we categorized companies into three groups: “Small”, Small
Large
“Medium”, and “Large”, by the level of total assets. Medium 2.20%
Medium
“Small” presents the companies with total assets less 9.20% Large
than £2million, while the ones with total assets between
£2 million and £15 million are regarded as “Medium”.
Those with greater than £15 million of total assets are
regarded as “Large”. Figure 8 illustrates company size
distribution within the sample. Small company, medium-
size and large company account for 88.6%, 9.2%, and
2.2% of the entire sample, respectively.
SME Z-Score Distribution by Company Size under
different scenarios are shown in Figure 9 – Figure 11.
Small
SME Z-Score, which higher value correspond to lower 88.60%
risk, could provide us the overview on the sink of the
creditability of tourism companies. In the baseline
situation as shown in Figure 9, the small and medium
companies are seemed to be more agile to respond Figure 9 / SME Z-Score Distribution
the current situation, with more proportion located well by Company Size – Baseline
above the investment score, compared to the large
Baseline Scenario: SME Z-score Distribution
companies. However, larger companies have less
Small Medium Large
proportion that rated down below 100, which is the
score range indicates the high possibility of default of 0-50 12% 1% 5%
the rated companies, compared to small companies. 50-100 2% 1% 0%
All companies have higher presence among the 100-150 8% 7% 9%
medium rating level between 150 and 300. 150-200 16% 18% 23%
200-250 15% 26% 41%
250-300 11% 9% 14%
Small Medium Large
300-350 10% 7% 5%
45% 350-400 6% 7% 0%
400-450 6% 12% 5%
40%
450-500 6% 5% 0%
35%
% of companies within category
500-550 5% 5% 0%
550-600 3% 2% 0%
30%
Total 100% 100% 100%
25%
20%
15%
10%
5%
0%
0-50 50-100 100-150 150-200 200-250 250-300 300-350 350-400 400-450 450-500 500-550 550-600
SME Z-Score
11Results Sample Distribution Comparisons by Company Size
Figure 10 / SME Z-Score Distribution by Company
Size – Mild Stress
Under the mild stress, the companies move into Mild Stress Scenario: SME Z-score Distribution
higher risk areas, and large companies show the most Small Medium Large
pronounced shift, as noted before. However, there is
0-50 18% 7% 5%
also a relatively high spike in the highest risk band from
50-100 11% 9% 14%
small companies.
100-150 8% 11% 18%
Small Medium Large 150-200 19% 25% 41%
200-250 24% 33% 18%
45%
250-300 14% 12% 5%
40% 300-350 7% 4% 0%
Total 100% 100% 100%
35%
% of companies within category
30%
25%
20%
15%
10%
5%
0%
0-50 50-100 100-150 150-200 200-250 250-300 300-350 350-400 400-450 450-500 500-550 550-600
SME Z-Score
Figure 11 / SME Z-Score Distribution by Company
Size – Severe Stress
Under severe scenario, the companies of all sizes Severe Stress Scenario: SME Z-score Distribution
move even further into the highest risk area, with Small Medium Large
larger companies appear to be hit hardest, and 0-50 36% 32% 41%
smaller companies’ ratings also dramatically plunge. 50-100 17% 20% 23%
100-150 22% 26% 18%
Small Medium Large
150-200 22% 23% 18%
45% 200-250 2% 0% 0%
40% 250-300 0% 0% 0%
Total 100% 100% 100%
% of companies within category
35%
30%
25%
20%
15%
10%
5%
0%
0-50 50-100 100-150 150-200 200-250 250-300 300-350 350-400 400-450 450-500 500-550 550-600
SME Z-Score
12Results Sample Distribution Comparisons by Company Size
Looking at the companies’ PD distributions under
stressed scenarios, large companies seem to be more
sensitive to the shock caused by Covid-19. As shown
in Figure 12, under baseline scenario, companies of all
sizes are predominantly in the low risk segment, with
PD under 10%. In mild-stress scenario, the companies
shift into higher risk segments. Large companies show
the most pronounced shift into 10% -20% risk segment,
whilst small and medium companies are more resilient
and are spread across the risk levels. Under severe
stress all companies migrate to the highest risk
segment with the PD above 30%, with large companies
demonstrating the highest presence in this band (68.18%).
No companies remain in the lowest risk segment.
