Which stablecoins behave alike, once size is taken out?
Over the 90-day window, the 20 stablecoins with sufficient data fall into four behavioral segments, containing 13 coins, four coins, two coins and one coin respectively. The largest group is labelled Volatile / Emerging.
The segments are descriptive rather than definitive. Separation between them is limited, and a stablecoin close to a boundary may move from one group to another when the observation window or feature set changes. A coin is therefore better understood by its position across the underlying behavioral measures than by its cluster label alone.
K-means groups stablecoins by how they behave rather than by size. The four behavioral features are velocity (how actively a coin circulates), 30-day growth rate (gaining or losing market share), market cap volatility (supply stability), and market beta (sensitivity to market-wide flows).
All features are z-score normalized before clustering, so a $5B coin with the same behavioral profile as USDC will appear in the same segment. Archetype names label the segment’s center, not a category a coin provably belongs to.
| Segment | Coins | Members | Velocity | Growth | Volatility | Beta |
|---|---|---|---|---|---|---|
| Declining / Contracting | 5 | USDT, USDC, USD1, USDG, FDUSD | 0.311 | -0.1% | 0.41% | 0.49 |
| Volatile / Emerging | 13 | USDS, USDE, DAI, PYUSD, USDF, USDD, USD0, TUSD, GHO, EURC, U, BUSD, FRAX | 0.028 | 1.5% | 0.53% | 0.25 |
| Institutional / High-Velocity | 1 | RLUSD | 0.121 | 38.8% | 2.61% | 15.12 |
| Growth Phase | 1 | USDTB | 0.003 | 35.0% | 9.44% | -14.67 |
| Coin | Market cap | Segment | Velocity | Growth | Volatility | Beta |
|---|---|---|---|---|---|---|
| USDT | $183.4B | Declining / Contracting | 0.348 | 0.2% | 0.03% | 0.15 |
| USDC | $74.1B | Declining / Contracting | 0.203 | 3.2% | 0.37% | 3.08 |
| USDS | $9.82B | Volatile / Emerging | 0.018 | 0.4% | 0.32% | -0.14 |
| USDE | $4.61B | Volatile / Emerging | 0.014 | 16.4% | 0.69% | -0.12 |
| DAI | $4.60B | Volatile / Emerging | 0.054 | 0.4% | 0.35% | 0.60 |
| USD1 | $4.33B | Declining / Contracting | 0.294 | 7.9% | 0.32% | 0.10 |
| USDG | $3.30B | Declining / Contracting | 0.277 | -5.0% | 0.64% | -0.96 |
| PYUSD | $2.77B | Volatile / Emerging | 0.034 | -0.4% | 2.17% | 4.43 |
| RLUSD | $2.38B | Institutional / High-Velocity | 0.121 | 38.8% | 2.61% | 15.12 |
| USDD | $1.53B | Volatile / Emerging | 0.007 | -1.3% | 0.98% | 0.27 |
| USDF | $1.34B | Volatile / Emerging | 0.000 | 0.2% | 0.05% | 0.04 |
| U | $1.31B | Volatile / Emerging | 0.128 | 5.8% | 0.62% | -0.72 |
| GHO | $0.698B | Volatile / Emerging | 0.007 | 0.0% | 0.03% | 0.07 |
| USD0 | $0.547B | Volatile / Emerging | 0.002 | -0.6% | 0.06% | 0.12 |
| TUSD | $0.494B | Volatile / Emerging | 0.031 | 0.1% | 0.04% | 0.09 |
| USDTB | $0.483B | Growth Phase | 0.003 | 35.0% | 9.44% | -14.67 |
| EURC | $0.469B | Volatile / Emerging | 0.066 | -0.1% | 0.98% | -1.49 |
| FDUSD | $0.326B | Declining / Contracting | 0.432 | -7.1% | 0.69% | 0.09 |
| BUSD | $0.281B | Volatile / Emerging | 0.005 | -0.7% | 0.50% | 0.22 |
| FRAX | $0.217B | Volatile / Emerging | 0.002 | 0.0% | 0.10% | -0.18 |
| Segment | Coins | Members | Velocity | Growth | Volatility | Beta |
|---|---|---|---|---|---|---|
