In valuation, sophistication is sometimes mistaken for quality.
I have seen advisers use increasingly complex techniques partly because they make the work appear more advanced or rigorous. But if the complexity doesn’t improve the estimate of fair value or help us understand the material drivers, it can actually make the analysis worse: more assumptions, more uncertainty, less transparency and greater false precision.
Coming from an academic and quantitative-finance background, I am certainly not against sophisticated models. Quite the opposite. But that experience has also made me skeptical of complexity that does not add information.
A model should be sophisticated because the problem requires sophistication — not because sophistication makes the analysis look more rigorous.
This becomes particularly visible in early-stage valuation.
At seed or very early stage, there may be little operating history, limited evidence of unit economics and a business plan that will almost certainly change. Yet I have seen highly sophisticated models and simulations applied to precisely these companies, generating thousands of possible valuation outcomes.
The output may look impressive. But the distributions, ranges and relationships used in the simulation have to come from somewhere.
If the underlying assumptions cannot themselves be estimated with reasonable confidence, simulation does not remove uncertainty. It packages uncertain assumptions into a more sophisticated output.
In my experience, at this stage it is often more useful to spend less time trying to mathematically refine an inherently uncertain valuation and more time understanding the fundamentals behind the opportunity.
Who is the team? Can they execute? What is the idea or product? How large could the addressable market become?
For very early-stage businesses, these questions can matter far more than whether a model produces €7.8 million rather than €8.2 million.
For more established businesses, the same principle applies differently. If a DCF assumes a sustainable margin of 3%, I want to understand what happens to value if it is 2% or 1%. If growth falls materially below the business plan, I want to understand the impact.
Sensitivity analysis makes this visible. Where several assumptions move together, scenario analysis can show a credible downside, base and upside case.
These approaches may look less sophisticated than thousands of simulated outcomes, but they often make the assumptions and consequences easier to understand and challenge.
This is not an argument against sophisticated techniques when the valuation problem genuinely requires them.
It is an argument against complexity for complexity’s sake.
Every additional layer of sophistication should answer a simple question:
“What do we understand now that we did not understand before?”
If the answer is very little, the additional complexity may not be improving the valuation.
More computation does not necessarily mean more information.