In March 2020, the S&P 500 fell 34% in twenty-three trading days. Most people spent that month reading headlines about ventilator shortages. Some of them were also trying, for the first time, to figure out which stocks to buy. That's a bad time to start.
There are two decisions in equity investing: what to own, and when to buy it. Most people make them simultaneously, under pressure, and get both wrong.
The idea here is to separate them. Do the research when markets are calm and prices are high. Build a list of businesses you'd want to own. Score them. Understand the risks. Then wait.
When markets eventually break — and they do, reliably, every few years — you're not starting from scratch. You already know what you want. The only question is whether the price is right.
This sounds obvious. In practice, almost nobody does it.
We track 17 quantitative indicators, all fully automated. Eleven are primary — price-based signals, volatility crosses, breadth measures, valuation extremes, and drawdown thresholds. Six are supplementary — small-cap confirmation, short-term breadth, breadth momentum, long-term trend, and consumer sentiment.
None of them predict the future. They describe the present. When eight or more primary signals trigger simultaneously, conditions are consistent with periods where long-term forward returns have historically been highest. That's all.
The signals don't find bottoms. They find neighborhoods. Getting within 10-15% of the low and deploying gradually is far more realistic than calling the turn, and historically it has been more than enough.
Each stock goes through nine analytical frameworks. The first eight are about quality — the business, its competitive position, management, risks, accounting. The ninth is about price.
The quality frameworks draw on Porter, Fisher, and Damodaran. Business model, competitive moat, management depth, hidden risks, accounting red flags, the bear case. Each framework is scored 1-10. A company that scores 7+ across the quality frameworks is a business worth owning. That's a necessary condition, not a sufficient one.
The price framework is a reverse DCF. It decodes the growth rate the current stock price implies and compares it against what's probable. A stock passes both filters when the business is high quality and the implied growth rate is at or below the probable rate.
During bull markets, most quality businesses fail the price filter. That's expected. The research is done and recorded. When prices eventually fall 30-50%, the price filter starts passing for businesses whose quality scores haven't changed. That's when the list becomes actionable.
The framework is straightforward. Living with it is not.
In bull markets, you do research on companies whose stocks you can't buy because the price is wrong. You watch them go up without you. Your friends are making money. You're scoring accounting red flags on a spreadsheet.
In bear markets, you deploy capital into companies whose stock prices are falling while the news gets worse every day. The research says the business is fine. Your stomach says otherwise.
A framework doesn't remove the discomfort. It just makes it harder to act on. When the decision is documented — scored, dated, and grounded in specific assumptions — there's something to point to besides instinct. That's not nothing.
This doesn't work for momentum trading, or short-term moves, or a market that goes up for a decade without a meaningful drawdown. It assumes that bear markets happen, that quality persists, and that valuations eventually matter. All of those have been true historically. None are guaranteed.
The framework also can't handle things it can't measure. Regime changes, structural breaks, fraud. It's a tool for disciplined deployment, not a substitute for judgment.
Mostly it's a way to do the boring work early, so the hard work later is just execution.