IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v92y2024ics1057521924000383.html
   My bibliography  Save this article

Non-standard errors in the cryptocurrency world

Author

Listed:
  • Fieberg, Christian
  • Günther, Steffen
  • Poddig, Thorsten
  • Zaremba, Adam

Abstract

Motivated by recent findings from the equity market, we investigate non-standard errors in cryptocurrency research. We examine ten prevalent decisions related to data sources, sample preparation, and portfolio construction, generating 20,736 research designs for 43 sorting variables. Our findings reveal remarkable variation in portfolio performance tied to seemingly trivial choices. The non-standard errors in cryptocurrency studies not only surpass those in the stock market but also clearly exceed standard errors—though varying considerably across coin characteristics. Notwithstanding the above, the most prominent cryptocurrency factors, such as size and momentum, remain consistently robust across numerous specifications. Lastly, we find that reducing the influence of the smallest coins effectively decreases the non-standard errors.

Suggested Citation

  • Fieberg, Christian & Günther, Steffen & Poddig, Thorsten & Zaremba, Adam, 2024. "Non-standard errors in the cryptocurrency world," International Review of Financial Analysis, Elsevier, vol. 92(C).
    • Handle: RePEc:eee:finana:v:92:y:2024:i:c:s1057521924000383
    DOI: 10.1016/j.irfa.2024.103106
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521924000383
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irfa.2024.103106?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bianchi, Daniele & Babiak, Mykola & Dickerson, Alexander, 2022. "Trading volume and liquidity provision in cryptocurrency markets," Journal of Banking & Finance, Elsevier, vol. 142(C).
    2. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020. "Taming the Factor Zoo: A Test of New Factors," Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
    3. Bali, Turan G. & Cakici, Nusret & Whitelaw, Robert F., 2011. "Maxing out: Stocks as lotteries and the cross-section of expected returns," Journal of Financial Economics, Elsevier, vol. 99(2), pages 427-446, February.
    4. Grobys, Klaus & Sapkota, Niranjan, 2019. "Cryptocurrencies and momentum," Economics Letters, Elsevier, vol. 180(C), pages 6-10.
    5. Campbell R. Harvey, 2017. "Presidential Address: The Scientific Outlook in Financial Economics," Journal of Finance, American Finance Association, vol. 72(4), pages 1399-1440, August.
    6. Hollstein, Fabian, 2022. "The world of anomalies: Smaller than we think?," Journal of International Money and Finance, Elsevier, vol. 129(C).
    7. Banz, Rolf W., 1981. "The relationship between return and market value of common stocks," Journal of Financial Economics, Elsevier, vol. 9(1), pages 3-18, March.
    8. Long, Huaigang & Demir, Ender & Będowska-Sójka, Barbara & Zaremba, Adam & Shahzad, Syed Jawad Hussain, 2022. "Is geopolitical risk priced in the cross-section of cryptocurrency returns?," Finance Research Letters, Elsevier, vol. 49(C).
    9. Miller, Merton H & Scholes, Myron S, 1982. "Dividends and Taxes: Some Empirical Evidence," Journal of Political Economy, University of Chicago Press, vol. 90(6), pages 1118-1141, December.
    10. Vidal-Tomás, David, 2022. "Which cryptocurrency data sources should scholars use?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    11. R. David Mclean & Jeffrey Pontiff, 2016. "Does Academic Research Destroy Stock Return Predictability?," Journal of Finance, American Finance Association, vol. 71(1), pages 5-32, February.
    12. repec:bla:jfinan:v:59:y:2004:i:5:p:2145-2176 is not listed on IDEAS
    13. Chen, Rongxin & Lepori, Gabriele M. & Tai, Chung-Ching & Sung, Ming-Chien, 2022. "Explaining cryptocurrency returns: A prospect theory perspective," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    14. Shen, Dehua & Urquhart, Andrew & Wang, Pengfei, 2020. "A three-factor pricing model for cryptocurrencies," Finance Research Letters, Elsevier, vol. 34(C).
    15. 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.
    16. Kewei Hou & Chen Xue & Lu Zhang, 2020. "Replicating Anomalies," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2019-2133.
    17. Andrew Ang & Robert J. Hodrick & Yuhang Xing & Xiaoyan Zhang, 2006. "The Cross‐Section of Volatility and Expected Returns," Journal of Finance, American Finance Association, vol. 61(1), pages 259-299, February.
    18. Brauneis, Alexander & Mestel, Roland & Riordan, Ryan & Theissen, Erik, 2021. "How to measure the liquidity of cryptocurrency markets?," Journal of Banking & Finance, Elsevier, vol. 124(C).
    19. Long, Huaigang & Zaremba, Adam & Demir, Ender & Szczygielski, Jan Jakub & Vasenin, Mikhail, 2020. "Seasonality in the Cross-Section of Cryptocurrency Returns," Finance Research Letters, Elsevier, vol. 35(C).
    20. Enoksen, F.A. & Landsnes, Ch.J. & Lučivjanská, K. & Molnár, P., 2020. "Understanding risk of bubbles in cryptocurrencies," Journal of Economic Behavior & Organization, Elsevier, vol. 176(C), pages 129-144.
    21. Uri Simonsohn & Joseph P. Simmons & Leif D. Nelson, 2020. "Specification curve analysis," Nature Human Behaviour, Nature, vol. 4(11), pages 1208-1214, November.