Figure 12 / Probability of Default Distribution by Company Size
Small Medium Large
PD: 0% – 10% PD: 10% – 20% PD: 20% – 30% PD > 30%
70%
65.22%
% of companies within category
60% 57.45%
59.09% Baseline: PD Distribution by Company Size
50%
40%
30% 26.09% 27.27%
20.77%
20%
14.33%
9.09%
10% 7.45% 6.52%
4.55%
2.17%
0%
PD: 0% – 10% PD: 10% – 20% PD: 20% – 30% PD > 30%
70%
% of companies within category
60% Mild Stress: PD Distribution by Company Size
50.00%
50%
40.22%
40%
33.86% 32.61% 31.49%
30% 28.56% 27.27%
20% 16.30%
13.64%
10.87%
9.09%
10% 6.09%
0%
PD: 0% – 10% PD: 10% – 20% PD: 20% – 30% PD > 30%
70% 68.18%
60.16%
% of companies within category
60% Severe Stress: PD Distribution by Company Size 58.70%
50%
40%
30%
21.78%
20.65% 20.65%
20% 18.18% 17.61%
13.64%
10%
0.45%
0%
13Results Sample Distribution Comparisons by Company Size
BRE distribution shows similar patterns in Figure 13:
Figure 13 / BRE Distribution by Company Size
BBB+ Baseline: Small BRE Baseline: Medium BRE Baseline: Large BRE
BBB
BBB+ CC- CC-
BBB- CC- CC 1.09% BBB+ BBB
11.74% 7.34% CCC- 1.09% 4.35% 4.55% 4.55% B+
10.87% 4.55%
BB+ CC BBB CCC-
1.13% BBB 13.64 B
9.71% 15.22% 4.55%
CC+
BB 2.14%
BB-
CCC- BBB-
13.77% 8.13%
B+ CCC B-
BB+ 17.39% BBB- 18.18%
0.90% 9.78%
B BB CCC
2.93% BB+
1.09% 18.18%
B- BB-
BB
2.6%
B+ 1.09%
CCC+ B+
CCC 3.27% 3.26% BB-
11.96% 1.09%
B CCC+ B
CCC 6.66% 14.13% 5.43%
CCC- CCC+ B- B-
8.47% 9.26% 14.13% CCC+
31.82%
CC+
CC
CC- Mild Stress: Small BRE Mild Stress: Medium BRE Mild Stress: Large BRE
BB
BB- 1.09%- CC-
0.34% B+ CC- B+ B-
3.72% 6.52% 4.55% 4.55%
CC 3.26% B CC
CC- B
18.96% 6.52% 5.43% 9.09% CCC+
10.16%
13.64%
CC+
7.61% B-
B- 15.22%
9.93% CC+
13.64%
CC
9.48%
CCC-
11.96%
CC+ CCC+
5.08% 13.43%
CCC+ CCC-
18.48% 13.64% CCC
CCC-
7.56% 40.91%
CCC
CCC 23.91%
21.33%
Severe Stress: Small BRE Severe Stress: Medium BRE Severe Stress: Large BRE
B-
0.11% CCC+ CCC CCC
0.79% 6.52% 4.55%
CCC
12.19%
CC-
CC- 32.61%
36.79% CCC-
27.27%
CCC-
27.17%
CC-
CCC- 40.91%
22.46%
CC+
4.55%
CC+ CC
17.39% CC+
CC 11.96% 16.30% CC
15.69% 22.73%
14Results
Sample Distribution Comparisons
by Age of Company
Company’s age distribution in the sample is shown in Figure 14 / Company Age Overview
Figure 14, where we divided the companies into 4 main
age groups: young – companies who are less than or 30
(“[20,30]”), and mature – those that have been active 10.50%
over 30 years (“>30”).
In terms of the company age, the young companies
dominate the overall sample, with nearly half of the sample
being in this group, they are followed by companies
with ages falling into [11-20], and [21-30] age brackets, 30%).
15Results Sample Distribution Comparisons by Age of Company
Figure 15 / SME Z-Score Distribution by Company Age under 3 Scenarios
30
Baseline Scenario
30% 28.97%
27.88%
% of companies within Age Group
24.76% 24.05%
25% 22.78%
20.00%
19.05%
20% 20.25%
15.76% 16.46% 16.46%
18.10%
16.46%
15% 14.33%
15.15%
10.59%
9.66%
10% 8.89%
9.52% 8.57%
4.44%
5%
3.80%
0%
100 200 300 400 500 600
Mild Stress Scenario
50% 46.84%
% of companies within Age Group
40% 38.01%
34.95% 31.46% 33.74%
30%
23.05% 25.32%
20% 21.90%
13.92%
13.92%
10% 7.48%
4.04%
0%
100 200 300 400
Severe Stress Scenario
70%
38.01%
34.95%
% of companies within Age Group
60%
38.01%
23.05%
50% 21.90%
33.74%
40% 13.92%
33.74%
30%
20%
10%
4.04%
0%
100 200 300
Notes: In SME Z-Score graphs in this analysis, x-axis represents score
band. For example, 100 at x-axis means SME Z-score ranging from 0 to 100;
200 represents SME Z-score between 101 and 200. The y-axis represents
the proportion of company within respective age group falling in the
corresponding SME Z-score range. For instance, under baseline scenario,
3.8% on the dark orange line represents only 3.8% of “old companies”
(greater than 30 years) assigned with “Results Sample Distribution Comparisons by Age of Company
Figure 16 / PD Distribution by Company Age
30
PD: 0% – 10% PD: 10% – 20% PD: 20% – 30% PD > 30%
80% 75.95%
% of companies within age group
62.93% Baseline Scenario
61.90%
60%
51.52%
40%
22.83% 23.81%
19.63%
20% 16.46% 16.36%
9.29% 10.90% 10.48%
6.54%
3.81% 3.80% 3.80%
0%
30 30 30 30
PD: 0% – 10% PD: 10% – 20% PD: 20% – 30% PD > 30%
80%
% of companies within age group
Mild Stress Scenario
60%
46.84%
45.71%
40% 37.07% 36.97%
35.44%
32.40%
27.47% 27.47% 24.92%
24.76% 24.76%
20%
13.92%
8.08%
5.61%
4.76% 3.80%
