Should We Expect Bitcoin Markets to Be Efficient?

Abstract

We investigate whether Bitcoin markets demonstrate month-of-the-year effects, and whether such anomalies are present across markets that differ in terms of fees, trading requirements, size as well as the extent of legal support in their host countries. We use monthly return data for the period of 2015-2018 for Bitfinex, Bitstamp and Okcoin and find that returns were similar across the markets suggesting lack of internal frictions, and that all three Bitcoin markets showed a positive effect in December and a negative effect in January, followed by a positive effect in February. One explanation for the anomalies in the Bitcoin markets could be spillovers from the seasonal anomalies in broader markets, such as those posited by tax-loss or portfolio rebalancing hypotheses, which could result in some investors selling equity in December and repurchasing it in January and parking the proceeds in Bitcoin in the interim. If related to tax considerations, this situation could change as various jurisdictions start to enforce tax regulations for cryptocurrencies.

Keywords

• Bitcoin
• Efficient Markets Theory
• Month-of-the-Year Effect
• Exchange Location
• Calendar Anomaly
• Cryptocurrency

References

1. AHARON, D.Y. and QADAN, M. (2019). Bitcoin and the Day-of-the-Week Effect. Finance Research Letters, 31 (C).
2. AKRAM, Q.F., RIME, D. and SARNO, L. (2008). Arbitrage in the Foreign Exchange Market: Turning on the Microscope. Journal of International Economics, 76, 237–253.
3. AYDOĞAN, K. and BOOTH, G. (2003). Calendar Anomalies in the Turkish Foreign Exchange Markets. Applied Financial Economics, 13(5), 353-360.
4. BAUR, D.G., CAHILL, D., GODFREY, K. and LIU, Z. (2019). Bitcoin Time-of-Day, Day-of-Week and Month-of-Year Effects in Returns and Trading Volume. Finance Research Letters, 31, 78–92.
5. BITFINEX EXCHANGE REVIEW (2019). Cointelligence Research and Analysis for the Crypto-Economy. Retrieved from: https://www.cointelligence.com/exchanges_list/bitfinex/
6. BLOCKCHAIN TRANSPARENCY INSTITUTE (2018). Market Surveillance Report – December 2018. Retrieved from https://www.bti.live/reports-december2018/
7. BRANDVOLD, M., MOLNAR, P., VAGSTAD, K. and VALSTAD, O.C.A. (2015). Price Discovery on Bitcoin Exchanges. Journal of International Financial Markets, Institutions and Money, 36, 18-35.
8. BRIERE, M., OOSTERLINCK, K. and SZAFARZ, A. (2015). Virtual Currency, Tangible Return: Portfolio Diversification with Bitcoin. Journal of Asset Management, 16(6), 365–373.

