Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Technical Trading Rules for Bitcoin Futures

비트코인 선물의 기술적 거래 규칙

  • Kim, Sun Woong (Trading System Major, Graduate School of Business IT, Kookmin University)
  • 김선웅 (국민대학교 비즈니스IT전문대학원 트레이딩시스템전공)
  • Received : 2021.04.19
  • Accepted : 2021.05.20
  • Published : 2021.05.28
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Abstract

This study aims to propose technical trading rules for Bitcoin futures and empirically analyze investment performance. Investment strategies include standard trading rules such as VMA, TRB, FR, MACD, RSI, BB, using Bitcoin futures daily data from December 18, 2017 to March 31, 2021. The trend-following rules showed higher investment performance than the comparative strategy B&H. Compared to KOSPI200 index futures, Bitcoin futures investment performance was higher. In particular, the investment performance has increased significantly in Sortino Ratio, which reflects downside risk. This study can find academic significance in that it is the first attempt to systematically analyze the investment performance of standard technical trading rules of Bitcoin futures. In future research, it is necessary to improve investment performance through the use of deep learning models or machine learning models to predict the price of Bitcoin futures.

본 연구의 목적은 비트코인 선물의 투자 전략으로 기술적 거래 규칙들을 제안하고, 실증 분석을 통해 투자 성과를 분석하는 것이다. 투자 전략은 표준적인 거래 전략인 VMA, TRB, FR, MACD, RSI, BB 등이며, 2017년 12월 18일부터 2021년 3월 31일까지의 비트코인 선물 일별 자료를 이용하였다. 실증 분석 결과, 추세 추종형 거래 규칙들 모두 비교전략인 Buy & Hold 보다 투자 성과가 높게 나타났다. 코스피200 주가지수 선물과의 비교에서는 비트코인 선물 투자 성과가 높게 나타났다. 특히, 비트코인 선물의 투자 성과는 하방 위험을 반영하는 Sortino Ratio에서 큰 폭으로 증가하였다. 본 연구는 비트코인 선물의 표준적인 기술적 거래 규칙들의 투자 성과를 체계적으로 분석한 첫 시도라는 점에서 학술적 의미를 찾을 수 있다. 향후 연구에서는 비트코인 선물의 가격 예측을 위한 딥러닝 모형이나 기계학습 모형의 활용을 통해 투자 성과를 개선할 필요가 있다.

