Governance tokens and cash-flo… than peer-reviewed findings. Checking what holders control: the Ownership Token Framework A cash-flow analogy assumes holders have a claim an analyst can find. Aragon (/wiki/dao-governance/tooling/frameworks/aragon) 's Ownership Token Framework, published on 18 December 2025, treats that claim as the thing to verify, starting from the position that ERC-20 tokens are financial assets first, whatever their issuers call them ( Aragon (https://blog.aragon.org/the-ownership-token-index-a-verifiable-standard-for-tokens-as-an-investable-asset-class/) ). An assessment works from contracts rather than documents, in five steps: identify the relevant contracts; map who holds which permissions; separate what can be changed from what cannot; trace where value flows and how it is routed; and use execution history to separate rights that exist on paper from rights that are exercised. Off-chain assets such as trademarks, intellectual property and the legal entity count for how far they support or undercut holders' control. Aragon launched a public dashboard on 4 February 2026 with profiles of Aave, Aerodrome, Curve, Lido and Uniswap ( Metaverse Post (https://mpost.io/aragon-launches-ownership-token-framework-and-dashboard-to-bring-evidence-based-transparency-to-crypto-assets/) ). On 19 September 2026 it profiled twelve, having added CoW Protocol, Ethena, ether.fi, Liquity, Ondo, Sky and YieldBasis, each scored by the number of criteria met under four headings: on-chain control, value accrual, verifiability and token distribution ( Ownership Token Framework (https://otf.aragon.org/) ). Three facts qualify the scores. Aragon's launch announcement says each profile was developed with the protocol's own team; Aragon sells tokenomics advisory alongside the framework ( Aragon (https://blog.aragon.org/making-tokens-investable-in-2026-through-ownership-automation-and-value-accrual/) ); and YieldBasis runs on a vote-escrow system Aragon built. How automated market makers se…arginal price at every moment.

References - Lin William Cong,…Research forum (ethresear.ch). - Aragon (2025). The Ownership Token Framework: A Verifiable Standard for Tokens as an Investable Asset Class (https://blog.aragon.org/the-ownership-token-index-a-verifiable-standard-for-tokens-as-an-investable-asset-class/) . Aragon blog; dashboard at otf.aragon.org (https://otf.aragon.org/) .

Tokens as claims on platform a…hen stabilization — driven by network effects (/wiki/economics/network-effects-and-platforms) . The model also identifies an…by supports the token's value.

References - Lin William Cong,…Research forum (ethresear.ch). - John Pfeffer (2017). An (Institutional) Investor’s Take on Cryptoassets (https://medium.com/john-pfeffer/an-institutional-investors-take-on-cryptoassets-690421158904) . Medium. - Mike Sall (2018). Valuing Cryptoassets from the Ground Up (https://medium.com/@sall/valuing-cryptoassets-from-the-ground-up-441ad5a9ff03) . Medium. - Vitalik Buterin (2018). A signaling theory model of cryptocurrency issuance and value (https://ethresear.ch/t/a-signaling-theory-model-of-cryptocurrency-issuance-and-value/1081) . Ethereum Research forum (ethresear.ch).

