Over the last 18 months, our collective understanding of token economics has progressed significantly. To be clear, we’re still at the very initial stages of the burgeoning field of token economics (also known as crypto-economics or tokenomics) and there is still a huge amount of work to be done.
However, this is to be expected; financial markets have existed since the 1600s and yet it wasn’t until Ben Graham’s 1934 “Security Analysis” that the now ubiquitous Discounted Cash Flow (DCF) methodology became widespread. Unsurprisingly, with 10 only years of history, token economics is still an extremely young field with a lot of room for innovation.
Nevertheless, significant progress has been made over this short timeframe; our understanding of existing token models is far stronger than it was even a year ago, valuation methodologies have become more sophisticated and we’ve seen the birth of entirely new and fascinating token models.
Despite the headway that has been made in furthering our collective understanding of token designs, there’s still work to be done in simplifying the complex and often conflicting token types currently on the market, exacerbated by the confusing nomenclature assigned to them. Even for those of us who follow the field closely, token economics is a tough space to navigate, which can serve as a deterrent to new entrants. This is a shame, as we need as many smart people as possible to contribute and help accelerate our understanding of tokenomics.
In this post I will attempt to cut through the clutter by creating an updated taxonomy of the various token models as I currently see them: the main cryptoasset categories and where each token model fits in, how each token model works, how we currently think about valuing them and examples of these valuations in action. Given the speed at which this field evolves, this attempt will almost certainly be incomplete, and prone to ageing swiftly. Nevertheless, it is only through clearly delineating existing token types that we can iterate and evolve our understanding of these digital assets, whose manifold applications are still being discovered. I would greatly appreciate the feedback and contributions of the crypto community in helping improve this framework and my thinking on this subject more generally.
Inspired by the excellent Fabric Ventures “State of the Token Market” report, I propose the following updated taxonomy:
The Main Categories
Inspired by Greer’s seminal “What is an asset class anyway?” and its excellent adaptation to crypto by Chris Burniske, the main distinction I will draw is between productive cryptoassets (referred to as “cryptocapital”) and non-productive cryptoassets (referred to as “cryptocommodities”). As the names imply, the primary difference between these is that crypto capital is what Greer refers to as “an ongoing source of something of value… valued on the basis of net present value of its expected returns” whereas cryptocommodities have no ongoing value flow.
Cryptocapital: A token whose ownership provides ongoing access to something of value and can therefore be valued based on the Net Present Value of its future cashflows. Any asset that is staked, bonded or otherwise committed in order to get a claim on value flows can be considered crypto capital. These can be valued by taking the Net Present Value of the future cashflows/value flows they are expected to generate.
Cryptocommodities: Tokens whose ownership doesn’t yield an ongoing stream of value. The key determinant of this is whether ownership of the token is a requirement to participate in the system and qualify to receive cash and value flows. If the asset is a requirement, it’s cryptocapital. If it isn’t a requirement, it’s a cryptocommodity (Ethereum 1.0 is a cryptocommodity, Ethereum 2.0 is cryptocapital).
Within cryptocapital we can further subdivide this into security tokens and work tokens.
Security tokens are tokens which pass the Howey Test in that the cashflows that are generated for holders result from the effort of others. Broadly, these can be valued using well-understood methodologies from traditional markets, both in absolute terms using tools like the venture capital method, discounted cashflow analyses (DCF) and in relative terms by looking at comparable company analyses or precedent transaction analyses.
On the other hand, value flows generated by work tokens are always contingent on some kind of active participation or contribution to the network by the holder. Both can be valued by taking the NPV of future cashflows/value flows; the traditional DCF in case of security tokens and a DVF (discounted value flow) in the case of work tokens.
Within cryptocommodities, we can further subdivide these into currency tokens and collectibles.
Currency tokens are tokens which seek to fulfil one or more of the three roles of currency: unit of account, store of value, or medium of exchange. In absolute terms, they can be valued using some variation of the equation of exchange. In relative terms, we can use metrics like the NVT ratio and its many variations to compare these tokens.