    22. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    23. Harvey, Campbell R., 2019. "Editorial: Replication in Financial Economics," Critical Finance Review, now publishers, vol. 8(1-2), pages 1-9, December.
    24. 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.
    25. Uri Simonsohn & Joseph P. Simmons & Leif D. Nelson, 2020. "Publisher Correction: Specification curve analysis," Nature Human Behaviour, Nature, vol. 4(11), pages 1215-1215, November.
    26. Borri, Nicola, 2019. "Conditional tail-risk in cryptocurrency markets," Journal of Empirical Finance, Elsevier, vol. 50(C), pages 1-19.
    27. repec:grz:wpsses:2021-08 is not listed on IDEAS
    28. Chen, Rongxin & Lepori, Gabriele M. & Tai, Chung-Ching & Sung, Ming-Chien, 2022. "Can salience theory explain investor behaviour? Real-world evidence from the cryptocurrency market," International Review of Financial Analysis, Elsevier, vol. 84(C).
    29. Chordia, Tarun & Subrahmanyam, Avanidhar & Anshuman, V. Ravi, 2001. "Trading activity and expected stock returns," Journal of Financial Economics, Elsevier, vol. 59(1), pages 3-32, January.
    30. Juhani T Linnainmaa & Michael R Roberts, 2018. "The History of the Cross-Section of Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2606-2649.
    31. Kewei Hou & Tobias J. Moskowitz, 2005. "Market Frictions, Price Delay, and the Cross-Section of Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 981-1020.
    32. Kim, Jae H. & Ji, Philip Inyeob, 2015. "Significance testing in empirical finance: A critical review and assessment," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 1-14.
    33. Li, Yi & Urquhart, Andrew & Wang, Pengfei & Zhang, Wei, 2021. "MAX momentum in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 77(C).
    34. Zhang, Wei & Li, Yi & Xiong, Xiong & Wang, Pengfei, 2021. "Downside risk and the cross-section of cryptocurrency returns," Journal of Banking & Finance, Elsevier, vol. 133(C).
    35. Andrew Y. Chen, 2022. "Most claimed statistical findings in cross-sectional return predictability are likely true," Papers 2206.15365, arXiv.org, revised Sep 2024.
    36. Barry, Christopher B. & Brown, Stephen J., 1984. "Differential information and the small firm effect," Journal of Financial Economics, Elsevier, vol. 13(2), pages 283-294, June.
    37. Grobys, Klaus & Junttila, Juha, 2021. "Speculation and lottery-like demand in cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    38. Cosemans, Mathijs & Frehen, Rik, 2021. "Salience theory and stock prices: Empirical evidence," Journal of Financial Economics, Elsevier, vol. 140(2), pages 460-483.
    39. Todd Mitton, 2022. "Methodological Variation in Empirical Corporate Finance," The Review of Financial Studies, Society for Financial Studies, vol. 35(2), pages 527-575.
    40. Shane A. Corwin & Paul Schultz, 2012. "A Simple Way to Estimate Bid‐Ask Spreads from Daily High and Low Prices," Journal of Finance, American Finance Association, vol. 67(2), pages 719-760, April.
    41. Yang, Dennis & Zhang, Qiang, 2000. "Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices," The Journal of Business, University of Chicago Press, vol. 73(3), pages 477-491, July.
    42. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    43. Burggraf, Tobias & Rudolf, Markus, 2021. "Cryptocurrencies and the low volatility anomaly," Finance Research Letters, Elsevier, vol. 40(C).
    44. Dirk G. Baur & Thomas Dimpfl, 2021. "The volatility of Bitcoin and its role as a medium of exchange and a store of value," Empirical Economics, Springer, vol. 61(5), pages 2663-2683, November.
    45. Bogumił Kamiński & Michał Jakubczyk & Przemysław Szufel, 2018. "A framework for sensitivity analysis of decision trees," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(1), pages 135-159, March.
    46. Ozgur S. Ince & R. Burt Porter, 2006. "Individual Equity Return Data From Thomson Datastream: Handle With Care!," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 29(4), pages 463-479, December.
    47. Hanauer, Matthias X. & Windmüller, Steffen, 2023. "Enhanced momentum strategies," Journal of Banking & Finance, Elsevier, vol. 148(C).
    48. Yukun Liu & Aleh Tsyvinski & Xi Wu, 2022. "Common Risk Factors in Cryptocurrency," Journal of Finance, American Finance Association, vol. 77(2), pages 1133-1177, April.
    49. Yukun Liu & Aleh Tsyvinski, 2021. "Risks and Returns of Cryptocurrency," The Review of Financial Studies, Society for Financial Studies, vol. 34(6), pages 2689-2727.
    50. B. B. Mandelbrot, 2001. "Scaling in financial prices: IV. Multifractal concentration," Quantitative Finance, Taylor & Francis Journals, vol. 1(6), pages 641-649.
    51. Melisa Ozdamar & Levent Akdeniz & Ahmet Sensoy, 2021. "Lottery-like preferences and the MAX effect in the cryptocurrency market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
    52. Tzouvanas, Panagiotis & Kizys, Renatas & Tsend-Ayush, Bayasgalan, 2020. "Momentum trading in cryptocurrencies: Short-term returns and diversification benefits," Economics Letters, Elsevier, vol. 191(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).