| Declining / Contracting | 5 | USDT, USDC, USD1, USDG, FDUSD | 0.234 | 0.0% | 0.38% | 0.46 |
| Volatile / Emerging | 13 | USDS, USDE, DAI, PYUSD, USDF, USDD, USD0, TUSD, GHO, EURC, U, BUSD, FRAX | 0.026 | 3.8% | 0.78% | 0.71 |
| Institutional / High-Velocity | 1 | RLUSD | 0.088 | 56.9% | 2.86% | 11.29 |
| Growth Phase | 1 | USDTB | 0.005 | 6.6% | 7.28% | -5.59 |
| Coin | Market cap | Segment | Velocity | Growth | Volatility | Beta |
|---|---|---|---|---|---|---|
| USDT | $183.4B | Declining / Contracting | 0.268 | -0.4% | 0.04% | 0.17 |
| USDC | $74.1B | Declining / Contracting | 0.162 | 1.3% | 0.31% | 2.46 |
| USDS | $9.82B | Volatile / Emerging | 0.016 | -1.4% | 0.55% | 1.43 |
| USDE | $4.61B | Volatile / Emerging | 0.012 | 15.3% | 0.68% | 2.05 |
| DAI | $4.60B | Volatile / Emerging | 0.043 | -0.9% | 0.34% | 0.33 |
| USD1 | $4.33B | Declining / Contracting | 0.225 | 1.5% | 0.40% | 1.54 |
| USDG | $3.30B | Declining / Contracting | 0.172 | 3.7% | 0.60% | -1.29 |
| PYUSD | $2.77B | Volatile / Emerging | 0.035 | -1.7% | 1.76% | 3.82 |
| RLUSD | $2.38B | Institutional / High-Velocity | 0.088 | 56.9% | 2.86% | 11.29 |
| USDD | $1.53B | Volatile / Emerging | 0.023 | 2.3% | 0.85% | 0.39 |
| USDF | $1.34B | Volatile / Emerging | 0.000 | -5.7% | 0.75% | 2.02 |
| U | $1.31B | Volatile / Emerging | 0.093 | 25.0% | 1.11% | -0.15 |
| GHO | $0.698B | Volatile / Emerging | 0.006 | 16.7% | 1.45% | -0.16 |
| USD0 | $0.547B | Volatile / Emerging | 0.002 | -1.0% | 0.09% | 0.08 |
| TUSD | $0.494B | Volatile / Emerging | 0.034 | 0.2% | 0.05% | 0.10 |
| USDTB | $0.483B | Growth Phase | 0.005 | 6.6% | 7.28% | -5.59 |
| EURC | $0.469B | Volatile / Emerging | 0.062 | 9.2% | 1.11% | -1.90 |
| FDUSD | $0.326B | Declining / Contracting | 0.344 | -5.9% | 0.52% | -0.56 |
| BUSD | $0.281B | Volatile / Emerging | 0.003 | -0.8% | 0.47% | -0.00 |
| FRAX | $0.217B | Volatile / Emerging | 0.003 | -8.3% | 0.91% | 1.18 |
| Segment | Coins | Members | Velocity | Growth | Volatility | Beta |
|---|---|---|---|---|---|---|
| Institutional / High-Velocity | 4 | USDT, USDC, USD1, FDUSD | 0.230 | -4.0% | 0.34% | 0.66 |
| Volatile / Emerging | 13 | USDE, DAI, PYUSD, USDG, USDF, USDD, USD0, TUSD, GHO, EURC, U, BUSD, FRAX | 0.032 | 6.2% | 0.83% | 0.30 |
| Growth Phase | 2 | USDS, RLUSD | 0.047 | 20.4% | 2.20% | 7.22 |
| Declining / Contracting | 1 | USDTB | 0.007 | -47.3% | 7.47% | 1.87 |
| Coin | Market cap | Segment | Velocity | Growth | Volatility | Beta |
|---|---|---|---|---|---|---|
| USDT | $183.4B | Institutional / High-Velocity | 0.260 | -1.6% | 0.09% | 0.38 |
| USDC | $74.1B | Institutional / High-Velocity | 0.158 | -0.9% | 0.29% | 1.35 |
| USDS | $9.82B | Growth Phase | 0.013 | -4.3% | 1.49% | 6.36 |
| USDE | $4.61B | Volatile / Emerging | 0.017 | 2.3% | 1.05% | 1.34 |
| DAI | $4.60B | Volatile / Emerging | 0.041 | 9.7% | 1.00% | -0.00 |
| USD1 | $4.33B | Institutional / High-Velocity | 0.214 | -6.0% | 0.55% | 1.05 |
| USDG | $3.30B | Volatile / Emerging | 0.126 | 19.5% | 0.77% | -0.32 |
| PYUSD | $2.77B | Volatile / Emerging | 0.035 | 0.7% | 1.52% | 1.98 |
| RLUSD | $2.38B | Growth Phase | 0.080 | 45.1% | 2.92% | 8.08 |
| USDD | $1.53B | Volatile / Emerging | 0.021 | 12.0% | 1.22% | 1.15 |
| USDF | $1.34B | Volatile / Emerging | 0.001 | -8.2% | 0.68% | 0.32 |
