About Intrinsiqq

Why I Started Intrinsiqq

I started buying stocks in 2019, while I was studying Economics and Business Economics. I went on to do a master's in International Economics, which is mostly about the world economy: trade, tariffs, how countries fit together. It is not a course in valuing companies. That part came from the finance side of the bachelor.

The combination turned out to be more useful than either half on its own. The macro training explains why markets move the way they do, and the finance training explains how to work out what a single business is actually worth. Most of the arguments people have about a stock are really one of those two things wearing the other's clothes.

What I wanted as an investor was simple, and I could not find it. I wanted to look at a company and know, in about a second, whether it is fundamentally any good. Not a badge someone had assigned it. The actual facts: does it earn a decent return on the capital it uses, are its margins holding up, would it survive a bad year, and has any of that been true consistently for the last decade rather than just last quarter.

What the tools gave me instead was a coloured shape, a star rating, or a fair value with no derivation attached. Nobody could tell me where those numbers came from: not the growth rate, not the discount rate, not which line of which filing the cash flow had been taken from. A number you cannot check is not analysis.

So I did what most people told to do their own research end up doing, and built spreadsheets. One tab per company, then one file per company, then a folder full of files. That was the routine for years. It worked, in that I trusted the output, and it was slow, in that every new idea cost an evening of copying figures out of a 10-K, and a single restatement quietly broke assumptions three tabs away.

Somewhere in there it became obvious that the hard part was never the valuation. The maths fits on one page. The hard part is the plumbing: pulling ten years of comparable fundamentals out of filings where the same concept is tagged four different ways, where banks and REITs do not fit the standard template, and where last quarter's number can be revised after you have already used it. That is an engineering problem, and engineering problems get solved once and then reused.

So I built it, on my own, and I still use it every day before I buy anything. It reads the filings, does the unglamorous work of making the numbers comparable, and gives me the read I wanted: one score from eight checks, computed identically for every company, in about a second. I invest in a mix of quality growth and value, so it had to answer both questions at once, which is why the quality score and the DCF sit side by side.

The rule I will not trade away is the one that made me build it: every number has to be checkable. Every formula, threshold, and tag mapping is published. Nothing here is a recommendation, no company pays to be scored differently, and where the data is not good enough to answer a question, the honest answer is a blank rather than a confident-looking guess.

If you find a number that looks wrong, tell me. I would rather hear it from you than leave it sitting there: hello@intrinsiqq.com.

What We Commit To

Coverage

Built for fundamental analysis of businesses that file regularly and generate meaningful cash flows. It is not built for day trading, options strategies, or momentum speculation.

Quality scores, DCF fair value across three scenarios, dividend sustainability, a decade of income statement, balance sheet and cash flow history, and price charting. Every calculation behind them is documented on the methodology page.