9. BOLLERSLEV, T. and DOMOWITZ, I. (1993). Trading Patterns and Prices in the Interbank Foreign Exchange Market. Journal of Finance, 48, 1421–1443.
10. CAPORALE, G.M. and PLASTUN, A. (2017). Calendar Anomalies in the Ukrainian Stock Market. Investment Management and Financial Innovations, 14(1), 104–114.
11. CAPORALE, G.M. and PLASTUN, A. (2019). The Day of the Week Effect in the Cryptocurrency Market. Finance Research Letters (in press).
12. CRYPTO-COMPARE (2019a). Coins List. Cryptocompare Website. Retrived from: https://www.cryptocompare.com/coins/list/USD/1
13. CRYPTO-COMPARE (2019b). Exchanges. Cryptocompare Website. Retrived from: https://www.cryptocompare.com/exchanges/#/overview
14. DAVIDSON, R. and MACKINNON, J. (2004). Econometric Theory and Methods. Oxford University Press, 244: New York.
15. DECOURT, R.F., CHOHAN, U.W. and PERUGINI, M.L. (2017). Bitcoin Returns and the Monday Effect. Horizontes Empresariales, 16, 4-14.
16. DICKEY, D. and FULLER, W. (1979), Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74, 427- 431.
17. DICKEY, D. and FULLER, W. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49, 1057- 1072.
18. DURAI, S.R.S. and PAUL, S. (2018). Calendar Anomaly and the Degree of Market Inefficiency of Bitcoin. Madras School of Economics Working Paper No. 168/2018: Chennai, India.
19. ELKAMHI, R., and STEFANOVA, D. (2015). Dynamic Hedging and Extreme Asset Co-movements. The Review of Financial Studies, 28(3), 743-790.
20. EYUBOGLU, K. (2018). Examining Day of the Week and Month of the Year Effects in Bitcoin and Litecoin Markets. Cankırı Karatekin University Journal of the Faculty of Economics and Administrative Sciences, 8(1), 165-183.
21. FAMA, E.F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25, 383-417.
22. FAMA, E.F. (1991). Efficient Capital Markets: II. Journal of Finance, 46(5), 1575–1617.
23. FRAZ, A., HASSAN, A. and CHUGHTAI, S. (2019). Seasonality in Bitcoin Market. Nice Research Journal, 12(1), 2013-2017.
24. GREEN, R. (2019). Watch Out Cryptocurrency Owners, The IRS Is On The Hunt. Forbes. Retrieved from https://www.forbes.com/sites/greatspeculations/2019/07/31/watch-out-cryptocurrency-owners-the-irs-is-on-the-hunt/
25. HAUGEN, R. and JORION, P. (1996). The January Effect Still Here After All These Years. Financial Analysts Journal, 52(1), 27-31.
26. HAUGEN, R.A. and LAKONISHOK, J. (1987). The Incredible January Effect. Dow Jones-Irwin: Illinois.
27. HUILLET, M. (2018). Okcoin, Payments Processer Simplex Partmer on Card-Enabled USD Deposits for International Users. Cointelegraph News, June 17. Retrieved from: https://cointelegraph.com/news/okcoin-payments-processer-simplex-partner-on-card-enabled-usd-deposits-for-intl-users
28. JACOBSEN, B., and ZHANG, C. Y. (October 1, 2018). The Halloween Indicator, 'Sell in May and Go Away': Everywhere and All the Time. Available at SSRN: https://ssrn.com/abstract=2154873 or http://dx.doi.org/10.2139/ssrn.2154873
29. KAISER, L. (2019). Seasonality in Cryptocurrencies. Finance Research Letters (in press).
30. KAUL, A. and SAPP, S. (2009). Trading Activity, Dealer Concentration and Foreign Exchange Market Quality. Journal of Banking and Finance, 33, 2122–2131.
31. KLOCK, S. and BACON, F. (2014). The January Effect: A Test of Market Efficiency. ASBBS Annual Conference: Las Vegas, Nevada, USA, 21(1), 423-434.
32. KURIHARA, Y. and FUKUSHIMA, A. (2017). The Market Efficiency of Bitcoin: a Weekly Anomaly. Journal of Applied Finance and Banking, 7(3), 57–64.
33. LAKONISHOK, J. and SMIDT, S. (1988). Are Seasonal Anomalies Real? A Ninety-Year Perspective. Review of Financial Studies, 1, 403-425.
34. LAW LIBRARY OF CONGRESS REPORT (2018). Regulation of Cryptocurrency Around the World, June. The Law Library of Congress, Global Legal Research Directorate: Washington, D.C.,USA.
35. MAKAROV, I. and SCHOAR, A. (2018). Trading and Arbitrage in Cryptocurrency Markets. SSRN Working Paper No: 3171204: London, UK.
36. MBANGA, C.L. (2018). The Day-of-the-Week Pattern of Price Clustering in Bitcoin. Applied Economics Letters, 26(10), 807-811.
37. NADARAJAH, S. and CHU, J. (2017). On the Inefficiency of Bitcoin. Economic Letters. 150, 6–9.
38. NAKAMOTO, S. (2008). Bitcoin: A Peer-to-peer Electronic Cash System, Unpublished undated Working Paper. Retrieved from: http://bitcoin.org/bitcoin.pdf
39. NEWEY, W. K. and WEST, K. D. (1987). A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix. Econometrica, 55(3), 703–08.
40. POPOVIC, S. and DUROVIC, A. (2014). Intraweek and Intraday Trade Anomalies: Evidence from Forex Market. Applied Economics. 46 (32), 3968–3979.
41. REYNOLDS, J., SOEGNER, L., WAGNER, M. and WIED, D. (2018). Deviations from Triangular Arbitrage Parity in Foreign Exchange and Bitcoin Markets. SSRN Working Paper No. 3148094; Lugano, Switzerland.
42. RITTER, J. (1988).The Buying and Selling Behavior of Individual Investors at the Turn of the Year. Journal of Finance, 43 (3), 701–719.
43. ROZEFF, M. S. and KINNEY, W. R. Jr. (1976). Capital Market Seasonality: The Case of Stock Returns. Journal of Financial Economics, 3, 379-402.
44. STATA FAQ (undated). How to Run a Likelihood and Wald Test. UCLA Institute for Digital Research and Education. Retrieved from: https://stats.idre.ucla.edu/stata/faq/how-can-i-perform-the-likelihood-ratio-wald-and-lagrange-multiplier-score-test-in-stata/
45. THOMSON REUTERS REPORT (2017). A World of Cryptocurrencies. Thomson Reuters Blog. Retrieved From: https://blogs.thomsonreuters.com/answerson/wpcontent/uploads/sites/3/2017/10/World-of-Cryptocurrencies-graphic.pdf
46. URQUHART, A. (2016). The Inefficiency of Bitcoin. Economics Letters, 148, 80–82.
47. WARREN, R. (2015). Your Portfolio has more risk than you think. Forbes. Retrieved from https://www.forbes.com/sites/randywarren/2015/11/04/your-portfolio-has-more-risk-than-you-think/
48. YAYA, O.S. and OGBONNA, E.A. (2019). Do We Experience Day-of-the-Week Effects in Returns and Volatility of Cryptocurrency?, MPRA Working Paper No. 91429: Ibadan, Nigeria.