Will Cong, Ye Li, Neng Wang,
Based on: Review of Financial Studies, March 2021 (Editor’s Choice), 34(3), 1105–1155 DOI: https://doi.org/10.1093/rfs/hhaa089
Blockchain-based applications and cryptocurrencies have taken a center stage among technological breakthroughs in FinTech, attracted much attention, and also generated much controversy. Ben Bernanke stated in a recent interview that “I believe that so far cryptocurrencies have not been shown to have any economic value at all.”[1] Yet, the global market capitalization of crypto tokens grew to trillions of U.S. dollars at their peak and still amounts to hundreds of billions despite the market crashes triggered by the collapse of Terra Luna stablecoin and the scandalous fraud of FTX and Alameda. Given the divergent views, understanding token functionality and valuation is urgent and crucial for academic research, practical design, and policy making.
Not all tokens are created equal, and President Biden’s executive order concerning digital assets specifically asks that the nature and types of digital assets be clarified, “the report should consider the particular features of various types of digital assets and include recommendations that address the identified financial stability risks posed by these digital assets.”[2] Among the various categories of cryptocurrencies and tokens, the most dominant are general payment tokens such as Bitcoin and Ripple, and platform tokens such as Dfinity for decentralized world computation and Avalanche for distributed smart contracting. Improving our understanding of how user adoption concerning these tokens affects market volatility and generates “fundamental values” can help policy makers in assessing system risk and setting regulations.
The theory represents a first-pass effort to answer a set of important questions regarding token valuation, price dynamics, and platform growth. For example, what are the sources of token valuation as many tokens generate no cash flows? Why are token price returns so volatile and how is this volatility related to user adoption? And, how do token prices vary over the user adoption life cycle?
A tokenized ecosystem typically involves users, contributors (investors and third-party innovators), and initial developers (“founders”). Tokens circulate among users, providing means of payment or specific utilities such as collateralizing tokens for rewards (i.e., “staking”). Tokens are also used by ecosystem stakeholders to raise capital and induce efforts. For example, the quality of a Bitcoin payment network or Ethereum smart contracting depends crucially on the maintenance of a decentralized ledger, which is achieved by compensating competing miners, or stakers, with newly minted tokens or user fees collected via tokens. Finally, founders typically design the token issuance policies, hold a significant fraction of tokens, and benefit from the platform’s development and adoption.
Most cryptocurrencies and tokens are hybrid assets. On the one hand, like commodities and money, they serve as a means of payments, allowing holders to transact on specialized platforms or to enjoy governance rights and rewards associated with the network communities. For example, transactions give agents a convenience benefit that depends on agent-specific needs, the size of the userbase, and the quality of the platforms. On the other hand, because tokens represent durable goods typically in limited supply that capture the value of the platform, they serve as investment vehicles for network users and participants, exposing them to token price changes.
Cryptocurrency pricing therefore depends on both the supply policy and the users’ demand, arising from both platform usage and investment motives. Importantly, the token valuation interacts with the size of the network in the underlying digital network. The larger and more active the network, the more likely it is that other users will adopt it. The early stage of adoption for social and payment networks, such as Facebook, Twitter, YouTube, WeChat, and PayPal, features the positive feedback effects between the size of the userbase and the rate at which new users join the network. Cryptocurrency adoption features similar effects: The more users adopt the platform, the easier it is to find transaction counterparties and collectively innovate or improve the platforms through data contribution. But, unlike fiat currency adoption, which is location dependent, users can switch among digital platforms easily or multihome (adopting multiple platforms and tokens). As a result, token value reflects users’ voluntary participation and the associated network externality effects. The dynamic feedback between user adoption and token price accelerates adoption and dampens user-base volatility. Platform adoption builds on the positive spillover from other users’ adoption. As a result, user adoption exhibits an S-curve: it starts slowly, becomes volatile, and eventually tapers off.
