Human beings love predictability. It provides knowledge about the future on a subject of interest. This is evident throughout life. From weather forecasts to world cup matches.

These predictions help us make plans and decisions when we don’t know what the future holds. As such, they are not only great but necessary.

But predictions, especially accurate ones, are not mere guesswork. They are the result of much analyses and the considerations of all sorts of probabilities.

This is where knowledge on statistics comes in handy.

Statistics offers a great way of understanding the trends.

As such, statistics are widely used to make predictions.

Making predictions can be made possible through a formula developed for that purpose.

### The Altman Z-Score

The Altman Z-Score was developed from the original z-score. Z-Scores are used to determine how far a value is from the mean.

Depending on the data being analyzed, the values lead to various conclusions. For example, it may show a student’s performance in comparison to the average performance.

Source: makemeanalyst

Building on the accuracy of the Z-Score, Edward I. Altman developed a formula for predicting the chances of a company becoming bankrupt.

This formula was picked up by those who wanted to know the profitability of their investments. Also standing to benefit from it were the potential investors.

Obviously, you would not want to put your money in a company which was about to file for bankruptcy.

But preventing bankruptcy is more than an investor’s desire.

When companies become bankrupt, many other challenges come up.

If big companies become bankrupt, an entire economy can be affected because of the many direct and indirect losses suffered.

### How accurate is the Altman Z-Score?

Reading the above section, you can tell that this model is accurate since it predicted the 2008 recession.

More than that, there have been studies done to affirm its accuracy.

• A prediction of bankruptcy 2 years before it happened had an accuracy of 72%. The false positives accounted for 6%.
• Further tests in the course of 31 years after its launch, the Altman Z-Score proved accurate by 80%-90%.
• In predicting bankruptcy 1 year before it happened, false positives were recorded at 15%-20%

Note: False positives are bankruptcy predictions where the company never became bankrupt.

## THE ALTMAN Z-SCORE FOR MANUFACTURING COMPANIES

In 1968, Altman published his Z-Score model which was primarily intended for use with publicly-held manufacturing companies.

The reason for targeting this specific group was because of the availability of their financial records.

Moreover, public companies have more transparency when it comes to reporting their financial status.

This is not the case with many private firms. Especially financial institutions.

Altman started evaluating the status of 66 companies.

Out of these, half of them had already filed for bankruptcy between 1946 and 1965.

At the beginning, he considered a total of 22 ratios for his calculations.

These ratios were classified into categories of liquidity, profitability, leverage, solvency and activity. Later on, he narrowed the number of ratios to 5.

The formula he came up with is shown below.

Altman Z Score = (1.2 x A) + (1.4 x B) + (3.3 x C) + (0.6 x D) + (0.999 x E)

Where:

A = Working Capital / Total Assets

B = Retained Earnings / Total Assets

C = Earnings Before Interest and Tax / Total Assets

D = Market value of equity / Total Liabilities

E = Sales / Total Assets

### Interpreting the Score

Upon calculating the Altman Z-Score, you will get a number which is the indicator you are looking for.

For investment purposes, or whatever other reason, it is important to interpret the score correctly.

The score is interpreted by checking it against a range of values.

There are three ranges of numbers which show the financial status of the company in regards to bankruptcy.

• Score of 3 and above – a score of more than 3 indicates that the company is in the “safe zone.” This means that the company’s financial status is okay. It is financially healthy. If you are investing, this would be a good company to invest in.
• Score from 2.9 and 1.8 – this range is considered a “gray area.” Companies which have a score lying in this range are not very safe. Their finances are not stable and the companies may get into the “danger zone” if there are improvements.

If you are looking for an investment, this would not be a very good bet. If you already have your money there, you will need to act fast.

Decide to either follow keenly everything affecting the company or just sell off your investment.

• Score of below 1.8 – any score below 1.8 should scare you. Do not give it much thought as the company is in the “red area” or “distress zone.” The lower the score, the more danger there is in the company soon becoming insolvent.

#### Example Calculation

Belta manufacturers in China produce car engines. They have been in the business for almost 20 years.

They have been profitable enough to employ more staff and increase their production. But with a recent loan taken to facilitate automation, investors want to know how the company is doing.

Their total assets are worth \$3,500,000 while they have a working capital of \$4,200,000. Their liabilities stand at \$5,000,000 while retained earnings amount to \$800,000. Earnings Before Interest and Tax come to \$6,500,000. Sales total \$8,300,000 while the market value of equity is \$7,000,000.

