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Decisions in Economics and Finance

Erschienen in:

18.06.2021

The rise and fall of cryptocurrency coins and tokens

verfasst von: Neil Gandal, J. T. Hamrick, Tyler Moore, Marie Vasek

Erschienen in: Decisions in Economics and Finance | Ausgabe 2/2021

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Abstract

Since Bitcoin’s introduction in 2009, interest in cryptocurrencies has soared. One manifestation of this interest has been the explosion of newly created coins and tokens. In this paper, we analyze the dynamics of this burgeoning industry. We consider both cryptocurrency coins and tokens. The paper examines the dynamics of coin and token creation, competition and destruction in the cryptocurrency industry. In order to conduct the analysis, we develop a methodology to identify peaks in prices and trade volume, as well as when coins and tokens are abandoned and subsequently “resurrected”. We also study trading activity. Our data spans more than 4 years: there are 1082 coins and 725 tokens in the data. While there are some similarities between coins and tokens regarding dynamics, there are some striking differences as well. Overall, we find that 44% of publicly-traded coins are abandoned, at least temporarily. 71% of abandoned coins are later resurrected, leaving 18% of coins to fail permanently. Tokens experience abandonment less frequently, with only 7% abandonment and 5% permanent token abandonment at the end of the data. Using linear regressions, we find that market variables such as the bitcoin price are not associated with the rate of introducing new coins, though they are positively associated with issuing new tokens. We find that for both coins and tokens, market variables are positively associated with resurrection. We then examine the effect that the bursting of the Bitcoin bubble in December 2017 had on the dynamics in the industry. Unlike the end of the 2013 bubble, some alternative cryptocurrencies continue to flourish after the bursting of this bubble.

Vorheriger Artikel Cross-listings of blockchain-based tokens issued through initial coin offerings: Do liquidity and specific cryptocurrency exchanges matter?

Nächster Artikel Introduction to Special Issue on Energy Finance

Although the market capitalization dropped following the last Bitcoin bubble it sits around $195 billion at the time of writing, which puts cryptocurrencies just below the market capitalization of Wells Fargo & Co.

Foley et al. (2019) find that approximately one-quarter of bitcoin users are involved in illegal activity. They estimate that 46% of bitcoin transactions involve illegal activity. Based on their estimates, the illegal use of bitcoin generates approximately $76 billion of illegal activity per year. In terms of comparison, the scale of the US and European markets for illegal drugs is only slightly larger (Foley et al. 2019).

Rob developed a theoretical model that shows that under uncertainty regarding the size of the market, entry will occur in waves (Rob 1991). Vettas obtained similar results in an extension of the Rob model to a setting with uncertainty on both sides of the market (Vettas 1998).

For a good summary of early work, see the Audretsch and Mata (1995) on the post-entry performance of firms.

See Geroski and the references cited within for a survey of the literature (Geroski 1995).

For an in-depth overview of how the Bitcoin ecosystem works, see Böhme et al. (2015).

This is true as long as at least one such API reports positive trade volume.

This term is particularly confusing because only tokens have ICOs, not coins.

For this analysis we exclude any price or volume rises from peaks occurring in the first week of a coin’s operation, as well as any falls within the last week of its operation. This is to deal with edge effects from the 7-day rolling average used to compute peaks.

See Fig. 9 for a graphic representation of the latter correlation.

Many of these trends can be seen in Fig. 12. The Bitcoin price was again included as the bottom plot of this figure because it continues to be the market leader and set the trend for other cryptocurrencies. These Bitcoin backed trends are apparent through the examination of high correlations found involving the log transformed Bitcoin price in Table 4.

Tokens typically give access to a product or service. On the other hand, recent research has shown that during the period we analyze, Bitcoin is primarily used for speculative and criminal activity. Hence, it is very unlikely that tokens’ life cycle (i.e., resurrection) affects Bitcoin’s value—and thus it is reasonable to treat the BTC price as exogenous.

We chose 52 day periods in order to have three periods to analyze: the rise, the fall, and the aftermath.

Adhami, S., Giudici, G., Martinazzi, S.: Why do businesses go crypto? an empirical analysis of initial coin offerings. J. Econ. Bus. 100, 64–75 (2018)CrossRef

Aggarwal, R.K., Wu, G.: Stock market manipulations. J. Bus. 79(4), 1915–1953 (2006)CrossRef

Amsden, R., Schweizer, D.: Are blockchain crowdsales the new ‘gold rush’? success determinants of initial coin offerings, available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3163849 (2018)

Audretsch, D.B., Mata, J.: The post-entry performance of firms: Introduction. Int. J. Ind. Organ. 13(4), 413–419 (1995)CrossRef

Benedetti, H.E., Kostovetsky, L.: Digital Tulips? Returns to Investors in Initial Coin Offerings, SSRN Electronic Journal (2018)

Böhme, R., Holz, T.: The effect of stock spam on financial markets. In: Workshop on the Economics of Information Security (2006)

