IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v16y2023i10p461-d1265145.html
   My bibliography  Save this article

An Investigation of the Predictability of Uncertainty Indices on Bitcoin Returns

Author

Listed:
  • Jinghua Wang

    (Martin Tuchman School of Management, New Jersey Institute of Technology, 184-198 Central Ave, Newark, NJ 07103, USA)

  • Geoffrey M. Ngene

    (Stetson School of Business and Economics, Mercer University, Macon, GA 31201, USA)

  • Yan Shi

    (Computer Science and Software Engineering Department, College of Engineering, Mathematics and Science, University of Wisconsin-Platteville, Platteville, WI 53181, USA)

  • Ann Nduati Mungai

    (Cameron School of Business, University of North Carolina Wilmington, 601 South College Street, Wilmington, NC 28403, USA)

Abstract

Policymakers and portfolio managers pay keen attention to sources of uncertainties that drive asset returns and volatility. The influence of uncertainty on Bitcoin has the potential to drive fluctuations in the entire cryptocurrency market. We investigate the predictability of thirteen economic policy uncertainty indices on Bitcoin returns. Using the Random Forest machine learning algorithm, we find that Singapore’s economic policy uncertainty (EPU) has the strongest predictive power on Bitcoin returns, followed by financial crisis (FC) uncertainty and world trade uncertainty (WTU). We further categorize these uncertainties into different groups. Interestingly, the predictability of uncertainty indices on Bitcoin returns within the international trade group is stronger compared to other uncertainty categories. Additionally, we observed that internet-based uncertainty measures have more predictive power of Bitcoin returns than newspaper- and report-based measures. These results are robust using various additional machine learning methods. We believe that these findings could be valuable for policymakers and portfolio managers when making decisions related to uncertainty drivers of cryptocurrency prices and returns.

Suggested Citation

  • Jinghua Wang & Geoffrey M. Ngene & Yan Shi & Ann Nduati Mungai, 2023. "An Investigation of the Predictability of Uncertainty Indices on Bitcoin Returns," JRFM, MDPI, vol. 16(10), pages 1-12, October.
  • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:10:p:461-:d:1265145
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/16/10/461/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1911-8074/16/10/461/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Smith, Simon C., 2022. "Time-variation, multiple testing, and the factor zoo," International Review of Financial Analysis, Elsevier, vol. 84(C).
    2. Liu, Mingxi & Li, Guowen & Li, Jianping & Zhu, Xiaoqian & Yao, Yinhong, 2021. "Forecasting the price of Bitcoin using deep learning," Finance Research Letters, Elsevier, vol. 40(C).
    3. 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.
    4. Zheng-Zheng Li & Chi-Wei Su & Meng Nan Zhu, 2022. "How Does Uncertainty Affect Volatility Correlation between Financial Assets? Evidence from Bitcoin, Stock and Gold," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 58(9), pages 2682-2694, July.
    5. Li, Zhiyong & Wan, Yifan & Wang, Tianyi & Yu, Mei, 2023. "Factor-timing in the Chinese factor zoo: The role of economic policy uncertainty," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
    6. 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.
    7. Gozgor, Giray & Tiwari, Aviral Kumar & Demir, Ender & Akron, Sagi, 2019. "The relationship between Bitcoin returns and trade policy uncertainty," Finance Research Letters, Elsevier, vol. 29(C), pages 75-82.
    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. 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).
    2. Vasilios Plakandaras & Elie Bouri & Rangan Gupta, 2019. "Forecasting Bitcoin Returns: Is there a Role for the U.S. – China Trade War?," Working Papers 201980, University of Pretoria, Department of Economics.
    3. Kreuzer, Christian & Laschinger, Ralf & Priberny, Christopher & Benninghoff, Sven, 2024. "Cryptocurrencies as a vehicle for capital exodus: Evidence from the Russian–Ukrainian crisis," Finance Research Letters, Elsevier, vol. 69(PB).
    4. Ben Nouir, Jihed & Ben Haj Hamida, Hayet, 2023. "How do economic policy uncertainty and geopolitical risk drive Bitcoin volatility?," Research in International Business and Finance, Elsevier, vol. 64(C).
    5. Matkovskyy, Roman & Jalan, Akanksha & Dowling, Michael, 2020. "Effects of economic policy uncertainty shocks on the interdependence between Bitcoin and traditional financial markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 150-155.
    6. Chengying He & Yong Li & Tianqi Wang & Salman Ali Shah, 2024. "Is cryptocurrency a hedging tool during economic policy uncertainty? An empirical investigation," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    7. Elie Bouri & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting Realized Volatility of Bitcoin: The Role of the Trade War," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 29-53, January.
    8. Simran, & Sharma, Anil Kumar, 2023. "Asymmetric impact of economic policy uncertainty on cryptocurrency market: Evidence from NARDL approach," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
    9. Ngo Thai Hung & Toan Luu Duc Huynh & Muhammad Ali Nasir, 2024. "Cryptocurrencies in an uncertain world: Comprehensive insights from a wide range of uncertainty indices," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3811-3825, July.
    10. Khaled Mokni & Elie Bouri & Ahdi Noomen Ajmi & Xuan Vinh Vo, 2021. "Does Bitcoin Hedge Categorical Economic Uncertainty? A Quantile Analysis," SAGE Open, , vol. 11(2), pages 21582440211, May.
    11. Tayyaba Ahsan & Krystian Zawadzki & Mubashir Khan, 2024. "Cryptocurrencies as a Speculative Asset: How Much Uncertainty is Included in Cryptocurrency Price?," SAGE Open, , vol. 14(2), pages 21582440241, June.
    12. Cynthia Weiyi Cai & Rui Xue & Bi Zhou, 2023. "Cryptocurrency puzzles: a comprehensive review and re-introduction," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 46(1), pages 26-50, June.
    13. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    14. Lars Hornuf & Paul P. Momtaz & Rachel J. Nam & Ye Yuan, 2023. "Cybercrime on the Ethereum Blockchain," CESifo Working Paper Series 10598, CESifo.
    15. Erik Johannesson & James A. Ohlson & Sophia Weihuan Zhai, 2024. "The explanatory power of explanatory variables," Review of Accounting Studies, Springer, vol. 29(4), pages 3053-3083, December.
    16. Hau, Liya & Zhu, Huiming & Shahbaz, Muhammad & Sun, Wuqin, 2021. "Does transaction activity predict Bitcoin returns? Evidence from quantile-on-quantile analysis," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    17. Parthajit Kayal & Purnima Rohilla, 2021. "Bitcoin in the economics and finance literature: a survey," SN Business & Economics, Springer, vol. 1(7), pages 1-21, July.
    18. Hui Xiao & Yiguo Sun, 2020. "Forecasting the Returns of Cryptocurrency: A Model Averaging Approach," JRFM, MDPI, vol. 13(11), pages 1-15, November.
    19. Jiang, Yonghong & Wu, Lanxin & Tian, Gengyu & Nie, He, 2021. "Do cryptocurrencies hedge against EPU and the equity market volatility during COVID-19? – New evidence from quantile coherency analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).
    20. Wang, Kai-Hua & Zhao, Yan-Xin & Jiang, Cui-Feng & Li, Zheng-Zheng, 2022. "Does green finance inspire sustainable development? Evidence from a global perspective," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 412-426.

    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:16:y:2023:i:10:p:461-:d:1265145. 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.