How to value DAO tokens?

Original Title: "How to Value DAO Tokens?"

Original Author: Kristof Lommers

Translated by: Deep Tide TechFlow

In this article, we discussed the valuation framework for decentralized autonomous organizations (DAOs). Although we based our framework on corporate finance concepts, we introduced native valuation concepts for DAOs. *Note: the proposed DAO valuation framework is preliminary as we are just beginning to understand the uniqueness of DAOs and how market participants evaluate them.

Compared to the development company behind DAO, the valuation of DAO Token is an independent valuation framework. The valuation framework will enable the community to measure the performance of DAO in creating value for stakeholders and help to introduce more responsibility to the development team behind DAO.

This article focuses on the valuation of DAO Tokens and the specific factors that may need to be considered in the valuation process.

DAO Token Valuation

First, we need to define our pricing correctly. In this article, we view DAOs as organizations that run autonomously on smart contracts and are managed by a community of stakeholders. Many protocols adopt a dual structure of company-DAO, with the company serving as the development company of the DAO and the operator of the underlying protocol. The company behind the protocol is often created before the DAO and retains ownership of relevant intellectual property and assets. For example, Uniswap can be seen as a DAO with governance token (UNI), with development work carried out by Uniswap Labs.

Web3 uses token economics to incentivize equity and token creation, creating appropriate decentralization. Tokens are also used as a partial solution to the cold start problem, where token incentives are used to reward appropriate user behavior. Fundamentally, the value of tokens is reflected through community membership, token utility in the ecosystem, and accompanying governance rights.

The value created by DAOs is usually not for the distribution of value among token holders, but for providing utility and governance participation.

Without involving securities (because this issue is far from resolved), DAO has always been cautious about directly assigning value to Token holders, as this could lead to Tokens being classified as securities.

The value created by DAO can indirectly flow to Token holders through various channels. For example, people can value the value of staking, community benefits, membership qualifications, and the overall growth of DAO (partially). Depending on the specific situation, people can also allocate value directly to Token holders, like some protocols do.

Valuation Method

First of all, there is no universal valuation method, and a DAO can cover many different concepts. For example, Hennekes (2022) divides DAOs into eight categories: Protocol DAO, Grant DAO, Charity DAO, Social DAO, Collection DAO, Risk DAO, Media DAO, and Sub-DAO.

Friend With Benefits DAO is a community-based effort, with a significant amount of value derived from social capital, while Orange DAO is a collective venture capital effort.

Therefore, each DAO needs a valuation method tailored to its category, and the importance of different factors will vary depending on the category of the DAO.

However, we can provide a general framework for thinking about valuation that can be roughly applicable to different DAOs with appropriate adjustments.

We believe that the valuation of DAO Tokens is mainly based on fundamental valuation or comparable analysis.

In the fundamental valuation method, people attempt to evaluate DAO Tokens based on the fundamentals related to the utility and expected value of holding DAO Tokens.

In the comparability method of investment, people try to compare DAO Tokens based on various indicators.

As the name suggests, fundamental valuation methods are more suitable for pricing tokens based on fundamentals, while the comparable method is a more market-oriented valuation method.

Basic Valuation Model

People can think about the basic valuation model of DAO Token in the following ways.

Valuation is both an art and a science, and many sub-components require discretionary estimation and assumption.

A) Discount factor

The discount rate can be estimated through the weighted average cost of capital (WACC), which is calculated by taking the weighted average discount rate of its sources of funding. These sources of funding can include debt or equity (tokens). Specifically, the calculation method for WACC is as follows:

其中 D represents the debt value of DAO, and T represents the total value of DAO Tokens.

For debt cost, people should simply take the value-weighted average of debt charging rates. However, in most cases, there is no leverage in the funding structure of DAO, which means that WACC is basically equal to the cost of token capital.

We can estimate the cost of token capital by calculating expected returns within the DAO native factor model framework. Literature (such as Liu and Tsyvinski, 2021) suggests that the cryptocurrency market is an independent asset class with its own unique movements and limited correlation with other macro asset classes.

However, the correlation with stocks is relatively strong and increasing, specifically with the technology stock market. This should not come as a surprise, especially for DAO tokens, as these tokens can be seen as shareholder qualifications of Web3 organizations. We can borrow the concept of asset pricing in stocks, but given the uniqueness of this asset class, a DAO-native model approach is needed. Factor models are popular in the stock market for estimating expected returns and discount rates.