Keywords

References

  1. S. Nakamoto. (2008). Bitcoin: A peer-to-peer electronic cash system (Online). https://www.lopp.net/pdf/bitcoin.pdf
  2. K. Grobys, S. Ahmed & N. Sapkota. (2020). Technical trading rules in the cryptocurrency market. Finance Research Letters, 32, 101396, 1-7. DOI : 10.1016/j.frl.2019.101396
  3. D. F. Gerritsen, E. Bouri, E. Ramezanfar & D. Roubaud. (2020). The profitability of technical trading rules in the Bitcoin market. Finance Research Letters, 34, 1-10. DOI : 10.1016/j.frl.2019.08.011
  4. S. Corbet, V. Eraslan, B. Lucey & A. Sensoy. (2019). The effectiveness of technical trading rules in cryptocurrency markets. Finance Research Letters, 31, 32-37. DOI : 10.1016/j.frl.2019.04.027
  5. D. Aggarwal, S. Chandrasekaran & B. Annamalai. (2020). A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices. Journal of Behavioral and Experimental Finance, 27, 100335, 1-12. DOI : 10.1016/j.jbef.2020.100335
  6. M. Liu, G. Li, J. Li, X. Zhu & Y. Yao. (2020). Forecasting the price of Bitcoin using deep learning. Finance Research Letters, In press. DOI : 10.1016/j.frl.2020.101755
  7. S. Xiaolei, L. Mingxi & S. Zeqian. (2020). A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Research Letters, 32, 1-6. DOI : 10.1016/j.frl.2018.12.032
  8. M. Gang, B. Kim, M. Shin, U. Baek & M. Kim. (2020). LSTM-based prediction of Bitcoin price fluctuation using sentiment analysis. Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, 561-562.
  9. D. Pant, P. Neupane, A. Poudel, A. Pokhrel & B. Lama. (2018). Recurrent neural network based Bitcoin price prediction by Twitter sentiment analysis. International Conference on Computing, Communication and Security. DOI : 10.1109/CCCS.2018.85886824
  10. Y. Ahn & D. Kim. (2020). Emotional trading in the cryptocurrency market. Finance Research Letters, in Press. DOI : 10.1016/j.frl.2020.101912
  11. S. W. Kim. (2021). Profitability of trading system for cryptocurrency. Journal of Digital Contents Society, 22(3), 555-562. DOI : 10.9728/dcs.2021.22.3.555
  12. M. Y. Day, P. Huang, Y. Cheng, Y. T. Lin & Y. Ni. (2021). Profitable day trading Bitcoin futures following continuous bullish (bearish) candlesticks. Applied Economics Letters, 28, In press. DOI : 10.1080/13504851.2021.1899115
  13. M. Latif, S. Arshad, M. Fatima & S. Farooq. (2011). Market efficiency, market anomalies, causes, evidences, and some behavioral aspects of market anomalies. Research Journal of Finance and Accounting, 2(9), 1-13.
  14. S. Corbet, B. Lucey & L. Yarovaya. (2018). Datestamping the Bitcoin and Ethereum bubbles. Finance Research Letters, 26, 81-88. DOI : 10.1016/j.frl.2017.12.006
  15. E. T. Cheah & J. Fry. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32-36. DOI : 10.1016/j.econlet.2015.02.029
  16. Introduction to Bitcoin Reference Rate. https://www.cmegroup.com/education/courses/introduction-to-bitcoin/introduction-to-bitcoin-reference-rate.html
  17. W. Kim, J. Lee & K. Kang. (2020). The effects of the introduction of Bitcoin futures on the volatility of Bitcoin returns. Finance Research Letters, 33, 1-8, 101204. DOI : 10.1016/j.frl.2019.06.002
  18. H. Sebastiao & H. Godinho. (2020). Bitcoin futures: An effective tool for hedging cryptocurrencies. Finance Research Letters, 33, 1-6, 101230. DOI : 10.1016/j.frl.2019.07.003
  19. B. Kapar & J. Olmo. (2019). An analysis of price discovery between Bitcoin futures and spot markets. Economics Letters, 174, 62-64. DOI : 10.1016/j.econlet.2018.10.031
  20. E. Akyildirim, S. Corbet, P. Katsiampa, N. Kellard & A. Sensoy. (2020). The development of Bitcoin futures: Exploring the interactions between cryptocurrency derivatives. Finance Research Letters, 34, 1-9, 101234. DOI : 10.1016/j.frl.2019.07.007
  21. J. C. Hung, H. C. Liu & J. J. Yang. (2021). Trading activity and price discovery in Bitcoin futures markets. Journal of Empirical Finance, 62, 107-120. DOI : 10.1016/jjempfin.2021.03.001
  22. W. Brock, J. Lakonishok & B. LeBaron. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764. DOI : 10.2307/2328994
  23. H. Bessembinder & K. Chan. (1995). The profitability of technical trading rules in the Asian stock markets. Pacific-Basin Finance Journal, 3(2-3), 257-284. DOI : 10.1016/0927-538x(95)0002-3
  24. A. Detzel, H. Liu, J. Strauss, G. Zhou & Y. Zhu. (2021). Learning and predictability via technical analysis: Evidence from Bitcoin and stocks with hard-to-value fundamentals. Financial Management, 50, 107-137. DOI : 10.1111/fima.12310
  25. E. Fama. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417. https://doi.org/10.2307/2325486
  26. I. Psaradellis, J. Laws, A. Pantelous & G. Sermpinis. (2019). Performance of technical trading rules: Evidence from the crude oil market. The European Journal of Finance, 25(17), 1793-1815. DOI : 10.1080/1351847x.2018.1552172
  27. S. Alexander. (1961). Price movements in speculative markets: Trends or random walks. Industrial Management Review, 2(2), 7-26.
  28. R. Sullivan, A. Timmermann & H. White. (1999). Data-snooping, technical trading rule performance and the Bootstrap. The Journal of Finance, 54(5), 1647-1691. DOI : 10.1111/0022-1082.00163
  29. A. Vo & C. Yost-Bremm. (2020). A high-frequency algorithmic trading strategy for cryptocurrency. Journal of Computer Information Systems, 60(6), 555-568. DOI : 10.1080/08874417.2018.1552090
  30. W. F. Sharpe. (1966). Mutual fund performance. The Journal of Business, 39(1), 119-138. https://doi.org/10.1086/294846
  31. F. A. Sortino & L. N. Price. (1994). Performance measurement in a downside risk framework. The Journal of Investing, 3(3), 59-64. DOI : 10.3905/joi.3.3.59
  32. C. Eom, T. Kaizoji & E. Scalas. (2019). Fat tails in financial return distributions revisited: Evidence from the Korean stock market. Physica A, 526, 121055, 51-10. DOI : 10.1016/j.physa.2019.121055