I built API for SEC 13F data with 57 endpoints as a site project. The data collection and the collectors are running on AWS, so the resources can be expended. I am thinking to add more data and endpoints. It is for free if someone wants to try it out.
I built API for SEC 13F data with 57 endpoints as a site project. The data collection and the collectors are running on AWS, so the resources can be expended. I am thinking to add more data and endpoints. Did not find on reddit answers to my questions about data quality and data cleaning process.
Thank you!
We back-tested 4 ways how 13f filings can be traded and compared the results against S&P500. If you are looking for a way to get rich quickly - this is not it. If someone want to see our data, we are open.
The 4 strategies we tested:
1. Patient capital
2. Net-flow momentum
3. Crowding fragility
4. Best Ideas clone
Full insight - findatafox
Question: If you track 13F filings, can you spot genuinely skilled managers ahead of time and ride their picks? I had the data to test it, and the answer looks like no.
Data & method: I pulled SEC EDGAR 13F filings for ~9,000 managers (2013–2026), cleaned them, and priced each fund's quarterly returns. Keeping funds with ≥16 quarters left 6,902. For each, I estimate a Fama-French 4-factor skill alpha (the intercept after stripping out market/size/value/momentum tilts, so style bets aren't mistaken for skill). I split each fund's history in half: skill is estimated on the earlier half and measured independently on the later half — a clean out-of-sample boundary (the standard Carhart 1997 framework), at the cost of statistical power.
Results:
- Formation→holding skill correlation: Spearman ρ = 0.11 — "significant" only because n is huge; ~1% of variance.
- Top vs bottom past-skill quartiles differ ~16 pts/yr in-sample; out-of-sample that spread is +0.02 pts/yr.
- Of 121 funds significantly skilled in the first half, one stayed significant in the second.
This also answers the follower question: I'm measuring each fund's own skill — best case. A copycat acting on a 45-day-stale filing can't beat that, so if the funds' own skill doesn't persist, monitoring them won't hand you persistent skill either.
Caveat: 13F is long-only US equity, quarterly, and split-half is low-power — but none of that flips the result toward persistence.
Disclosure first: I built a small 13F-analytics site and ran this on my own data, so I'm the author. Posting here because the result is about as Boglehead-confirming as it gets, and I'd honestly like people to poke holes in the method.
The question: can you follow "smart money"? I tested whether a fund's stock-picking skill in the first half of its history predicts its skill in the second half — out-of-sample, so the test half is never used to pick the fund.
Two things I did to avoid fooling myself:
What I found:
None of this is new in spirit — it's Carhart (1997) and everything since — but it was sobering to watch it fall straight out of raw 13F data tested the fair way. The famous names (Vanguard, BlackRock, Geode) read as market-beta ≈ 1, skill ≈ 0 in their 13F books. There usually isn't a secret.
Honest caveats, because they matter: 13F is long US equity only — no shorts, options, bonds, international, or cash — so a market-neutral or macro fund's real edge can live entirely outside what I can see. Quarterly snapshots miss intra-quarter trades. And I measure the fund's own paper return, not a follower's — a copycat couldn't act until the 13F is public ~45 days later, which only makes the "follow smart money" case weaker, not stronger.
Full methodology and the de-biasing details: https://findatafox.com/insights/institutional-skill-doesnt-persist
But mostly I'm asking: where would you attack this? What would change your mind that 13F-based "skill" is real and followable?