Utility value, store of value and burned fees Practitioner analysis in 2017 and 2018 asked what a utility token is worth once its network is mature and competition has run its course. John Pfeffer (https://medium.com/john-pfeffer/an-institutional-investors-take-on-cryptoassets-690421158904) , an investor and former partner at the private equity firm Kohlberg Kravis Roberts, argued in December 2017 that open code lets anyone fork a protocol into a copy that charges less, so at equilibrium the value of a utility token converges on the cost of the computing resources it pays for: the processing power, memory and bandwidth that the network's operators supply. That cost is low and falling, and the tokens in use can circulate as fast as the network processes transactions, so in his analysis scaling and cheaper hardware help users and hurt token holders. His one exception was a cryptoasset that becomes the dominant non-sovereign store of value, which he said could be worth many trillions of dollars. Mike Sall (https://medium.com/@sall/valuing-cryptoassets-from-the-ground-up-441ad5a9ff03) rebuilt the argument from its parts in April 2018. He derived velocity from queuing theory, treating each use of a token as a service nobody has to queue for, and the share of tokens users keep on hand from the Baumol–Tobin model of cash balances, in which a holder weighs the cost of each conversion against the interest the money could earn elsewhere. With competition pushing the value of the network's activity down to its cost, he applied the formula to Ethereum carrying the volume of card payments, email or file storage, and got velocities above one million and values below a dollar per ether in each case. He read the result as a network that processes large volumes at a thin margin, and treated any store-of-value premium as a separate, speculative question. Vitalik Buterin offered two other accounts in a 2018 post on the Ethereum Research forum (https://ethresear.ch/t/a-signaling-theory-model-of-cryptocurrency-issuance-and-value/1081) . In a signaling model, issuing a new coin costs almost nothing, but issuing one that people value requires spending on technical development and marketing; the market can then settle where each coin's value is proportional to that spending, and new coins keep appearing without pushing every price to zero. The alternative he preferred values a coin like a company, as the discounted sum of the transaction fees its protocol will burn, because under it the only way to earn money is to build a blockchain people use. A coin that burns fees grows faster than one that does not, so over time he expected the best stores of value to be tokens that are useful first.

References - Lin William Cong,…i and Joshua S. Gans (2018). Initial Coin Offerings and the Value of CryptoFinancing Ventures with Fungible Tokens (https://papers.ssrn.c…rs.cfm?abstract_id=3137213) . MIT Sloan School Working Paper 5347-18 / NBER Working Paper 24418SSRN 3137213 (DOI 10.2139/ssrn.3137213); MIT Sloan School Working Paper 5347-18. The same paper circulates under its earlier title, Initial Coin Offerings and the Value of Crypto Tokens (https://www.nber.org/papers/w24418) , as NBER Working Paper 24418 (March 2018, revised March 2019), and the two records still carry the two different names. - Lin William Cong, Ye Li, a…Research forum (ethresear.ch).

See also: token velocity (/wi… how much the network is used. See also: virtual economies (/wiki/economics/virtual-economies) — the measurement tradition that network-level valuation descends from, and the record of what issuers did with the policy power it left them.

Governance tokens and cash-flo…ement of what became known as MEV (/wiki/economics/mev-and-value-extraction) . The consequence for valuatio…arginal price at every moment.

Topic Economic models of how b…ormerly MakerDAO) (/wiki/daos/organizations/stablecoins/sky-dao)

Governance tokens and cash-flow analogs See also: fee switches and value accrual (/wiki/economics/fee-switches-and-value-accrual) — how the cash flow a governance token is valued on gets switched on in the first place, and what the DAO then does with it. Tokens whose protocols route …akerDAO (now Sky (/wiki/daos/organizations/stablecoins/sky-dao) ), writte…arginal price at every moment.

See also: token velocity (/wiki/economics/token-velocity) — why a token that is only spent, never held, accrues little value regardless of how much the network is used.

Topic Economic models of how b…(formerly MakerDAO) (/wiki/dao/ecosystems/organizations/stablecoins/sky-dao)

Governance tokens and cash-flo… MakerDAO (now Sky (/wiki/dao/ecosystems/organizations/stablecoins/sky-dao) ), written when the …arginal price at every moment.

Topic Economic models of how blockchain tokens acquire and sustain market value Key approaches Transactional-demand asset pricing, ICO theory, equity-like cash-flow analogs, automated market maker microstructure Related Bonding curve (/wiki/markets/bonding-curve) , Trading (/wiki/markets/trading) , Sky (formerly MakerDAO) (/wiki/dao/ecosystem/sky-dao)

Token valuation is the study of how blockchain tokens acquire and sustain market value. Because most tokens confer no legal claim on a firm's earnings, the discounted-cash-flow tools of equity analysis apply only imperfectly; economic research has instead modelled tokens as claims on the future usage of the platforms they power, with prices determined by transactional demand, expectations about adoption, and the mechanics of the markets in which they trade. The literature spans formal asset-pricing theory, empirical work on Bitcoin and initial coin offerings (ICOs), practitioner valuations of governance tokens with equity-like value accrual, and analysis of the automated market makers that quote token prices algorithmically.

