Skip to content
← Home

Evaluating Stocks Like Products: The Hypothesis

In the first post I described why I evaluate companies through two lenses: a quantitative one for timing and a qualitative one for conviction. In the second I showed the machinery: the weights, the thresholds, the geometric mean that penalizes imbalance. Both posts answer the same question from different angles: is this a good company to buy right now?

What neither post answers is the third layer of business quality: where is the world going, and does it favor this company? The scores confirm the numbers are real, the problem is must-have, and the moat is defensible. The hypothesis asks whether the world is moving in a direction that sustains it.

The implicit belief

Every investment carries a hypothesis about the world. When I bought Intuitive Surgical, I was betting that robotic surgery will become the standard of care for most elective procedures. When I bought Veeva, I was betting that the top twenty pharma companies will consolidate their clinical, regulatory, and commercial workflows onto a single cloud platform. These beliefs existed in my head but were never written down, never given concrete signals that would confirm or invalidate them, and never connected to each other to reveal shared risk.

The scores told me Veeva had a 4.64 and Intuitive Surgical had a 4.40. They did not tell me what to do when the stock drops thirty percent. Is this panic, or is my thesis broken? Without the hypothesis written down, the answer depends on how I feel that day. That is not a system.

What goes wrong

Three things go wrong when the belief stays implicit.

First, you cannot distinguish between poor execution and a wrong thesis. If a company misses earnings, is it because management failed or because the world moved in a different direction than you expected? The quarterly numbers cannot answer that. The hypothesis can.

Second, you cannot see hidden correlations. My portfolio has two companies that both depend on P&C insurers modernizing their core systems to cloud. If that migration wave stalls, both positions suffer simultaneously. The statistical correlation check (weekly returns over three years) might not catch this because the stocks do not co-move yet. But the hypothesis makes the shared risk visible immediately.

Third, re-evaluations become mechanical. Every quarter I update the quantitative score with fresh earnings data. But the quantitative score only tells me what happened last quarter. It does not ask the harder question: is the world still moving in the direction I bet on?

What a hypothesis looks like

A hypothesis is not a vague belief. "AI will change the world" is not a hypothesis. "Enterprise AI spending will grow more than twenty-five percent annually through 2028" is, because you can check it, and you know when you are wrong.

Each hypothesis in my system has three layers:

  • A macro belief about the world, with a time horizon. This must not mention a specific company. "Pharma will consolidate on cloud platforms by 2030" is a macro belief. "Veeva will win the pharma market" is not.
  • Company links that explain how each position captures the trend, including the capture risk: who else could capture it, how, and what evidence exists today. This is the most important field. The macro can be correct and the company can still lose.
  • Signals with measurable thresholds, both confirming and falsifying, plus a status: active, weakening, or falsified.

The status matters more than it seems. "Weakening" without a date is dangerous. You can sit in weakening for eighteen months without acting because the hypothesis never formally crossed to falsified. So I added a rule: if a hypothesis has been weakening for more than twelve months without new confirming evidence, treat it as falsified and re-evaluate every linked position.

Two examples

Veeva, where the separation matters most.

Macro belief: The top twenty pharma companies will consolidate from dozens of point solutions to a unified cloud platform by 2030. Every customer on Veeva's Salesforce-hosted CRM must migrate by September 2030.

Macro checkpoint: Fifteen or more of the top twenty committed to a cloud CRM (any vendor) by 2028.

Capture risk:

  • Named competitor: Salesforce Life Sciences Cloud, launched September 2025 with IQVIA as partner
  • Loss mechanism: Pharmas already in the Salesforce ecosystem consolidate CRM there instead of migrating to Vault
  • Why it's plausible: AstraZeneca, Novartis, Takeda, and Pfizer chose Salesforce. About eight of the top twenty remain undecided.

Signals:

  • Confirming: nine of the top twenty committed to Vault CRM. Revenue 3.2 billion dollars, up sixteen percent.
  • Falsifying: five or more of the top twenty choose Salesforce over Vault CRM. Currently at three to five. Approaching threshold.
  • Status: active, capture risk elevated.