Here is the calculation of Belta’s Altman z-score:

Altman Z Score = (1.2 x A) + (1.4 x B) + (3.3 x C) + (0.6 x D) + (0.999 x E)

(1.2 x (4,200,000 / 3,500,000)) + (1.4 x (800,000 / 3,500,000)) + (3.3 x (6,500,000 / 3,500,000)) + (0.6 x (7,000,000 / 5,000,000)) + (0.999 x (8,300,000 / 3,500,000))

= (1.2 x 1.2) + (1.4 x 0.229) + (3.3 x 1.857) + (0.6 x 1.4) + (0.999 x 2.371)

Altman Z-Score = 11.097

The investors can comfortably toss away their fears and relax, expecting higher returns. With a score of 11.097, Belta is firmly in the safe zone.

## THE ALTMAN Z-SCORE FOR PRIVATE COMPANIES

There was a need for predicting the possibilities of private companies filing bankruptcy. This made Altman come up with a formula for these too. The formula is as below.

Altman Z Score = (0.717 x A) + (0.847 x B) + (3.107 x C) + (0.420 x D) + (0.998 x E)

Where:

A = (Working Capital) / Total Assets

B = Retained Earnings / Total Assets

C = Earnings Before Interest and Taxes / Total Assets

D = Book Value of Equity / Total Liabilities

E = Sales / Total Assets

### Interpreting the Score

The interpretation of the score for private companies is slightly different from that of the public manufacturers.

Here is the guide:

• Score of 3 and above – this score indicates that a private company is in the “safe zone.” A company with this score is financially stable and the chances of going bankrupt are very low.
• Score from 2.99 to 1.23 – this is the “gray area.” The chances of bankruptcy are moderate and these companies should work on improving their financial standing.
• Score of below 1.23 – this is the “distress zone.” A company in this position is highly likely to become bankrupt. Investors in such companies should be on high alert.

#### Example Calculation

Keriko is a medium-sized marketing agency.

They have been in operation for 2 years. Business has not yet hit their expectation but things are running smoothly.

Wanting to know the outlook of their business, they seek to calculate their Altman z-score.

Keriko’s earnings before tax and interest are \$650,000. Their revenues (sales) are \$857,000 while total assets amount to \$1,200,000 with liabilities being \$320,000. Book value of equity stands at \$7,000,000. Retained earnings are \$135,000 and working capital is at \$400,000.

Altman Z Score = (0.717 x A) + (0.847 x B) + (3.107 x C) + (0.420 x D) + (0.998 x E)

= (0.717 x (400,000 / 1,200,000)) + (0.847 x (135,000 / 1,200,000)) + (3.107 x (650,000 / 1,200,000)) + (0.420 x (7,000,000 / 320,000)) + (0.998 x (857,000 / 1,200,000))

Altman Z-Score = 11.917

The management of Keriko can rest assured that they are on the right path.

Their score shows they are not anywhere near bankruptcy.

## THE ALTMAN Z-SCORE FOR NON-MANUFACTURING COMPANIES AND THOSE IN EMERGING MARKETS

Altman also worked on a formula for non-manufacturers as well as those companies in emerging markets.

Below is the guide on calculating their scores.

#### For non-manufacturers

Altman Z-Score = (6.56 x A) + (3.26 x B) + (6.72 x C) + (1.05 x D)

#### For companies in emerging markets

Altman Z-Score = 3.25 + (6.56 x A) + (3.26 x B) + (6.72 x C) + (1.05 x D)

Where:

A = Working Capital / Total Assets

B = Retained Earnings / Total Assets

C = Earnings Before Interest and Taxes / Total Assets

D = Book Value of Equity / Total Liabilities

### Interpreting the Score

The score interpretation is as below:

• Score above 2.6 – this is the “safe zone” for these companies.
• Score from 2.6 to 1.1 – this is the range for those companies in the “gray area.” This means some work should be done to avoid getting into the danger zone.
• Score below 1.1 – this is the “distress zone.” These companies might be filing for bankruptcy any time.

## LIMITATIONS OF THE ALTMAN Z-SCORE

Altman’s Z-Score provides any investor or regulator with a great way of knowing what is yet to come. With accuracy rates of over 80%, this model is certainly worth utilizing.

As is normally the case, advantages rarely come alone. Strengths are usually accompanied by weaknesses.

The Altman Z-Score model has some limitations as it’s usage in some scenarios may not be ideal. Below are some of the limitations.

