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Which stock screeners actually performed? The first month of data

June 2, 2026 · ~4 min read

Every stock screener shows you a list of tickers that pass some filter. Almost none of them show you whether those picks went on to make money. That gap is the whole reason this project exists. I log the daily picks of 20+ screening strategies and then track how each batch performs forward over time.

The first full month of data is in. Here's what it shows, what stood out, and (just as importantly) why I don't trust it yet.

How this was measured

For each strategy I take the cohort of tickers it picked on a given date, enter at the first available close after that date, and measure the average percent return to the latest close. The numbers below come from picks made on May 2, 2026, measured to June 1, 2026, roughly a one-month forward window. Each cohort is about 22 equal-weighted names.

Bar chart of 1-month forward returns by screening strategy, May 2 to June 1 2026. Momentum and quality strategies lead near 18%, while value strategies lag near 1%.
1-month forward return by screening strategy · picks from May 2 → measured June 1, 2026.

The full leaderboard

#Screening strategy1-mo return
1Momentum / Earnings Acceleration+18.1%
2GARP+16.5%
3Quality / Compounder+13.7%
4High Quality Growth (Sales)+12.8%
5Small Cap Momentum+12.4%
6Capital-Light / Asset-Light+12.3%
7Fortress Balance Sheet+11.8%
8Low Volatility Defensive+11.6%
9Margin Expansion+11.4%
10High Quality Growth (PEG)+11.3%
11Breakout + Earnings+10.0%
12Free Cash Flow Machine+9.2%
13Insider Buying + Quality+8.5%
14Sustained Growth / FCF+6.9%
15Shareholder Yield+5.0%
16Dividend Growth+4.8%
1752w Low Value+3.0%
18Small Cap Value+2.1%
19Deep Value / Graham-style+0.9%

What stood out

The honest caveat. This is a single cohort per strategy, ~22 names each, over one month of a strong up-market, so momentum leading isn't evidence of durable edge. Every strategy with data finished positive; a rising tide lifted everything. The entire point of tracking this continuously is to separate skill from a good month. One data point is a story, not a conclusion.

What's next

The interesting question isn't "who won last month," it's "who wins consistently." The next piece looks at forward returns across every past pick date for each strategy, so we can see which screeners hold up across different conditions rather than catching one good tape. I'm also digging into whether consensus picks (tickers selected by several independent strategies at once) beat any single screener.

Want the data behind this?

Daily picks, consensus signals, and cohort-based performance, all as a simple JSON API, with a free tier.

Get it on RapidAPI →