Some companies begin with a business plan. Valemont Invest Inc began with a rule about the order in which questions and answers should be trusted.
A principle, not an answer
Evan Valemont studied mathematics, probability and the structure of financial markets. His training left him with a specific habit of mind: when a judgment is offered, ask what has to be true underneath it, and ask under what circumstances it would fail. Ryan Mercer arrived from applied mathematics and computational finance and then moved towards data and engineering. His preoccupation was narrower but just as demanding — whether a theory that looks powerful on a page can be written into something workable, and then tested repeatedly without falling apart.
At Cambridge the two shared a tutor, and through that tutor a long-running argument about how a judgment should be earned. What they carried away from those discussions was not a technique. It was a principle, stated plainly and meant to be lived with: understand the problem first, then trust the answer.
The order is the whole point. The principle is not a claim that answers are worthless. It is a rule that an answer without an understood problem cannot be checked, and an uncheckable answer cannot be relied on — however elegant it looks, and however confidently it is stated. This is an uncomfortable rule to build a career on, because it means the first duty is always to characterise the problem more precisely, and that duty never really ends.
Their paths then diverged, as paths do. But wherever each of them went, the same challenge kept surfacing, and it was a challenge about access rather than about intelligence.
The gap they kept returning to
Mature institutions can support a decision with four things working together: data, research, models and risk management. Each is ordinary on its own. Together they form a loop in which a view can be formed, challenged, constrained and reviewed before any money is committed to it.
Data is the raw record: prices, volumes, macroeconomic releases, market structure, flows. Research is the process that turns that record into a question worth asking. Models are the formal statements of a relationship that can be tested against history and against out-of-sample data. Risk management is the constraint layer — the part that decides how much of a view is survivable if the view is wrong.
An individual facing the same market rarely has any of that machinery. What they have instead is news, opinion and immediate reaction: a continuous stream of conclusions with the reasoning stripped out. The conclusions arrive fast and confidently, and there is almost no way to ask the one question the Cambridge principle demands — under what conditions would this be wrong?
Mature institutions can support a decision with data, research, models and risk management. Many individuals, facing the same market, can only find direction among news, opinions and immediate reactions.
Evan Valemont and Ryan Mercer wanted to know something specific, and it was not whether individuals could be given better tips. They wanted to know whether institutional research methods could be taken apart and reassembled into something clearer and more verifiable — a capability in which the reasoning survived the trip, rather than being compressed into a verdict.
That question is harder than it sounds. Institutional research is not a single object that can be copied; it is a set of habits embedded in teams, in process and in infrastructure. Deconstructing it and rebuilding it for a different setting means rebuilding the habits too, and the habits are the expensive part.
That is why the question they settled on was never really about tools. A tool can be borrowed; a habit of verification has to be built, and then maintained under pressure. For anyone who has tried to make sense of markets without an institution behind them, this is the whole difference between a system that can be examined and a stream of confident assertions that cannot be checked at all.
2015: deciding to answer it seriously
In 2015 they decided to answer the question properly. It is worth being precise about what that decision was, because it was not a product launch and it produced nothing immediately. It was a commitment to work on the problem until the method itself could be trusted — a decision about how they would spend their time rather than about what they would sell.
From there the two took up opposite ends of the same problem. Evan Valemont analysed market structure, risk boundaries and the logic of judgment: what a market is actually made of, where the limits of a position sit, and how a conclusion should be assembled from evidence. Ryan Mercer worked to embed that logic into data architecture and models: how the record is stored, how it is joined, how a relationship is expressed in a form a machine can evaluate.
The failures arrived quickly, and they were instructive in a way that successes are not. An assumption might hold up on paper and lose its persuasiveness the moment it met historical data. A model might perform well inside a sample and then expose a weakness in a different market environment. Neither outcome was treated as a verdict on the idea; both were treated as information about what had not yet been understood.
What they learned was not how to make every test successful. It was how to find the parts they had not yet understood when a test failed.
That distinction is easy to say and difficult to hold to over years. The natural reaction to a failed test is to adjust the model until the test passes. The discipline they were building pointed the other way: to leave the failure in view, and to go looking for the part of the problem that had been misdescribed.
Two disciplines, deliberately held in tension
When people describe a quantitative research effort, they tend to describe one skill. In practice two are required, and they pull in opposite directions.
Evan Valemont supplies the research framework and the risk constraints. He defines what counts as an acceptable question, which relationships are worth testing, and what a position is allowed to look like once it exists. This work is essentially financial: it decides whether a result means anything in market terms, and it refuses results that cannot be given an economic interpretation.
Ryan Mercer supplies the data architecture and the engineering system: how information enters, how models are deployed, how results are produced reproducibly. This work is essentially structural. It decides whether an idea can be executed reliably, at a scale and a cadence that a research process can actually sustain.
These two capabilities do not align easily. Some market phenomena resist being coded neatly — the relationship is real, but it will not sit still inside a clean formulation. In the other direction, some technically impressive results cannot withstand scrutiny from financial logic: they are accurate on the data and meaningless in the market.
It is precisely this mutual requirement that does the work. Because each discipline is obliged to answer to the other, neither can quietly mistake works for proven. A result that satisfies the engineer but not the risk framework, or the risk framework but not the data, is not allowed to graduate.
Read plainly, this is a description of a working relationship rather than a technical claim. Evan Valemont and Ryan Mercer did not set out to make two skills compatible; they set out to make each one answerable to the other. The friction is the point, and the friction is what makes the resulting method defensible to someone outside the room.