Collectibles do not generate cashflow and, due to their lack of fungibility, cannot be used as currency (although some successful ones may end up being used as a Store of Value), instead representing some kind of unique digital or physical good. In fundamental terms, since collectibles are neither a productive asset nor a currency, they can only be valued by seeking to estimate and model supply and demand curves. Since supply is generally (although not always) known and predictable, the real challenge is modeling demand, which can be done through researching the asset’s specific demand characteristics in order to select the independent variables with the most explanatory power and place them into a regression. For some ideas of how this is done quite effectively in the fine art market, see some of the various econometric publications on the subject. In relative terms, art can be valued by comparables such as era, style, artist, etc and we expect NFTs to be valued similarly.
I’ll now go into the subcategories within each one of these and corresponding valuation methodologies.
As mentioned, security tokens are tokenized representations of assets that qualify as a security under specific jurisdictions. In terms of the sub-classifications, I followed the work done by Newtown Partners in their excellent research report on security tokens, dividing them into four main categories: Derivative tokens, debt tokens, equity tokens and convertible or hybrid tokens.
What they are: These are fairly self-explanatory and probably the simplest and most intuitive of the bunch. As the name suggests, equity tokens represent equity in an underlying company, functioning similarly to shares on the stock market.
Valuation methodology: There are many well-understood ways to value different types of equities, including venture capital method, discounted cashflow analyses (DCF), comparable company analyses or precedent transaction analyses.
What they are: Debt security tokens are tokenized assets that are or represent debt instruments. We can further subdivide this into two subcategories: tokenized debt and on-chain debt. Tokenized debt refers to a tokenized representation of existing debt vehicles (e.g. corporate debt, government bonds, etc). On-chain debt refers to the fully automated flow of funds on the blockchain.
Examples: An example of tokenized debt is the world bank’s bond-i (blockchain operated new debt instrument) which raised A$110M in an Australian-domiciled offering. Securitize has also announced it joined IBM’s blockchain accelerator with the goal of modernizing the $82T corporate debt market.
In terms of on-chain debt, a great example is Dharma, a blockchain protocol that enables the creation and management of tokenized debt assets based on its four fundamental components of debtors, underwriters, relayers and creditors.
Valuation methodology: Debt is the oldest financial instrument and we have well understood and generally accepted ways to value it. Basically, a bond asset’s valuation will depend on the amount of cashflow it produces over its lifetime (i.e. its “coupon”) and the riskiness of these cashflows (i.e. default risk of the underlying entity). As such, once the above two variables are computed, a bond can be valued using traditional DCF methodology, with the cashflows being discounted based on their perceived risk.
What they are: Derivative tokens, like traditional financial derivatives, are instruments which derive their value from an underlying asset or group of assets. Specifically, derivatives are contracts between two or more parties in which each agrees to pay each other cash (or other assets) based on price movements in the underlying asset. Derivatives effectively create a market for risk, enabling market participants to hedge risks that are otherwise impossible to buy or sell.
The original innovation behind derivatives was that of enabling market participants to take a position in a given asset without having to actually own it, replacing physical custody of the asset with a financial contract (enforced by the legal system) which references the price of those underlying assets. However, this innovation introduced an additional problem: the ability to trust a) that the counterparty of the derivative contract will honor its terms b) that the legal system will enforce the terms of the contracts cheaply and efficiently. As the UMA whitepaper tells us, this trust means that derivatives have only been made accessible to a small number of sophisticated institutional investors who rely on traditional due diligence and costly legal process to “trust” each other.
Derivative tokens are the natural evolution of derivatives as financial technology, using smart contracts, margin, economic incentives and the transparency of the blockchain to allow anyone to gain exposure to derivatives while greatly minimizing counterparty risk.
As per Jesus Rodriguez’s classification, derivative tokens can be further subdivided into forward models, option models and swaps.
Examples: There are many protocols seeking to create different parts of the derivative stack. For instance, dYdX facilitates margin trading through its dYdX margin tokens, ERC20 compatible tokens which move positively or negatively based on the performance of the underlying asset.
Set protocol enables the composition of different tokens which collateralize a single tradable unit, effectively allowing for the trustless, permissionless creation of decentralized crypto ETFs and index funds.