The macro is confirmed. The market is bifurcating, not winner-take-all. That distinction only becomes visible when you separate the macro from the capture.

Visa, where the macro is almost a fact.

Macro belief: Digital payments will continue displacing cash globally through 2030.

Macro checkpoint: More than sixty percent of global consumer payments digital by 2028. This is barely a hypothesis.

Capture risk (two independent threats):

  • Threat 1, real-time payments: India's UPI processes thirteen billion transactions per month. Credit card market share in India fell from forty-three percent to twenty-one percent in six years. Brazil's Pix is following the same trajectory. In the US, FedNow has sixteen hundred participating banks, up forty-four percent year over year.
  • Threat 2, regulatory: The DOJ filed an antitrust lawsuit alleging Visa has a debit monopoly, with a potential trial in late 2027. The Credit Card Competition Act was reintroduced in January 2026 and would force cards to offer two unaffiliated networks.

Signals:

  • Confirming: 2026 is the first year more than half of global consumer payments use card credentials.
  • Falsifying: FedNow or A2A captures more than five percent of US consumer point-of-sale transactions. Currently near zero. DOJ ruling restricts debit routing. Credit Card Competition Act passes into law.
  • Status: active, weakest hypothesis in the portfolio. Two independent capture threats, either alone would hurt.

Both companies score well. Both passed the same quantitative and product evaluation. In both cases, the macro is confirmed. The difference is in the capture: Veeva faces a real competitor for the first time, and Visa faces two structural threats converging independently. The hypothesis card makes that visible in a way the scores alone cannot.

What hypotheses do not do

They do not change the scores. A great hypothesis does not save a company with a score total below 3.0. They do not determine position sizing; the scores and classification do that. And they do not add a top-down screening step. The framework remains bottom-up: screen the universe, score the companies, then document the belief that explains why you looked at this company in the first place.

Not every position needs a macro hypothesis. Some businesses are structural. The world needs credit scoring regardless of what happens with AI or autonomous vehicles or interest rates. Those positions get tagged as structural with a one-line reason. Structural does not mean unmonitored. It means the demand is permanent, not that the position is risk-free. The monitoring shifts from macro signals to company-specific risk: can someone else capture that structural demand? Is regulation changing who provides it?

What I know now

After building this layer, I can see things the scores alone could not show me. I can see that my insurance stack (three companies, twenty-five percent of the portfolio) shares risk at the industry level even though two of them depend on the same macro hypothesis and one is structural. I can see that Visa is the position where I should pay the most attention, not because the score is low, but because the world might be moving in a direction that bypasses the company's moat entirely. And I can see that Veeva is the position where my conviction is highest, backed by the most concrete evidence, on a timeline I can actually verify.

The hypothesis is the thing I check when the stock drops thirty percent and I need to decide whether this is panic or a broken thesis. The score tells me what happened. The hypothesis tells me whether what happened matters.

What I changed after using it

After running the hypothesis layer for a few weeks, I noticed a structural problem with where it lived. The hypothesis was a post-entry monitoring tool. I wrote it after the scores were calculated, sometimes after the buy was already executed. That meant the belief that was supposed to give me conviction was being written to justify a decision I had already made. Rationalization, not falsification.

The fix was to move it earlier. The hypothesis now sits between comprehensibility triage and the Product Score. Before I spend the time evaluating the moat and the problem, I have to articulate a falsifiable belief about why the future favors this company. If I cannot, the company goes to watchlist. Not discarded. I might form the conviction later as new evidence appears or as I learn more about the sector. But I do not invest research time or capital without it.

This changes the sequence from screen, score, buy, document the belief, to screen, understand, believe, evaluate, buy. The scoring mechanics are identical. The weights, the thresholds, the geometric mean, all unchanged. What changed is that the hypothesis is no longer an afterthought. It is the reason you sit down to do the work in the first place.