Evaluating Stocks Like Products: The Business
The previous post was about the thesis. The need described without any company attached. By the end of it, the Credit Scoring thesis was alive and its roster had names on it.
This is the post about the names.
A player on a roster is a candidate, not a position. Whether to commit capital is its own question. What I keep for each candidate makes sure that question gets answered against evidence, not impulse.
For each candidate I keep the same set of pieces, each defined the same way every time:
- What it does. The business in a sentence.
- The moat. Its durable advantages, each evidenced and given a direction.
- The three legs. Whether it dominates on revenue, profit, and growth.
- Falsifiers. Specific events that would break its hold on the need.
- Watch signals. Softer signals I track between falsifiers, defined just as tightly: what I watch, what trips it, how often, and the source.
- The pre-mortem. Ranked ways it fails, each one tied to a falsifier.
- Exit criteria. Mechanical triggers to leave.
- The numbers. The financials, last, as confirmation.
Two more only matter once I act: what management did with the cash, and the entry context I freeze if I buy.
I'll use FICO, the example from the last post, as the illustration. The point isn't FICO. It's the shape of the read. What follows is some of what I keep, not all of it.
What it does
Before anything else I make sure I can say, in one sentence, what the company sells and where the money comes from. For FICO: it licenses the credit-score algorithm to lenders, mostly through the three credit bureaus, and that licensing is the slice anchored to the thesis. A separate software business rides alongside but isn't the core. That's enough to start.
The deep-dive behind it
That one-sentence summary is the easy part. None of the pieces that follow come that cheaply. Behind each is a long-form deep-dive: years of filings, earnings calls, regulator pages, primary sources, a few thousand words of notes nobody else reads. The structured pieces are what that reading distills to.
It matters because the moves that decide an investment rarely show up in a single data point. They show up when the pieces sit together. On FICO, reading across a few years surfaced a deliberate pricing-and-distribution play that no single filing called a strategy, but that plainly was one once laid side by side. That pattern is the kind of thing the deep-dive exists to catch.
One deep-dive per player before I write anything down, then quarterly and annual reviews keep it current.
The moat
A moat, in my notes, is a set of named advantages, each written as prose with a primary source behind it. There are ten types I work with (brand, switching costs, network effects, scale, pricing power, regulatory protection, and a handful more), and an element either earns its place with evidence or it doesn't. What each one carries is a direction: stable, eroding, or strengthening, so the picture moves as the world does.
For FICO the most load-bearing advantage is regulatory: federal rules effectively require its score across large parts of mortgage lending. That used to be exclusive, and a 2026 ruling cracked the door open to a competitor, so I tag it eroding and watch it harder than the rest. Brand, switching costs, and pricing power read as mostly stable. The direction tags are where the attention goes, not the labels.
Does it dominate on all three legs
A company can lead on revenue and still be weak on profit, or grow fast and keep none of the cash. So I check three legs on their own: revenue, profit, and growth. Each gets a plain pass or fail with evidence behind it. I keep them separate on purpose, because a strong leg shouldn't be allowed to paper over a failing one.
FICO passes all three today: it leads its market, the economics are genuinely high-margin, and it's still growing. The verdict matters less than the habit of looking at each leg separately before nodding along.
What management did with the cash
Between the business and the numbers sits management. I keep ten years of major capital decisions, each logged with what it was, roughly how much, and whether it created value, destroyed it, or did neither. Just the call and what came of it.
Reading them in sequence is how I tell whether management has been a friend or a tax over time. For FICO the recent record is mixed: some sharp value-creating moves alongside buybacks at rich prices I've tagged neutral-for-now, with a note to revisit. Keeping the judgment attached to its date is what makes the record useful five years later.
What would kill it
The thesis has falsifiers about the need. Each player gets its own falsifiers about whether this particular company can keep capturing it: a concrete event, a mechanism, an observable signal, a date, a primary source, plus a rough probability. A player falsifier firing doesn't kill the thesis. It just knocks this company off it.
For FICO I keep several, each pointing at a different way the capture could break: a regulator stripping its required status, a forced price cut, a competitor finally taking share, an enforcement action. Writing them down in advance is the discipline. The bad news gets a name and a threshold before it arrives, instead of being explained away when it does.
Watch signals are the falsifiers' lighter siblings, written with the same parts: what I watch, what would trip it, how often I check, and the source. The difference is the job. A falsifier breaks the case when it fires. A watch signal only moves my confidence and points to where the next falsifier might form. For FICO, one is its distribution growing more concentrated in a few channels, worth tracking long before it would ever break anything.
The pre-mortem ties to the falsifiers
Before deploying capital I write a pre-mortem: three to five ways the position fails over five years, ranked by how likely I think each is. The rule I hold myself to is that every cause has to map to one of the falsifiers. If a failure I can imagine maps to nothing, either I'm missing a falsifier and I add it, or the cause is too vague and I sharpen it until it points at something I could actually observe.
For FICO the top cause maps to its top falsifier: regulatory pressure forcing a price concession. The mapping runs both ways over time. At each review, quarterly and annual, I take the falsifiers and ask whether the failure they describe has moved closer. The pre-mortem isn't a one-time exercise, it's a check I keep running.
What would make me leave
Exit criteria sit next to the falsifiers but do a different job. A falsifier lowers my conviction. An exit criterion is a mechanical trigger: if it fires, I'm out, with no re-litigating it in the moment. The point is to make the decision now, calmly, instead of later when the position is moving and I'm tempted to talk myself out of it.
