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Quantitative Finance and Economics

2020, Volume 4, Issue 3: 365-381. doi: 10.3934/QFE.2020017
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Research article

Bitcoin transactions, information asymmetry and trading volume

  • 1.
    Blockchain Research Lab, Colonnaden 72, 20354 Hamburg, Germany
  • 2.
    University of Hamburg, Faculty of Business, Economics & Social Sciences, Von-Melle-Park 5, 20146 Hamburg, Germany
  • Received: 26 April 2020 Accepted: 28 May 2020 Published: 02 June 2020

  • JEL Codes: C12, C58, G10, G12, G14

  • The underlying transparency of the Bitcoin blockchain allows transactions in the network to be tracked in near real-time. When someone transfers a large number of Bitcoins, the market receives this information and traders can adjust their expectations based on the new information. This paper investigates trading volume and its relation to asymmetric information around transfers on the Bitcoin blockchain. We collect data on 2132 large transactions on the Bitcoin blockchain between September 2018 and November 2019, where 500 or more Bitcoins were transferred. Using event study methodology, we identify significant positive abnormal trading volume for the 15-minute window before a large Bitcoin transaction as well as during and after the event. Using public information about Bitcoin addresses of cryptocurrency exchanges as proxies for information asymmetry, we find that transactions with high levels of information asymmetry negatively affect abnormal trading volume once the event becomes public knowledge, while some effects are even opposite for transactions with lower information asymmetry. The results show that blockchain transaction activity is a relevant aspect of Bitcoinns microstructure, as informed traders make use of the information in general and adjust their expectations based on the degree of information asymmetry.

    Citation: Lennart Ante. Bitcoin transactions, information asymmetry and trading volume[J]. Quantitative Finance and Economics, 2020, 4(3): 365-381. doi: 10.3934/QFE.2020017

    Related Papers:

  • The underlying transparency of the Bitcoin blockchain allows transactions in the network to be tracked in near real-time. When someone transfers a large number of Bitcoins, the market receives this information and traders can adjust their expectations based on the new information. This paper investigates trading volume and its relation to asymmetric information around transfers on the Bitcoin blockchain. We collect data on 2132 large transactions on the Bitcoin blockchain between September 2018 and November 2019, where 500 or more Bitcoins were transferred. Using event study methodology, we identify significant positive abnormal trading volume for the 15-minute window before a large Bitcoin transaction as well as during and after the event. Using public information about Bitcoin addresses of cryptocurrency exchanges as proxies for information asymmetry, we find that transactions with high levels of information asymmetry negatively affect abnormal trading volume once the event becomes public knowledge, while some effects are even opposite for transactions with lower information asymmetry. The results show that blockchain transaction activity is a relevant aspect of Bitcoinns microstructure, as informed traders make use of the information in general and adjust their expectations based on the degree of information asymmetry.