UMA protocol is focusing on the swaps portion of the derivative stack with its most recent product, the USStocks, being an ERC20 token representing synthetic ownership of an index of the 500 largest exchange-listed US stocks, allowing anyone with access to the internet and digital currencies to participate in the US stock market.
How to value them: In general terms, the value of a derivative is always related to the value of the underlying asset such that as the value of the asset changes, so does the net present value (NPV) of the derivative contract. Depending on the type of derivative, other factors such as volatility, time value, strike price, etc will also come into play. There are various valuation models for derivatives such as the famous Black-Scholes model for options, the Heston model for swaps, Monte Carlo option model, and Binomial options pricing model.
What they are: A hybrid security token is composed of two or more financial instruments, mixing and matching the features and risk profile of each one, possibly creating entirely new and previously unseen innovations (in the traditional world, we can think of convertible debt and convertible equity as examples of hybrid instruments). Hybrid security tokens allow instruments to be created which provide different risk and reward balances and/or hedges against specific market conditions, providing investors with added opportunities for customization. They may also allow for the intermingling of utility features such as discounts into a security token, creating hybrid utility/security instruments.
The design space for hybrid tokens is still wide open and given the ease of programmability of security tokens compared to traditional securities, we should see some significant experimentation here over the next few years.
Examples: The only example I know of a hybrid token is tZERO’s preferred equity issuance, which pays out 10% of adjusted gross revenue to tokenholders. tZERO also mentioned plans for additional utility features although these are yet to be released.
How to value it: Valuation models here will vary depending on the specific hybrid token and the financial instruments it is composed of. That said, in general the same methodology used to value convertible bonds and equity in traditional markets can be used. That is, the value of each of the components must be computed independently and then summed to arrive at the value of the hybrid instrument.
I believe all good utility token models can in some way be seen as work tokens. Work tokens can be thought of as similar to taxi medallions in that they are tokens which are staked by a service provider/contributor in exchange for the right to provide (potentially) profitable work to the network. This has two major benefits:
(1) Network adoption: Work tokens can be used to bootstrap and coordinate the supply-side of a network in ways that would otherwise be very difficult/impossible, by providing an incentive in the form of potential yield for those staking to provide services to the network. This yield can be paid for by inflation in which case it’s a tax on holders (Steem), by transaction fees in which case it’s a tax on users (Augur), or by a mixture of both (Ethereum 2.0).
(2) Incentive alignment: In some cases, work tokens also serve as a mechanism design tool to enable ‘skin in the game’ for service providers, ensuring they are not only rewarded for good work but can also be punished (slashed) for work that harms the network without relying on identity/reputation.
Fundamentally, these work tokens can generally be valued by taking the NPV of the future value flows to supply-siders (a Discounted Value Flow or DVF). In relative terms, they could be compared on various fronts, including perhaps a total work to network value ratio comparing cashflows paid to supply-siders (a proxy for utility/earnings) to the current network value (a proxy for price).
Work tokens can be further subdivided into service tokens, discount tokens and governance tokens, based on the nature of the work being provided to the network.
What they are: Service tokens are the most obvious kind of work token, where a user stakes tokens in order to provide a given service to a network in exchange for cashflows. If the work is done correctly (where ‘correctness’ is generally defined by some sort of consensus mechanism), the user receives a reward comprised of either fees paid by the demand side or inflation. Crucially, these fees need not be paid in the native token as long as the native token must be staked in order to qualify to receive fees. If work is done incorrectly or maliciously, the stake can be slashed.
Different implementations of service tokens designs include: ‘Skin in the game’ tokens, where the amount a user has staked influences the amount they can earn. Access tokens, where the amount users stake act is fixed similar to a license fee. Token Curated Registries, where users can stake tokens in order to filter data in and create trusted lists. Burn & Mint Equilibrium where, rather than paying fees to service providers, customers burn tokens (denominated in USD) in the name of a service provider and service providers receive a pro-rata share of monthly inflation (denominated in native token) based on percentage of tokens burned in their name. While in the burn & mint model users don’t have to stake tokens, they still have to hold them in order to benefit from the token burn.
Examples of service tokens include Ethereum 2.0, Steem, Bancor, Jur, Kleros, Augur, and Keep Network. They also include token curated registries such as Ocean Protocol and District0X.
