Tokenomics: Dynamic Adoption and Valuation

44 Pages Posted: 25 Apr 2018 Last revised: 24 May 2019

See all articles by Lin William Cong

Lin William Cong

Cornell University

Ye Li

Ohio State University

Neng Wang

Columbia Business School - Finance and Economics; National Bureau of Economic Research (NBER)

Date Written: May 2019

Abstract

We develop a dynamic asset-pricing model of cryptocurrencies/tokens that facilitate peer-to-peer transactions on digital platforms. The equilibrium value of tokens is determined by users' transactional demand rather than cashflows as in standard valuation models. Endogenous platform adoption exhibits an S-curve -- it starts slow, becomes volatile, and eventually tapers off. Users' adoption generates positive network externality, which leads to endogenous token-price risk and boom-bust price dynamics. Tokens allow users to capitalize on platform growth, inducing an intertemporal feedback between user adoption and token price that accelerates platform adoption, reduces user-base volatility, and improves welfare.

Keywords: Asset Pricing, Blockchain, Cryptocurrency, Digital Marketplace, FinTech, Intertemporal Feedback Effect, Network Externality, Tokens

JEL Classification: G12, L86

Suggested Citation

Cong, Lin and Li, Ye and Wang, Neng, Tokenomics: Dynamic Adoption and Valuation (May 2019). Columbia Business School Research Paper No. 18-46; 2nd Emerging Trends in Entrepreneurial Finance Conference. Available at SSRN: https://ssrn.com/abstract=3153860 or http://dx.doi.org/10.2139/ssrn.3153860

Lin Cong (Contact Author)

Cornell University ( email )

Ithaca, NY 14853
United States

HOME PAGE: http://www.linwilliamcong.org

Ye Li

Ohio State University ( email )

Fisher Hall 836, 2100 Neil Ave
Columbus, OH 43210
United States

HOME PAGE: http://yeli-macrofinance.com

Neng Wang

Columbia Business School - Finance and Economics ( email )

3022 Broadway
New York, NY 10027
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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