Böhme, R., Christin, N., Edelman, B., Moore, T.: Bitcoin: Economics, technology, and governance. J. Econ. Persp. 29(2), 213–38 (2015)CrossRef

Bolt, W., van Oordt, M.: On the value of virtual currencies. J. Money Credit Bank. (2020)

Corbet, S., Meegan, A., Larkin, C., Lucey, B., Yarovaya, L.: Exploring the dynamic relationships between cryptocurrencies and other financial assets. Econ. Lett. 165, 28–34 (2018)CrossRef

Foley, S., Karlsen, J.R., Putniņš, T.J.: Sex, drugs, and bitcoin: How much illegal activity is financed through cryptocurrencies? Rev. Financ. Stud. 32(5), 1798–1853 (2019)CrossRef

Frieder, L., Zittrain, J.: Spam works: Evidence from stock touts and corresponding market activity. Hastings Comm & Ent LJ 30, 479 (2007)

Gandal, N.: Compatibility, standardization, and network effects: Some policy implications. Oxf. Rev. Econ. Policy 18(1), 80–91 (2002)CrossRef

Gandal, N., Halaburda, H.: Can we predict the winner in a market with network effects? competition in cryptocurrency market. Games 7(3), 16 (2016)CrossRef

Gandal, N., Hamrick, J., Moore, T., Oberman, T.: Price manipulation in the bitcoin ecosystem. J. Monet. Econ. 95, 86–96 (2018)CrossRef

Geroski, P.A.: What do we know about entry? Int. J. Ind. Organ. 13(4), 421–440 (1995)CrossRef

Griffin, J.M., Shams, A.: Is bitcoin really un-tethered? J. Finance (2020). ((forthcoming))

Hamrick, J., Rouhi, F., Mukherjee, A., Feder, A., Gandal, N., Moore, T., Vasek, M.: An examination of the cryptocurrency pump and dump ecosystem. Available at SSRN 3303365 (2018)

Hanke, M., Hauser, F.: On the effects of stock spam e-mails. J. Financ. Markets 11(1), 57–83 (2008)CrossRef

Huang, W., Meoli, M., Vismara, S.: The geography of initial coin offerings. Small Bus. Econ. pp 1–26 (2019)

Krafft, P.M., Della Penna, N., Pentland, A.S.: An experimental study of cryptocurrency market dynamics. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery (2018)

Li, X., Wang, C.A.: The technology and economic determinants of cryptocurrency exchange rates: The case of bitcoin. Decis. Support Syst. 95, 49–60 (2017)CrossRef

Lyandres, E., Palazzo, B., Rabetti, D.: Do Tokens Behave like Securities?. An Anatomy of Initial Coin Offerings, SSRN Electronic Journal (2019)

Makarov, I., Schoar, A.: Trading and arbitrage in cryptocurrency markets. J. Financ. Econ. 135(2), 293–319 (2020)CrossRef

Momtaz PP (2018) Initial Coin Offerings. SSRN Scholarly Paper ID 3166709, Social Science Research Network, Rochester, NY, https://papers.ssrn.com/abstract=3166709

Moore, T., Christin, N.: Beware the middleman: Empirical analysis of Bitcoin-exchange risk. In: Financial Cryptography and Data Security, Springer, pp 25–33 (2013)

Popper, N.: Rise of Bitcoin competitor Ripple creates wealth to rival Zuckerberg. The New York Times Available at: https://www.nytimes.com/2018/01/04/technology/bitcoin-ripple.html (2018a)

Popper, N.: Worries grow that the price of bitcoin is being propped up. The New York Times Available at: https://www.nytimes.com/2018/01/31/technology/bitfinex-bitcoin-price.html (2018b)

Rob, R.: Learning and capacity expansion under demand uncertainty. Rev. Econ. Stud. 58(4), 655–675 (1991)CrossRef

Vasek, M., Moore, T.: There’s no free lunch, even using Bitcoin: Tracking the popularity and profits of virtual currency scams. In: Financial Cryptography and Data Security, Springer, pp 44–61 (2015)

Vettas, N.: Demand and supply in new markets: diffusion with bilateral learning. RAND J. Econ. pp 215–233 (1998)

Xie, P., Chen, H., Hu, Y.J.: Signal or noise in social media discussions: Investigating the role of network cohesion, available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2894089 (2017)

Xu, J., Livshits, B.: The anatomy of a cryptocurrency pump-and-dump scheme. In: 28th USENIX Security Symposium, pp 1609–1625 (2019)

Titel: The rise and fall of cryptocurrency coins and tokens
verfasst von: Neil Gandal
J. T. Hamrick
Tyler Moore
Marie Vasek
Publikationsdatum: 18.06.2021
Verlag: Springer International Publishing
Erschienen in: Decisions in Economics and Finance / Ausgabe 2/2021
Print ISSN: 1593-8883
Elektronische ISSN: 1129-6569
DOI: https://doi.org/10.1007/s10203-021-00329-8

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