Service tokens also include Burn & Mint Equilibrium.
How to value them: Because staking of the tokens and providing work to the network generates value flows for supply-siders, these tokens can valued by taking the NPV of these valueflows. This is already done in the traditional world as can be seen by this Hymn capital valuation framework for taxi medallions. For examples of crypto work token valuations, see MultiCoin Capital’s Factom analysis or their Augur valuation.
What it is: At the highest level, a discount token grants the user discounts on transactions performed on the network. These are still a type of work token in that the benefit is contingent on the user transacting and thus contributing to the network. Discount tokens can be implemented as a “Use” model in which case a discount is provided for users paying for the service using the discount token (e.g. Binance’s BNB) or as a “Stake” model in which users must stake the discount token in order to qualify for a pro-rata share of the total available discount (e.g. SweetBridge’s SWC). In this author’s opinion, the SweetBridge “Stake” model leads to far higher value capture than the “Use” one, as in the latter the discount token is used as a medium of exchange to pay for discounted fees and thus suffers from the aforementioned velocity problem.
The “Stake” discount token model possesses several interesting features: (1) since the discount provided by each token is mathematically equivalent to a cashflow, it can be valued using a discounted cash-flow. (2) As a result, the token’s value is directly linked to transaction volume and grows alongside network adoption (3) Despite the fact it can be valued similarly to an equity or other cashflow-generating instrument, it necessarily qualifies as a utility token since the value flows are only received if the user utilizes the platform (4) The token doesn’t interfere with the UX as users do not have to transact in it (5) The token actually benefits users over speculators.
While the value of a traditional utility token is identical to both users and passive speculators since its value is spent when utilized and thus selling is identical to spending it, this is not the case for a discount token. This is because a discount token possesses both resale value (similar to the utility token) and discount value, which can only be realized through discounts on actual services. As such, as Alexander Bulkin tells us: “an investor holding discount tokens for passive appreciation is by definition underutilizing them, only able to capture their resale value, but not the discount value.”
Examples: Binance’s BNB is an example of a spend discount token. Sweetbridge’s SWC is an example of a “stake” discount token.
How to value them: Once again, these tokens can be valued by taking the NPV of valueflows to supply-siders, in this case holders of the token who are receiving value flows in the form of discounts on transactions. Indeed, we can think of discount tokens as entitling holders to a perpetual discount on transaction fees but structured in such a way that the discount is mathematically equivalent to a revenue share/royalty, but only if one utilizes platform services. From the perspective of the token issuer, this is a royalty model but rather than offering rights to a proportion of total cashflows, it represents rights to a proportion of total discount offered. As a result, the absolute amount of discount provided by each token (and consequently its value) grows linearly alongside network adoption, providing holders with an ever increasing discount which they can either use or sell to others. For examples of discount token valuation models, see Phil Bonello’s work here or Michal Bacia’s SweetBridge valuation model.
What it is: A governance token gives users the ability to influence the way the network is run, including anything from electing representatives, proposing and voting on network upgrades, deciding how funds are spent and even determining monetary policy. While almost all cryptocurrencies possess some level of governance through off-chain communication as well as the ever present possibility of forking the protocol, this kind of informal governance is generally referred to as “off-chain” governance, whereas governance tokens refer to projects implementing formalized, automated systems of “on-chain” governance in which governance rules are encoded onto the protocol and the results of the governance process are automatically executed. Generally, but not always, governance tokens also possess some kind of productivity in addition to their governance features.
Although these could arguably come under service tokens, I would argue governance is sufficiently differentiated to warrant its own category.
Examples: Examples of governance tokens include 0x, MakerDAO, Decred and Dfinity.
How to value them: The intuition behind governance token valuations is that as the value of a network goes up, the ability to influence how it is run should become a scarce resource, with some, like Fabric Ventures, even arguing the value of this influence will actually scale exponentially with the value it secures.
That said, the best quantitative work done on this topic so far is this post by Phil Bonello who argues the value of governance tokens is bound by the cost associated with a fork, where this cost can be computed as the difference between the NPV of the pre-fork and post-fork business.
