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⟁⊿⋔Returns, factors, and adoption · predictive
Are crypto returns linked to network adoption rather than production costs?
The source study reports exposure to its cryptocurrency network-factor construction and no exposure to its production-factor construction.
Conditional
The source study reports exposure to its cryptocurrency network-factor construction and no exposure to its production-factor construction.
The result depends on the paper's definitions of user adoption, production costs, assets, and sample. A live network metric is not automatically the same factor.
Literature record
ConditionalWhat the reviewed source and linked counterevidence support.
Bathymark reproduction
Not startedA point-in-time factor specification and source-vintage plan are still required.
Live validity
Not monitoredCurrent network instruments are related observations, not this paper's factor test.
where the claim applies
Scope and horizon
Assets
Bitcoin, XRP, and Ethereum
Venues or data
Paper-specific market and network data
Geography
Global crypto markets
Sample
Historical samples ending before the 2021 journal publication
Horizon
Paper-specific return horizons
source result
What the work reported
The paper reports return exposure to network factors, but not to its production-factor proxies.
structured numbers
This claim record depends on a reported relationship or method, not a single headline number. No summary metric is manufactured.
how the result was made
Method and implementation boundary
Design
Factor-exposure and predictive-return tests.
Measures
Network variables intended to proxy for adoption and production variables intended to proxy for mining costs.
Reality gap
Bathymark has not rebuilt the paper's factor definitions from point-in-time source vintages.
Assumptions
The selected network variables are useful adoption proxies rather than only activity or speculation proxies.
The production variables capture the relevant cost channel for each covered asset.
Limits
Active addresses, transactions, fees, and supply each have different semantics and manipulation risks.
A relationship found for three assets cannot be generalized to every chain or token design.
Exposure is not proof that network growth causes a future price move.
Required reality checks
Map every factor input to the Bathymark metric dictionary before testing.
Separate user activity, transfer activity, fees, and token-price effects.
Retest on assets with different consensus and token-demand mechanisms.
What this cannot mean
That more transactions guarantee higher returns.
That mining cost creates a price floor.
That one on-chain activity number measures adoption by people.
source and version trail
The works behind this record
Bathymark stores curated bibliographic facts and its own paraphrase. It does not store the source abstract or full text. Open the original work to inspect the complete analysis.
Yukun Liu, Aleh Tsyvinski · The Review of Financial Studies · 2021
Journal version reviewed. The NBER record is used only as an accessible version trail.
Publication state
peer reviewed
Reviewed version
Journal version of record
Identifier
DOI 10.1093/rfs/hhaa113
Source locator
Abstract
Metadata reviewed
2026-07-13
Source text
Not stored
What it supports
Network-factor exposure and the reported absence of production-factor exposure.
append-only assessment memory
Status history
ConditionalPrimary journal claim verified, conditional on the paper's factor definitions and sample.
current Bathymark context
Related live evidence, not a replication
Current network instruments are related observations, not this paper's factor test.
Reviewed 2026-07-13; next review 2026-10-13. The paper record is not a recommendation, forecast, or proof of current profitability. Information, not financial advice.