Please note this was written in September of 2019, so some of the figures might be out of date.

Executive Summary

This report further expands on the report written by Joubert, Garvie and Parle in 2017 by using the Altman formula (1968) to examine the financial position of companies listed on the Australian Securities Exchange (ASX).

To conduct this analysis, the following data was extracted from Morningstar’s DatAnalysis database:

ASX Ticker
GICS Industry Group
Working Capital (Annual Sundry Analysis)
Total Assets (Annual Balance Sheet)
Retained Earnings (Annual Balance Sheet)
Earnings Before Interest and Tax (Annual Profit and Loss)
Market Capitalization (Market Value of Equity – Annual Ratio Analysis)
Total Liabilities (Annual Balance Sheet)
Operating Revenue (Sales – Annual Profit and Loss)

They were then substituted into the following formula:

Z-score = 1.2 x (working capital / total assets) + 1.4 x (retained earnings / total assets) + 3.3 x (earnings before interest and taxes / total assets) + 0.6 x (market value of equity / book value of total liabilities) + 0.999 x (sales / total assets)

After this, they were grouped into industry sector and then into the following “Zones of discrimination”:

Z > 2.99 – “Safe” Zone
1.81 < Z < 2.99 – “Gray” Zone
Z < 1.81 – “Distress” Zone

A summary of the findings are below:

Although this may seem alarming at first, it’s also important to note that interest rates are far lower today than they were when Altman model was created in 1968. This has likely led to companies increasing their use of debt to fund growth (Eg: issuing bonds instead of share capital), which is likely to increase their shareholders returns despite the additional financial risk, particularly for cyclical businesses.

Purpose

The purpose of this report is to inform the Chief Accountant of the Australian Securities & Investments Commission (ASIC) about the financial stability of listed companies for their respective 2018 financial years.

The Altman formula calculates a Z-score which is designed to “predict the probability that company will collapse within 2 years” (Wilkinson 2013). In general, the lower the Z-score, the higher the risk of collapse. Calculating the Z-score of each company and then summarising it by sector, can be used as an indication of the financial stability of the sector. This could then be used to guide ASIC when examining regulations, for example when reviewing the capital requirements for banks.

Method

The information required for each company listed on the ASX as at the end of their 2018 financial year (typically as at 30^th of June 2018) is extracted using Morningstar’s DatAnalysis tool. The Z-score is then calculated for each company using Excel and then the count of the Z-scores for each zone of discrimination & industry classification is calculated. Further details about the formulas used can be found in the Appendix.

The Zones of discrimination are as follows (Joubert, Garvie and Parle 2017):
Z > 2.99 – “Safe” Zone
1.81 < Z < 2.99 – “Gray” Zone
Z < 1.81 – “Distress” Zone

The 2 digit industry classifications were extracted from the MSCI website (2018) and are as follows:

10	Energy
15	Materials
20	Industrials
20	Industrials
25	Consumer Discretionary
25	Consumer Discretionary
30	Consumer Staples
35	Health Care
40	Financials
40	Financials
45	Information Technology
45	Information Technology
50	Communication Services
55	Utilities
60	Real Estate

Findings

The highest risk sector is Utilities, where 65.5% of companies are in ‘Distress’. The lowest risk sector is Materials where 38.3% are in ‘Distress’. Overall, 44.4% of companies are in the distress zone. This may seem alarming at first, however it’s also important to note that interest rates are far lower today than they were when Altman model was created in 1968. According to the RBA (2019), the cash rate target on the 23^rd of January 1990 was 17.00% to 17.50% and currently it is 1%. This has resulted in companies being able to manage a higher level of debt than they were able to in the past, due to the lower interest cost (for loans) and/or coupon payments they are required to pay (for bonds). This could indicate that this figure is somewhat overstated and that the zones of discrimination should be reviewed. Some examples of companies that are classed as in “distress” but are unlikely to be struggling financially are Sydney Airport with a Z-score of 0.81 and Commonwealth Bank of Australia with a Z-score of 1.18.

Companies with defensive revenue streams such as Transurban (Z-score 0.83) and Goodman Group (Z-score 2.6) are likely to have consistent cash flows, even during a recession. Therefore, even though their Z-score is low, they are unlikely to go bankrupt.

Companies which have cash flows that vary martially from year to year, particularly in a recession may not be as safe. For example, although there will always be demand for loans & asset management, investment bank Macquarie Group (Z-score 0.58) may struggle during a recession due to their high debt load and fluctuating cash flows. This can be compared to Lehman Brothers (also an investment bank) who were performing well up to the GFC but declared bankruptcy in 2008 due to being unable to meet its debt obligations.