Previous research has shown that the three-factor model captures a large portion of the systematic returns in the cryptocurrency market. From Botte and Nigro's (2021) principal component analysis, it can be seen that the first three factors account for approximately 70% of the co-movements, which is similar to the three-factor model in stocks. We see that the cryptocurrency market has changed from token returns being closely related to Bitcoin, to the correlation between tokens and Bitcoin decreasing. Therefore, there is more idiosyncratic risk-return in the cryptocurrency market, and one can consider that the correlation between DAO tokens and the DAO token market is higher, and the correlation with the cryptocurrency market is usually lower in the medium to long term.

For DAO tokens with sufficient trading history, we can use a factor model to estimate expected returns. For tokens without trading history (or with too few data points), we can use a set of comparable DAO tokens and adjust for relevant factors such as size and illiquidity. The factor model is constructed in two steps: first, constructing factor returns, and second, regressing factor returns on DAO token returns. Factor returns are estimated as the returns of long-short investment portfolios that are rebalanced monthly and calculated based on value weighting.

However, Jiasun Li and Guanxi Yi (2020) found that the bullish factors in cryptocurrency are more significant. This may be due to the relatively high cost of shorting in cryptocurrency, resulting in less downward pressure. A standard practice in factor research is to value-weight portfolios. However, in cryptocurrency, certain tokens often have a significant advantage in market capitalization, which would skew the value-weighted structure towards a few tokens. Therefore, we recommend using a logarithmic value-weighting scheme to confirm the market capitalization of top tokens, but without overly biasing the portfolio towards a few selected tokens. Additionally, factors should be estimated on data without survivorship bias and include failed DAO projects.

We recommend using DAO market, ecosystem, scale, value, liquidity, and momentum as factors in the DAO factor model. This is a traditional 5-factor model supplemented with blockchain ecosystem factors. The market factor captures broad cryptocurrency market risks and is calculated as the average return of DAO tokens. It should be emphasized that we specifically use DAO market rather than the general cryptocurrency market. The ecosystem factor captures the systemic relevance of tokens in the L1 ecosystem where DAOs exist (such as Ethereum, Solana, Avalanche). One can think of the existence of DAO tokens as fundamentally linked to the ecosystem in which they operate. For example, DAOs on Ethereum rely on the Ethereum network to execute smart contracts and will have tokens within the ERC-20 framework.

The size factor reflects the size risk and is used to calculate the return of an investment portfolio that goes long on small DAO tokens and short on large DAO tokens. The traditional value factor is difficult to define in DAOs. People can construct a value factor based on financial and business variables using some of the indicators discussed in the comparability section of this article, as these indicators represent value measures based on financial and business variables. The liquidity factor reflects liquidity risk and is typically used to calculate the return of an investment portfolio that goes long on low liquidity DAO tokens and short on high liquidity DAO tokens. Various liquidity measures can be used, such as trading volume, bid-ask spread, or Amihud illiquidity measure. Finally, the momentum factor reflects momentum or trend risk and is calculated as the return of an investment portfolio that goes long on the best-performing DAO tokens and short on the worst-performing DAO tokens.

Finally, the DAO token's factor return regression is defined by the following equation:

Where Rt represents the return of DAO tokens, RFt represents the risk-free or funding rate return, representing the alpha return of DAO tokens, i represents the risk of DAO tokens to factor i, and Fi,t represents the return of factor i. The "risk-free" financing rate reflects the cost of liquidity. For example, liquidity in the Ethereum ecosystem is processed through ETH, where the staking return of ETH can be seen as the basic funding rate. The estimated beta coefficient can be used to calculate the appropriate expected return and discount rate.

C) Valuation of Future Token Flow

One of the most common basic valuation models is the discounted cash flow (DCF) model, which is based on the discounted valuation of entity's future (free) cash flows. Using a similar approach, one can consider discounting token flows, where DAO's free cash flow can be calculated. The DCF model uses three main components: cash flows, discount rate, and assumptions about future expected cash flows (growth). In the case of DAO, we can introduce the term free token flow (FTF), which includes the true token liquidity generated by DAO and can be calculated using net income, asset depreciation, changes in work token liquidity, and system token expenditures. Specifically, free token flow can be calculated as:

Net income, asset depreciation, working capital liquidity, and capital expenditures can be defined according to traditional accounting and corporate financial practices. It should be noted that the DAO accounting field is still very young - see Lommers, Ghanchi, Ngo, Song, and Xu (2022), who discuss DAO accounting - and various methodological adjustments can be made based on the unique characteristics of cryptocurrency accounting and DAOs.

The token flow of discounted tokens (DTF) can be calculated as:

其中 FTFt represents the free token flow in year t, rt represents the discount factor in year t, and TV represents the final value.

People can also assume that DAO will grow with the development of its segmented market and use this growth rate to estimate future free token flow. A recent report by McKinsey (2016) advocates using a discounted cash flow model to evaluate the value of technology startups. They suggest analyzing the expected long-term development of the segmented market targeted by the startup and working backwards from there.

