Finance is an infinitely dynamic field and no investment course can survive the progress of time ad infinitum.
As markets become more efficient once an investment strategy is known, the course maintains its relevance but has to constantly be on the search for new investment strategies.
The user of Altman’s Z is a novel idea suggested by Dr Wealth staff Irving Soh and has great potential as a new strategy to be taught to the new students of the program.
Altman’s Z was developed by Edward Altman, a New York University Professor who wanted a metric to measure how likely a company would go bankrupt.
It is a linear combination of five financial ratios:
- Working Capital divided by Total Assets
- Retained earnings divided by Total Assets
- Operating Earnings divided by Total Assets
- Market Capitalization divided by Total Liabilities
- Sales divided by Total Assets
The coefficient of each factor is unimportant as Altman is known to vary them based on whether they are private companies or non-manufacturing firms.
A score below 1.8 means it’s likely the company is headed for bankruptcy, while companies with scores above 3 are not likely to go bankrupt.
In its initial test, the Altman Z-Score was found to be 72% accurate in predicting bankruptcy two years before the event, with a Type II error (false negatives) of 6% (Altman, 1968).
In a series of subsequent tests covering three periods over the next 31 years (up until 1999), the model was found to be approximately 80%–90% accurate in predicting bankruptcy one year before the event, with a Type II error (classifying the firm as bankrupt when it does not go bankrupt) of approximately 15%–20% (Altman, 2000).
In 2007, the credit ratings of specific asset-related securities had been rated higher than they should have been. The Altman Z-score indicated that the companies’ risks were increasing significantly and may have been heading for bankruptcy.
Altman calculated that the median Altman Z-score of companies in 2007 was 1.81. These companies’ credit ratings were equivalent to a B. This indicated that 50% of the firms should have had lower ratings, were highly distressed and had a high probability of becoming bankrupt.
Altman’s calculations led him to believe a crisis would occur and there would be a meltdown in the credit market. Altman believed the crisis would stem from corporate defaults, but the meltdown began with mortgage-backed securities. However, corporations soon defaulted in 2009 at the second-highest rate in history.Investopedia
Step One: Develop a Hypothesis on This New Strategy
If you inspect these financial ratios individually, you can develop this hypothesis that perhaps companies with a higher Altman’s Z ratio will be better investments over time.
Why is this so?
Working capital divided by Total Assets measures how much cash they have on hand to deal with expenses that may arise as part of business operations.
- Greater retained earnings can possibly lead to higher growth if the money can be deployed to money-making projects.
- Operating earnings are crucial to the calculation of Free Cash Flow, and it is the lifeblood of dividends.
- Sales divided by total assets is a measure of how quickly the company turns over its assets.
The only ratio I do not like is Market capitalization divided by total liabilities because this goes against the small firm effect that predicts that small firms tend to outperform over time.
Remember that Edward Altman wanted to predict an insolvency event, not build a factor model to aid him with stock selection.
Nevertheless, we now have a hypothesis “Firms with a larger Altman Z ratio outperforms the rest of the market.”
Step Two: Create a Baseline of Results
Before we can conclude whether the Altman’s Z strategy would be successful, we need to build a benchmark to compare against.
We set up two baselines, the first being a back-test of a portfolio consisting of equally-weighted STI counters.
The second being a back-test of a portfolio of all stocks in the Singapore stock exchange. In these tests, we omit REITs and remove all China domiciled companies (for the possibility of fraud).
For each baseline, we go to a Bloomberg terminal and create a back-test that goes back 10 year, rebalancing the portfolio annually.
We end up with the following results:
|All SGX counters||6.60%||8.23%|
Semivariance measures the downside risk of an investment strategy.
Step 3: Test a Subset of stocks based on the Proposed Hypothesis
Armed with a baseline, it is now time to test our hypothesis.
For each baseline of stock, we select half of the stocks with a higher Altman Z ratio and run through the same back-test again on the Bloomberg terminal. Obviously, the back-test involves only a subset that is half the size of the back-test we have built previously.
Here are our results.
|STI Components – Higher than median Altman Z||0.64%||2.54%|
|All SGX counters – Higher than median Altman Z||9.80%||7.55%|
From this observation, we can conclude that Altman’s Z may not function too well for STI components and can lead to underperformance.
I might even suspect that Altman’s Z design biases the back-test to pick up larger market capitalization counters.
For the smaller cap stocks in SGX, higher Altman Z showed great promise as a strategy with a huge bump in returns and a lower downside risk.
Step 4: Refine the Strategy to generate a subset of stocks that are Investable by a Retail Investor
We are now armed with information that Altman’s Z works well for all SGX stock counters. Stocks with above-median Altman’s Z number in the several hundred. The next step would be to generate hypotheses that combine ERM’s existing strategies with Altman Z to see if returns can be supercharged further.
One possible hypothesis may look like this: “Will choosing stocks with a higher Altman Z improve the performance of a portfolio of stocks with sustainable dividend yields above 4%”
Eventual refinement led to a strategy with 19.12% returns and 8.23% semivariance.
This is an investable set of 30 stock counters.
You can join the Early Retirement Masterclass to find out more. The Early Retirement Masterclass is a data-driven program that arms retail investors with investment concepts and tools that are designed to provide them with a high probability of achieving Early Retirement.
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