Tokens as claims on platform adoption The most direct theoretical treatment prices a token from the demand of the people who use it. In the dynamic asset-pricing model of Cong, Li, and Wang (https://academic.oup.com/rfs/article-abstract/34/3/1105/5891182) , tokens enable peer-to-peer transactions on a digital platform, and the equilibrium token price is determined by aggregating heterogeneous users' transactional demand rather than by discounting cash flows. Introducing a token lowers users' transaction costs, and platform adoption follows an S-curve — slow initial growth, volatile expansion, then stabilization — driven by network effects. The model also identifies an intertemporal feedback between user adoption and token price that accelerates adoption and dampens user-base volatility relative to a tokenless platform. Catalini and Gans (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3137213) analyse why ventures sell tokens to raise funds (/wiki/markets/raising-funds) at all. In their model of ICOs, an entrepreneur issues tokens and commits to accept only those tokens as payment for future platform use, so competition among buyers reveals consumer value without the entrepreneur needing to know willingness to pay in advance — a demand-revelation property that, in the paper's analysis, may raise entrepreneurial returns beyond what traditional equity financing achieves. Initial funds raised are maximized when token supply growth is set to zero, which encourages early participants to save their tokens; conversely, a lack of commitment in the token's "monetary policy" undermines saving, and the cost of token financing is potential inflexibility in future capital raising. The paper also documents the scale of the phenomenon as of 2018: over $7 billion raised through ICOs since 2017, against roughly $1 billion of venture capital into the sector, with Tezos ($232M) and Filecoin ($205M) among the largest offerings. A companion model by the same authors extends the analysis to token supply policy (https://www.sciencedirect.com/science/article/abs/pii/S0304405X21004414) . When tokens both serve as a means of payment and finance investment in platform productivity, the optimal policy issues tokens when productivity-normalized supply is low and burns them when it is high. The platform's financial constraint creates an endogenous cost of issuing tokens, which leads to underinvestment through a conflict of interest between platform insiders and token-holding outsiders; blockchain-based commitment to token policy mitigates this time-inconsistency problem and thereby supports the token's value.

Empirical evidence from Bitcoin Athey, Parashkevov, Sarukkai, and Xia (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2826674) built one of the earliest models linking a cryptocurrency's exchange rate to fundamentals: bitcoin's value is determined by transactional usage and beliefs about future adoption, with expectations about adoption feeding into present valuation through a speculative channel. Transaction volume correlates with, but does not solely determine, value — investor and speculator behaviour adds pricing pressure beyond transaction-based fundamentals. Their analysis of blockchain data through mid-2015 found that active transactional usage was not growing quickly and that investors and infrequent users held the majority of bitcoins; the study is frequently cited as early evidence that speculative holding, rather than payments usage, dominated bitcoin demand. Speculation can do more than add noise. In a Bank of England model by Zimmerman (https://www.bankofengland.co.uk/working-paper/2020/blockchain-structure-and-cryptocurrency-prices) , a cryptocurrency's price is determined by the extent of its usage as money, but the blockchain's limited settlement capacity forces users to compete for settlement space. Speculative activity can crowd out monetary usage, undermining the currency's function as a medium of payment and lowering its value — so, contrary to standard economic models in which more demand raises price, higher speculative demand can reduce prices. The mechanism also raises the riskiness of investing in cryptocurrency, offering one explanation for the high observed volatility of crypto assets.