0%
30 30 30 30
PD: 0% – 10% PD: 10% – 20% PD: 20% – 30% PD > 30%
80%
% of companies within age group
53.58%
Severe Stress Scenario
60% 55.24%
68.08%
44.30%
39.24%
40%
26.79%
23.81%
20.95% 19.31%
20% 15.56% 15.76% 16.46%
0.61%
0.31%
0%
30 30 30 30
17Results Sample Distribution Comparisons by Age of Company
Figure 17 / BRE Distribution by Company Age
BBB+ Baseline: BRE with Age 30
CC- CCC+
CC+ 2.53% CC-
1.27% B+ 1.27%
CCC- BBB+ 8.86% 10.13%
6.33% 17.72% CC
3.80% CC- CCC
CC+ 21.52% 21.52%
CCC 2.53%
12.66% B
CCC- 16.46%
8.86%
BBB
13.92%
CC
11.39%
CCC+
12.66%
B-
15.19%
CCC CCC-
B- BBB- 21.52% 29.11%
6.33% 16.46% CC+
B 15.19%
5.06% BB CCC+
2.53% BB+ 12.66%
B+ 1.27%
1.27%
18Results
Sample Distribution Comparisons
by Company’s Profitability
Profitability is one the major factor determining company’s Figure 18 / Company Profitability Overview
creditworthiness. In this analysis, we use Return on
Assets (ROA), that is, Net Income divided by Total Low
Assets, as the profitability proxy. And the ROA ratios Profitability
are categorized into 3 groups: (1) low profitability group, 23.80%
having ROA < 0%, (2) medium profitability group, with
ROA between 0 and 5%, (3) high profitability group, with
more than 5% ROA. Figure 18 provides an overview of
company profitability level under the baseline scenario.
The low profitability group, medium profitability and
high profitability group accounts for 23.8%, 12.9%, and
63.3% of the entire sample, respectively. Medium
Profitability
23.80%
High
Profitability
63.30%
Figure 19 (overleaf) shows SME Z-Score Distribution by
Profitability (ROA) under 3 Scenarios. Even under the
baseline scenario, over half of low profitable companies
have less than 100 SME Z-score assigned, which signals
the riskier credit profile. For the rest of two groups,
the proportion of having less than 100 SME Z-score is
around zero, and the high profitability group is likely to
achieve higher score. As situation get worse, all ROA
groups’ credit score suffers and slide to lower score
levels, having greater portion of falling into score band
that is less than 100. In the mild-stress situation, none of
them obtains score greater than 400, and the maximum
score further drops to 300 in the severe stress scenario.
Regardless of scenarios, high profitability group generally
has better score than other groups.
19Results Sample Distribution Comparisons by Company’s Profitability
Figure 19 / SME Z-Score Distribution by Profitability (ROA) under 3 Scenarios
Low Medium High
Baseline Scenario
70%
% of companies within Profitability Group
61.24%
60%
51.26%
50%
40% 37.21%
28.57% 27.65%
30% 24.80%
19.43%
20%
19.33%
15.01% 12.64%
10% 0.47% 0.42%
1.55%
0.00% 0.00%
0%
100 200 300 400 500 600
Mild Stress Scenario
80% 73.95%
% of companies within Profitability Group
56.67%
60%
40.19% 39.25%
40%
28.65%
20.56%
20%
18.07% 11.31%
7.98%
3.47%
0.00%
0%
100 200 300 400
Severe Stress Scenario
% of companies within Profitability Group
90% 91.60% 78.53%
80%
70%
60% 63.97%
50%
34.31%
40%
30%
20%
18.93% 7.56%
10% 0.84%
0%
100 200 300
Notes: In SME Z-Score graphs in this analysis, x-axis represents score
band. For example, 100 at x-axis means SME Z-score ranging from 0 to 100;
200 represents SME Z-score between 101 and 200. The y-axis represents
the proportion of company within respective profitability group falling in the
corresponding SME Z-score range.
20Results Sample Distribution Comparisons by Company’s Profitability
Looking at PD bands (Figure 20) with different levels
of profitability clusters, we can see that the higher
profitability is associated with lower default risk. The low
profit cluster is more likely to default in baseline case and
becomes even more vulnerable in the stressed situations.
The findings are consistent with BRE distribution by
profitability (Figure 21): under the baseline scenario,
companies more capable of generating higher return
have better rating assigned (around 75% of profitable
group achieving BRE rating of B- and above), while
companies in the low profitability group have generally
poor rating with 75% within the group assigned CCC
and worse ratings. The divergences of BRE among 3
profitability clusters also exist in mild-stress and severe-
stress scenarios. The rating deterioration in the higher
profitability group is less severe than lower profit group.