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    [1] Aalborg HA, Molnár P, de Vries JE (2019) What can explain the price, volatility and trading volume of Bitcoin? Financ Res Lett 29: 255-265. doi: 10.1016/j.frl.2018.08.010
    [2] Admati AR, Pfeiderer P (1988) A Theory of Intraday Patterns: Volume and Price Variability. Rev Financ Stud 1: 3-40. doi: 10.1093/rfs/1.1.3
    [3] Ajinkya BB, Jain PC (1989) The behavior of daily stock market trading volume. J Account Econ 11: 331-359. doi: 10.1016/0165-4101(89)90018-9
    [4] Alameda Research (2019) Investigation into the Legitimacy of Reported Cryptocurrency Exchange Volume. Available from: https://ftx.com/volume_report_paper.pdf
    [5] Ante L (2020) A place next to Satoshi: scientific foundations of blockchain and cryptocurrency in business and economics. Scientometrics Available from: https://doi.org/10.1007/s11192-020-03492-8
    [6] Ante L, Fiedler I (2020) Market Reaction to Large Transfers on the Bitcoin Blockchain-Do Size and Motive Matter? Financ Res Lett. Available from: https://doi.org/10.1016/j.frl.2020.101619.
    [7] Bariviera AF (2017) The inefficiency of Bitcoin revisited: A dynamic approach. Econ Lett 161: 1-4. doi: 10.1016/j.econlet.2017.09.013
    [8] Baur DG, Cahill D, Godfrey K, et al. (2019) Bitcoin time-of-day, day-of-week and month-of-year effects in returns and trading volume. Financ Res Lett 31: 78-92. doi: 10.1016/j.frl.2019.04.023
    [9] Beaver WH (1968) The Information Content of Annual Earnings Announcements. J Account Res 6: 67-92. doi: 10.2307/2490070
    [10] Bitfinex (2020) Bitfinex Security Features. Available from: https://support.bitfinex.com/hc/en-us/articles/213892469-Bitfinex-Security-Features.
    [11] Black F (1986) Noise. J Financ 41: 528-543. doi: 10.1111/j.1540-6261.1986.tb04513.x
    [12] Brown SJ, Warner JB (1985) Using daily stock returns. The case of event studies. J Financ Econ 14: 3-31. doi: 10.1016/0304-405X(85)90042-X
    [13] Campbell CJ, Wasley CE (1996) Measuring abnormal daily trading volume for samples of NYSE/ASE and NASDAQ securities using parametric and nonparametric test statistics. Rev Quant Financ Account 6: 309-326. doi: 10.1007/BF00245187
    [14] Caporale GM, Plastun A (2019) The day of the week effect in the cryptocurrency market. Financ Res Lett 31: 258-269. doi: 10.1016/j.frl.2018.11.012
    [15] Chae J (2005) Trading volume, information asymmetry, and timing information. J Financ 60: 413-442. doi: 10.1111/j.1540-6261.2005.00734.x
    [16] Ciaian P, Rajcaniova M, Kancs Artis (2016) The economics of BitCoin price formation. Appl Econ 48: 1799-1815. doi: 10.1080/00036846.2015.1109038
    [17] Cready WM, Ramanan R (1991) The power of tests employing log-transformed volume in detecting abnormal trading. J Account Econ 14: 203-214. doi: 10.1016/0165-4101(91)90005-9
    [18] Decker C, Wattenhofer R (2016) Information propagation in the Bitcoin network, in: 13-Th IEEE International Conference on Peer-to-Peer Computing, 1-10.
    [19] Dorfleitner G, Lung C (2018) Cryptocurrencies from the perspective of euro investors: a re-examination of diversification benefits and a new day-of-the-week effect. J Asset Manage 19: 472-494. doi: 10.1057/s41260-018-0093-8
    [20] Easley D, Hvidkjaer S, O'Hara M (2002) Is information risk a determinant of asset returns? J Financ 57: 2185-2221. doi: 10.1111/1540-6261.00493
    [21] Fama EF (1970) Efficient Capital Markets: A Review of Theory and Empirical Work. J Financ 25: 383-417. doi: 10.2307/2325486
    [22] Foster G, Olsen C, Shevlin T (1984) Earnings Releases, Anomalies, and the Behavior of Security Returns. Account Rev 59: 574-603.
    [23] Fusaro T, Hougan M (2019) Bitwise Asset Management - Presentation to the U.S. Securities and Exchange Commission. Available from: https://www.sec.gov/comments/sr-nysearca-2019-01/srnysearca201901-5164833-183434.pdf.
    [24] Gervais A, Ritzdorf H, Karame GO, et al. (2015) Tampering with the delivery of blocks and transactions in Bitcoin, in: Proceedings of the ACM Conference on Computer and Communications Security, 692-705.
    [25] Harris L (1986) Cross-Security Tests of the Mixture of Distributions. J Financ Quant Anal 21: 39-46. doi: 10.2307/2330989
    [26] Jain PC, Joh GH (1988) The Dependence between Hourly Prices and Trading Volume. J Financ Quant Anal 23: 269-283. doi: 10.2307/2331067
    [27] James C, Edmister RO (1983) The Relation Between Common Stock Returns Trading Activity and Market Value. J Financ 38: 1075-1086. doi: 10.1111/j.1540-6261.1983.tb02283.x
    [28] Kaiser L (2019) Seasonality in cryptocurrencies. Financ Res Lett 31: 232-238. doi: 10.1016/j.frl.2018.11.007
    [29] Karalevicius V (2018) Using sentiment analysis to predict interday Bitcoin price movements. J Risk Financ 19: 56-75. doi: 10.1108/JRF-06-2017-0092
    [30] Karpoff JM (1986) A Theory of Trading Volume. J Financ 41: 1069-1087. doi: 10.1111/j.1540-6261.1986.tb02531.x
    [31] Koutmos D (2018) Bitcoin returns and transaction activity. Econ Lett 167: 81-85. doi: 10.1016/j.econlet.2018.03.021
    [32] Kristoufek L (2018) On Bitcoin markets (in)efficiency and its evolution. Phys A Stat Mech Appl 503: 257-262. doi: 10.1016/j.physa.2018.02.161
    [33] Kyle AS (1985) Continuous Auctions and Insider Trading. Econometrica 53: 1315-1335. doi: 10.2307/1913210
    [34] Lakonishok J, Vermaelen T (1986) Tax-induced trading around ex-dividend days. J Financ Econ 16: 287-319. doi: 10.1016/0304-405X(86)90032-2
    [35] Li Z, Dong H, Huang Z, et al. (2018) Asymmetric Effects on Risks of Virtual Financial Assets (VFAs) in different regimes: A Case of Bitcoin. Quant Financ Econ 2: 860-883. doi: 10.3934/QFE.2018.4.860
    [36] Milgrom P, Stokey N (1982) Information, Trade and Common Knowledge. J Econ Theory 26: 17-27. doi: 10.1016/0022-0531(82)90046-1
    [37] Nadarajah S, Chu J (2017) On the inefficiency of Bitcoin. Econ Lett 150: 6-9. doi: 10.1016/j.econlet.2016.10.033
    [38] Sapuric S, Kokkinaki A, Georgiou I (2020) The relationship between Bitcoin returns, volatility and volume: asymmetric GARCH modeling. J Enterp Inf Manage.
    [39] Vidal-Tomás D, Ibañez A (2018) Semi-strong efficiency of Bitcoin. Financ Res Lett 27: 259-265. doi: 10.1016/j.frl.2018.03.013
    [40] Wang JN, Liu HC, Hsu YT (2019) Time-of-day periodicities of trading volume and volatility in Bitcoin exchange: Does the stock market matter? Financ Res Lett, 1-8. doi: 10.1016/j.frl.2019.04.031
    [41] Wilcoxon F (1945) Individual Comparisons by Ranking Methods. Biometrics Bull 1: 80-83. doi: 10.2307/3001968
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