IDEAS home Printed from https://ideas.repec.org/a/vrs/ijomae/v58y2022i4p351-370n6.html
   My bibliography  Save this article

Is the cryptocurrency market efficient? Evidence from an analysis of fundamental factors for Bitcoin and Ethereum

Author

Listed:
  • Łęt Blanka

    (Department of Applied Mathematics, Poznań University of Economics and Business, Poznań, Poland .)

  • Sobański Konrad

    (Department of International Finance, Poznań University of Economics and Business, Poznań, Poland .)

  • Świder Wojciech

    (Department of Public Finance, Poznań University of Economics and Business, Poznań, Poland .)

  • Włosik Katarzyna

    (Department of Investment and Financial Markets, Poznań University of Economics and Business, Poznań, Poland .)

Abstract

This article sheds new light on the informational efficiency of the cryptocurrency market by analyzing investment strategies based on structural factors related to on-chain data. The study aims to verify whether investors in the cryptocurrency market can outperform passive investment strategies by applying active strategies based on selected fundamental factors. The research uses daily data from 2015 to 2022 for the two major cryptocurrencies: Bitcoin (BTC) and Ethereum (ETH). The study applies statistical tests for differences. The findings indicate informational inefficiency of the BTC and ETH markets. They seem consistent over time and are confirmed during the COVID-19 pandemic. The research shows that the net unrealized profit/loss and percent of addresses in profit indicators are useful in designing active investment strategies in the cryptocurrency market. The factor-based strategies perform consistently better in terms of mean/median returns and Sharpe ratio than the passive “buy-and-hold” strategy. Moreover, the rate of success is close to 100%.