Figure 20 / PD Distribution by Profitability Level
Low Medium High
PD: 0% – 10% PD: 10% – 20% PD: 20% – 30% PD > 30%
90%
80.41%
80%
% of companies within Profitability Group
Baseline Scenario
70%
60%
52.94%
50% 45.74%
40% 26.09%
27.91%
30% 26.36%
19.33%
16.39% 17.22% 14.33%
20%
11.34%
10% 6.52%
1.74% 0.63%
0%
0.05 0.05 0.05 0.05
PD: 0% – 10% PD: 10% – 20% PD: 20% – 30% PD > 30%
90%
80% Mild Stress Scenario 76.47%
% of companies within Profitability Group
70%
60% 55.66%
50% 43.93%
40% 35.98% 35.22%
30%
20%
10.08% 11.21%
8.88%
6.72%
10% 6.72%
4.74% 4.38%
0%
0.05 0.05 0.05 0.05
PD: 0% – 10% PD: 10% – 20% PD: 20% – 30% PD > 30%
100%
92.44%
% of companies within Profitability Group
90% Severe Stress Scenario
80%
69.36%
70%
60%
50% 43.79%
40%
30% 27.97% 27.97%
14.33%
20% 13.24%
16.67%
9.09%
6.52% 7.45%
10% 4.62% 2.17%
4.55%
0.74% 0.28% 2.94%
0%
0.05 0.05 0.05 0.05
21Results Sample Distribution Comparisons by Company’s Profitability
Figure 21 / BRE Distribution by Profitability Level
BBB+ Baseline BRE with High Profitability Baseline BRE with Medium Profitability Baseline BRE with Low Profitability
BBB CC-
0.32% B+
BBB- CC+ B+ B BBB 0.42% B
CCC- 0.32% BBB+ BB- 0.78% 1.55% 0.42%
6.00% 10.90% 0.78% B- 3.36% B-
BB+ 8.53% 6.30%
CCC
11.06% CCC+
BB 7.56%
BBB
BB- 15.80%
CCC+ CCC+
B+ 8.37% 18.60% CC- CCC
CCC- 43.70% 11.76%
B 48.06%
B-
CCC+ B-
11.53% BBB-
12.80%
CCC CCC-
BB+ CCC 14.71%
CCC- B BB 1.42% 21.71%
8.69% B+ BB- 4.27% CC CC+
4.90% 3.63% 4.62% 7.14%
CC+
CC
Mild Stress BRE with High Profitability Mild Stress BRE with Medium Profitability Mild Stress with Low Profitability
CC-
CC-
2.01%
CC BB- CC- B- B-
CC+ 0.91% 0.73% B+ 14.95% 11.21%
CCC+
11.21% CCC+
3.47% 6.57% 0.93%
CCC- 13.71% CCC
6.39% 11.84%
B
17.34% CCC-
CC
7.79% 28.35%
CCC CC+
26.09% CCC 14.33%
26.48%
CC
13.40%
B- CC-
16.61% 31.15%
CC+
4.05%
CCC+ CCC-
19.89% 6.54%
Severe Stress BRE with High Profitability Severe Stress BRE with Medium Profitability Severe Stress with Low Profitability
B-
4.76% CCC+ CCC
CC- CCC+ CCC-
0.98% CCC 0.84% 5.88%
7.62% 8.57% CC+
CC 6.37%
2.10%
1.90%
CCC CC
13.33% 6.30%
CCC-
17.65%
CC-
36.76%
CC+
0.95%
CC+
12.25%
CC-
CCC- 84.87%
11.43% CC
25.98%
22Results
Sample Distribution Comparisons
by Company’s Leverage
Leverage level is an important factor in predicting Figure 22 / Company’s Leverage Overview
the resilience of companies to economic shocks. We
categorized leverage ratios (Total Debt divided by High
Shareholder Equity) into 3 clusters: (1) low leverage Leverage
group, with leverage ratio less than 1; (2) medium 12.50%
leverage group, with their leverage ratio higher than 1; (3)
high leverage group with negative leverage ratio, which
results from negative total shareholder’s equity in that
sample. It means that liabilities exceed assets which Medium
Leverage
could happen when company has accumulated losses
18.10%
over several periods, and company chooses to borrow
money to cover accumulated losses. Figure 22 provides
an overview of company’s leverage level under the
baseline scenario. Low leverage group, Medium
Low
leverage and High leverage group accounts for 69.4%, Leverage
18.1%, and 12.5% of the entire sample, respectively. 69.40%
Figure 23 (overleaf) shows SME Z-Score Distribution by
Leverage under 3 scenarios. Starting from the baseline
scenario, more than half of high leverage companies
have less than 100 SME Z-score assigned. For the low-
and medium leverage groups, the proportion of having
less than 100 SME Z-score is relatively low. These
suggest that the higher leverage level is associated with
greater default risk.
Comparing low leverage group and medium leverage
one, the latter has generally lower portion of company
that fall into distress zone (SME Z-score from 0 to 100,
see risk zone mapping in Figure 2) in all 3 scenarios.
Low leverage group achieve average higher score in
the baseline scenario by having over 50% of companies
assessed as medium- and low- risk entities, while
medium leverage group turns to slightly better position
in severe stress scenario, having higher SME Z-score
on average.
