Quality as an Algorithm: How I Evaluate Businesses

The question "Is this a good company?" sounds simple. In practice, it is extraordinarily difficult to answer because the term "quality" is notoriously vague. Everyone has an intuitive sense of what makes a good company — but intuition is not a reliable compass when it comes to capital allocation.

My approach is different: to operationalize quality as far as possible. Not as a rigid formula, but as a systematic framework that translates qualitative judgments into verifiable criteria. An algorithm in the figurative sense — a sequence of steps that leads to a reasoned assessment.

The Five Dimensions of Quality

After years of analyzing companies — first at AlleAktien, later through the work on Eulerpool — five dimensions have crystallized that reliably capture the quality of a business:

1. Return on Capital

The single most important metric is the return on invested capital (ROIC). It measures how efficiently a company converts capital into profit. A ROIC that consistently exceeds the cost of capital is the clearest signal of a competitive advantage.

A company with a 25% ROIC effectively earns 25 cents per euro deployed. If it can reinvest those profits at 25%, it grows exponentially — this is compounding at the business level.

2. Competitive Position

High returns on capital are only sustainable if they are protected by a durable competitive advantage. Warren Buffett calls this the "moat" — the protective barrier that shields the company from competition.

Typical sources of a moat include:

• Network effects: Every additional user makes the product more valuable for all other users.
• Switching costs: The cost of changing providers is so high that customers stay even when cheaper alternatives exist.
• Cost advantages: Scale effects or proprietary processes that enable permanently lower production costs than competitors.
• Intangible assets: Brands, patents, regulatory licenses that are difficult to replicate.

3. Financial Solidity

A high-quality company has a solid balance sheet. Concretely, this means: low debt relative to cash flow, adequate liquidity reserves, and no dependence on constant refinancing.

The debt metric I find most informative is Net Debt / EBITDA. A value below 2 is comfortable. Above 3, debt begins to constrain strategic flexibility. Above 5, it becomes dangerous.

4. Growth Profile

Growth alone is not a quality marker — profitable growth is. A company growing rapidly while burning capital creates no value. A company growing slowly while generating high margins and cash flows can be an excellent investment.

The relevant question is: Can the company grow without diluting its return on capital? The best companies achieve precisely this — they grow profitably because their business model is scalable.

5. Management Quality

The hardest dimension to quantify — yet decisive. Good management is characterized by: rational capital allocation, long-term orientation, transparency in communication, and the willingness to name uncomfortable truths.

I pay particular attention to capital allocation decisions over the last ten years. How has management deployed free cash flow? Were acquisitions made at reasonable prices? Were share buybacks executed at the right time? The capital allocation track record is the most reliable window into management quality.

The Algorithm in Practice

Concretely, I follow these steps in every business analysis:

1. Screening: Filter for ROIC above 15%, positive free cash flow, and revenue growth over 5 years.
2. Moat analysis: Qualitative assessment of competitive advantage based on the four sources.
3. Financial check: Debt levels, cash flow stability, margin development over 10 years.
4. Valuation: Compare the current price with estimated intrinsic value based on DCF analysis and multiples.
5. Risk assessment: Identify the largest risk factors and estimate their probability.

No single step provides the answer. The interplay of all five produces a differentiated picture that goes far beyond a simple buy/sell recommendation.

Why Most Companies Fail the Test

When this algorithm is applied rigorously, the vast majority of publicly listed companies fall through the filter. This is intentional. Quality investing means not buying many mediocre stocks, but few outstanding ones.

Peter Lynch said that investors need only a few truly good ideas in their lifetime. That is true — but only if the selection is rigorous. The algorithm helps temper one's enthusiasm and maintain high quality standards.

The Limits of the Approach

No framework is perfect. The approach described here has clear limitations:

• It is backward-looking. Historical metrics do not guarantee future performance.
• It favors established business models. Young companies in early growth phases often fall through the filter, even though they have the potential to become tomorrow's quality businesses.
• It requires judgment. The quantitative criteria provide a shortlist; the final decision remains qualitative.

Despite these limitations, a systematic approach consistently delivers better results than an unsystematic one. Not because it is always right, but because it reduces error rates and makes the process reproducible.

FAQ

Can quality really be captured in numbers? Not completely, but largely. The five dimensions — return on capital, competitive position, financial solidity, growth, and management — can be quantified to a great extent. What remains is a qualitative residual that requires judgment and experience. But a systematic approach with clear criteria is superior to any purely intuitive method.

How many companies typically pass the quality test? Of the roughly 3,000 publicly listed companies we regularly analyze, approximately 5 to 10% pass the strict quality criteria in my experience. This is a small universe, but it contains the most reliable long-term investments. Quality investing is selective by definition.

Why not simply buy the broadest index? Index investing is an excellent strategy for many investors. But an index by definition also contains mediocre and poor companies. Those who have the time, knowledge, and discipline to systematically identify quality can outperform the index over the long term — provided they maintain high standards and keep costs low.