Suggested Citation

  • Łęt Blanka & Sobański Konrad & Świder Wojciech & Włosik Katarzyna, 2022. "Is the cryptocurrency market efficient? Evidence from an analysis of fundamental factors for Bitcoin and Ethereum," International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, vol. 58(4), pages 351-370, December.
    • Handle: RePEc:vrs:ijomae:v:58:y:2022:i:4:p:351-370:n:6
    DOI: 10.2478/ijme-2022-0030
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/ijme-2022-0030
    Download Restriction: no
    File URL: https://libkey.io/10.2478/ijme-2022-0030?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Achraf Ghorbel & Ahmed Jeribi, 2021. "Volatility spillovers and contagion between energy sector and financial assets during COVID-19 crisis period," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 449-467, September.
    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. Adedeji Daniel Gbadebo & Ahmed Oluwatobi Adekunle & Wole Adedokun & Adebayo-Oke Abdulrauf Lukman, 2021. "BTC price volatility: Fundamentals versus information," Cogent Business & Management, Taylor & Francis Journals, vol. 8(1), pages 1984624-198, January.
    4. John M. Griffin & Amin Shams, 2020. "Is Bitcoin Really Untethered?," Journal of Finance, American Finance Association, vol. 75(4), pages 1913-1964, August.
    5. Sensoy, Ahmet, 2019. "The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies," Finance Research Letters, Elsevier, vol. 28(C), pages 68-73.
    6. Grobys, Klaus & Sapkota, Niranjan, 2019. "Cryptocurrencies and momentum," Economics Letters, Elsevier, vol. 180(C), pages 6-10.
    7. Alvarez-Ramirez, J. & Rodriguez, E. & Ibarra-Valdez, C., 2018. "Long-range correlations and asymmetry in the Bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 948-955.
    8. Mnif, Emna & Jarboui, Anis & Mouakhar, Khaireddine, 2020. "How the cryptocurrency market has performed during COVID 19? A multifractal analysis," Finance Research Letters, Elsevier, vol. 36(C).
    9. Mawuli Segnon & Stelios Bekiros, 2020. "Forecasting volatility in bitcoin market," Annals of Finance, Springer, vol. 16(3), pages 435-462, September.
    10. Robert Hudson & Andrew Urquhart, 2021. "Technical trading and cryptocurrencies," Annals of Operations Research, Springer, vol. 297(1), pages 191-220, February.
    11. P. S. Lintilhac & A. Tourin, 2017. "Model-based pairs trading in the bitcoin markets," Quantitative Finance, Taylor & Francis Journals, vol. 17(5), pages 703-716, May.
    12. Khuntia, Sashikanta & Pattanayak, J.K., 2018. "Adaptive market hypothesis and evolving predictability of bitcoin," Economics Letters, Elsevier, vol. 167(C), pages 26-28.
    13. Schiereck, D. & De Bondt, W. & Weber, M., 1999. "Contrarian and Momentum Strategies in Germany," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 35306, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    14. Caporale, Guglielmo Maria & Gil-Alana, Luis & Plastun, Alex, 2018. "Persistence in the cryptocurrency market," Research in International Business and Finance, Elsevier, vol. 46(C), pages 141-148.
    15. Cheng, Qing & Liu, Xinyuan & Zhu, Xiaowu, 2019. "Cryptocurrency momentum effect: DFA and MF-DFA analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    16. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    17. Shen, Dehua & Urquhart, Andrew & Wang, Pengfei, 2020. "A three-factor pricing model for cryptocurrencies," Finance Research Letters, Elsevier, vol. 34(C).
    18. De Bondt, Werner F M & Thaler, Richard, 1985. "Does the Stock Market Overreact?," Journal of Finance, American Finance Association, vol. 40(3), pages 793-805, July.
    19. C. Alexander & M. Dakos, 2020. "A critical investigation of cryptocurrency data and analysis," Quantitative Finance, Taylor & Francis Journals, vol. 20(2), pages 173-188, February.
    20. Raphael Auer & Marc Farag & Ulf Lewrick & Lovrenc Orazem & Markus Zoss, 2022. "Banking in the shadow of Bitcoin? The institutional adoption of cryptocurrencies," BIS Working Papers 1013, Bank for International Settlements.
    21. Ledoit, Oliver & Wolf, Michael, 2008. "Robust performance hypothesis testing with the Sharpe ratio," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 850-859, December.
    22. Max J. Krause & Thabet Tolaymat, 2018. "Author Correction: Quantification of energy and carbon costs for mining cryptocurrencies," Nature Sustainability, Nature, vol. 1(12), pages 814-814, December.
    23. Kristoufek, Ladislav, 2018. "On Bitcoin markets (in)efficiency and its evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 257-262.
    24. ANGHEL, Dan-Gabriel, 2021. "A reality check on trading rule performance in the cryptocurrency market: Machine learning vs. technical analysis," Finance Research Letters, Elsevier, vol. 39(C).