Governance tokens and cash-flow analogs Tokens whose protocols route value to holders invite more conventional analysis. An influential practitioner example is Kang's 2019 valuation of MKR (https://medium.com/@Rewkang/maker-mkr-valuation-fundamentals-the-case-for-trillion-dollar-maker-9b6e3f0150f1) , the governance token of MakerDAO (now Sky (/wiki/dao/ecosystem/sky-dao) ), written when the project traded near a $1 billion market capitalization. Kang argued that MakerDAO had achieved genuine product-market fit, distinguishing it from most crypto projects, yet observed widespread confusion about MKR's long-term worth despite the token being integral to the system's functioning. His method proceeds by "searching for an analog" in traditional finance: tokens with burn models are compared to equity structures in which value flows directly to holders through buyback-like accrual, tying MKR's value to the growth of the DAI stablecoin system. As a blog post by an author who disclosed holding MKR, it illustrates the genre of fundamental token analysis rather than peer-reviewed findings. How automated market makers set marginal prices Whatever a token is worth in theory, its quoted on-chain price is usually set by an algorithm. The foundational design is the constant-product market maker described by Buterin in 2018 (https://ethresear.ch/t/improving-front-running-resistance-of-x-y-k-market-makers/1281) and later implemented by Uniswap: a contract holds reserves of two tokens and prices every trade so that the product of the reserves stays constant. Pricing is path-based along this invariant curve, so larger orders move the reserve point further and face progressively worse marginal prices — natural slippage — while a small trading fee (such as 0.3%) makes providing liquidity profitable and the invariant prevents the mechanism from being drained. The same post identified the front-running vulnerability in which miners trade ahead of and behind a user's order to extract profit from its price impact, an early statement of what became known as MEV. The consequence for valuation is that such curves translate net demand into price deterministically: the market's position on the curve, not any market maker's judgement, fixes the marginal price at every moment.

Relevance to Caper Caper tokens are priced continuously by a bonding curve (/wiki/markets/bonding-curve) , a deterministic pricing function in the same family as the automated market makers above: every purchase or sale moves the marginal price along the curve, so a token's price at any moment reflects the cumulative balance of buying and selling since launch. The research surveyed here therefore applies directly. If demand for a caper's token derives from expected usage of, and participation in, the venture behind it, then models in which token price aggregates transactional demand and expectations of future adoption describe the forces that move the token along its curve when trading (/wiki/markets/trading) occurs — while the empirical literature cautions that speculative demand can push prices in ways usage-based fundamentals alone do not predict.

References - Lin William Cong, Ye Li, and Neng Wang (2021). Tokenomics: Dynamic Adoption and Valuation (https://academic.oup.com/rfs/article-abstract/34/3/1105/5891182) . The Review of Financial Studies 34(3): 1105–1155. - Christian Catalini and Joshua S. Gans (2018). Initial Coin Offerings and the Value of Crypto Tokens (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3137213) . MIT Sloan School Working Paper 5347-18 / NBER Working Paper 24418. - Lin William Cong, Ye Li, and Neng Wang (2022). Token-Based Platform Finance (https://www.sciencedirect.com/science/article/abs/pii/S0304405X21004414) . Journal of Financial Economics 144(3): 972–991. - Susan Athey, Ivo Parashkevov, Vishnu Sarukkai, and Jing Xia (2016). Bitcoin Pricing, Adoption, and Usage: Theory and Evidence (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2826674) . Stanford University Graduate School of Business Research Paper No. 16-42. - Peter Zimmerman (2020). Blockchain structure and cryptocurrency prices (https://www.bankofengland.co.uk/working-paper/2020/blockchain-structure-and-cryptocurrency-prices) . Bank of England Staff Working Paper No. 855. - Andrew Kang (2019). Maker (MKR) Valuation Fundamentals — The Case for Trillion Dollar Maker (https://medium.com/@Rewkang/maker-mkr-valuation-fundamentals-the-case-for-trillion-dollar-maker-9b6e3f0150f1) . Medium. - Vitalik Buterin (2018). Improving front running resistance of x*y=k market makers (https://ethresear.ch/t/improving-front-running-resistance-of-x-y-k-market-makers/1281) . Ethereum Research forum (ethresear.ch).