In addition, Jake Brukhman has done some interesting work on valuing governance tokens through his “decisiveness” model, which seeks to determine the ability of a particular tokenholder’s stake to influence the decision relative to a given token distribution. These stakes can then be ordered and valued relatively, providing the basis for an absolute valuation. Interestingly, in Brukhman’s model the value of a governance token can be shown to not only be positively correlated but in some cases produces an exponential relationship. This also means that under certain distributions, small stakes are valueless or close to it, auguring potential liquidity problems for certain governance tokens.
Burn & Mint Equilibrium
What it is: These are arguably a type of service token but one in which, rather than paying fees to service providers, customers burn tokens (denominated in USD) in the name of a service provider and service providers receive a pro-rata share of monthly inflation (denominated in native token) based on percentage of tokens burned in their name.
This is the token model I understand least well and at this point it’s unclear to me whether this even qualifies as a work token or whether it’s something else altogether as users don’t actually have to stake the native token in order to receive value flows. Instead, the token acts as a proprietary payment currency with value flows being passed on indirectly through deflation.
How to value it: These are extremely difficult to value since value flows are not paid out directly but rather captured through decreases or increases in total supply. The only valuation model I’ve been able to find on this is Multicoin’s Factom valuation.
Currency tokens aim to fulfil one or more of the three purposes of currency, namely:
(1) A medium of exchange — eliminating the inefficiencies of barter
(2) A unit of account — facilitating valuation and calculation
(3) A store of value — allowing economic transactions to be conducted over long periods as well as geographical distances.
This is what most people think of when they think of blockchain and also represents the original promise of cryptocurrencies, to create a digital currency that is decentralized and therefore global, permissionless, censorship resistant, non-sovereign, trustless and programmable, providing a superior money technology for the modern world.
While the goal of all currency tokens is to eventually fulfil all three purposes of money, most have chosen to initially focus on only one. The rationale for this is that rather than trying to do them all and struggling to achieve any of them, it’s far better to begin by focusing on one and doing it extremely well, with the other attributed then following.
Currency tokens can therefore be sub-classified further based on the particular purpose of currency they are initially focused on fulfilling. Specifically, most currency tokens have chosen to optimize to become either a store of value or a medium of exchange.
Store of value
What they are: Store of value tokens basically seek to realize the vision of being a superior version of gold (i.e. “digital gold” meme). As such, these tokens prioritize a few key characteristics at the expense of all others:
- Scarcity : A fixed, or at the very least, predictable supply that cannot be changed or gamed.
- Security : People must feel confident they won’t lose their money due to technical reasons (e.g. failed update, broken smart contract).
- Permissionless: No one can prevent the owner from acquiring or spending their store of value.
- Censorship resistance: Absent physical violence, no one can take the store of value away from its owner.
With the more extreme store of value tokens it is argued that these traits should never be compromised at all, no matter how great the marginal utility some trade-off provides (e.g. Bitcoin block size debate). The argument is that, similarly to gold, a digital SoV doesn’t need to be cheap, scalable, programmable, private, flexible or generally usable. As long as it can be converted into other, more practical assets/currencies or used to back these more practical assets/currencies, it has fulfilled its purpose of storing value. This is the vision of Bitcoin so eloquently outlined by Saifedean Ammous in The Bitcoin Standard and Murad Mahmudov on Off the Chain; a vision of Bitcoin as a settlement layer, providing a layer 1 reserve currency for the financial system, similar to gold (except better in every way) which sits in central bank vaults and is only moved around once a year or so in armored trucks.
There are a few other, less extreme types of Store of Value tokens whose proponents argue that a few extra characteristics should be added to the above four.
Privacy coins : Privacy coin purveyors argue that privacy is essential as Bitcoin’s pseudonymous nature means it is completely traceable by firms such as Chainalysis, creating “tainted” coins which compromise Bitcoin’s fungibility. Within privacy coins, there are different implementations, all of which make different design trade-offs, from ZCash’s zk-Snarks, to Monero’s ring signatures to the Mimblewimble implementations chosen by Grin and Beam. For a good summary of some of the privacy coin design tradeoffs, see Delphi Digital’s Thematic Insights on Zero-knowledge Proofs.