| U | $1.31B | Volatile / Emerging | 0.064 | 30.1% | 0.92% | 0.02 |
| GHO | $0.698B | Volatile / Emerging | 0.007 | 16.7% | 1.18% | 0.14 |
| USD0 | $0.547B | Volatile / Emerging | 0.002 | -1.1% | 0.07% | 0.03 |
| TUSD | $0.494B | Volatile / Emerging | 0.035 | 0.1% | 0.05% | 0.04 |
| USDTB | $0.483B | Declining / Contracting | 0.007 | -47.3% | 7.47% | 1.87 |
| EURC | $0.469B | Volatile / Emerging | 0.064 | 8.2% | 1.13% | -1.00 |
| FDUSD | $0.326B | Institutional / High-Velocity | 0.289 | -7.4% | 0.44% | -0.14 |
| BUSD | $0.281B | Volatile / Emerging | 0.003 | -0.7% | 0.39% | -0.03 |
| FRAX | $0.217B | Volatile / Emerging | 0.004 | -9.2% | 0.78% | 0.22 |
The clusters divide a behavioral space in which stablecoins vary continuously. They are a way of summarizing that distribution, not evidence that the market consists of naturally distinct categories.
Three implications follow. First, a coin close to a cluster boundary can be reassigned even when its underlying behavior has changed very little. That is why the 30-, 60- and 90-day results are all shown: a classification that persists across all three windows carries more information than one that appears only under a single specification.
Second, the labels describe the center of each cluster rather than every coin within it. An individual stablecoin can therefore sit within a named segment while matching that description only loosely.
Third, the number of clusters is a modeling choice informed by the diagnostic curve above. It should not be interpreted as an inherent property of the stablecoin market.
For that reason, the tables provide the more informative reading. A coin’s position on each of the four underlying features, relative to its peers, conveys more than the segment name alone.
High velocity (volume/mcap) signals active use in payments, settlement, and exchange flows. These coins are the workhorses of the stablecoin system. The segment does not require large size, a newer coin that circulates actively will appear here.
Lower velocity, moderate growth, and low beta. These coins tend to be locked in DeFi protocols (lending vaults, AMM liquidity) rather than circulating in payment flows. Supply is stable and relatively independent of market-wide events.
Above-average growth rate and often elevated beta. These coins are gaining market share rapidly, typical of newly launched or recently adopted stablecoins. High beta means their expansion closely tracks broader market inflows.
Negative or near-zero growth sustained over the selected window. These coins are losing market share. The segment does not imply insolvency, it may reflect competition, regulatory pressure, or capital rotation to alternatives.
This segment shows the greatest swings in market capitalization over the period, with little evidence of a sustained direction. Supply can rise or fall sharply from one interval to the next, a pattern more common among smaller or recently launched coins whose ownership base is still taking shape. In such markets, a single large issuance or redemption can materially alter the total supply. The label is applied to the remaining segment with the highest supply volatility and therefore appears only when at least four distinct segments are identified.
