🪞 Brad Gerstner · Altimeter

🔮 Predicted next buys: quarter ending 2026-09-30

The model orders Brad Gerstner · Altimeter’s candidates for the next 13F filing. No public confirmation yet. The rate beside each name is the model’s own score rewritten as a measured quantity: how often a name in that position turned out to be the next buy across 2,030 investor-quarters of holdout. Picking at random from this board lands 0.62%.

📉 Predicted exits, conviction fading

Of Brad Gerstner · Altimeter’s current disclosed holdings, the names a model flags as most likely to be EXITED or TRIMMED next quarter, before the 13F shows it. Model score (not a calibrated probability), ranked. Holdings as of 2026-06-30.

⚠️ This predicts the ACTION (whether Brad Gerstner · Altimeter reduces the position), not a return and not a short. A 13F shows long positions only, so a “sell” here means exiting or cutting a long, never shorting the stock. Walk-forward validated on the decision (AUC ≈ 0.66), but acting on it is not shown to make money. Research, not advice.

📜 Top disclosed holdings: Jun 2026

Source: SEC Form 13F (public domain), long U.S. equity positions only, disclosed up to 45 days after quarter-end. Top 15 of 20 shown.

💲 Where they bought: accumulation cost

13F shows holdings, not trade prices. Implied average cost is total value ÷ shares; the zone is the price range that quarter, where they accumulated. “vs cost” is today versus that average. This is where they bought, not a future price.

Dip target = the recent high discounted by this investor’s historical tendency to add on weakness. Behavioral, not a guarantee.

🧬 Factor fingerprint, the style behind the book

82% of Brad Gerstner · Altimeter’s day-to-day swings are explained by public, buyable factors (Fama-French 5 + momentum), regressed on their current disclosed book (13 of 13 names, ~273 trading days). Market beta 1.51.

⚠️ This is the part the great investors can’t hide: their style is a tilt toward known factors, and every one of these is sold as a cheap index ETF. Buying the factors gets you most of the book for a few basis points. What’s left after the factors (the residual) is noisy on a single concentrated book and is not a reliable edge, read this as transparency about the style you’re copying, not a secret signal. Loadings are in-sample on current static weights, U.S. long positions only. Research, not advice.

📈 This mirror’s track: backtest vs S&P 500

⚠️ Read this honestly. Leak-free walk-forward backtest: top-10 predicted new buys ranked from a broad ~2,000-name universe known at the rebalance date, equal-weight, 1-quarter holds, complete quarters only, gross (before costs). Small samples, per-investor edges are noisy and not statistically significant. The consensus mirror's backtest shows +2.2 pts/quarter net vs the S&P 500 over 35 complete quarters (2017Q3 to 2026Q1), directional, NOT statistically significant (t≈1.3; 95% CI includes zero). Most of that average is one quarter: 2026Q1 posted a +47.5 pt edge, roughly half of it two AI-hardware names (SNDK +258%, AMD +186%) marked at what proved to be a local peak; excluding it the edge is +0.9 pts/quarter. Past performance does not predict future results. Not investment advice.

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Prior Moves is a research publication: one impersonal model portfolio per investor, identical for every reader. You place any trades yourself at your own broker. No execution, no custody, no individualised advice.