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Using crypto assets pricing methods to build technical oscillators for short-term bitcoin trading

Author

Listed:
  • Yang, Zixiu
  • Fantazzini, Dean

Abstract

This paper examines the trading performances of several technical oscillators created using crypto assets pricing methods for short-term bitcoin trading. Seven pricing models proposed in the professional and academic literature were transformed into oscillators, and two thresholds were introduced to create buy and sell signals. The empirical back testing analysis showed that some of these methods proved to be profitable with good Sharpe ratios and limited max drawdowns. However, the trading performances of almost all methods significantly worsened after 2017, thus indirectly confirming an increasing financial literature that showed that the introduction of bitcoin futures in 2017 improved the efficiency of bitcoin markets.

Suggested Citation

  • Yang, Zixiu & Fantazzini, Dean, 2022. "Using crypto assets pricing methods to build technical oscillators for short-term bitcoin trading," MPRA Paper 115508, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:115508
    as

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    References listed on IDEAS

    as
    1. Dean Fantazzini, 2022. "Crypto-Coins and Credit Risk: Modelling and Forecasting Their Probability of Death," JRFM, MDPI, vol. 15(7), pages 1-34, July.
    2. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CIRJE F-Series CIRJE-F-1078, CIRJE, Faculty of Economics, University of Tokyo.
    3. Köchling, Gerrit & Müller, Janis & Posch, Peter N., 2019. "Does the introduction of futures improve the efficiency of Bitcoin?," Finance Research Letters, Elsevier, vol. 30(C), pages 367-370.
    4. García-Monleón, Fernando & Danvila-del-Valle, Ignacio & Lara, Francisco J., 2021. "Intrinsic value in crypto currencies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    5. Fry, John, 2018. "Booms, busts and heavy-tails: The story of Bitcoin and cryptocurrency markets?," Economics Letters, Elsevier, vol. 171(C), pages 225-229.
    6. Xiong, Jinwu & Liu, Qing & Zhao, Lei, 2020. "A new method to verify Bitcoin bubbles: Based on the production cost," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    7. Dean Fantazzini & Raffaella Calabrese, 2021. "Crypto Exchanges and Credit Risk: Modeling and Forecasting the Probability of Closure," JRFM, MDPI, vol. 14(11), pages 1-23, October.
    8. Dean Fantazzini & Stephan Zimin, 2020. "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 19-69, March.
    9. Dean Fantazzini & Nikita Kolodin, 2020. "Does the Hashrate Affect the Bitcoin Price?," JRFM, MDPI, vol. 13(11), pages 1-29, October.
    10. Akanksha Jalan & Roman Matkovskyy & Andrew Urquhart, 2021. "What effect did the introduction of Bitcoin futures have on the Bitcoin spot market?," The European Journal of Finance, Taylor & Francis Journals, vol. 27(13), pages 1251-1281, September.
    11. Hattori, Takahiro & Ishida, Ryo, 2021. "Did the introduction of Bitcoin futures crash the Bitcoin market at the end of 2017?," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    12. Jiaqi Liang & Linjing Li & Daniel Zeng, 2018. "Evolutionary dynamics of cryptocurrency transaction networks: An empirical study," Papers 1808.08585, arXiv.org.
    13. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CARF F-Series CARF-F-430, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    14. John Hargrave & Navroop Sahdev & Olga Feldmeier, 2019. "How Value is Created in Tokenized Assets," World Scientific Book Chapters, in: Melanie Swan & Jason Potts & Soichiro Takagi & Frank Witte & Paolo Tasca (ed.), Blockchain Economics: Implications of Distributed Ledgers Markets, Communications Networks, and Algorithmic Reality, chapter 7, pages 125-143, World Scientific Publishing Co. Pte. Ltd..
    15. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CARF F-Series CARF-F-441, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    16. Ruan, Qingsong & Meng, Lu & Lv, Dayong, 2021. "Effect of introducing Bitcoin futures on the underlying Bitcoin market efficiency: A multifractal analysis," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    17. Marcel Thum, 2018. "The Economic Cost of Bitcoin Mining," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 19(01), pages 43-45, March.
    18. Delgado-Mohatar, Oscar & Felis-Rota, Marta & Fernández-Herraiz, Carlos, 2019. "The Bitcoin mining breakdown: Is mining still profitable?," Economics Letters, Elsevier, vol. 184(C).
    19. Jiaqi Liang & Linjing Li & Daniel Zeng, 2018. "Evolutionary dynamics of cryptocurrency transaction networks: An empirical study," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-18, August.
    20. D'aniel Kondor & M'arton P'osfai & Istv'an Csabai & G'abor Vattay, 2013. "Do the rich get richer? An empirical analysis of the BitCoin transaction network," Papers 1308.3892, arXiv.org, revised Mar 2014.
    21. Yulin Liu & Luyao Zhang, 2022. "Cryptocurrency Valuation: An Explainable AI Approach," Papers 2201.12893, arXiv.org, revised Jul 2023.
    22. Alexandre Bovet & Carlo Campajola & Francesco Mottes & Valerio Restocchi & Nicol`o Vallarano & Tiziano Squartini & Claudio J. Tessone, 2019. "The evolving liaisons between the transaction networks of Bitcoin and its price dynamics," Papers 1907.03577, arXiv.org.
    23. Liu, Ruozhou & Wan, Shanfeng & Zhang, Zili & Zhao, Xuejun, 2020. "Is the introduction of futures responsible for the crash of Bitcoin?," Finance Research Letters, Elsevier, vol. 34(C).
    24. Dániel Kondor & Márton Pósfai & István Csabai & Gábor Vattay, 2014. "Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.
    25. Nakano, Masafumi & Takahashi, Akihiko & Takahashi, Soichiro, 2018. "Bitcoin technical trading with artificial neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 587-609.
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    More about this item

    Keywords

    C32; C51; C53; C58; G11; G12; G17;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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