DTF calculation becomes:

Where FTFt represents the free token flow in year t, rt represents the discount rate in year t, gt represents the growth rate in year t, and TV represents the terminal value. One of the main assumptions in the model includes future growth rates, which can be handled through scenario analysis (e.g. continuation of growth rates, halving of growth rates, doubling of growth rates, etc.).

This type of token flow can accumulate directly and indirectly to token holders. For example, buyback and burn mechanisms, direct distribution, etc. It expands the value of token holders.

B) The value of DAO benefits.

The ownership of tokens in DAO can bring various benefits, including becoming part of the community, certain member rights, governance rights, and so on. As mentioned earlier, there is no one-size-fits-all approach in DAO, and various DAOs can provide different benefits through a series of available options currently enabled by DAO. For example, the benefits of most DAOs are composed of community members. As Agarwal (2021) believes, communities themselves are valuable because they allow for the establishment of community-based cooperative initiatives, the use of shared resources, the sharing of knowledge and skills, and so on.

Governance power is another common benefit of holding DAO tokens, reflecting the degree of community participation in decision-making. To measure the quality of governance, Regner (2022) has proposed various measures such as the number of proposals, proposal thresholds, proposal acceptance rates, and voting participation rates. Recently, the community has been discussing whether governance power has an impact on token value - for example, Buterin (2022) believes that governance power should not affect token value. We believe that this can be determined based on specific circumstances: whether value is allocated to token holders, the degree of community participation in DAO operations, and so on.

It should be noted that the benefits of DAO may vary depending on the amount of tokens held. For example, Mnema DAO divides the benefits offered into several levels based on the amount of tokens held, with the highest level of benefits being much higher than the lowest level. In addition, as a major shareholder, there is a certain strategic advantage in governance, which can be seen as a control premium in traditional corporate financing. Furthermore, each token holder may have a different estimation of the benefits of DAO, so there is a discretionary factor in this estimation work. However, people can try to estimate the approximate average of the benefits provided, which may be a way for future research.

Finally, the present value of benefits (DVB) can be calculated as:

Where DBt represents the value of DAO benefit in year t, rt represents the discount factor in year t, and TV represents the final value of DAO benefit after year t.

We can also assume that the benefits of DAO will increase with the segmentation of the market, and use this growth rate to estimate future benefits. In this case, the calculation result of DVB is:

其中，DBt represents the benefit value of year t, rt represents the discount rate of year t, gt represents the growth rate of year t, and TV represents the final value of benefits. One of the main assumptions in the model is the future growth rate, which can be handled through scenario analysis (such as continuation of growth rate, halving of growth rate, doubling of growth rate, etc.).

D) Expected Pledge Reward

Most Web3 projects with native tokens allow the tokens to be used for blockchain consensus generation, token economics incentives, or protocol development, with token rewards going to these holders (Cong, He and Tang, 2022). In finance, this can be seen as creating passive income by locking up capital for a certain period of time. Therefore, people can calculate the expected staking return discounted at an appropriate discount rate. As mentioned earlier, the staking yield of ETH can be seen as the liquidity cost in the Ethereum ecosystem. Therefore, it can be considered that the token liquidity cost in the Ethereum ecosystem is equal to the ETH staking yield, plus a risk premium. The cost of staking mainly includes losing flexibility, as tokens are locked up for a period of time: fluctuations in the price of native tokens, project failures, and potential platform hacks (Royal, 2022). It should be noted that in the early stages, staking yields can be relatively high to incentivize early users. In these cases, staking yields may be significantly higher than the risk-adjusted liquidity cost.

Comparability

Assuming that the value of DAO tokens is tied to the value of DAO, we can use comparability of various relevant indicators to evaluate this entity. Comparable analysis uses ratios or indicators to determine valuation estimates. More importantly, it uses valuations based on similar competitors, making it a more market-oriented valuation method. Comparable analysis utilizes fundamental accounting and business information to calculate key indicators related to profitability, financial condition, user attractiveness, and more. What we want to say is that native indicators for DAO need to be developed to evaluate organizations under this new paradigm.

Comparability of DAOs can be defined by category and scale. If there are no comparable DAOs in the correct scale and category, the closest one needs to be found. If a comparable DAO is found in the same category but the scale is significantly different, a scale adjustment can be estimated. For example, the average valuation difference between large and small DAOs within the same category can be estimated and used as a valuation adjustment. If possible, people should try to take various competitors and calculate a valuation range.

Standard comparability includes indicators such as revenue, profit, EBITDA, and profit margin. There are many popular indicators used for Web2 companies, some of the most important of which include monthly unique visitors, bounce rate, average order value, active users, customer conversion rate, churn rate, cost per visitor, and viral coefficient (Corporate Finance Institute, 2022). These indicators can be used to compare companies and perform relative valuations based on them. To build relevant comparability, it is necessary to distinguish DAOs from traditional (Web2) tech companies.

