Value Premium, Network Adoption, and Factor Pricing of Crypto Assets

49 Pages Posted: 15 Dec 2021

See all articles by Lin William Cong

Lin William Cong

Cornell University - Samuel Curtis Johnson Graduate School of Management

George Andrew Karolyi

Cornell University - Samuel Curtis Johnson Graduate School of Management

Ke Tang

Institute of Economics, School of Social Sciences, Tsinghua University

Weiyi Zhao

Tsinghua University

Date Written: December 2021

Abstract

We document characteristic-based anomalies in over 4000 cryptocurrencies. We find that cryptocurrency returns exhibit momentum in the largest-cap group but reversal in other size groups. We identify strong crypto value and network adoption premia, from which we derive two novel factors to add to crypto market, size, and momentum factors. The resulting C-5 model outperforms extant models in pricing the cross-section of crypto assets, as revealed in most standard tests. We also provide the first comprehensive categorization of about 700 cryptocurrencies based on their economic functionality and use tools from international asset pricing to demonstrate strong segmentations across token categories.

Keywords: Asset Pricing, Cryptocurrency, Factor Model, Network Effect, Segmentation

Suggested Citation

Cong, Lin and Karolyi, George Andrew and Tang, Ke and Zhao, Weiyi, Value Premium, Network Adoption, and Factor Pricing of Crypto Assets (December 2021). Available at SSRN: https://ssrn.com/abstract=3985631 or http://dx.doi.org/10.2139/ssrn.3985631

Lin Cong (Contact Author)

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

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

George Andrew Karolyi

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

Ke Tang

Institute of Economics, School of Social Sciences, Tsinghua University ( email )

No.1 Tsinghua Garden
Beijing, 100084
China

Weiyi Zhao

Tsinghua University ( email )

Beijing, 100084
China

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