### Cannot be used with financial companies

Like other companies, financial organizations can have their scores calculated using the Altman Z-Score model. The problem is that data for these companies may not be available.

This is not because the data doesn’t exist, but because it isn’t readily available or easily accessible.

If inaccurate data causes inaccurate results, what would unavailable data do?

Financial organizations tend to keep much of their records private. This may be seen as just the nature of anything to do with money.

People keep their financial status secret and so do banks. Though they are businesses like the others, this seems to be widely practiced in the finance sector.

Reports on fraud also put a lot of pressure on the banks. And as they work on tightening security, the criminals seem to be improving their skills too.

Watch the below video to see how easy cloning a credit card is. There is definitely a need to be more cautious with your card.

Reporting the full amount lost is seen as possibly damaging the image of the company.

When customers hear the amounts lost and the frequency of the losses, they might conclude that the company is insecure.

And no-one wants to put his money where it’s not safe.

With this mode of operation, there is little predicting which can be done from the outside. But from the inside, it’s possible.

Yet it’s not the inside prediction that is needed because that can be kept secret.

The people who need the information i.e. shareholders and other investors, may be inconvenienced by this.

### Only as accurate as the data used

This is quite expected. This model uses data from a company to calculate the possibility of insolvency.

Obviously, the results can only be as good as the data used.

If the company provides inaccurate data, then that is what will go into the model. After calculations, the model will still give a score and that score is what will be used to determine possible bankruptcy.

The challenge lies in the possibility of a company’s management or accounting department deciding to falsify information.

As much as the model was initially developed using publicly-available records, there is no guarantee that these can’t be inaccurate.

For example, a CFO or other high-ranking employee may have his job on the line. If the records available prove his under-performance, he may decide to falsify the information.

Doing the calculations himself, he may alter the records and provide data which will result in a high Z-Score.

This will make the public, shareholders or regulators happy. But the problem in the company will still exist.

If the company eventually goes down, everyone will be taken by surprise. Many will lose their money since they never saw the need to sell off their stock.

### Cannot be used with new companies

Many companies are being registered everyday as entrepreneurs venture into business. As good as this is, many startups fail to become the big businesses they were envisioned to be.

Many new companies lack finances. Though they may have managed well enough with the much they had, piling debt can bring them down fast. With sales being low, the situation may get too hard for the entrepreneur to handle.

Yet even new companies often have investors. There are people who put their money in new companies. Others invest by giving products on credit trusting that the company will sell and pay its debts on time.

Wouldn’t it be good if these people could tell if the company would be unable to stand?

It certainly would. Unfortunately, the hero model, Altman’s Z-Score can’t help in such a situation.

The biggest problem is that there is simply too little data to facilitate this calculation. Two particular variables are not well captured in a new company, making the Altman Z-Score unusable. These are:

1. Retained earnings – these earnings are determined at least after 1 year of business operation. This is because you have to calculate profits or losses so as to know whether you have any retained earnings. If you have some, then you will be able to have the figures to use.

Without retained earnings in the formula, there will be no accurate results. Every piece of information required in the formula must be available. With this situation, it means that the model cannot be utilized.

2. Market value of equity – the market value of a new company’s equity can be very difficult to determine. This is because the company may not have traded enough to be known. This may be because there are no investors interested in the company.

Also, it’s products may not have been well received by the public yet. Maybe the company was still in the marketing period working on some strategies when it went down.

Maybe it was looking for funds and not attracting an investor in good time, it closed down.

Such scenarios make it difficult to put a number on the market value of the company’s equity. With an unconfirmed figure, the results may not reflect the true state of the company. The calculation may not even be possible.

### The model is based on old discriminatory data

The Altman Z-Score model was published in 1968, which is over 50 years ago. At that time, the dataset used by Altman was for 66 manufacturing companies whose net worth was more than \$1 million.

The dataset in itself was discriminatory.

Although picking publicly-help companies for the task was okay, why discriminate against smaller companies?

Many reasons can be given but using this as the foundation of the model can be limiting to its use.

More than that, the sheer age of the model creates room for opponents to discredit it. The business environment in 1968 was very different from the current one. You may wonder whether there are considerations the model is missing.

Could it be that smaller companies have a Z-Score which might be interpreted differently? Or the score might differ greatly depending on the size of the company?

## CONCLUSION

The Altman Z-Score is a good model which can accurately show the direction a company is taking. If the alarm is raised in good time, then corrective measures can be taken.