23Results Sample Distribution Comparisons by Company’s Leverage
Figure 23 / SME Z-Score Distribution by Leverage under 3 Scenarios
Low Medium High
Baseline Scenario
60%
% of companies within Leverage Group
52.49%
56.80%
50%
38.67%
40%
29.60%
30%
22.91%
21.33%
20% 17.44%
19.45%
12.00%
7.49% 11.38%
10% 6.08%
1.66% 0.80%
1.10% 0.80% 0.80% 0.00%
0%
100 200 300 400 500 600
Mild Stress Scenario
80%
% of companies within Leverage Group
78.74%
60%
43.52%
42.86%
40%
39.02%
24.06% 23.89%
20%
16.54% 8.53%
13.94% 4.72%
4.18%
0.00%
0%
100 200 300 400
Severe Stress Scenario
100%
% of companies within Leverage Group
96.12%
80%
58.81%
54.10%
60%
44.03%
40%
39.40%
20%
2.33% 1.55%
0%
100 200 300
Notes: In SME Z-Score graphs in this analysis, x-axis represents score
band. For example, 100 at x-axis means SME Z-score ranging from 0 to 100;
200 represents SME Z-score between 101 and 200. The y-axis represents
the proportion of company within respective leverage group falling in the
corresponding SME Z-score range.
24Results Sample Distribution Comparisons by Company’s Leverage
Negative leverage ratio usually signals potential financial
distress. According to PD distribution by different leverage
levels (see Figure 24), over 60% of high leverage companies
are more likely to suffer financial distress (PD >30% –
high risk band) and thereby default, compared to other
two leverage groups. This portion surges above 80%,
and even 95%, in mild and severe downturn respectively.
On the contrary, the majority of less leveraged companies
have lower PD and stay in the “low risk zone” under the
baseline scenario. Medium leverage company group is
roughly spread around the “low-and-medium risk zone”
according to their PD distribution.
Figure 24 / PD Distribution by Leverage Level
Low Medium High
PD: 0% – 10% PD: 10% – 20% PD: 20% – 30% PD > 30%
90%
80% Baseline Scenario
% of companies within Leverage Group
69.60%
70%
60.00%
60%
46.96%
50% 42.54%
40% 35.98%
30%
18.40%
20% 16.43%
11.20% 10.08% 9.39% 10.40%
8.88%
6.72% 7.64%
10% 6.34% 4.74% 4.38%
1.10%
0%
Low Medium High Low Medium High Low Medium High Low Medium High
PD: 0% – 10% PD: 10% – 20% PD: 20% – 30% PD > 30%
90%
81.89%
80% Mild Stress Scenario
% of companies within Leverage Group
70%
60%
50% 48.43%
44.20%
40% 35.98%
30% 26.48%
24.57%
25.60%
20% 16.03%
8.88% 8.66% 9.06%
6.72%
10% 3.94% 5.63% 5.51% 4.38%
0%
Low Medium High Low Medium High Low Medium High Low Medium High
PD: 0% – 10% PD: 10% – 20% PD: 20% – 30% PD > 30%
100% 96.12%
Severe Stress Scenario
% of companies within Leverage Group
80%
59.33%
60%
47.76%
40%
25.6%
26.87% 25.37%
23.13%
16.03%
16.98%
20% 9.06%
5.63% 4.38%
0.56% 0.78% 1.55% 1.55%
0% Low Medium High Low Medium High Low Medium High Low Medium High
25Results Sample Distribution Comparisons by Company’s Leverage
These insights are also supported by BRE distributions
segmented by the leverage level (Figure 25). Under the
baseline scenario, companies bearing less debt are as-
signed to better rating (around 60% of them achieving B-
and above rating), while high leveraged companies have
worse ratings with only around 30% of them obtaining
B- and above rating. The portion of having B- and above
rating drops to 5% in the medium leverage group. As
more severe stress factors applied, the sample’s overall
BRE turns worse regardless of leverage level, and the
rating of high leverage company deteriorates the most.
Figure 25 / BRE Distribution by Leverage Level
BBB+ Baseline BRE with Low Leverage Baseline BRE with Medium Leverage Baseline BRE with High Leverage
BBB
BBB BBB- BB+
1.10% 1.10% 0.55% BBB-
BBB- CC+ 0.80% B
CC CC- 0.55% CC BB BB- B+ BBB+
6.34% BBB+ 0.55% 2.76% 1.10% 0.55% 0.80% B-
CC+ 0.86% 9.80% 0.80% 4.80%
BB+ CCC- CCC+
0.58% 17.68% B
4.97% 5.60%
CCC-
BB 12.39%
BBB CCC
BB- 14.27% 9.60%
B-
B+ 18.78%
CC-
CCC 49.60%
B 8.93%
B- BBB-
11.24% CCC-
13.60%
CCC+ CCC+ CCC
7.06% BB+ 28.73%
CCC 1.15%
BB
B-
BB- 3.17% CCC+ CC+
CCC- 8.50%
B B+ 3.17% 21.55% 11.20%
4.61% CC
CC+ 7.93% 3.20%
CC
CC- Mild Stress BRE with Low Leverage Mild Stress BRE with Medium Leverage Mild Stress with High Leverage
BB- B-
0.68% B+ CC- B+ 1.57% CCC+
CC 3.14% 3.83% B 2.36% CCC
CC- 4.27%
12.80% 8.01% 6.27% 4.72%
B
13.14% B-
CC+ 6.27% CCC-
8.71% 8.66%
CC
10.24% CC+
3.94%
CCC+
CC+ 15.68% CC
4.27% B- CCC- 7.09%
14.16% 12.54%
CCC-
5.80% CC-
71.65%
CCC+
CCC 15.53%
19.11% CCC
35.54%
Severe Stress BRE with Low Leverage Severe Stress BRE with Medium Leverage Severe Stress with High Leverage
B- CCC+
0.19% CCC+ 0.30% CCC+ CCC CCC-
0.93%
CCC 0.78% 0.78% 1.55% CC+
CCC 1.55% CC
12.31% CC- 14.33%
21.49% 5.43%
CC-
33.02%
CCC-
24.44%
CC
16.12% CCC-
28.96%
CC CC+
18.47% 10.63% CC+ CC-
18.81% 89.82%
26The speed of the
economic recovery
Economic crises have often been defined as black Often, we hear reference to the new normal. As for any
swans, rare and unpredictable events. In the case of the big change, most people respond with denial and tend
current economic crisis, its unpredictability is somewhat to believe that this “new normal” will only be temporary.