Features. Four behavioral features are computed per coin for the selected look-back window (30D/60D/90D): (1) Velocity = mean(volume_24h ÷ market_cap), how actively the coin circulates; (2) Growth rate = (latest mcap − first mcap) ÷ first mcap; (3) Mcap volatility = standard deviation of daily market cap % changes; (4) Market beta = cov(coin daily %, market daily %) ÷ var(market daily %). All four are z-score normalized (StandardScaler) so no single feature dominates by scale.
Coverage. Coins covered on fewer than two-thirds of the window by valid data are excluded from that window entirely rather than counted as zero.
Clustering. K-means with k-means++ initialisation (scikit-learn), 20 restarts for the WCSS curve and 30 for the final fit. Optimal k is selected via the elbow method: WCSS is computed for k = 2 to 6 and the k with the largest second difference (sharpest bend) is chosen. The curve is published above so the selection is auditable.
Projection. The 2D scatter uses PCA on the normalized behavioral feature matrix for axis placement, distinct from the market-cap-returns PCA on the Market Factor Analysis page. It is a projection for display and no inference is drawn from the axes.
Updated daily. See the methodology for data sources and coverage.
K-means groups stablecoins by how they actually behave, their velocity, growth trajectory, supply stability, and market sensitivity. Because all features are size-normalized, a small coin that behaves identically to USDC will appear in the same cluster. This makes it possible to identify behavioral peers and outliers that raw market cap rankings obscure.
For dollar-pegged stablecoins, price variance is near-zero for every coin, it differentiates nothing. For yield-bearing tokens like sUSDE, rising “price” just reflects yield accrual, not a behavioral signal. Market cap volatility is used instead: it captures supply dynamics, minting surges, redemption waves, and market stress, which are far more informative about behavioral type.
The elbow method: we compute within-cluster sum of squares (WCSS) for k=2 through 6, then find the k where marginal improvement drops most sharply (the largest second-derivative of the WCSS curve). The elbow chart is shown on this page so the selection is fully auditable.
It can mean either of two things, and the page does not distinguish them. A coin whose behavior has genuinely changed will move: growth normalizing and circulation deepening moves a coin out of a growth segment, and a sustained loss of share moves it toward a contracting one. But a coin sitting close to a boundary can also move without any change in its behavior, because the segments are cut from a continuous space. The 30D/60D/90D toggle is the check: a shift that holds across all three windows is more likely to be the first case.
The two axes are the first two principal components of the normalized behavioral feature matrix, not the market-cap-returns PCA on the Market Factor Analysis page. PCA here serves purely as a 2D projection to make the four-dimensional feature space visually interpretable. Coins close together on the scatter have similar behavioral profiles.
Less than the label might imply. Cluster names describe the typical characteristics of a segment’s center; they do not establish that every coin within it belongs to a distinct economic category. The four inputs also capture only part of a stablecoin’s behavior.
Because behavior varies continuously, the boundaries between clusters are imposed by the model rather than discovered as natural dividing lines. A coin close to one of those boundaries can move between segments under a different observation window, feature set or clustering method without any material change in its underlying behavior.
The most useful comparison is therefore the coin’s position relative to its peers across each of the four measures. Those values are shown directly in the coin assignments table.
The first clustering feature on its own Turnover relative to supply, ranked across stablecoins and tracked over time rather than compressed into a segment.
Growth viewed through a different model It uses the same market-cap history, but fits that history to an S-curve rather than reducing it to a single growth measure.
Trading activity adjusted for size It uses the same reported-volume input under a different normalization, without the z-score transformation applied in the clustering model.
A dimension deliberately excluded from the clustering model Price stability is measured separately rather than included among the behavioral features used to form the segments.
The market-share counterpart to behavioral clustering How much stablecoin supply is represented by each segment is a different question from how many coins fall within it.
Definitions and coverage Universe definitions, the hierarchy of source authority, and the coverage rules behind the supply and volume series.