IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v18y2025i7p373-d1695311.html
   My bibliography  Save this article

Margin Trading and Cryptocurrency Investment Among U.S. Investors: Evidence from the National Financial Capability Study

Author

Listed:
  • Ferdous Ahmmed

    (Department of Finance, Economics, and Risk Management, Missouri State University, Springfield, MO 65897, USA)

  • Boakye Yam Boadi

    (School of Financial Planning, Texas Tech University, Lubbock, TX 79409, USA)

  • Michael Guillemette

    (School of Financial Planning, Texas Tech University, Lubbock, TX 79409, USA)

Abstract

This study examined the relationship between margin trading and cryptocurrency investment using data from the 2018 and 2021 waves of the National Financial Capability Study (NFCS) Investor Survey. Guided by behavioral finance theory, which suggests that cognitive biases may influence risk-taking, the study explored whether margin loan use and margin calls are associated with higher cryptocurrency participation. Margin loans are inherently risky, as they must be repaid regardless of investment outcomes, and margin calls are triggered when an investor’s equity falls below a required threshold. The results showed a positive and statistically significant association between margin activity and cryptocurrency investment. Specifically, individuals with a margin loan were 17 percentage points more likely to invest in cryptocurrency, while those who have experienced a margin call were 23 percentage points more likely. Given the extreme volatility of cryptocurrencies, these results highlight the increased risks investors face when using leverage in speculative markets. The analysis is based on cross-sectional data from U.S. investors; therefore, the findings should be interpreted as correlational rather than causal.

Suggested Citation

  • Ferdous Ahmmed & Boakye Yam Boadi & Michael Guillemette, 2025. "Margin Trading and Cryptocurrency Investment Among U.S. Investors: Evidence from the National Financial Capability Study," JRFM, MDPI, vol. 18(7), pages 1-24, July.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:7:p:373-:d:1695311
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/18/7/373/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/18/7/373/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Łukasz Goczek & Ivan Skliarov, 2019. "What drives the Bitcoin price? A factor augmented error correction mechanism investigation," Applied Economics, Taylor & Francis Journals, vol. 51(59), pages 6393-6410, December.
    2. Eugene Bland & Chuleeporn Changchit & Robert Cutshall & Long Pham, 2024. "Behavioral and Psychological Determinants of Cryptocurrency Investment: Expanding UTAUT with Perceived Enjoyment and Risk Factors," JRFM, MDPI, vol. 17(10), pages 1-22, October.
    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. Panagiotidis, Theodore & Papapanagiotou, Georgios & Stengos, Thanasis, 2024. "A Bayesian approach for the determinants of bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 91(C).
    2. Burcu Kapar & Jose Olmo, 2021. "Analysis of Bitcoin prices using market and sentiment variables," The World Economy, Wiley Blackwell, vol. 44(1), pages 45-63, January.
    3. Gaies, Brahim & Nakhli, Mohamed Sahbi & Sahut, Jean-Michel & Schweizer, Denis, 2023. "Interactions between investors’ fear and greed sentiment and Bitcoin prices," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    4. Alexander, Carol & Deng, Jun & Feng, Jianfen & Wan, Huning, 2023. "Net buying pressure and the information in bitcoin option trades," Journal of Financial Markets, Elsevier, vol. 63(C).
    5. Taha Zaghdoudi & Kais Tissaoui & Mohamed Hédi Maâloul & Younès Bahou & Niazi Kammoun, 2024. "Can Economic, Geopolitical and Energy Uncertainty Indices Predict Bitcoin Energy Consumption? New Evidence from a Machine Learning Approach," Energies, MDPI, vol. 17(13), pages 1-15, July.
    6. Zhang, Shunqi & Xu, Qiuhua & Ding, Xuerou & Han, Kefei, 2025. "Risk spillover between cryptocurrencies and traditional currencies: An analysis based on neural network quantile regression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 667(C).
    7. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    8. Qiao, Xingzhi & Zhu, Huiming & Hau, Liya, 2020. "Time-frequency co-movement of cryptocurrency return and volatility: Evidence from wavelet coherence analysis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    9. Aljinović Zdravka & Marasović Branka & Milićević Tea Kalinić, 2022. "The Risk and Return of Traditional and Alternative Investments Under the Impact of COVID-19," Business Systems Research, Sciendo, vol. 13(3), pages 8-22, October.
    10. John E. Marthinsen & Steven R. Gordon, 2022. "The Price and Cost of Bitcoin," Papers 2204.13102, arXiv.org.
    11. Ben Osman, Myriam & Galariotis, Emilios & Guesmi, Khaled & Hamdi, Haykel & Naoui, Kamel, 2024. "Are markets sentiment driving the price bubbles in the virtual?," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 272-285.
    12. Joubert, Thomas H.A., 2024. "Unraveling Bitcoin price unpredictability: The role of hard forks," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    13. Murat Akkaya, 2021. "The Determinants of the Volatility in Cryptocurrency Markets: The Bitcoin Case," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 35(1), pages 87-97.
    14. Zdravka Aljinović & Branka Marasović & Tea Šestanović, 2021. "Cryptocurrency Portfolio Selection—A Multicriteria Approach," Mathematics, MDPI, vol. 9(14), pages 1-21, July.
    15. Ozkan Haykir & Ibrahim Yagli, 2022. "Speculative bubbles and herding in cryptocurrencies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-33, December.
    16. Joseph J. French, 2021. "#Bitcoin, #COVID-19: Twitter-Based Uncertainty and Bitcoin Before and during the Pandemic," IJFS, MDPI, vol. 9(2), pages 1-7, May.
    17. 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).
    18. Yae, James & Tian, George Zhe, 2022. "Out-of-sample forecasting of cryptocurrency returns: A comprehensive comparison of predictors and algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    19. Theodore Panagiotidis & Thanasis Stengos & Orestis Vravosinos, 2020. "A Principal Component-Guided Sparse Regression Approach for the Determination of Bitcoin Returns," JRFM, MDPI, vol. 13(2), pages 1-10, February.
    20. Daehan Kim & Doojin Ryu & Robert I. Webb, 2024. "Does a higher hashrate strengthen Bitcoin network security?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-15, December.

    More about this item

    Keywords

    ;
    ;
    ;

    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:gam:jjrfmx:v:18:y:2025:i:7:p:373-:d:1695311. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.