Asset similarity
Peers and diversifiers, when the sector label isn't the answer
Two stocks in the same sector can behave nothing alike, and two in different sectors can move as one. This study builds a similarity measure that ignores the industry label and blends two things instead: how a name behaves (market-residual returns, PCA-denoised) and what the company is (fundamental ratios and sector structure). Blended 0.55 / 0.45 across 2,068 common stocks, it produces a full similarity matrix and a 25-way partition of the universe. The useful output is not the peer list but its inverse: the names that are genuinely independent of what you already hold.
Getting to a clean universe
Most of the attrition is funds, not filtering for quality. Leaving ETFs in would have poisoned the behavioural block, because a fund's returns are a blend of its holdings and would look similar to everything.
- 01 Liquid names requested 3,000 names
- 02 Common stocks after dropping funds 2,141 names
859 dropped: 738 ETF, 39 ETV, 9 ETS, 6 ETN, 3 PFD
- 03 With a full ~1y return window 2,068 names
The two blocks
Behaviour alone overfits to a single window; characteristics alone just rediscover the sector label. Blending them is what makes a cross-sector twin findable.
Behavioural
Market-residual returns over 252 days, denoised by keeping only the 22 eigenvalues that clear the Marchenko-Pastur bound of 14.9 — everything below it is indistinguishable from noise in a matrix this shape.
Characteristic
35 dimensions — 10 numeric fundamentals plus 25 sector indicators. Coverage is the weak point: ratios do not resolve for every name, so this half of the blend is thinner for loss-making companies.
Worked example — AAPL
AAPL sits in electronic computers, but nothing in its own industry makes the top of its peer list. What it actually trades like is large, stable, cash-generative consumer and payments names.
Most similar
Most independent
Structure of the universe
Clustering the matrix recovers groupings that mostly look sensible and occasionally do not — cluster 12 pulls together international large-caps and materials names that share little except a dollar sensitivity. Showing the six largest of 25.
Consumer & travel cyclicals
Mega-cap platforms
Enterprise software
International & materials
Biotech & pharma
Speculative frontier tech
Limitations
- Similarity is measured over one 252-day window. Relationships that only appear in a different regime are invisible here, and the whole matrix shifts as the window rolls.
- Characteristic coverage is uneven — price-to-earnings resolves for only 63% of names, so the characteristic half of the blend is thinner for loss-making companies.
- Sector labels come from SIC codes, which are coarse and occasionally missing entirely. Several genuine peers show a blank sector.
- The blend weights (0.55 behavioural / 0.45 characteristic) are a judgement call, not an optimisation. A different split reorders the middle of every peer list.
- Clusters are a partition of this universe at this moment, not stable industry definitions. Names near a boundary move between runs.
Window 2025-07-28 to 2026-06-26. Blend weights 0.55 behavioural / 0.45 characteristic. Matrix 2068×2068 (34 MB).