The fast-evolving userbase plays a critical role in driving token valuation and explaining the cross-section variation of cryptocurrency returns, the time variations of volatility, and the run-up and crashes of the crypto market. One reason for this is that it makes cryptocurrency returns more volatile. Consider a technology breakthrough that improves the functionality of the platform. Greater user adoption and the network externality further amplify the impact of the fundamental shock on the ecosystem valuation, which has a one-to-one relation to token valuation when token supplies do not drastically change over the short term. Data on token pricing and adoption for 16 major cryptocurrencies from 2010 to 2018 allows a calibration exercise to link the user-based growth trajectory and token valuation, as shown in Figure 1. The crypto sector naturally experiences large valuation fluctuations in the initial phase of adoption. Multiple empirical studies have also confirmed that user-base measures constitute an important pricing factor for crypto assets. In general, the pricing of platform tokens satisfies an equation with network size, user heterogeneity, platform trajectory, and market expectations of price appreciation all as key determinants.
Figure 1. Token Pricing throughout Adoption Life Cycle
This figure plots token price evolution as a function of a measurement of user adoption of blockchain platform, Nt. The solid blue curve is the equilibrium token price that matches well with the gray dots, the real data of major cryptocurrencies. Token valuation is the most sensitive to user adoption in the initial and final phase of the user adoption life cycle of a platform.
Given that the adoption curve affects token valuation, it is natural to also ask how tokens affect a platform’s userbase. Entrepreneurs often speak heuristically of the “bootstrapping” benefits crypto tokens bring about. User adoption is below the socially optimal level for a conventional platform with improving quality because users do not internalize the network effects in their adoption decisions. Tokens indeed can accelerate adoption and increase welfare because agents foresee a growing demand for the platform, which implies token price appreciation when token supply is fixed or its increases are moderated by algorithms, and they therefore adopt more. Introducing tokens lowers the effective carry cost of conducting platform transactions and hence accelerates the adoption of productive platforms, as illustrated in Figure 2. For an unproductive platform whose quality is expected to deteriorate, tokens similarly precipitate its demise.
Figure 2. User Adoption on Tokenized and Tokenless Platforms
This figure plots four cases of userbase evolution against a measurement of the quality of the blockchain platform, ln(At). The solid blue curve is the userbase from the model of user adoption and token valuation, which matches well with the gray dots, the real data of the userbase (proxied by active addresses) of major cryptocurrencies. The green vertical line marks the level of platform quality, above which the adoption level that maximizes the aggregate welfare should be 100%, and below which, it should be zero. The tokenized economy (blue solid) curve brings user adoption close to the ideal level relative to the same platform without native cryptocurrency (the red dashed curve).
The introduction of tokens also reduces fluctuations in the userbase because agents’ expectations about long-term growth in token value (the investment motive) weaken the impact of productivity or regulatory shocks to the platform. Consider a negative fundamental shock that lowers user adoption. This direct negative effect is mitigated by an indirect effect through token price: a lower adoption now means more agents can be brought onto the platform in the future. The expected stronger token price appreciation therefore induces agents to adopt.
Researchers are still exploring the various economic functionalities of crypto tokens. How to design token supply policy and develop a platform requires an integrated analysis of monetary economics and finance frameworks. None of them can do away with a basic understanding of crypto asset valuation and time-varying network adoption. Appropriate regulatory policies, too, can only be introduced after understanding the economic functionality of cryptocurrencies and how they derive value from platform fundamentals and inherit volatility from fluctuations in the userbase.
[1] Lucas, Dan, 2022 (Dec. 7), Economy Award Winner: Cryptocurrencies Have No Value Whatsoever. Dagens Nyheter. https://www.dn.se/ekonomi/ekonomipristagare-kryptovalutor-har-inte-nagot-varde-over-huvud-taget/
[2] The White House, 2022 (Mar. 9). Executive Order on Ensuring Responsible Development of Digital Assets. https://www.whitehouse.gov/briefing-room/presidential-actions/2022/03/09/executive-order-on-ensuring-responsible-development-of-digital-assets/
The Rudd Family Professor of Management and Associate Professor of Finance
Johnson Graduate School of Management, SC Johnson College of Business, Cornell University
William W. Alberts Endowed Professor in Finance and Assistant Professor
Foster School of Business. University of Washington