For FICO they're written as specific, observable combinations, the kind of thing I can't argue my way around when it actually happens.
The numbers come last
Only here do I look hard at the financials, and only to confirm what the rest already said. They're facts the company already produced, pulled from its 10-K and 10-Q filings and verifiable against EDGAR. They get calculated straight from the line items. If the business and the moat read like a fortress and the returns on capital came back mediocre, something in my read would be wrong. For FICO, the numbers and the story agree. In plain terms:
- It keeps an unusually high share of what it sells as profit (operating margin).
- It turns more than all of its reported profit into actual cash (free cash flow against net income).
- It earns extraordinary returns on the money it puts to work (return on invested capital), near 70% where even strong businesses usually sit under 20%.
- It carries a moderate amount of debt against its earnings (net debt to EBITDA).
The underlying figures, from the snapshot pulled 2026-05-15:
| Metric | TTM |
|---|---|
| Revenue | $2.26B |
| Return on invested capital | 68.1% |
| Gross margin | 84.2% |
| Operating margin | 50.4% |
| Free cash flow / Net income | 1.19 |
| Net debt / EBITDA | 3.01 |
I don't forecast these forward, and I don't lean on price targets, mine or anyone's. Every projection bets on growth I can't verify, and that bet quietly becomes the thing I own instead of the business. What the numbers do tell me is whether today's price looks rich, fair, or cheap against the business behind it. That's one input to the enter-wait-skip call, not the answer. A good business at the wrong price is still a bad call.
If the numbers and the story ever disagree, I have a couple of diagnostics to find out which one is lying: a reverse-DCF read of what today's price implies, and an accrual check for when reported earnings drift from actual cash. Flashlights, not gates.
After qualification
When all five pieces hold and the numbers confirm them (business described, moat documented, three legs pass, falsifiers and pre-mortem mapped, exit criteria set, numbers consistent with the rest), the player has cleared the bar. That's the end of what the discipline does. The rest is a separate judgment.
Qualified means "worth capital." It doesn't mean "buy now." Whether I actually deploy, how much, when, and across one name or several, depends on things the discipline doesn't speak to. How close earnings are. Whether I'd be doubling up on something I already hold (Visa and Mastercard are functionally the same exposure, I might want one and not both). Whether I'm averaging in or buying it all at once. What else is qualified and competing for the same capital. Whether the price today is one I want to live with for years. Sometimes a player qualifies and I still don't enter. That's a valid outcome.
When I do enter, sizing lives in three qualitative buckets:
- Core (10-20%). Maximum conviction, business I understand deeply, odds clearly in my favor.
- Normal (5-10%). Solid business, solid thesis, regular size.
- Tracking (2-5%). Exposure I want with relevant uncertainty, or where I'm still learning the operational shape.
The ranges are percentages of total net worth, not of any sub-bucket. I pick the bucket by judgment, against the same factors above.
A thesis can carry more than one qualified player at the same time, and the qualified set can shift as the world moves. Under Credit Scoring, FICO is the obvious candidate today, and Equifax sits on the same roster capturing a different slice of the need (consumer-credit data, distribution to lenders, a co-owner stake in VantageScore). If FICO's regulatory floor erodes and Equifax's distribution share strengthens, either or both could qualify at a given moment. The discipline says "this is worth capital" for any that pass. Which one, how much, and when stay calls I make against the factors above.
If I deploy
When I do commit capital, a snapshot of the moment gets frozen. Evaluator, date, price, target weight, rationale paragraph, comparative context against alternatives, and a list of sources I read before deciding. Set once. No rewriting it later.
The reason for the freeze is the principle that decisions get judged by what was known at the time, not by outcomes. A year later at half the price or twice the price, the question is whether the rationale was honest given the evidence I had, not whether the outcome turned out well. A frozen entry context is the only way that question stays answerable.
After deployment, two regular checks run. Quarterly: read the 10-Q, refresh the numbers, log an observation with a primary source. Annually: bear-case-first deep review, ask whether the model of the business has changed in the year, update the trajectory tag if it has.
Where this leaves me
The thesis describes the need. The player is the company that captures it. Both have to hold for capital to move.
Whether FICO is worth holding at any given moment is a decision I keep separately. The discipline's job is to make sure I can answer "what did I know when I decided" without reconstructing it under pressure later.
The discipline prevents the obvious mistakes: unmapped causes, ungated entries, hidden judgment. It doesn't produce edge. If there's any edge in concentrated investing, it comes from the research itself (10-Ks, transcripts, regulator filings, news, interviews, watching how the business and the world around it shift over time), not from the notes that organize the thinking. The notes are bookkeeping. The research is the work. And most of that work is on the need itself, not on the players. When I understand the problem well enough, the players get easier to read.
There's another piece to it. Because the attachment is to the problem and not to the company, I find it easier to discard. When a company I follow announces a new product, an acquisition, or a new investment, the first question is whether it serves the need they're supposed to capture, or whether it's drifting away from the moat. If it's drifting, that's a signal, not noise to explain away. Confirmation bias has less to grab onto when what I care about is the problem, not the name on the stock.
None of this is a framework I'd hand to someone else, and I'm not out to convince anyone of it. People invest in plenty of ways, and none of them is wrong. This is just the angle I landed on, the one that gives me conviction when I sit with it.
In the end, what I'm looking for is a good business anchored to a need that won't go away easily, with a moat to keep capturing it. The rest is bookkeeping that keeps me honest with myself.