Valuation of DeSci Projects

This article examines the applicability of different valuation methods for blockchain-based projects to tokenized decentralized science projects. It concludes that the quantity theory of money (QTM) provides the most reliable valuation for utility tokens in the absence of comparable projects. QTM can be complemented with the market valuation method if comparable projects exist.

Rise of DeSci

In the last few years, we have seen an explosion in the number of science-related projects using blockchain to achieve their vision. We will refer to such projects as Decentralize Science (DeSci) projects. Figure 1 illustrates the breadth of the DeSci landscape as of May 2022 (Pearl, 2022). These DeSci projects, alongside those yet to emerge, may revolutionize the scientific process as we know it.

At the same time, we cannot help but notice a growing number of stalled or outright abandoned DeSci projects. At this pivotal moment in the development of the DeSci community, the role of metascience is to develop tools and frameworks facilitating the creation, maintenance, and scaling up of DeSci projects. This article contributes to this goal by providing a valuation framework that can help the scientific community estimate the worth of new and existing projects and facilitate the fundraising process, essential to scale up those projects.

Figure 1. DeSci Project Landscape (Pearl, 2022)

While most of the DeSci projects are at the early stages of development, in the nearest future, they will require raising funds to scale up and achieve their goals. In fact, Molecule has recently raised $13 million in seed funding (Matsuda, 2022), and other projects are likely to follow suit.

An imperative prerequisite to attracting investors is obtaining a project valuation. Estimating the value of a project is of utmost importance to the founders to know whether the idea is worth their efforts and to the investors interested in impactful projects. While valuation is never an easy endeavour, it is especially difficult to estimate the true value of a DeSci project due to its inherent novelty.

Many of DeSci projects choose to issue their own tokens to create an incentive mechanism within their ecosystem or facilitate the project governance, e.g., Research Coin by ResearchHub or $VITA token by VitaDAO. Selling these tokens or options to acquire the tokens to investors is likely to become the primary funding mechanism for DeSci, as has already happened in other decentralized domains. For this reason, we will focus on the token valuation of DeSci projects.

Next, we investigate the three standard asset valuation methods: market, income, and asset (Shapiro et al., 2019). We complement those methods with the quantity theory of money method, as Ernst and Young (2019) suggested for crypto assets. For each method, we discuss its applicability to the emerging DeSci field.

Market Method

The market method is the most direct valuation method. It relies on the idea that efficient markets capture all the available information and thus provide the best estimate of the true value of an asset (Malkiel, 1989). Therefore, if a DeSci project has already listed its token on exchanges, its quoted price would be the most reliable estimate of its true value. The problem with this approach is that most projects seek funding and require valuation before they issue their tokens.

At the pre-launch stage, it might be possible to find comparable organizations with market quotes available for their tokens. Comparable, however, does not mean identical. The market capitalization of comparable organizations has to be adjusted to account for differences (Akkaya, 2020). A typical adjustment approach entails developing a scorecard, as shown in Table 1.

Table 1. Scorecard Approach for Adjusting the Value of Comparable Organizations

Parameter	Weight	Comparable Organization Factor
Market Size	$w_1$	$f_1$
Team Experience	$w_2$	$f_2$
Technology Maturity	$w_3$	$f_3$
Product Functionality	$w_4$	$f_4$
Weighted Factor		$\Sigma_{i=1}^4 w_i f_i$

In the above table, $w_i$ represents the weight of each parameter, and the sum of all weights should add up to $1$ . The comparable organization factor works as follows: If $f_i=1$ , the comparable organization is equivalent to the focal one based on parameter $i$ . If $f_i<1$ , the comparable organization is superior to the focal one, and the opposite is true for $f_i>1$ . For example, if the comparable organization's market size is twice as large as the focal one's, $f_1=0.5$ . Similarly, if the focal organization's team has $20\%$ more experience than the comparable one, $f_2=1.2$ .

Finally, the weighted factor serves as the multiplier for the comparable organization's market capitalization. For example, if it equals $1.4$ and the market capitalization of the comparable organization is $\$100$ , the valuation for the focal one will be $1.4\times \$100=\$140$ . It is highly recommended to repeat this valuation process for multiple comparable organizations to obtain a reliable valuation range.

The limitation of the market approach is that it assumes comparable organizations exist. While this might be true for some projects, many new DeSci ideas are unique without any extant competitors. Furthermore, the comparison process is rather subjective, which may result in a very wide range of value estimates.

Income Method

The income method is a very common method in traditional valuation. Its most well-known technique is obtaining the net present value of the project. However, the fundamental assumption of the income method is that the asset owner has a right to receive the income generated by the asset. This is simply not true for a wide majority of DeSci projects. For example, holding Research Coins does not entail the right to receive a fraction of ResearchHub's revenue. The only exception from this rule would be DeSci projects that issue security tokens, but such projects are relatively uncommon.

Therefore, the income approach cannot be applied to most tokenized DeSci projects.

