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Empirical Analysis of Indirect Internal Conversions in Cryptocurrency Exchanges

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  • Paz Grimberg
  • Tobias Lauinger
  • Damon McCoy

Abstract

Algorithmic trading is well studied in traditional financial markets. However, it has received less attention in centralized cryptocurrency exchanges. The Commodity Futures Trading Commission (CFTC) attributed the $2010$ flash crash, one of the most turbulent periods in the history of financial markets that saw the Dow Jones Industrial Average lose $9\%$ of its value within minutes, to automated order "spoofing" algorithms. In this paper, we build a set of methodologies to characterize and empirically measure different algorithmic trading strategies in Binance, a large centralized cryptocurrency exchange, using a complete data set of historical trades. We find that a sub-strategy of triangular arbitrage is widespread, where bots convert between two coins through an intermediary coin, and obtain a favorable exchange rate compared to the direct one. We measure the profitability of this strategy, characterize its risks, and outline two strategies that algorithmic trading bots use to mitigate their losses. We find that this strategy yields an exchange ratio that is $0.144\%$, or $14.4$ basis points (bps) better than the direct exchange ratio. $2.71\%$ of all trades on Binance are attributable to this strategy.

Suggested Citation

  • Paz Grimberg & Tobias Lauinger & Damon McCoy, 2020. "Empirical Analysis of Indirect Internal Conversions in Cryptocurrency Exchanges," Papers 2002.12274, arXiv.org.
    • Handle: RePEc:arx:papers:2002.12274
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    References listed on IDEAS

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    1. Stanis{l}aw Dro.zd.z & Ludovico Minati & Pawe{l} O'swik{e}cimka & Marek Stanuszek & Marcin Wk{a}torek, 2019. "Signatures of crypto-currency market decoupling from the Forex," Papers 1906.07834, arXiv.org, revised Jul 2019.
    2. Makarov, Igor & Schoar, Antoinette, 2020. "Trading and arbitrage in cryptocurrency markets," LSE Research Online Documents on Economics 100409, London School of Economics and Political Science, LSE Library.
    3. Makarov, Igor & Schoar, Antoinette, 2020. "Trading and arbitrage in cryptocurrency markets," Journal of Financial Economics, Elsevier, vol. 135(2), pages 293-319.
    4. Aiba, Yukihiro & Hatano, Naomichi & Takayasu, Hideki & Marumo, Kouhei & Shimizu, Tokiko, 2002. "Triangular arbitrage as an interaction among foreign exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 310(3), pages 467-479.
    5. Daniel J. Fenn & Sam D. Howison & Mark McDonald & Stacy Williams & Neil F. Johnson, 2008. "The Mirage of Triangular Arbitrage in the Spot Foreign Exchange Market," Papers 0812.0913, arXiv.org.
    6. Thomas Günter Fischer & Christopher Krauss & Alexander Deinert, 2019. "Statistical Arbitrage in Cryptocurrency Markets," JRFM, MDPI, vol. 12(1), pages 1-15, February.
    7. Yukihiro Aiba & Naomichi Hatano & Hideki Takayasu & Kouhei Marumo & Tokiko Shimizu, 2002. "Triangular arbitrage as an interaction among foreign exchange rates," Papers cond-mat/0202391, arXiv.org, revised Mar 2002.
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    Cited by:

    1. Binh Nguyen Quang & Thai‐Ha Le & Canh Nguyen Phuc, 2022. "Influences of uncertainty on the returns and liquidity of cryptocurrencies: Evidence from a portfolio approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2497-2513, April.
    2. Ye Wang & Yan Chen & Haotian Wu & Liyi Zhou & Shuiguang Deng & Roger Wattenhofer, 2021. "Cyclic Arbitrage in Decentralized Exchanges," Papers 2105.02784, arXiv.org, revised Jan 2022.

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