Throughout the previous articles, we have looked at a company from many different perspectives. We analysed liquidity, profitability, debt, asset productivity, receivables, inventories and liabilities.

Each of these indicators tells us something important.

The problem begins when we analyse them separately.

A company may have a high ROE while also carrying significant debt. It may be increasing sales while taking longer and longer to collect receivables. It may improve its net profit while simultaneously requiring more and more assets to support its operations.

At the next level of financial analysis, therefore, we no longer ask:

“What is the value of the indicator?”

We ask:

“Why does the indicator have this particular value?”

This is where pyramid analysis becomes useful.

ROE Is Only the Beginning

Let us recall:

ROE = Net Profit / Equity × 100%

Suppose a company has:

Net profit: PLN 1.2 million

Equity: PLN 8 million

Its ROE is:

1.2 / 8 × 100% = 15%

We can therefore say:

every PLN 100 of owners’ equity generated PLN 15 of net profit.

15% looks good.

But we still do not know where it came from.

This is where pyramid analysis begins.

The DuPont Pyramid – Breaking ROE Down into Its Components

ROE can be expressed as:

ROE = ROA × Equity Multiplier

And ROA as:

ROA = ROS × Asset Turnover

Ultimately:

ROE = ROS × Asset Turnover × Equity Multiplier

This is one of the most important connections between all the areas discussed previously.

It shows that the return generated on owners’ equity depends on three things:

margin × efficiency of asset utilisation × financing structure.

Example – Two Companies Both Have an ROE of 15%

Imagine two companies.

Both achieve:

ROE = 15%

At first glance, their performance appears identical.

But let us look deeper.

Company A

ROS = 10%

Asset turnover = 1.0

Equity multiplier = 1.5

10% × 1.0 × 1.5 = 15%

Company B

ROS = 5%

Asset turnover = 1.5

Equity multiplier = 2.0

5% × 1.5 × 2.0 = 15%

The same result.

But a completely different story.

Company A has a higher margin and lower financial leverage.

Company B earns considerably less on every unit of sales, but uses its assets more intensively and relies more heavily on external financing.

The ROE is the same. The risk is not.

Why Can ROE Increase Even When the Company Is Not Performing Better?

Suppose that one year earlier the company had:

ROS = 8%

Asset turnover = 1.2

Equity multiplier = 1.5

ROE:

8% × 1.2 × 1.5 = 14.4%

One year later:

ROS = 6%

Asset turnover = 1.1

Equity multiplier = 2.5

ROE:

6% × 1.1 × 2.5 = 16.5%

ROE increased:

14.4% → 16.5%

Is the company performing better?

Not necessarily.

Its margin has declined.

Its asset productivity has declined.

What increased was the equity multiplier, meaning that external financing became more significant.

The higher ROE was achieved through greater financial leverage.

This is an excellent example of why a single indicator can be misleading.

Pyramid Analysis Changes the Conversation with a Manager

Instead of simply saying:

“ROE fell from 18% to 14%,”

we can ask:

What happened?

Did the margin decline?

Did costs increase?

Did prices fail to keep pace with costs?

Are the assets being used less efficiently?

Did we purchase new machinery or vehicles that have not yet reached full utilisation?

Has the financing structure changed?

Did equity increase?

This is no longer an accounting conversation.

It is a business conversation.

Pyramid Analysis + Causal Analysis

If:

ROE = ROS × Asset Productivity × Equity Multiplier

then a change in ROE can be broken down into the impact of:

changes in sales profitability,

changes in asset productivity,

changes in the financing structure.

Instead of merely saying:

“ROE decreased by 3 percentage points,”

we can try to determine how much of that decline resulted from the margin, how much from asset utilisation, and how much from changes in financing.

From a management perspective, this is a major difference.

Only then do we know where to look for the cause.

Now Let Us Go One Step Further: Is the Company Heading Towards Financial Problems?

Financial analysis is not only about describing the past.

One of its most interesting applications is attempting to answer the question:

Can deteriorating financial results signal future financial difficulties?

This is where discriminant models can be used.

The best-known example is Edward Altman’s Z-score model.

Its underlying idea is particularly interesting.

Instead of looking at one indicator, several different pieces of financial information are combined into one synthetic score.

It is therefore not only about debt.

Not only about liquidity.

Not only about profitability.

The model attempts to assess the company from multiple dimensions simultaneously.

Why Might One Indicator Fail to Detect a Problem?

Imagine a company that is still reporting a profit.

At first glance, everything appears fine.

But at the same time:

receivables are rising rapidly,

cash is becoming increasingly scarce,

short-term debt is increasing,

inventory is accumulating,

asset productivity is declining,

and margins are gradually shrinking.

Each signal on its own might still be explained.

But when they occur simultaneously, the picture begins to look very different.

This is precisely why early warning models attempt to combine several variables.

A Company Rarely Gets into Trouble Overnight

Serious financial problems are often preceded by a sequence of events.

For example:

1. The margin declines.

The company tries to maintain sales.

2. Customers are offered longer payment terms.

Sales are temporarily maintained.

3. Receivables increase.

Revenue still appears in the accounts, but the cash does not.

4. The company begins using working-capital financing.

Debt increases.

5. Finance costs rise.

Net profit declines.

6. The company begins paying suppliers later.

The payment period for liabilities increases.

7. Liquidity deteriorates.

Only then does everyone say:

“The company has financial problems.”

Yet financial analysis may have revealed the first symptoms much earlier.

Profit Does Not Protect a Company from Losing Liquidity

This is one of the most important lessons a manager should remember from financial analysis:

profit ≠ cash.

A company may sell a service worth PLN 1 million.

Revenue will appear in the income statement.

But if the customer pays after 90 days, the company may have to finance three months of:

salaries,

fuel,

taxes,

leasing payments,

suppliers,

and loan repayments.

This is why financial distress analysis should combine the income statement, balance sheet and cash flow statement.

Can a Model Predict Bankruptcy?

No model should be treated as an oracle.

A statistical model does not say:

“This company will definitely go bankrupt.”

Instead, it may indicate:

“The combination of this company’s financial parameters is beginning to resemble the profiles of businesses experiencing financial distress.”

That is an important distinction.

Models can serve as early warning systems, but their results should always be supplemented with business analysis.

Financial statements do not reveal everything.

They do not directly show the loss of a key customer, conflicts between owners, a major future contract, management quality, or technology that may transform the business within a few months.

The Direction Matters More Than a Single Number

When we analyse a company over several years, we begin to see much more.

For example:

ROS: 9% → 7% → 5%

ROA: 8% → 6% → 4%

Receivables: 38 → 51 → 72 days

Debt ratio: 48% → 57% → 66%

Liquidity: 1.8 → 1.4 → 1.1

Each indicator viewed separately may not yet appear alarming.

Together, however, they tell a very clear story:

the company is gradually losing its financial resilience.

This is where pyramid analysis, causal analysis and discriminant models begin to connect.