    25. Gerritsen, Dirk F. & Bouri, Elie & Ramezanifar, Ehsan & Roubaud, David, 2020. "The profitability of technical trading rules in the Bitcoin market," Finance Research Letters, Elsevier, vol. 34(C).
    26. Camilo Mora & Randi L. Rollins & Katie Taladay & Michael B. Kantar & Mason K. Chock & Mio Shimada & Erik C. Franklin, 2018. "Bitcoin emissions alone could push global warming above 2°C," Nature Climate Change, Nature, vol. 8(11), pages 931-933, November.
    27. 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.
    28. Vidal-Tomás, David & Ibañez, Ana, 2018. "Semi-strong efficiency of Bitcoin," Finance Research Letters, Elsevier, vol. 27(C), pages 259-265.
    29. Naeem, Muhammad Abubakr & Bouri, Elie & Peng, Zhe & Shahzad, Syed Jawad Hussain & Vo, Xuan Vinh, 2021. "Asymmetric efficiency of cryptocurrencies during COVID19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    30. Olivier Ledoit & Michael Wolf, 2018. "Robust performance hypothesis testing with smooth functions of population moments," ECON - Working Papers 305, Department of Economics - University of Zurich.
    31. Max J. Krause & Thabet Tolaymat, 2018. "Quantification of energy and carbon costs for mining cryptocurrencies," Nature Sustainability, Nature, vol. 1(11), pages 711-718, November.
    32. Jegadeesh, Narasimhan & Titman, Sheridan, 1995. "Overreaction, Delayed Reaction, and Contrarian Profits," The Review of Financial Studies, Society for Financial Studies, vol. 8(4), pages 973-993.
    33. Jobson, J D & Korkie, Bob M, 1981. "Performance Hypothesis Testing with the Sharpe and Treynor Measures," Journal of Finance, American Finance Association, vol. 36(4), pages 889-908, September.
    34. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    35. Wei, Wang Chun, 2018. "Liquidity and market efficiency in cryptocurrencies," Economics Letters, Elsevier, vol. 168(C), pages 21-24.
    36. Hu, Yang & Valera, Harold Glenn A. & Oxley, Les, 2019. "Market efficiency of the top market-cap cryptocurrencies: Further evidence from a panel framework," Finance Research Letters, Elsevier, vol. 31(C), pages 138-145.
    37. Kakinaka, Shinji & Umeno, Ken, 2022. "Cryptocurrency market efficiency in short- and long-term horizons during COVID-19: An asymmetric multifractal analysis approach," Finance Research Letters, Elsevier, vol. 46(PA).
    38. 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.
    39. Eric Masanet & Arman Shehabi & Nuoa Lei & Harald Vranken & Jonathan Koomey & Jens Malmodin, 2019. "Implausible projections overestimate near-term Bitcoin CO2 emissions," Nature Climate Change, Nature, vol. 9(9), pages 653-654, September.
    40. Nikolay Miller & Yiming Yang & Bruce Sun & Guoyi Zhang, 2019. "Identification of technical analysis patterns with smoothing splines for bitcoin prices," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(12), pages 2289-2297, September.
    41. Gandal, Neil & Hamrick, JT & Rouhi, Farhang & Mukherjee, Arghya & Feder, Amir & Moore, Tyler & Vasek, Marie, 2018. "The Economics of Cryptocurrency Pump and Dump Schemes," CEPR Discussion Papers 13404, C.E.P.R. Discussion Papers.
    42. Nadarajah, Saralees & Chu, Jeffrey, 2017. "On the inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 150(C), pages 6-9.
    43. Brauneis, Alexander & Mestel, Roland, 2018. "Price discovery of cryptocurrencies: Bitcoin and beyond," Economics Letters, Elsevier, vol. 165(C), pages 58-61.
    44. Andrew Detzel & Hong Liu & Jack Strauss & Guofu Zhou & Yingzi Zhu, 2021. "Learning and predictability via technical analysis: Evidence from bitcoin and stocks with hard‐to‐value fundamentals," Financial Management, Financial Management Association International, vol. 50(1), pages 107-137, March.
    45. Corbet, Shaen & Hou, Yang (Greg) & Hu, Yang & Larkin, Charles & Lucey, Brian & Oxley, Les, 2022. "Cryptocurrency liquidity and volatility interrelationships during the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 45(C).
    46. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    47. Kristoufek, Ladislav & Vosvrda, Miloslav, 2019. "Cryptocurrencies market efficiency ranking: Not so straightforward," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    48. Ball, Ray & Kothari, S. P. & Shanken, Jay, 1995. "Problems in measuring portfolio performance An application to contrarian investment strategies," Journal of Financial Economics, Elsevier, vol. 38(1), pages 79-107, May.
    49. Feng, Wenjun & Wang, Yiming & Zhang, Zhengjun, 2018. "Informed trading in the Bitcoin market," Finance Research Letters, Elsevier, vol. 26(C), pages 63-70.