questionable considering that most of the developed Unfortunately, there is no certainty with regards to the
world had been enjoying one of the longest benign length of this new normal stage and waiting for an end
periods in the recent financial history. However, the can generate wrong behaviours. People and business
trigger of this crisis was definitely unexpected. A global will need to accept this new status quo and adapt.
pandemic is a fairly rare event in history and the last time This is the only way to ensure a faster recovery.
that something similar was experienced, the world was
Individuals can adapt faster than businesses. Many
far less connected than it is today.
people learnt how to cook or cut their hair, even enjoyed
Our way of living gave the virus a great help to spread exercising outside rather than going to gym. Many
quickly throughout the world. Governments responded businesses managed to respond quickly. Even during
slowly and in a fragmented way. Lockdown measures lockdown, some businesses modified their business
were introduced and lifted at different speed creating model to adapt to the new context. These businesses
public confusion. These measures significantly altered not only managed to survive the lockdown, but some
the habits of the vast majority of the population affecting increased their revenues.
every aspect of our life.
The speed of the recovery will depend strongly on
At the beginning of the pandemic, few understood how these behaviours. The faster businesses will adapt, the
long it would be before life returned to normal, and many shorter will be the downturn. SMEs will have an advantage
analysts talked of V-shaped recoveries. Many analysts compared to larger, more complex organizations. Small
now believe that, barring major improvements in COVID businesses have a leaner structure, lower fixed costs
treatment (which would make the disease less dangerous), and faster decision times. These elements will play a
only a vaccine can allow economic activity to return to major role in the next months and hopefully provide
the pre-pandemic baseline. Even once the economy SMEs with a competitive advantage.
starts to reopen, measures will likely be put in place
that curtail economic activity to some degree – travel
will be less common, businesses will have to space
workers and customers further apart, restaurants will be
serving fewer customers at a time, and sporting events,
concerts, and other activities involving large crowds
probably will remain off limits for a long time. And even if
the rules allow, many people may be reluctant to return
to life as it was before the pandemic.
27The speed of the economic recovery
Most optimistic: The Z
The economy suffers a downturn during the pandemic, Figure 26 / Z-Shaped Recovery
but then bounces back up above the level it would have
been in a pre-pandemic baseline, as pent-up demand
Pre-Corona baseline
creates a temporary boom. In this scenario, a good part
of the GDP foregone during lockdowns – the shopping
we didn’t do, the restaurant meals we didn’t enjoy, trips
we didn’t take – was simply delayed, and is made up
once the risk from the pandemic passes.
GDP
Time
Still very optimistic: The V
The economy permanently loses the production that Figure 27 / V-Shaped Recovery
would have occurred absent the pandemic, but very
quickly returns to its pre-pandemic baseline once social
Pre-Corona baseline
distancing is lifted. Trips not taken, restaurant meals
not purchased, and concerts not attended are forgone,
rather than delayed, but once life returns to normal,
everything is just as it would have been before.
GDP
Time
28The speed of the economic recovery
Somewhat pessimistic,
and probably more likely: The U
The effects of the pandemic on economic activity last Figure 28 / U-Shaped Recovery
well beyond the end of the social distancing, and GDP
recovers slowly. Even after the health risks recede, the
Pre-Corona baseline
economy still doesn’t quickly go back to where it would
have been, though it does get there eventually. This
basic story has many possible shapes. In the U-shape,
the level of GDP stays low for a while (perhaps because
social distancing norms last a long time), but then
recovers back to baseline slowly.
GDP
Time
Another possible estimation: The W
If the response to the pandemic is a first round of Figure 29 / W-Shaped Recovery
openings that is followed by a surge in COVID-19 cases
and another round of closures in the fall, the recovery
Pre-Corona baseline
could be W-shaped. But then the question will be, what
does the recovery from the second (or third, if we do
that multiple times) downturn look like?
GDP
Time
29The speed of the economic recovery
Most pessimistic: The L
The pandemic has a permanent effect on GDP. Lost Figure 30 / L-Shaped Recovery
investment during the crisis, a rethinking of global value
chains, a permanent change to fiscal policy, and a
Pre-Corona baseline
slowdown in productivity growth all have the potential to
cause the trajectory of GDP to be lower than it otherwise
would. This is basically what the recovery from the Great
Recession looked like.
GDP
Time
30Conclusions
The report provided forecasts of expected default rates For both stress scenarios we then made further
in the next twelve months for the Scottish tourism and conservative adjustments following the feedback from
hospitality sector. the tourist industry experts.
