Estimating Profitability of Alternative Cryptocurrencies (Short Paper)

Danny Yuxing Huang, Kirill Levchenko, Alex C. Snoeren

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Digital currencies have flourished in recent years, buoyed by the tremendous success of Bitcoin. These blockchain-based currencies, called altcoins, are associated with a few thousand to millions of dollars of market capitalization. Altcoins have attracted enthusiasts who enter the market by mining or buying them, but the risks and rewards could potentially be significant, especially when the market is volatile. In this work, we estimate the potential profitability of mining and speculating 18 altcoins using real-world blockchain and trade data. Using opportunity cost as a metric, we estimate the mining cost for an altcoin with respect to a more popular but stable coin. For every dollar invested in mining or buying a coin, we compute the potential returns under various conditions, such as time of market entry and hold positions. While some coins offer the potential for spectacular returns, many follow a simple bubble-and-crash scenario, which highlights the extreme risks—and potential gains—in altcoin markets.

Original languageEnglish (US)
Title of host publicationFinancial Cryptography and Data Security - 22nd International Conference, FC 2018, Revised Selected Papers
EditorsSarah Meiklejohn, Kazue Sako
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783662583869
StatePublished - 2018
Event22nd International Conference on Financial Cryptography and Data Security, 2018 - Nieuwpoort, Belgium
Duration: Feb 26 2018Mar 2 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10957 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Financial Cryptography and Data Security, 2018

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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