Quality, growth, and cash: a plain-English factor model
3 August 2026 · 7 min read
Factor investing has a jargon problem. Strip away the terminology and the idea is simple: some measurable characteristics of a company – how profitable it is, how fast it's growing, how much real cash it throws off – have historically correlated with how the stock performs, on average, across many companies and long periods. A factor model doesn't predict a single stock's future. It ranks companies against their peers on these characteristics, today, using data that was actually available today.
Three simple ingredients
Quality averages return on equity, net margin, gross margin, and a debt-to-equity term (inverted, since less leverage is the higher-quality end) – a rough answer to “how efficiently and safely does this company make money?” Growth averages revenue growth and earnings growth – “is the business getting bigger?” Cash uses free-cash-flow margin – “does the accounting profit show up as real cash?”, since profits that don't convert to cash are a recurring way companies quietly disappoint.
A composite blends the three (weighted toward quality and growth, with cash as a smaller check) into one percentile rank per company, relative to the universe it's compared against. A company at the 90th percentile isn't predicted to return more than one at the 40th – it's simply stronger on these measured fundamentals right now, relative to its peers.
The discipline that actually matters: point-in-time data
Here's the part that's easy to get wrong and expensive to get wrong quietly: a fundamentals figure is only usable in a historical ranking if it was actually public on that date. Companies revise and restate financials; a figure you'd pull from today's database for a date two years ago might be a revised number nobody could have seen at the time. Using it anyway is look-ahead bias – the model quietly gets to see the future, and every backtest built on it will look better than the strategy ever could have performed in real time.
The fix is unglamorous but non-negotiable: every fundamentals figure is timestamped by its actual SEC filing date, and only filings on or before the ranking date are ever used. It's the single most common way honest-looking factor research turns out to be an illusion, and the single most boring thing to get right.
Overfitting is the other trap
Test enough factor-weight combinations against history and some combination will look great – by chance, not by edge. That's why any factor-weight optimisation here runs through a combinatorial overfitting check (a published methodology, not invented for this purpose) that estimates how much of an apparent edge would survive out-of-sample. A high overfitting probability is a real, reportable finding, not something to hide by not asking the question.
None of this is a promise. It's a ranking methodology, disclosed plainly, that tries hard not to lie to itself.
Educational content only – not investment advice or a recommendation to trade. Nothing here estimates or implies future returns.