    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. Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
    2. Fieberg, Christian & Liedtke, Gerrit & Zaremba, Adam, 2024. "Cryptocurrency anomalies and economic constraints," International Review of Financial Analysis, Elsevier, vol. 94(C).
    3. Cakici, Nusret & Zaremba, Adam & Bianchi, Robert J. & Pham, Nga, 2021. "False discoveries in the anomaly research: New insights from the Stock Exchange of Melbourne (1927–1987)," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).
    4. Christian Fieberg & Gerrit Liedtke & Daniel Metko & Adam Zaremba, 2023. "Cryptocurrency factor momentum," Quantitative Finance, Taylor & Francis Journals, vol. 23(12), pages 1853-1869, November.
    5. Long, Huaigang & Demir, Ender & Będowska-Sójka, Barbara & Zaremba, Adam & Shahzad, Syed Jawad Hussain, 2022. "Is geopolitical risk priced in the cross-section of cryptocurrency returns?," Finance Research Letters, Elsevier, vol. 49(C).
    6. Yukun Liu & Aleh Tsyvinski & Xi Wu, 2022. "Common Risk Factors in Cryptocurrency," Journal of Finance, American Finance Association, vol. 77(2), pages 1133-1177, April.
    7. Kewei Hou & Chen Xue & Lu Zhang, 2017. "Replicating Anomalies," NBER Working Papers 23394, National Bureau of Economic Research, Inc.
    8. Leong, Minhao & Kwok, Simon, 2023. "The pricing of jump and diffusive risks in the cross-section of cryptocurrency returns," Journal of Empirical Finance, Elsevier, vol. 74(C).
    9. Dobrynskaya, Victoria, 2024. "Is downside risk priced in cryptocurrency market?," International Review of Financial Analysis, Elsevier, vol. 91(C).
    10. De Nard, Gianluca & Zhao, Zhao, 2022. "A large-dimensional test for cross-sectional anomalies:Efficient sorting revisited," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 654-676.
    11. Liebi, Luca J., 2022. "Is there a value premium in cryptoasset markets?," Economic Modelling, Elsevier, vol. 109(C).
    12. Kaplanski, Guy, 2023. "The race to exploit anomalies and the cost of slow trading," Journal of Financial Markets, Elsevier, vol. 62(C).
    13. Cederburg, Scott & O’Doherty, Michael S. & Wang, Feifei & Yan, Xuemin (Sterling), 2020. "On the performance of volatility-managed portfolios," Journal of Financial Economics, Elsevier, vol. 138(1), pages 95-117.
    14. Zhao, Xiaojuan & Wang, Ye & Liu, Weiyi, 2024. "Someone like you: Lottery-like preference and the cross-section of expected returns in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    15. Andrew Y. Chen & Tom Zimmermann, 2022. "Open Source Cross-Sectional Asset Pricing," Critical Finance Review, now publishers, vol. 11(2), pages 207-264, May.
    16. Cakici, Nusret & Zaremba, Adam, 2023. "Recency bias and the cross-section of international stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    17. Hoang, Khoa & Cannavan, Damien & Gaunt, Clive & Huang, Ronghong, 2019. "Is that factor just lucky? Australian evidence," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    18. Berggrun, Luis & Cardona, Emilio & Lizarzaburu, Edmundo, 2024. "Evaluating asset pricing anomalies: Evidence from Latin America," Research in International Business and Finance, Elsevier, vol. 70(PB).
    19. Wang, Feifei & Yan, Xuemin Sterling, 2021. "Downside risk and the performance of volatility-managed portfolios," Journal of Banking & Finance, Elsevier, vol. 131(C).
    20. Chen, Rongxin & Lepori, Gabriele M. & Tai, Chung-Ching & Sung, Ming-Chien, 2022. "Can salience theory explain investor behaviour? Real-world evidence from the cryptocurrency market," International Review of Financial Analysis, Elsevier, vol. 84(C).

    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:eee:finana:v:92:y:2024:i:c:s1057521924000383. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620166 .

    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.