How it works

A mirror is one model portfolio

For each of 58 famous investors we publish a single impersonal model portfolio, their latest disclosed 13F holdings plus the stocks a model predicts they will buy next. It is identical for every reader. We never execute trades, hold assets, or tailor anything to your account.

Predicting the next buy

13F filings disclose what each institution held at the end of a quarter, about six weeks after the fact. The model learns each investor’s historical buying pattern and ranks a broad ~2,000-name candidate universe, known at the rebalance date, by the probability that the name becomes a new buy in the next filing. The ranking is the PriorScore. The published page never changes that ranking after the fact.

Tested walk-forward, and one leak we did not solve

Every backtest is walk-forward: at each historical rebalance the model only sees data available on that date, holds the top predicted names equal-weight for one quarter, and scores complete quarters only, gross of costs. As-of joins are availability-lagged so no future feature information leaks in.

One leak survives that, and we will not call these results leak-free because of it. Each quarter’s positions are formed from 13F holdings as of quarter end, and those filings are not public for up to 45 days. Re-run so nothing is bought until the filing is actually public, the same pipeline returns −3.4 pts/q (t = −1.74) over 34 complete quarters. The measured edge does not survive that constraint, and the track record page states this next to the number rather than in a footnote.

What the accuracy number means

Accuracy is measured per investor as AUC: the chance the model ranks a name the investor actually bought above one they did not. It is not one number, and we refuse to quote only the best one. Across the 49 investor models that have a scored holdout it spans 0.38 to 0.80, median 0.61, and that spread is the honest story:

• Median 0.61 across the scored roster. Better than a coin flip at the next-buy task, but modestly so. This is the real edge and the number we lead with.
• 0.73 to 0.80 at the top, all of it broad systematic books (D.E. Shaw 0.80, Millennium 0.78, Citadel 0.78, Two Sigma 0.78). High, but it reflects breadth, not foresight: these books hold hundreds of names, so ranking a new entry against their own giant book is an easier task, and the lift on a live broad universe is near zero. Those same funds are excluded from the consensus Top Picks, so our best AUCs are not the ones the product ranks on.
• At or below chance for three models: Pabrai 0.38, Peltz 0.48, Engaged Capital 0.50. Their handful of high conviction bets are driven by private research the model cannot see, so it cannot predict them. A further nine of the 58 models have too few holdout events to score at all. We say so on their pages rather than hide it.

So the path to the number is plain: a separate model per investor, trained walk-forward on up to 294 public features per stock per quarter, calibrated on 72,268 out-of-sample calls across 24 investors. A label-shuffle canary collapses it to a coin flip, which rules out labels bleeding into features but is blind to survivorship and coverage leakage. The honest claim is a median 0.61, not a single big percentage.

The honest result

A research publication

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. You hold the trades; Prior Moves publishes the playbook. Nothing here is investment advice. Read the full disclaimer.