Cryptocurrency valuation and the role of tokens

12.Oct.2018Editorial

Blockchains can be seen as an innovative tool in FinTech and the sharing economy: they allow the creation of set of networks, and the reduction of market power concentration. A majority of blockchains use cryptocurrencies and cryptography-secured tokens. Over 1000 different “altcoins” have been introduced over the past few years and many central banks are exploring applications involving retail and payment systems. Indeed, cryptocurrencies are typically regarded as means of payment and primarily associated with their respective blockchains.

The so called blockchain-based crypto-tokens have also gained popularity. In initial coin offerings (ICOs), entrepreneurs sell “tokens” or “AppCoins” created on top of primary blockchains to be dispersed amongst investors around the globe. While some tokens are “security tokens” and derive their value from the company’s future cash flows, the vast majority of ICOs are “utility tokens”, often serving as media of exchange on platforms. Precisely here lies the innovation of the blockchain technology – allowing peer-to-peer interactions in decentralized networks. That is where utility tokens derive their value from: their collective usage on blockchain platforms.

Thus, in order to differentiate between reckless speculation and valuable financial innovation it is important to understand the interaction between user adoption and token. In a recent paper, Cong, Li and Wang (CLW, 2018) develop a tractable dynamic model to shed light on this important interaction.

Many blockchain-based platforms embed native tokens as means of payment, not only in the case of payment and settlement applications but also on other usages, such as decentralized file storage or computations. Tokens provide incentives both on the supply side (for example for miners maintaining decentralized consensus in proof-of-work protocols) and on the demand side (e.g. enabling smart contracting in a uniform currency). Of course there is a user-based externality for platforms: the more users on a platform, the easier for a user to find counterparties and enjoy the utility from entertainment and business activities and services, which is particularly true and important in the early stage of adoption of social and payment networks.

The three authors consider a continuous-time economy with a continuum of potential users who differ in their needs to conduct transactions on the blockchain giving them a particular utility flow which also depends on the size of user base and the productivity of the platform (modeled as a geometric Brownian motion) that loads on exogenous shocks. In the model, agents decide whether to incur the cost of adoption and how many tokens to hold for on-chain activities. Of course, the holding of tokens exposes agents to token price changes and introduces an “investment motive” in addition to the “transaction motive”. A positive productivity shock directly increases the current user base, by raising the utility flow on the platform. At the same time, agents expect more users to join the community in the future, which leads to a stronger future demand for tokens and thus token price appreciation. The investment motive then creates a stronger demand for tokens today and greater adoption. This feeds expectations of further increase in productivity, token appreciation and ever greater adoption giving life to a dynamic feedback mechanism.

Assuming there is no bubble in order to focus on fundamental valuation and using data on tokens for 16 major cryptocurrencies from 2010 to 2018, the three economists calibrate the model where the patterns in the initial phase of adoption match the empirically observed dynamics of token price and user base. This quantitative exercise allows them to develop an intuitive understanding of the role of tokens on user adoption and the determinants of fundamentals-based token valuation.

One key contribution of the study is the clarification of the roles of tokens on platform adoption. To this end, they compare the endogenous S-curve of platform adoption with and without tokens. Without tokens, user adoption is often below the socially optimal level for a promising platform with improving productivity. Tokens can increase welfare because agents foresee token price appreciation and adopt more. Embedding tokens on a promising platform therefore front-loads prospective growth. A caveat is that tokens may also lead to over-adoption in the very early stage, paving the way to overvaluation and “price exuberance”. Similarly, for a bad platform whose productivity is expected to deteriorate, tokens can precipitate its demise, which again can be welfare-improving.

Furthermore, the introduction of tokens can reduce user base volatility, making it less sensitive to platform productivity shocks. The key driver is again the agents’ investment motive: in other words, their decisions to participate depend on their expectations of future token price appreciation. If a negative shock reduces productivity, utility flow and user adoption also lower. This direct negative effect is mitigated by an indirect positive effect, i.e. a depreciation of the token price which will bring more token-holding agents on the platform in the future. Of course the opposite (i.e. an increase in productivity, utility, user base, an appreciation of prices and a decrease of potential users in the future) holds true as well. Thus tokens have the role of accelerating and stabilizing user adoption and this is the reason why entrepreneurs may want to introduce them in a platform, issuing and offering them to early investors through ICOs, enjoying their appreciation.

The dynamic feedback mechanism described above leads the authors to design a token pricing formula which incorporates the user base, agents’ expectation on the platform popularity, its productivity and the heterogeneity of users. The proposed model fits the real data pattern well at the early stage of adoption, i.e. when the token price runs up quickly. Furthermore, the model predicts a further increase as the adoption approaches 100%. Interestingly, model excess volatility is quite high in the early stage of a platform development, but then (when its development increases and quality improves) starts going down. This finding suggests that to obtain more drastic dynamics in token price volatility, other sources beyond endogenous user adoption might be considered.

Overall, the CWL model constitutes a framework flexible enough to accommodate multiple additional features observed in practice. For example, the platform productivity can be endogenized by incorporating consensus provision; one can also study behavioral elements to accommodate the possibility of bubbles, so as to better match empirical observations; finally, crypto-investors can use the cross-section predictions to construct portfolios of cryptocurrencies. More broadly, the CLW framework can be applied to dynamic pricing of assets associated with a platform or system with network externalities.