As Hsu (2022) believes, the valuation in Web2 tends to focus on customer acquisition and user activity. What I want to say is that the relevant factors in DAO (and the broader Web3) include decentralization, community participation, protocol users, protocol development activity, integration with other protocols, blockchain ecosystem indicators, and protocol revenue. Therefore, we can propose the following indicators for the valuation of Web3: single protocol users and token holders, protocol fees charged, token holder participation in governance, protocol transaction volume, token transaction volume and speed, community interaction (such as discord), treasury spending and growth, protocol ecosystem growth, cost of acquiring users through token incentives (such as vampire attacks), and user net present value (such as value received in tokens - value provided), and so on.

The best way to estimate valuation is through studying different indicators, which may be specific to different types of DAOs. For example, compared to social DAOs, DeFi protocols of structured DAOs require different measuring standards. Total Value Locked (TVL) is considered one of the core indicators that people focus on when analyzing DeFi protocols. For instance, Maker DAO (2021) proposed outstanding risk asset total, interest income correlation, net interest income, outstanding DAI total, DAI market share, on-chain DAI transaction volume, gross interest income, and vaults opened. People can compare different dimensions of DAOs that are relatively overvalued/undervalued and take a weighted average based on the relative importance of the dimensions.

Other Considerations Related to Tokens in Valuation

As the token price is the result of the balance between token demand and supply, considering token supply is also important. Sustainable and healthy token economics become part of the token valuation work. The composition of the protocol token economics should promote and best incentivize the production of value. As most circulating cryptocurrencies represent utility tokens, it can be argued that token value should represent the utility of holding the token and depict the utility provided by the protocol ecosystem. Fundamentally, a sustainable token economic design will facilitate a closer connection between token value and ecosystem utility. When comparing DAO tokens, future token supply should be adjusted for proper apples-to-apples comparison. For example, "open" token supply creates more uncertainty for future valuation compared to fixed token supply.

In addition, as Samani (2017) believes, when valuing tokens, the circulation speed of tokens must be taken into account. The growth of the ecosystem does not necessarily mean an increase in price, because tokens that circulate quickly will have less price appreciation if there is no motivation to hold them. Samani (2017) proposed some protocols to reduce speed, such as introducing profit-sharing mechanisms, establishing pledge functions, balancing burning and casting mechanisms, and gamification to encourage holding.

Most DAO tokens are listed on decentralized exchanges because listing on centralized exchanges can be a lengthy and expensive process. Especially for non-blue chip tokens, liquidity may be thin and trading prices can be heavily influenced. This poses significant risks to price fluctuations based on abnormal trading activity. For example, if a block holder sells their position, this could have a significant negative impact on token prices. Related to this argument is the distribution of tokens, where the situation of whale holders can be considered to bring significant risks. However, here people should abstract from tokens held in protocol libraries, tokens held in exchange pool contracts, etc., as these tokens are often more sticky or collected for specific purposes (such as token marketization).

In addition, early investors and development teams often have a vesting schedule, which means that tokens cannot be sold within a specified period of time. It has been proven that the vesting schedule is a consistent signal of token price fluctuations, as investors tend to take advantage of the opportunity to profit. Therefore, valuation should take into account the token vesting timeline, which could cause significant downward pressure on prices in the short term. In addition, liquidity in the treasury is also important. As Regner (2022) pointed out, DAOs tend to hold a large amount of their own tokens in the treasury. For treasury operations, having liquidity is important. If there is no liquidity for other tokens, the treasury will need to sell its own tokens, causing downward pressure on prices. Especially in the case of small and medium-sized DAO tokens, where liquidity is relatively scarce, the treasury's sale of tokens may have a significant impact on prices.

Finally, we want to emphasize the importance of providing value to and engaging with the token holder community. Many leading projects have underperformed in terms of utility or governance tokens because they have failed to provide value-added services to their token holders. This brings us back to the discussion between DAOs and enterprises, and more importantly, the misaligned incentives between the two.

As mentioned earlier, in many protocols, the company behind the protocol was often created before the DAO and retains ownership of the relevant intellectual property and assets. The protocol can experience strong growth, while the company behind it experiences rapid valuation growth, and the token remains stagnant. There may be a relatively large incentive misalignment between the owners of the company behind the protocol (i.e. the development team and investors) and the token holders. However, as Walden (a16z) argues, the protocol can gradually become decentralized, starting out highly centralized and then gradually becoming more decentralized (with IP transferred to the DAO), which can be taken into consideration.

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