via WSB:
This sort of blew up on r/ValueInvesting so posting here too.
I work in AI and started trading casually last year. Like any good regard, I immediately subscribed to every investing newsletter I could find on Substack. 23 paid subscriptions. $9,600/year, including Michael Burry’s.
The problem? I can’t actually read them all. And I have no idea which ones are worth the money.
So I did what any engineer would do — I wrote codes to find out.
What I Built
A pipeline that:
– Crawls every article from 23 paid Substack authors (1,782 articles over the past year)
– Uses Gemini AI to extract high-conviction stock picks only — not casual mentions, but tickers the author actually analyzed in depth
– Tracks returns at 1d, 7d, 15d, 30d, and 60d after publication
– Calculates alpha vs sector benchmarks (SOXX for semis, IGV for SaaS, XLF for financial services etc)
– Dedupes: if the same author calls the same ticker multiple times within 14 days, it only counts once (first mention wins). Different authors calling the same ticker are tracked independentlyTotal dataset: 3,519 high-conviction calls tracked over 1 year.
The Results
30-Day Absolute Return Leaderboard (Long Calls)
Rank Author Calls 30d Avg Return 1 Global Tech Research 50 +14.9% 2 Paulo Macro 21 +9.5% 3 Collyer Bridge 89 +8.7% 4 Doomberg 79 +7.8% 5 SemiAnalysis 80 +7.5% 6 Altay Capital 15 +7.2% 7 The Overshoot 24 +7.1% 8 The Setup Factory 285 +6.7% 9 Fabricated Knowledge 50 +5.8% 10 Macro Charts 72 +3.6% 30-Day Alpha vs Benchmark (Long Calls)
Rank Author Calls 30d Avg Alpha 1 Global Tech Research 50 +9.4% 2 Paulo Macro 21 +6.8% 3 Altay Capital 15 +5.2% 4 Collyer Bridge 89 +4.8% 5 The Setup Factory 285 +4.3% 6 Doomberg 79 +3.8% 7 SemiAnalysis 80 +3.4% 8 Lord Fed 86 +3.1% 9 The Overshoot 24 +1.8% 10 Shrubstack 100 +1.5% 30-Day Win Rate (Long Calls)
Rank Author Calls Win Rate 1 Paulo Macro 21 85% 2 Altay Capital 15 85% 3 Global Tech Research 50 81% 4 The Overshoot 24 79% 5 Doomberg 79 72% But 30 Days Isn’t the Whole Story
30d is a reasonable window for swing traders, but some of these authors are deep value investors with 6-12 month theses. Here’s what the 60-day numbers look like — the rankings shift significantly:
60-Day Absolute Return Top 10 (Long Calls)
Rank Author Calls 60d Avg Return 1 Global Tech Research 50 +26.7% 2 SemiAnalysis 80 +16.7% 3 Fabricated Knowledge 50 +14.2% 4 Altay Capital 15 +13.7% 5 Doomberg 79 +12.6% 6 Paulo Macro 21 +12.1% 7 Macro Charts 72 +11.1% 8 The Setup Factory 285 +10.8% 9 The Overshoot 24 +9.6% 10 TicToc Trading 180 +8.9% Notable shifts: Fabricated Knowledge jumps from #9 (30d: +5.8%) to #3 (60d: +14.2%). Altay Capital goes from +7.2% to +13.7%. Deep value theses need time to play out. Conversely, Collyer Bridge drops out of the top 10 at 60d — their edge is more short-term.
Take these numbers for what they are: one time horizon among many. A 60d or even 90d window would tell a different story for buy-and-hold investors. This is for information, not gospel.
And at the bottom…
Michael J Burry: 24 long calls, 30d avg return +0.1%, 60d avg return -11.1%, 30d alpha -2.7% (60d alpha: -11.4%). Then again, The Big Short took 2 years to play out — maybe his thesis just needs more time than our 60-day window can capture.
Methodology Caveats (Please Challenge This)
I want to be upfront about limitations:
- AI extraction isn’t perfect. Gemini parses articles and extracts ticker calls. To reduce noise, we only count high conviction — where the author dedicates multiple paragraphs, specific data, or explicit price targets. Passing mentions are filtered out.
- We validated this. Spot-checked extraction accuracy against manual reads, and cross-verified with alternative model outputs (codex / claude). It’s not 100%, but it’s consistent.
- Survivorship bias matters. We only track tickers with available price data. Delisted stocks, non-US tickers without yfinance data, and typos get counted as No Data and excluded from return calculations.
- This is a bull market. Many of these authors are long-biased. Absolute returns look good partly because the market went up. The alpha column adjusts for this using sector-specific ETF benchmarks.
- The full dataset is available. All 3,519 calls, every author, every ticker, every return at every horizon. You can audit everything. I will put up the link later.
What I Learned
- The expensive ones aren’t always the best. Some of the top performers cost 80−360/year.Some1,000+ newsletters are mid-table.
- Volume ≠ quality. Authors with 300+ calls often have mediocre win rates. The ones with 15-80 highly targeted calls tend to outperform.
- Shorts are hard. Almost every author has worse short performance than long. The few exceptions (Global Tech Research shorts: -20.5% at 60d) are impressive outliers.
- Michael Burry’s Substack picks haven’t worked yet — but his most famous trade took 2 years, so the jury’s still out.
Total Cost Breakdown
$9,599/year across 23 newsletters. Here’s every single one:
Author Annual Fee Author Annual Fee James Bulltard $1,099 Paulo Macro $360 Lord Fed ~$1,000 Collyer Bridge $350 10x Research $948 The Overshoot $330 Eliant Capital $760 Doomberg $300 TMT Breakout $589 TicToc Trading $290 SemiAnalysis $500 Global Tech Research $100 Shrubstack $500 Earnings Edge $100 The Setup Factory $450 Altay Capital $80 Best Anchor Stocks $449 Quality Stocks $70 Michael J Burry $439 Winter Gems $50 Fabricated Knowledge $400 Swiss Transparent Portfolio ~$40 Macro Charts $400 Total ~$9,599 If I could only keep 5 based on this data: Global Tech Research (100),PauloMacro(360), Doomberg (300),SemiAnalysis(500), The Setup Factory (450).That′s1,710/year — 82% cheaper and probably better returns.
Shoutout to every author on this list. Even the bottom-ranked ones taught me more about markets than any YouTube video. This isn’t meant to trash anyone — just data.
Happy to answer questions. Roast my methodology. Tell me I’m wrong. That’s how this gets better.
Full methodology + data / charts: https://x.com/pyhrroll/status/2027374283669066045?s=20
Positions: long several names mentioned by top authors. Not financial advice, obviously.