Stablecoins: Stablecoin issuers argue that stability is essential for a Store of Value since no one wants to store their wealth long-term in something that regularly experiences 50% drawdowns. Within stablecoins, there are various implementations, all of which make different tradeoffs along the capital efficiency, scalability, decentralization trilemma. That said, the three main types are:
a) Fiat collateralized : Kept stable by an equal reserve of fiat that is centrally held.
b) Crypto collateralized: Over-collateralized by cryptoassets such as Ether escrowed trustlessly.
c) Seigniorage share: Recreating an algorithmic central bank that keeps stability with levers on supply and demand.
For more on stablecoins, check out this great introductory article by Haseeb Qureshi.
Non PoS smart contracting platforms: Advocates of this technology argue that as the world and technology have evolved, so too must our conceptions of what a Store of Value is, and that utility, specifically programmability, is an essential component of a 21st century Store of Value. Once again, there are many different approaches to smart contract platforms, all of which make different design tradeoffs along the scalability, security, decentralization trilemma. The distinction between non PoS and PoS is important here, as PoS based chains generate yield and thus qualify as cryptocapital rather than cryptocommodities.
Examples: The leading example of a traditional Store of Value is Bitcoin. Privacy Store of Value coins include Zcash and Monero. Stablecoin Stores of Value include TrueUSD, Paxos and Gemini USD. Non PoS Smart contracting platforms include Ethereum 1.0.
How to value them: In terms of absolute or fundamental valuation methodologies, all currency tokens can be valued using some variation of the Equation of Exchange (MV=PQ). This valuation methodology was originally proposed by Chris Burniske in 2017 with his INET model. The primary criticism of this model was the velocity variable which was seen as arbitrary and very difficult to justify. Since then, the model has been refined and improved as different analysts have proposed solutions to this. Alex Evans from Placeholder proposed his VOLT model, using Baumol-Tobin cash inventories approach to come up with a better estimate of velocity. HASH CIB proposed the Rational Market Value approach, which allows us to model changing velocities over time rather than simply assuming a constant velocity.
In terms of relative valuation methodologies, many indicators and ratios have emerged to allow us to quickly compare these cryptocurrencies to each other. The original one in this category is Willy Woo’s Network Value to Transactions (NVT) Ratio which provides an equivalent to the Price to Earnings (P/E) Ratio in traditional markets, allowing investors to quickly discern whether or not a network is overpriced compared to its peers (a high ratio implies either overvaluation or high growth and a low ratio vice versa). The intuition behind this model is that in traditional markets, earnings effectively represent a company’s utility while price represents how much you’re paying for this utility. Since cryptocurrencies aim to store and transfer value rather than generate earnings, we can look at the money flowing through the currency as that currency’s utility and a proxy to company earnings.
While the NVT has performed well, several additional metrics have emerged to combat its faults. Dmitry Kalachkin’s NVT Signal Ratio uses a 90-day moving average of daily transaction volume rather than taking a snapshot as in traditional NVT, in order to make the NVT more predictive rather than reactive and thus more able to inform trading decisions. The Wookalich Ratio on the other hand seeks to correct long-term inflation skewing the NVT ratio by normalizing it by a dilution factor. Murad Mahmudov and Dave Puell’s MVRV use Nic Carter’s Realized Value instead of the more traditional Network Value in order to properly account for the effect of lost coins and hodlers on Bitcoin’s price.
More recently, Cryptopoiesis and others have expressed concern about the potential effects of transaction batching, Lightning Network and other L2 scaling technologies on the NVT since they will reduce the on-chain volume and thus make the NVT look artificially expensive. Solutions are now in the works for how to make NVT account for these off-chain transactions.
Medium of Exchange tokens (Payment tokens)
What they are: Medium of Exchange tokens optimize for the “Medium of Exchange” purpose of currency. This was the initial vision behind Bitcoin (as evidenced by Satoshi’s whitepaper title: “Peer to Peer Electronic Cash System”), predating the digital gold mantra which has since taken hold as a response to scalability shortcomings. As such, Medium of Exchange tokens prioritize for scalability and usability over all other features.
