Working Paper: NBER ID: w27222
Authors: Lin William Cong; Ye Li; Neng Wang
Abstract: We develop a dynamic asset-pricing model of cryptocurrencies/tokens that allow users to conduct peer-to-peer transactions on digital platforms. The equilibrium value of tokens is determined by aggregating heterogeneous users' transactional demand rather than discounting cashflows as in standard valuation models. Endogenous platform adoption builds upon user network externality and exhibits an S-curve — it starts slow, becomes volatile, and eventually tapers off. Introducing tokens lowers users' transaction costs on the platform by allowing users to capitalize on platform growth. The resulting intertemporal feedback between user adoption and token price accelerates adoption and dampens user-base volatility.
Keywords: Tokenomics; Cryptocurrency; Dynamic Valuation; User Adoption; Network Effects
JEL Codes: E42; G12; L86
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| user adoption (O36) | token price (P22) |
| token price (P22) | user adoption (O36) |
| platform productivity (L17) | user adoption (O36) |
| platform productivity (L17) | token price (P22) |
| user adoption (O36) | transactional utility (L14) |
| transactional utility (L14) | user adoption (O36) |
| user heterogeneity (R20) | platform adoption (L17) |
| user heterogeneity (R20) | token pricing (G13) |
| productivity shocks (O49) | user base (D16) |
| expected token price appreciation (G13) | effective carry cost (D61) |
| user adoption (O36) | network effects (D85) |
| user adoption (O36) | excess volatility (G17) |
| anticipated token price appreciation (G13) | user adoption stability (D16) |