Research note

Does factor similarity predict returns? We ran the leak-free test.

We tested our own dataset the way a skeptic would — and we're publishing the result whether it flatters the product or not. The short version: factor similarity does not forecast stock returns. It does something else, something real, and that distinction is the whole point of this note.

The findings, up front

20-day forward returns are not forecastable from this factor data — cosine similarity, a return-projected (PLS) variant, and a gradient-boosted model all land at a cross-sectional IC of essentially zero, leak-free, across 237 monthly cross-sections and 26 years.
Factor similarity is risk-coherent. Tickers the engine calls "similar" share a genuine forward risk profile — neighbours' forward volatility predicts the query's forward volatility at IC +0.062 (t +8.3), twelve times the random baseline.
So Factor Weave is a research substrate, not an alpha source. It is built to screen, group, and assemble clean leak-free datasets — not to tell you what happens next. Anyone selling you the latter on commodity technical factors is selling a backtest, not a signal.

1 · What's in the dataset

Factor Weave is a point-in-time factor library for US-listed equities and ETFs — roughly 10,800 tickers, daily, back to 2005 (about 28 million ticker-days). Every row is built to be joined to your own research, not consumed as a verdict.

28 daily factors

Trailing returns (1/5/20/60d), realized vol (20/60d), ATR%, RSI, 52-week z-score, momentum, mean-reversion, gap%, beta vs SPY, a composite score, plus cross-sectional ranks and quantiles.

32-D regime-aware embeddings

A vector per ticker-day summarising its factor state, with four similarity methods built on top — cosine, DTW, regime-filtered, and a return-projected (PLS) variant.

Leak-free forward-return labels

fwd_ret_1d / 5d / 20d and binary targets — each a strict price[t+h]/price[t]−1, the realised future. They are a backtest target, never an input.

Market-regime classification

Each date carries a SPY-volatility regime (low / mid / high) so any study can be split by market state.

2 · The test, and why it's leak-free

The question: if you find the historical setups whose factor profile most resembles a ticker today, does the average forward return of those analogues predict the ticker's forward return? We tested it with discipline:

The embeddings use no forward labels. Candidate analogues are restricted to strictly past dates (35–400 trading days before each query).
The return-projected (PLS) model and the gradient-boosted model are refit walk-forward — each query date sees a model trained only on data whose labels were fully realised before it.
The metric is the cross-sectional Information Coefficient: the Spearman rank correlation between prediction and realised forward return, computed within each date, then averaged. This strips out the market-wide period effect that makes a naïve pooled correlation look meaningful when it isn't. The t-statistic comes from the spread of that IC across dates.
Scale: 237 monthly cross-sections, 2005–2024, 28,440 leak-free queries for the similarity tests; 59 quarterly walk-forward cross-sections (2010–2024) for the gradient-boosted model.

As a sanity check every test includes a random-analogue baseline — picking K analogues at random. A correct measurement puts random at zero. Ours does.

3 · Result — returns are not predictable

Method	What it is	Cross-sec. IC	t-stat	Verdict
Random baseline	random analogues	+0.003	+0.5	≈ 0 ✓
Cosine similarity	nearest factor-profile analogues	−0.005	−0.8	no signal
Supervised (PLS)	return-projected similarity	+0.005	+0.8	no signal
Gradient boosting	nonlinear, 26 factors, walk-forward	−0.000	−0.0	no signal

Three different methods — a linear similarity, a return-supervised projection, and a nonlinear model with the full factor set — and the same answer each time: a cross-sectional IC indistinguishable from zero, and from random. The return-projected variant's mildly rising outcome buckets (a +0.70% top-vs-bottom spread) do not survive the t-test; on 237 dates it is noise.

This is not a surprise, and that's important. These are commodity technical factors on liquid US equities — the most heavily arbitraged corner of global markets. The academic and practitioner consensus is that such signals are competed flat. Our data agrees. A vendor who tells you otherwise is showing you a backtest with a leak in it.

4 · Result — but similarity is risk-coherent

Returns are a coin flip. Volatility is not — it clusters and persists. So we asked a second, fairer question: do tickers the engine calls "similar" share a forward risk profile? We measured whether cosine analogues' forward 20-day realized volatility predicts the query's, same leak-free design.

Predictor of forward 20-day volatility	Cross-sec. IC	t-stat
Random analogues' forward vol	+0.005	+0.8
Cosine analogues' forward vol	+0.062	+8.3
The ticker's own trailing vol	+0.830	+191.6

Cosine analogues carry real, strongly significant forward-risk information — twelve times the random baseline, t +8.3. The embeddings genuinely organise the universe by risk regime: "similar" means "similar risk profile."

We'll be just as straight about the limit. A ticker's own trailing volatility predicts its forward volatility far better (IC +0.83). So this is not a volatility-forecasting product — if you want one stock's vol, use its own history. What the result establishes is subtler and more useful: the similarity engine is coherent. It is not noise. When it groups setups, it is grouping them by a property that genuinely carries forward — which is exactly what makes it a sound tool for screening, peer sets, and regime-aware research, and exactly why it is not a return oracle.

5 · What to use it for

A good fit

Screening — find tickers in a given factor or risk state
Peer sets & substitutes — "what else looks like this?"
Regime-aware research — split any study by market state
Assembling clean, leak-free backtest datasets to test your own signal
Conversational research via the MCP server

Not what it does

Predict which stocks go up — the data does not contain that
Replace your alpha model — it is the substrate, not the signal
Forecast a single stock's volatility — its own history wins

Reproduce it

The probes behind every number here are in the repository under scripts/diagnostics/ — signal_probe_extended.py (similarity vs returns), signal_probe_gbm.py (the gradient-boosted model), and signal_probe_vol.py (the risk-coherence test). Leak-free by construction; run them yourself.

Honest data, honestly described

A free account gives you all 28 factors, 252 days of point-in-time history, four similarity methods, forward-return labels, the REST API and the MCP server.

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