The problem with pure Medium of Exchange tokens (i.e. ones that don’t also have Store of Value features and/or have very little chance of becoming Stores of Value) as investments is that these tokens do a terrible job capturing value due to the so-called velocity problem, variously identified and discussed by Vitalik, Kyle Samani, Cathy Barrera, James Kilroe, myself and many others. To summarize the problem, there’s no incentive to hold a pure Medium of Exchange token and incur price risk vs fiat or some other asset. As such, the MoE will be purchased in order to acquire a particular good or service and then sold immediately afterwards, resulting in the its velocity (the number of times it changes hands per year) being extremely high. Using Chris Burniske’s adaptation of the equation of exchange:
M= size of the asset base
V= velocity of the asset (the number of times that an average coin changes hands every day)
P= price of the digital resource being provisioned. This is not the price of the cryptocurrency but rather of the resource being provisioned by the network (i.e. price in $ per GB of storage in the case of Filecoin)
Q= quantity of the digital resource being provisioned (GBs of storage provided)
As Burniske tells us, in order to value the coin, we solve for M, where:
M = PQ/V.
M is the size of the monetary base necessary to support a cryptoeconomy of size PQ, at velocity V. In order to find the token price, we simply divide M by the total token supply. As we can see, the higher the velocity the lower the coin’s value.
As such, it is clear that the velocity of the coin is inversely proportional to the value of the token. As James Kilroy tells us:
“This is intuitive, because if the transactional activity of an economy is $100 billion (for the year) and coins circulate 10 times each over the course of the year, then the collective value of the coins is $10 billion. If they circulate 100 times, then the collective coins are worth $1 billion.”
Examples: Examples of pure medium of exchange tokens include Aventus, TicketChain, BlockTix, Bitstation, Bhired, Dentacoin, and Celsius Network. They are the most common type of token model, particularly during the 2017 mania where they were consistently and forcibly implemented into networks which did not actually need a token to function.
How to value them: Medium of Exchange tokens, similar to Store of Value tokens, can be valued using the Equation of Exchange. The main difference is that for pure Medium of Exchange tokens we must account for an extremely high velocity variable. For examples illustrating the drastic effects this can have on valuation, see Pfeffer’s analysis of Ethereum’s valuation under PoS given a high average velocity.
What it is: Collectibles are non-fungible tokens (NFTs) which, as the name implies, are not fungible and thus represent something unique and not interchangeable. These can include both digital collectibles such as in-game items as well as asset-backed collectibles like tokens representing artworks, plane tickets or jewelry.
Importantly, while all collectibles are NFTs, not all NFTs are collectibles as we can think of productive NFTs such as ownership rights in a property or some kind of digital asset that generates cashflows (e.g. a digital pickaxe that allows you to mine some valuable resource).
Examples: Examples of NFTs include the infamous CryptoKitties, land tokens on Decentraland, collectibles on Axie Infinity and the Picasso painting tokenized by Maecenas.
How to value them: In fundamental terms, since collectibles are neither a productive asset nor a currency, they can only be valued by seeking to estimate and model supply and demand curves. Since supply is generally (although not always) known and predictable, the challenge is modeling demand, which can be done through researching the asset’s specific demand characteristics in order to select the independent variables with the most explanatory power and model them in a regression. For some ideas of how this is done quite effectively in the fine art market, see some of the various econometric publications on the subject. In relative terms, collectibles can be compared by identifying relevant metadata about them (e.g. in the case of artwork, metadata like era, style or artist may be used).
This post represents my best attempt to create a taxonomy of all currently known token models and their corresponding valuation models. It is my hope that through this we can eliminate unhelpful poorly defined nomenclature such as “utility” tokens (a catch-all name that variously refers to currency tokens, work tokens and others) and replace them with more precise terminology referring to clearly delineated token models.
It is also my hope that this provides some visibility into the fantastic work that has been done by various token economic thinkers in valuing these various kinds of tokens, demonstrating that just because we cannot immediately apply existing valuation frameworks to a given cryptoasset, doesn’t mean the asset has no value or cannot be valued.
I welcome feedback on ways to refine this taxonomy to further our understanding of token models and valuation frameworks. This is still a nascent industry and I’m excited to be able to play a small part in developing the field of token economics, building on the stellar work done by some of the brightest minds in this space.