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Beyond Bitcoin

Paper Session

Saturday, Jan. 4, 2020 12:30 PM - 2:15 PM (PDT)

Manchester Grand Hyatt, Torrey Hills AB
Hosted By: Association of Financial Economists & American Economic Association
  • Chair: Zhiguo He, University of Chicago

What Drives the Covariation of Cryptocurrency Returns?

Amin Shams
,
Ohio State University

Abstract

Demand for cryptocurrencies not only signals investment motives but also can be a sign of user adoption that affects the fundamental value of these assets. Therefore, common demand shocks can be the primary driver of cryptocurrency return structure. This paper constructs a "connectivity" measure proxying for common demand shocks and shows that it explains substantial covariation in the cross-section of cryptocurrency returns. Cryptocurrencies trade on more than 150 exchanges, and due to geographical and other restrictions, these exchanges serve different investor clienteles who show correlated demand across currencies listed on the same exchange. I define connectivity as the degree of overlap in the set of exchanges two cryptocurrencies trade on. I show that connected currencies exhibit substantial contemporaneous covariation. In addition, currencies connected to those that perform well outperform currencies connected to those that perform poorly by 71 basis points over the next day and 214 basis points over the next week. Evidence from new exchange listings and a quasi-natural experiment exploiting the shutdown of Chinese exchanges shows that the results cannot be explained by endogenous sorting of currencies into exchanges. Using machine learning techniques to analyze social media data, I find that the demand effects are 40 to 50% larger for currencies that rely more heavily on network externalities of user adoption. This amplified effect is consistent with the notion that demand for a cryptocurrency originates not only from investment motives but also the user adoption that can affect the fundamental value of these assets.

Cryptocurrency Pump-And-Dump Schemes

Tao Li
,
University of Florida
Donghwa Shin
,
University of North Carolina-Chapel Hill
Baolian Wang
,
University of Florida

Abstract

Pump-and-dump schemes(P&Ds) are pervasive in the cryptocurrency market.We find that P&Ds lead to short-term bubbles featuring dramatic increases in prices,volume, and volatility.Prices peak within minutes and quick reversals follow. The evidence we document, including price run-ups before P&Ds start, implies signicant wealth transfers between insiders and outsiders. Bittrex, a cryptocurrency exchange, banned P&Ds on November 24, 2017. Using a difference-in-differences approach, we provide causal evidence that P&Ds are detrimental to the liquidity and price of cryptocurrencies. We discuss potential mechanisms why outsiders are willing to participate and describe how our findings shed light on manipulation theories.

De-crypto-ing Signals in Initial Coin Offerings: Evidence of Rational Token Retention

Tetiana Davydiuk
,
Carnegie Mellon University
Deeksha Gupta
,
Carnegie Mellon University
Samuel Rosen
,
Temple University

Abstract

Using the market for initial coin offerings (ICOs) as a laboratory, we provide evidence that entrepreneurs use retention as a method to alleviate information asymmetry. ICO investors face a high degree of uncertainty because of the unregulated and opaque nature of the ICO market. Using a detailed dataset on 5,644 ICOs, we show that ICOs that retain a larger fraction of their tokens are more successful in their funding efforts and are more likely to develop a working product or platform. Specifically, we estimate that a 1 p.p. increase in token retention leads to a 0.1-0.3 p.p. increase in fundraising success. Moreover, we find that retention is a stronger signal when markets are crowded and investors do not have as much time to conduct due diligence.
Discussant(s)
Katrin Tinn
,
McGill University
Sean Foley
,
University of Sydney
Sabrina T. Howell
,
New York University
JEL Classifications
  • G2 - Financial Institutions and Services
  • C6 - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling