Tokenized equities are the fastest-growing segment of on-chain real-world assets, yet almost the entire float is inert. The natural way to make it productive — supplying concentrated liquidity — has a poor empirical record: across the studied Uniswap v3 cohort a majority of providers finished unprofitable, and manual repositioning showed no measurable edge. This paper argues the failure is one of policy, not of effort, and specifies a per-asset managed vault that converts idle stock tokens into range liquidity under explicit price guards and separated permissions.
1Problem statement
An equity holder on chain has exactly two options today: hold, or sell. Neither pays anything. Meanwhile the pools that price those same tokens are thin, which widens spreads for everyone and suppresses the volume that would have paid for depth in the first place. The market is short liquidity precisely where the supply of the asset is greatest.
The obvious bridge — become a liquidity provider — is where retail capital has historically been destroyed rather than compounded. In the studied cohort, impermanent loss of $-260,100,000 outweighed $199,300,000 of fee income, and over 51% of providers ended under water. Critically, a peer-reviewed analysis found no statistical relationship between repositioning frequency and outperformance. Doing more did not help.
2Why equities are their own case
Generic automated liquidity managers apply one model to every pair. That is defensible when the underlying assets share a volatility regime; it is not defensible for equities, whose volatility is structured by an off-chain calendar. A stock token trades continuously, but the equity it tracks does not: variance clusters at the open, thins overnight, and gaps discontinuously across earnings and index events.
A range sized for a semiconductor name into an earnings print is not the range for a broad index ETF, and neither is the range for a high-retail-participation name whose realized volatility is several times either. Per-asset sizing is therefore not a refinement — it is the difference between a position that survives the week and one that spends it out of range holding a single asset.
3Construction
Each vault is a single USDG-quoted market for one stock token, deploying into the 0.3% fee tier. Depositors receive shares representing a proportional claim on the deployed position, uncollected fees and idle balances. Single-asset entry is served by a zap that swaps only the portion the active range ratio requires, under a 2% minimum-output floor and a 20-minute deadline.
Collected fees are split 85/15 in the vault's favour, charged only on newly collected amounts so the same assets are never charged twice. There is no principal-based management fee, because a fee on principal pays the operator whether or not the strategy earned anything.
4Guards
Every guarded action passes a 30-minute time-weighted price check and aborts on deviation beyond 1.5% or on a reference older than 15 minutes. This is deliberately conservative: the cost of a missed rebalance is some forgone fees, while the cost of rebalancing into a manipulated print is principal.
Permissions are separated so that deposits and shares stay under user control while rebalancing, compounding, pausing and parameter changes sit behind distinct authorized roles. No role can route user assets to a third party. Blocked actions are written to chain alongside successful ones.
5What would falsify this
The thesis is testable and the failure conditions are worth stating plainly. If per-asset range sizing does not produce materially higher time-in-range than a generic width on the same pools, the central claim is wrong. If realized fees net of the strategy share do not exceed the return on simply lending USDG — around 7% on this chain — then depositors are being paid nothing for carrying equity exposure and impermanent loss, and should not deposit.
Both are measurable from the public ledger this protocol publishes, which is the reason the ledger exists in the form it does. A managed strategy that only shows you its outcomes, and never the conditions under which it acted, cannot be evaluated at all.
This paper describes a demonstration template. Every performance figure shown across this site is simulated; the market statistics cited are drawn from published third-party research and are attributed where they appear.