Asset Method

This method aims to estimate the replacement value of the organization's assets. In the DeSci context, the key asset is the token. This asset's equivalent (or replacement) value can be interpreted in terms of services or goods exchanged for the token. For example, we know that the award pool in ResearchHub's competition is $5,000 and 100,000 Research Coins (RSC). Assuming the average award in similar competitions is $15,000, we can find that 100,000 RSC is equivalent to $10,000. From this, we can easily find the value of one token as well as the total value of tokens in circulation.

The main drawback of the asset approach is that it can only be applied to existing projects. It will not be possible to estimate the asset value of a project at the pre-launch stage.

Quantity Theory of Money (QTM) Method

A DeSci project with its native token forms a mini-economy. Leveraging this idea, we can use the QTM that links money supply $M$ , the velocity of money circulation $V$ , the average price level $P$ , and the volume of transactions in the economy $T$ (Friedman, 1989). The relationship can be captured as

$MV=PT$

Adapting it to the mini-economy of a particular DeSci project, we need to re-interpret the above parameters to match them with the project's specifics.

The right-hand side of the equation represents the total transaction value in the economy, i.e., the value of all goods and services consumed in the economy per year. For a DeSci project, this can be estimated as the overall market size times the projected market share of the project. The market size can be typically estimated based on existing market research. For example, for a DeSci project focusing on disseminating research articles, the market can be estimated as the total annual revenue of existing academic journals, which would be approximately $20 billion (Hagve, 2020). At the same time, the market share of a particular project is more uncertain. It is usually an excellent parameter to run scenario analyses, e.g., pessimistic, neutral, and realistic scenarios based on different market share projections.

On the left-hand side of the equation, the first parameter is the total money supply. The total money supply for a DeSci project would be the total circulating supply of the token. To find the circulating supply, we need to multiply the total supply by the floating factor. The floating factor represents the fraction of tokens in free circulation, i.e., not kept in reserves, restricted by the vesting period, or otherwise excluded from circulation.

The second parameter on the left-hand side is the velocity of the token, which is the number of times the token is expected to change hands per year. This value can be different from different blockchains. For example, if gas fees are high on a particular blockchain, users are more likely to keep their tokens rather than spend them. To estimate the velocity, one could check the average time (measured in years) that a particular wallet keeps tokens of other DeSci projects on the same blockchain. The inverse of this value will be the token velocity. For example, if users keep their tokens for an average of $2$ months before spending them, this needs to be converted to $\frac{2}{12}$ of a year, which yields a velocity of $\frac{12}{2}=6$ turns per year.

Combining the above, we can find the price of a token as

$p=\frac{D \times m}{N \times f \times V}$

where $p$ is the token price, $D$ is the annual demand (market size) for all products the token will be used for, $m$ is the projected market share of the project, $N$ is the total number of tokens to be issued, $f$ is the floating factor, and $V$ is the token velocity.

The downside of the QTM method is that it assumes a well-defined and measurable market for the project. While this assumption typically holds for utility tokens, it is problematic for governance tokens. A pure governance token is not intended to be spent on goods and services, which makes the QTM method inapplicable.

Concluding Remarks

We summarize our findings on the applicability of different valuation methods to DeSci projects in Table 2.

Table 2. Overview of DeSci Valuation Methods

Valuation Method	Applicability
Market	For mature projects or those with existing comparable projects
Income	Not applicable, except for projects with security tokens
Asset	For mature projects only
QTM	For all projects with utility tokens

Our analysis shows that only the QTM method is applicable for all projects at the pre-launch stage. At the same time, whenever feasible, we highly recommend using multiple methods to increase the reliability of the valuation.

We hope these insights will be useful for DeSci founders to estimate the value of their idea or existing project and for investors considering DeSci projects for their portfolio.

References

Akkaya, M. (2020). Startup Valuation: Theories, Models, and Future. In Valuation Challenges and Solutions in Contemporary Businesses (pp. 137-156). IGI Global.

Ernst and Young (2019). The valuation of crypto assets. Minds made for shaping financial services // URL https://assets.ey.com/content/dam/ey-sites/ey-com/en_gl/topics/emeia-financial-services/ey-the-valuation-of-crypto-assets.pdf accessed on 19 July 2022.

Friedman, M. (1989). Quantity theory of money. In Money (pp. 1-40). Palgrave Macmillan, London.

Hagve, M. (2020). The money behind academic publishing. In Tidsskriftet // URL https://tidsskriftet.no/en/2020/08/kronikk/money-behind-academic-publishing accessed on 21 July 2022.

Malkiel, B. G. (1989). Efficient market hypothesis. In Finance (pp. 127-134). Palgrave Macmillan, London.

Matsuda, T. (2021). Decentralized science platform Molecule raises $13 million in seed funding. In The Block, 13 June 2022 // URL https://www.theblock.co/post/151539/decentralized-science-platform-molecule-raises-13-million-in-seed-funding accessed on 21 July 2022.

Pearl, J. (2022). DeSci Wiki // URL https://docs.google.com/document/d/1aQC6zn-eXflSmpts0XGE7CawbUEHwnL6o-OFXO52PTc/edit#heading=h.l9xwzgwdig5a accessed on 19 July 2022.

Shapiro, E., Mackmin, D., & Sams, G. (2019). Modern methods of valuation. Estates Gazette.