The analysis used the risk models developed by With stressed financial inputs and macroeconomic
Wiserfunding to assess the chances of default/financial variables, forecasts for the average level of default varied
distress with financial ratios and macroeconomic between 28% (mild stress) and 43% (severe stress).
variables as inputs. The models were applied to a
Contrary to our expectations, medium and large
sample of the Scottish tourism and hospitality companies
companies appeared to be more sensitive to the shock
to estimate probability of default (PD) at the company
caused by Covid-19. In particular, for large companies
level under three scenarios: baseline, mild downturn and
the proportion in the highest PD band (over 30%)
severe downturn.
increased from 4.55% under baseline scenario to
The sample of 5000 companies was characterised by 27.27% in mild downturn and to 68.18% under severe
predominantly small firms (87% of sample had total assets stress. For small companies that are riskier in normal
less than £2m), relatively young (82% of the firms in the circumstances, the PD levels also increased but with
sample was less than 20 years old). The latest financial the less pronounced magnitude. This can be attributed
statements from 2019 showed a relatively healthy risk to the adaptability of smaller companies that enjoy
profile with a good profitability (ROA above 5% in 63% leaner structure and lower amount of tangible assets
of the sample) and a generally low level of debt (debt and fixed costs. As such, they can adjust faster to the
to equity ratio lower than 1 in 70% of the sample). challenging conditions.
The baseline scenario used 2019 financial ratios and
As for the company age, younger businesses appeared
the most recent macroeconomic variables. Given the
to be more vulnerable compared to the more established
deterioration in the economy and the impact of the
ones. The response to the Covid-19 shock varied also
lockdown on this sector, the average PD of these firms
according to business fundamentals. More profitable
increased to 15% even in the baseline scenario, almost
companies were less likely to experience default, and
the double of the one observed in other sectors in the UK.
the same applied to the companies with moderate levels
For the mild stress scenario, we examined the last of debt. The highest risk levels were exhibited by young
20 year of financial statements for the Scottish tourism companies with no profit and high levels of debt.
and hospitality sectors. The worst distress was observed
The analysis in this report did not address explicitly the
during the Global Financial Crisis (GFC) Period (2007 –
effect of the government support, this will be the subject
2008), and post-GFC recessions followed by the
of further research. However, the high expected default
European debt crisis (2009-2011). The tourism industry
rates confirm that the current government efforts (e.g.
also suffered the effects of swine flu in 2009-2010. We
VAT discount) to support the sector are going in the right
took the observed percentage change of each financial
direction. Nevertheless, we would recommend support
variable between the peak and through points in the
programs to be tailored to the company size to maximise
2008 crisis as the initial estimates of mild downturn for
their impact. Business fundamentals should be taken
our current event.
into account too. Firms that show the highest level of
For the severe downturn scenario, for balance sheet adaptability should be rewarded and offered additional
financials we used an additional 50% adjustment as support to overcome the crisis, in order to increase the
compared to the 2008 crisis. As for Profit & Loss (P&L) chances of success in the deployment of public funds.
variables, we took the recent values from the Scottish Finally, the withdrawal of the current borrowing schemes
government estimates of GDP, that showed the estimated should be carefully planned in order not to create additional
drop for Accommodation & Food industry in April shocks to the companies with high leverage.
2020 of 85%.
31Overview of risk
modelling methodology
The results presented above are based on Wiserfunding The Z-score model remained simple, transparent and
proprietary risk models, and this section provides a brief consistently accurate for many years, and this is a possible
overview of them in the context of the history of credit reason for its popularity in academic and practitioner
risk modelling for businesses. Wiserfunding is a fintech research in finance and accounting. This model opened
start-up offering innovative solutions for risk assessment. to door to many alternative models and frameworks
It provides consultancy services to business lenders, to predict bankruptcy or defaults. The success of the
including the alternative/non-bank ones. The core of its original model inspired more research into default risk
methodology is based on the famous Z-score model, assessment in many scientific disciplines. In addition to
pioneered by Wiserfunding co-founder - Prof Altman finance and accounting scholars, it attracted statisticians
(New York University) and one of the most popular and mathematicians, to experiment with better and
models in finance. more efficient methods and techniques, especially
with the arrival of more data. More on the history of the
The original Z-score model was proposed by Altman
Z-score can be found in Altman (2018).
(1968) and relied on ratios from financial statements to
predict business failures. It used a multiple discriminant For many years thereafter, MDA was the most popular
analysis technique (MDA) to a matched sample containing statistical technique used in bankruptcy and default
66 manufacturing firms (33 failed and 33 non-failed). prediction studies (to mention a few – Altman et al 1977;
From the 22 potentially relevant financial ratios, five were Blum 1974; Edmister 1972; Micha 1984; Lussier 1995;
selected into the final Z-score model as achieving the Taffler 1982; Taffler and Tisshaw 1977). However, the
best prediction of bankruptcy: Working Capital/Total problem which was often pointed out is that the two
Assets, Retained Earnings/Total Assets, EBIT/Total assumptions of MDA do not hold in most prediction
Assets, Market Value Equity/BV of Total Debt and Sales/ problems, i.e. 1) the independent variables should be
Total Assets. multivariate normally distributed (and often they are not);
2) the variance-covariance matrices should be equal
The name was inspired by statistical Z-measures and
for the failing and the non-failing groups. This topic was
also because it is the last letter in the English alphabet.
