FROM FINANCIAL ANALYSIS TO INVESTMENT CONVICTION
Every deal team can build a model. Fewer can explain, without opening it, why the return is credible. That gap, between a spreadsheet and something you would actually stake capital on, is where much of the work in this business happens. It is rarely the modelling itself.
THE MARKET HAS STOPPED FORGIVING WEAK THESES
For much of the 2010s, private equity returns benefited from relatively cheap debt, higher leverage and the possibility of valuation multiples expanding at exit. Those conditions are less reliable today. Bain’s 2026 Global Private Equity Report describes the change as “12 is the new 5”: deals that once required roughly 5% annual EBITDA growth may now require approximately 10–12% to achieve comparable returns.
At STAG, we naturally take these principles into consideration and strive to apply them thoughtfully throughout our investment decision-making process.
A deal that works only because financing remains favourable or exit valuations increase is exposed to factors outside the business itself. The investment case should show how the company or asset can generate the required return through identifiable operational performance before relying on supportive market conditions. What should replace a weak market-dependent thesis is a clear explanation of how value will be created, why that mechanism is realistic, and what evidence supports it.
WHY BEFORE THE SPREADSHEET
Before a model is built, the opportunity must stand on real reasoning. Not simply a market story, but a mechanism that can be identified and tested.
To illustrate the concept, consider a hotel business. At its core, performance depends on three key variables: occupancy, room rates, and operating costs. When demand is robust and capacity remains available, incremental increases in bookings or pricing can drive a disproportionate increase in profit, as many of the hotel’s costs are fixed and do not rise significantly with occupancy.
The opposite is also true. If demand weakens, revenue can fall faster than costs because the hotel still must pay for staff, maintenance, utilities and other basic operations. A good investment case therefore needs to explain not only why the hotel can generate more revenue, but also whether it can do so without costs rising at the same pace.
The investment question is not simply whether the hotel market is growing. It is whether the particular hotel has a credible reason to perform well. It could be because there is limited competing supply nearby, because it offers something customers value or because its operations can be improved.
The same discipline applies across other investments. Revenue growth from raising prices is not the same as growth from winning new customers, increasing volumes, improving retention or adding capacity. Each source has different evidence, costs and risks. Treating them as interchangeable is how a good analyst can end up supporting a weak business.
THE MODEL’S JOB IS TO EXPLAIN, NOT JUST CALCULATE
Once the reasoning holds up, the model’s task is to make it visible. A model that produces a return without showing which two or three assumptions drive it is not doing its job. It may be accurate as a calculation and still be weak as an investment tool.
If debt is part of the structure, the real question is not whether the plan works if everything develops broadly as expected. It is whether the investment remains acceptable if leasing takes longer, operating performance is weaker, costs are higher or refinancing becomes more expensive than assumed.
This is also where a model with forty tabs may stop adding value. Complexity does not automatically create rigor. It can make the assumptions that matter most harder to identify, especially when each assumption is reviewed separately.
A renewable energy project, for example, may look attractive if electricity prices, production and construction timing all develop as expected. But lower prices, weaker production or a delay in construction can change the return quickly. None of these assumptions needs to be particularly aggressive for the investment case to weaken. The risk often comes from several small disappointments happening at the same time. The model should therefore show not only what happens when one assumption changes, but also whether the investment remains sound when a few reasonable things go wrong together. This is usually more useful than adding further layers of complexity.
VALUATION IS A RANGE, AND THE RANGE IS THE POINT
That is why a single valuation figure is the wrong way to hold onto conviction. A number that comes out to exactly €100 million looks precise, but it is only as reliable as the assumptions behind it.
Change the main assumptions about growth, margins, capital expenditure, financing or the exit basis, and the value may move materially. The difference between €95 million and €105 million may not change the decision. The difference between €80 million and €120 million might, and the range helps show where that decision actually sits.
At STAG, the methodology we apply across our fund structures considers a range of downside, base case and upside scenarios, rather than relying solely on the most optimistic outcome. A deal that clears the investment hurdle only if occupancy, exit pricing and financing terms all develop favourably has limited room for error.
That does not automatically make it a bad deal. It means the risk needs to be recognized in the price, the structure, the leverage, the protections or the size of the investment.
WHAT THE DOWNSIDE ACTUALLY TESTS
The base case in most investments is often close to the expected plan, with only a modest discount applied. That makes it useful for planning, but not sufficient for testing the thesis. The downside case is where we find out whether the investment can absorb disappointment and whether the risks can be managed throughout its lifecycle.
Some risks can be managed through the structure of the investment. Funding can be staged, protections can be added or the entry price can be adjusted. Other risks are harder to manage, especially when the investment depends on one key assumption about demand, regulation, pricing or exit conditions. The important thing is to understand which risks can be reduced and which ones are simply being accepted before deciding to go for it.
In reality, the most useful test is not always an extreme or unlikely shock. It is often a small but plausible miss against the base case, such as slightly lower demand, a delay of a few months or financing that is somewhat more expensive.
If that alone breaks the return, the thesis is thinner than the headline case suggests. If the return survives, the investment may have genuine room for error. The purpose of the downside is not to make every deal look unattractive. It is to show what the investment can withstand and where its limits are.
WHERE THIS LEAVES US
The strongest investment cases are not necessarily those with the highest headline returns. They are the ones where the sources of the return are clear, the key assumptions are supported by evidence and the main risks are understood. Ultimately, the quality of an investment case comes down to how well the numbers reflect the underlying business and how much confidence there is in what needs to happen for the investment to deliver.
What the model cannot establish is whether a specific assumption, about a specific asset, in a market we actually know, is realistic. That still comes from visiting the site, meeting the people running the business, testing the commercial evidence and understanding the local market well enough to know where the opportunity, and its limits, really are.
None of this replaces judgment, and it is not meant to. A model forces assumptions into the open, shows which ones carry the return and makes the effect of downside scenarios easier to understand. A valuation range shows where the investment becomes fragile.