Following up on the companies Joubert, Garvie and Parle reported on in 2017, the return on assets for Qantas was 9.3%, Virgin was 3.6% and for Telstra was 13.5% compared to 14.2%, 37.6% and 1.5% respectively in 2016 (Joubert, Garvie and Parle 2017). This represented a decrease of 4.9%, 35% and an increase of 800%. Whilst Virgin & Telstra are over the 10% that would be considered material, they are unlikely to be the effect of the new accounting standard for leases (AASB 16) and instead are likely due to economic conditions, changing their product mix and a variety of other factors. Further research will be required to conclusively report on this (such as finding the total amount of leases that are now required to be reported as assets & liabilities and comparing it to the total assets/liabilities).

Assumptions & Limitations

Some of the key assumptions made are:

The data provided by Morningstar is accurate & complete
The correct data was extracted from Morningstar
The excel formulas used were correct
All companies traded on the ASX belong to a sector

A limitation is that some companies did have missing data and/or $0.

4 had $0 in working capital
1 had $0 in total assets
32 had $0 in retained earnings
301 had $0 in EBIT
63 had $0 in market capitalisation
10 had $0 in total liabilities
973 had $0 in operating revenue

If this was due to inaccurate data being processed, this could have significantly overstated the number of companies that are in distress. Another limitation as highlighted earlier is that the ranges for the zones of distress may need to be reviewed due to the impact of lower interest rates making larger amounts of debt manageable.

Conclusion

The Altman formula calculates a company’s Z-score by applying different weights to common accounting performance ratios. Historically, the Altman formula has had a 72% – 80% accuracy rate at predicting how likely a company is to collapse within two years (Wilkinson 2013). However, it is important to recognise its limitations. The fact that 44.4% of companies are in ‘Distress’, is unlikely to be the case and is likely to be reflective of the lower cost of debt in recent years. Therefore, it would be beneficial to review the different zones of discrimination.

AASB 16 has resulted in different ways of recording how leases are shown in the balance sheet. This will lead to assets & liabilities being higher than they used to be, but this effect is unlikely to be material, although a full examination of the effect is beyond the scope of this report.

In the future, there are likely to regularly be new accounting standards introduced, but these often do not result in material changes to the financial statements. Companies with defensive revenue streams are also likely to continue to use debt to deliver better returns to shareholders in this lower interest rate environment which is likely to continue. Although this adds to the financial risk of the business, this is likely to be in the best interest of shareholders.

Appendix

Z-Score Formula

The formula used to calculate the Z-score is the Altman model for public companies (Joubert, Garvie and Parle 2017):
Z-score = 1.2 x (working capital / total assets) + 1.4 x (retained earnings / total assets) + 3.3 x (earnings before interest and taxes / total assets) + 0.6 x (market value of equity / book value of total liabilities) + 0.999 x (sales / total assets)
Where a company has $0 in total assets or $0 in total liabilities, to avoid an error the number 0 was substituted (eg: for the purpose of this report, working capital / total assets = 0 if total assets were $0).

Excel Workbook

There were 3 main sheets used. A sample of the data and formulas used in each of those sheets is shown below. For a copy of the full workbook, please send an email to admin@tonydarcy.online

Sheet 1: Morningstar Data (this was compiled from 11 separate workbooks downloaded, one for each sector):

Sheet 2: Processed Data:

Sheet 3: Summary by Sector:

References

Altman, E. (1968). Financial Ratios, Discriminant Analysis, and the Prediction of Corporate Bankruptcy, Journal of Finance, 23(4), 589-609. Joubert, M., Garvie, L. & Parle, G. (2017).
Implications of the New Accounting Standard for Leases AASB 16 (IFRS 16) with the Inclusion of Operating Leases in the Balance Sheet. Journal of New Business Ideas & Trends. 15(2), 1-11.
Morningstar 2019, Financial Search – DatAnalysis Premium, Morningstar Inc, viewed 7 September 2019 < https://datanalysis-morningstar-com-au.ezproxy.lib.swin.edu.au/ftl/search/fincriteria?xtm-licensee=datpremium>
MSCI 2018, GICS – Global Industry Classification Standard, MSCI, viewed 7 September 2019 <https://www.msci.com/gics>
RBA 2019, Cash Rate – RBA, RBA, viewed 8 September 2019 <https://www.rba.gov.au/statistics/cash-rate/>
Wilkinson, J 2013. Z Score Model | Altman Z Score Purpose | Altman Z Score Formula, viewed 13 September 2019, <https://strategiccfo.com/z-score-model/>

A Study of The Financial Stability of Companies on the ASX (Using the Altman formula).