extensively discussed by Barnes (1982), Karels and
The financial ratios were used as inputs into multiple
Prakash (1987) and McLeay and Omar (2000).
linear discriminant analysis to estimate the bankrupt/
non-bankrupt group and the resulting discriminant Besides, MDA models are less intuitive as compared to
Z-score was compared to cut-offs between “Safe,”, regression when it comes to interpretation. In regression
“Grey” and “Distress” zones. If the firm had a Z-Score analysis the coefficients can be interpreted as the slopes
below 1.8 (Distressed Zone), it was classified as and therefore, indicate the relative importance of the
“bankrupt” and did, in fact, go bankrupt within one different independent variables. This is not the case with
year. Firms with a Z-score above 2.99 keep trading, MDA. Logistic regression does not have these limitations
at least until the end of the study period in 1966. For and was first applied to the bankruptcy prediction
bankrupt and non-bankrupt groups, the accuracy by Ohlson (1980). Ohlson analysed a sample of 105
was 100%, whilst there were 3 errors in classification bankrupt firms and 2,058 non-bankrupt firms from the
for firms in “Grey” zone. COMPUSTAT database between 1970-1976. Probit ⟶
32Overview of risk modelling methodology
⟶ analysis is another popular way to predict bankruptcy In 2016 a fintech start up (Wiserfunding Limited) was
(pioneered by Zmijewski 1984) but logistic regression is founded that focuses on developing credit risk models
more widely used in this field. for SMEs. It used the Z-Score as the starting point,
leveraging on its strengths (i.e. simplicity, transparency
The popularity of logistic regression is based on the fact
and consistency), but building a modern version that
that it fits well the demands of the default/bankruptcy
would be specific to the SME market. This is how the
prediction problem, where the dependant variable is
SME Z-Score was born.
binary (default/non-default). The output from logistic
regression is a score between zero and one which The SME Z-Score models are segmented by country
provides an estimate for the probability of default (PD). and industry to maximize their prediction power, but
The estimated coefficients provide the information on their structure is always the same with 3 modules: a
the statistical effect of each of the independent variables typical financial module using SME specific financial
on the estimated PD. Most of the academic literature in ratios; a corporate governance and qualitative module,
default/ bankruptcy prediction used logistic regression which includes important information about the company
(to name several - Becchetti and Sierra 2002; Gentry et structure, ownership and managerial skills; and last,
al 1985; Lin et al 2012; Mossman et al 1998; Orton et al a macroeconomic module, that allows to put all the
2015; Platt and Platt 1990; Zavgren 1983), and it is also information specific to the company in the economic
used in this study. context in which it operates. There are models for all
countries in Europe and the development of models for
North America and Asia is in progress.
Figure 31 / SME Z-score components
Option 1
or
1
Option 2
2
AI
SME Z-Score
Models
3
Manual feed: eg. interim accounts, management
Automated sourcing
accounts, projections, stressed financials
Source: Wiserfunding, Ltd.
33Appendix
34Appendix Table 2 / Financial Ratios definitions
Financial Ratios Definition
Total Shareholder The shareholders’ funds in a company’s balance sheet is the excess of the assets over the
Equity liabilities. Alternatively, you could view it as the shareholders’ investment in the company –
the share capital plus all the retained profits of the company.
Total Shareholder Equity is the sum of all items under shareholder funds including Share
capital, Share premiums and retained earnings (Net earnings available for reinvestment in
the firm).
Total Assets Total Assets is the sum of all current and non-current assets owned by the firm based on
the purchase price.
Turnover Total earnings of the business from its daily operations from sale of goods and services to
customers also to known as Revenue/Sales.
Short Term Debt Short-term debt, also called current liabilities, is a firm's financial obligations that are
expected to be paid off within a year.
Long Term Debt Long Term Debt (non-current liabilities) is the sum of all financial debt owed for a period
exceeding 12 months from date of reporting (bank loan, debenture and mortgage loans).
Cash ‘Cash’ stands for ‘cash and cash equivalents’ and refers to the item on the balance sheet
that reports the value of a company's assets that are in cash or can be converted into
cash immediately. Cash equivalents include bank accounts and marketable securities,
which are debt securities with maturities of less than 90 days.
Working Capital Capital of the business used in the daily operations calculated as the difference between
the current assets and the current liabilities. Finance provided to support the short-term
assets of the business (stocks and debtors) to the extent that these are not financed by
short-term creditors.
Tangible Assets A tangible asset is an asset that has a finite monetary value and usually a physical form.
Tangible assets can typically be transacted for some monetary value though the liquidity
of different markets will vary.
Intangible Assets An intangible asset is an asset that is not physical in nature. Goodwill, brand
recognition and intellectual property, such as patents, trademarks, and copyrights,
are all intangible assets.
EBITDA EBITDA, or earnings before interest, taxes, depreciation, and amortization, is a measure of
a company's overall financial performance and is used as an alternative to simple earnings
or net income in some circumstances.
Retained Earnings Retained earnings is the amount of net income left over for the business after it has paid
out dividends to its shareholders. It is the accumulated net income subsequent to any
withdrawals as dividends at the reported date under the shareholder funds section of the
balance sheet.
Interest Expenses The interest expenses is the annual accrued amount of interest that the company paid (or
sometimes will have to pay) to its creditors.
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