Vesquarnela — Examine Investment Thesis Assumptions

Why the quality of your research process matters more than the quantity of your information

Every investment thesis begins with a story. You identify a company, a sector, or a broader trend, and you construct a narrative that explains why the opportunity exists and why it is likely to persist. The problem is that narratives are built from bricks you may not have chosen consciously. When you say a business has a durable competitive advantage, you are implicitly assuming that the conditions which created that advantage will continue to hold. When you say a sector is undervalued, you are implicitly assuming that the market's current assessment is wrong and that your own reading of the evidence is more reliable. When you say a management team is exceptional, you are implicitly assuming that past decisions were made by the same people, under comparable pressures, with comparable information, and that the circumstances going forward will reward the same qualities. None of these assumptions is necessarily false, but each one is a load-bearing wall inside your thesis. If any of them collapses without your noticing, the entire structure can come down while you are still congratulating yourself on the quality of your reasoning.

The most dangerous hidden assumptions tend to cluster around three themes: continuity, comparability, and causation. Continuity assumptions hold that whatever has been true will remain true — that a market will keep growing, that a regulatory environment will stay stable, that consumer behaviour will follow its established pattern. Comparability assumptions hold that the situation you are analysing resembles a previous situation closely enough that lessons from the past apply directly to the present. Causation assumptions hold that the factor you have identified as the driver of a result is genuinely responsible for it, rather than merely correlated with it or coincidentally present alongside it. Private investors are particularly vulnerable to all three because the research process tends to be self-directed and confirmatory. You look for evidence that supports the story you have already begun to tell, and the assumptions embedded in that story go unexamined because they feel like background facts rather than choices. Recognising that they are choices — and therefore potentially wrong choices — is the first step towards a more rigorous process.

A practical method for surfacing hidden assumptions is to write what some analysts call a pre-mortem. Before you commit to a position, you imagine that a substantial period has passed and the investment has failed badly. You then work backwards, asking what would have had to be true for that outcome to occur. This exercise is uncomfortable precisely because it is productive. It forces you to articulate the specific ways in which your thesis could be wrong, which in turn reveals the assumptions you were relying on without realising it. You might discover that your thesis only holds if interest rates behave in a particular way, or if a specific competitor fails to enter the market, or if the regulatory framework in a given jurisdiction remains unchanged. Once you have made those dependencies explicit, you can assess how confident you genuinely are in each one, and you can monitor them over time rather than simply hoping they remain intact. The goal is not to talk yourself out of every position but to understand what you are actually betting on, as distinct from what you believe you are betting on.

A related discipline is to distinguish between assumptions that are testable and those that are not. Some hidden assumptions can be examined against observable evidence: you can look at historical patterns of industry consolidation, at how similar businesses have behaved during previous downturns, at how management teams have allocated capital across a range of conditions. Other assumptions are genuinely uncertain and cannot be resolved by research alone — they concern future decisions by regulators, competitors, or consumers that have not yet been made. For the first category, the task is to do the work and update your view honestly when the evidence does not support your prior belief. For the second category, the task is to be explicit about the uncertainty rather than papering over it with confidence. Many investment mistakes are not failures of analysis but failures of epistemic honesty — the investor knew, at some level, that a key assumption was fragile, but chose not to examine it too closely because doing so would have complicated a thesis they had already grown attached to. Keeping your assumptions visible, labelled, and periodically revisited is not a guarantee of good outcomes, but it is one of the most reliable ways to ensure that the outcomes you experience are at least the ones you understood yourself to be accepting.