    50. David Sanz-Bas & Carlos del Rosal & Sergio Luis Náñez Alonso & Miguel Ángel Echarte Fernández, 2021. "Cryptocurrencies and Fraudulent Transactions: Risks, Practices, and Legislation for Their Prevention in Europe and Spain," Laws, MDPI, vol. 10(3), pages 1-20, July.
    51. Marina Resta & Paolo Pagnottoni & Maria Elena De Giuli, 2020. "Technical Analysis on the Bitcoin Market: Trading Opportunities or Investors’ Pitfall?," Risks, MDPI, vol. 8(2), pages 1-15, May.
    52. Sergio Luis Náñez Alonso & Javier Jorge-Vázquez & Miguel Ángel Echarte Fernández & Ricardo Francisco Reier Forradellas, 2021. "Cryptocurrency Mining from an Economic and Environmental Perspective. Analysis of the Most and Least Sustainable Countries," Energies, MDPI, vol. 14(14), pages 1-22, July.
    53. Tran, Vu Le & Leirvik, Thomas, 2020. "Efficiency in the markets of crypto-currencies," Finance Research Letters, Elsevier, vol. 35(C).
    54. Hoang, Lai T. & Baur, Dirk G., 2022. "Loaded for bear: Bitcoin private wallets, exchange reserves and prices," Journal of Banking & Finance, Elsevier, vol. 144(C).
    55. Carmen López-Martín & Sonia Benito Muela & Raquel Arguedas, 2021. "Efficiency in cryptocurrency markets: new evidence," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 403-431, September.
    56. 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.
    57. Urquhart, Andrew, 2022. "Under the hood of the Ethereum blockchain," Finance Research Letters, Elsevier, vol. 47(PA).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Helder Miguel Correia Virtuoso Sebastião & Paulo José Osório Rupino Da Cunha & Pedro Manuel Cortesão Godinho, 2021. "Cryptocurrencies and blockchain. Overview and future perspectives," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 21(3), pages 305-342.
    2. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    3. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
    4. Carmen López-Martín & Sonia Benito Muela & Raquel Arguedas, 2021. "Efficiency in cryptocurrency markets: new evidence," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 403-431, September.
    5. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    6. Corbet, Shaen & Lucey, Brian & Urquhart, Andrew & Yarovaya, Larisa, 2019. "Cryptocurrencies as a financial asset: A systematic analysis," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 182-199.
    7. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    8. Bouri, Elie & Lau, Chi Keung Marco & Saeed, Tareq & Wang, Shixuan & Zhao, Yuqian, 2021. "On the intraday return curves of Bitcoin: Predictability and trading opportunities," International Review of Financial Analysis, Elsevier, vol. 76(C).
    9. Aslan, Aylin & Sensoy, Ahmet, 2020. "Intraday efficiency-frequency nexus in the cryptocurrency markets," Finance Research Letters, Elsevier, vol. 35(C).
    10. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    11. Ahmed, Walid M.A., 2022. "Robust drivers of Bitcoin price movements: An extreme bounds analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    12. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & David Martinez-Regoband & Fan Wu, 2020. "Cryptocurrency Trading: A Comprehensive Survey," Papers 2003.11352, arXiv.org, revised Jan 2022.
    13. Tran, Vu Le & Leirvik, Thomas, 2020. "Efficiency in the markets of crypto-currencies," Finance Research Letters, Elsevier, vol. 35(C).
    14. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2019. "The effects of markets, uncertainty and search intensity on bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 220-242.
    15. Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
    16. Helder Sebastião & Pedro Godinho, 2021. "Forecasting and trading cryptocurrencies with machine learning under changing market conditions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
    17. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Gabauer, David, 2019. "Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 37-51.
    18. Tetsuya Takaishi & Takanori Adachi, 2019. "Market efficiency, liquidity, and multifractality of Bitcoin: A dynamic study," Papers 1902.09253, arXiv.org.
    19. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
    20. Yi, Eojin & Ahn, Kwangwon & Choi, M.Y., 2022. "Cryptocurrency: Not far from equilibrium," Technological Forecasting and Social Change, Elsevier, vol. 177(C).

    More about this item

    Keywords

    active strategies; cryptocurrency; fundamental factors; informational efficiency; Ledoit and Wolf test;
    All these keywords.

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:ijomae:v:58:y:2022:i:4:p:351-370:n:6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: http://kolegia.sgh.waw.pl/en/KGS/Pages/default.aspx .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.