IDEAS home Printed from https://ideas.repec.org/p/pre/wpaper/201980.html
   My bibliography  Save this paper

Forecasting Bitcoin Returns: Is there a Role for the U.S. – China Trade War?

Author

Listed:
  • Vasilios Plakandaras

    () (Department of Economics, Democritus University of Thrace, University Campus, Komotini, Greece)

  • Elie Bouri

    (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

Abstract

Previous studies provide evidence that trade related uncertainty tends to predict an increase in Bitcoin returns. In this paper, we extend the related literature by examining whether the information on the U.S. – China trade war can be used to forecast the future path of Bitcoin returns controlling for various explanatory variables. We apply ordinary least square (OLS) regression, support vector regression (SVR), and the least absolute shrinkage and selection operator (LASSO) techniques that stem from the field of machine learning, and find weak evidence of the role of the trade war in forecasting Bitcoin returns. Given that out-of-sample tests are more reliable than in-sample tests, our results tend to suggest that future Bitcoin returns are unaffected by trade related uncertainties, and investors can use Bitcoin as a safe haven in this context.

Suggested Citation

  • 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.
  • Handle: RePEc:pre:wpaper:201980
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    References listed on IDEAS

    as
    1. Caldara, Dario & Iacoviello, Matteo & Molligo, Patrick & Prestipino, Andrea & Raffo, Andrea, 2020. "The economic effects of trade policy uncertainty," Journal of Monetary Economics, Elsevier, vol. 109(C), pages 38-59.
    2. Pavel Ciaian & Miroslava Rajcaniova & d’Artis Kancs, 2016. "The economics of BitCoin price formation," Applied Economics, Taylor & Francis Journals, vol. 48(19), pages 1799-1815, April.
    3. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2018. "On the determinants of bitcoin returns: A LASSO approach," Finance Research Letters, Elsevier, vol. 27(C), pages 235-240.
    4. Demir, Ender & Gozgor, Giray & Lau, Chi Keung Marco & Vigne, Samuel A., 2018. "Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation," Finance Research Letters, Elsevier, vol. 26(C), pages 145-149.
    5. John Y. Campbell, 2008. "Viewpoint: Estimating the equity premium," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 41(1), pages 1-21, February.
    6. Elie Bouri & Konstantinos Gkillas & Rangan Gupta, 2020. "Trade uncertainties and the hedging abilities of Bitcoin," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 49(3), September.
    7. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
    8. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    9. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
    10. Öğüt, Hulisi & Doğanay, M. Mete & Ceylan, Nildağ Başak & Aktaş, Ramazan, 2012. "Prediction of bank financial strength ratings: The case of Turkey," Economic Modelling, Elsevier, vol. 29(3), pages 632-640.
    11. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, Oxford University Press, vol. 131(4), pages 1593-1636.
    12. Aysan, Ahmet Faruk & Demir, Ender & Gozgor, Giray & Lau, Chi Keung Marco, 2019. "Effects of the geopolitical risks on Bitcoin returns and volatility," Research in International Business and Finance, Elsevier, vol. 47(C), pages 511-518.
    13. Corbet, Shaen & Meegan, Andrew & Larkin, Charles & Lucey, Brian & Yarovaya, Larisa, 2018. "Exploring the dynamic relationships between cryptocurrencies and other financial assets," Economics Letters, Elsevier, vol. 165(C), pages 28-34.
    14. Harrison Hong & Jeremy C. Stein, 1999. "A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets," Journal of Finance, American Finance Association, vol. 54(6), pages 2143-2184, December.
    15. Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2009. "Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(6), pages 512-534.
    16. Bouri, Elie & Gupta, Rangan & Lau, Chi Keung Marco & Roubaud, David & Wang, Shixuan, 2018. "Bitcoin and global financial stress: A copula-based approach to dependence and causality in the quantiles," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 297-307.
    17. 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.
    18. Rubio, Ginés & Pomares, Héctor & Rojas, Ignacio & Herrera, Luis Javier, 2011. "A heuristic method for parameter selection in LS-SVM: Application to time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 725-739, July.
    19. Klein, Tony & Pham Thu, Hien & Walther, Thomas, 2018. "Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performance," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 105-116.
    20. Jamal Bouoiyour & Refk Selmi, 2016. "Bitcoin: a beginning of a new phase?," Economics Bulletin, AccessEcon, vol. 36(3), pages 1430-1440.
    21. David Yermack, 2017. "Corporate Governance and Blockchains," Review of Finance, European Finance Association, vol. 21(1), pages 7-31.
    22. Shahzad, Syed Jawad Hussain & Bouri, Elie & Roubaud, David & Kristoufek, Ladislav & Lucey, Brian, 2019. "Is Bitcoin a better safe-haven investment than gold and commodities?," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 322-330.
    23. 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.
    24. Rubio, Ginés & Pomares, Héctor & Rojas, Ignacio & Herrera, Luis Javier, 2011. "A heuristic method for parameter selection in LS-SVM: Application to time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 725-739.
    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. Tobias Burggraf, 2020. "Bitcoin and Global Political Uncertainty – Evidence from the U.S. Election Cycle," Economics Bulletin, AccessEcon, vol. 40(1), pages 727-742.

    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. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2019. "Forecasting transportation demand for the U.S. market," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 195-214.
    2. 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.
    3. 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.
    4. Jareño, Francisco & González, María de la O & Tolentino, Marta & Sierra, Karen, 2020. "Bitcoin and gold price returns: A quantile regression and NARDL analysis," Resources Policy, Elsevier, vol. 67(C).
    5. Das, Debojyoti & Le Roux, Corlise Liesl & Jana, R.K. & Dutta, Anupam, 2020. "Does Bitcoin hedge crude oil implied volatility and structural shocks? A comparison with gold, commodity and the US Dollar," Finance Research Letters, Elsevier, vol. 36(C).
    6. Elie Bouri & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Forecasting Realized Volatility of Bitcoin: The Role of the Trade War," Working Papers 202003, University of Pretoria, Department of Economics.
    7. Aurelio F. Bariviera & Ignasi Merediz-Sol`a, 2020. "Where do we stand in cryptocurrencies economic research? A survey based on hybrid analysis," Papers 2003.09723, arXiv.org.
    8. Zhou, Siwen, 2018. "Exploring the Driving Forces of the Bitcoin Exchange Rate Dynamics: An EGARCH Approach," MPRA Paper 89445, University Library of Munich, Germany.
    9. Fang, Libing & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2019. "Does global economic uncertainty matter for the volatility and hedging effectiveness of Bitcoin?," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 29-36.
    10. Bedi, Prateek & Nashier, Tripti, 2020. "On the investment credentials of Bitcoin: A cross-currency perspective," Research in International Business and Finance, Elsevier, vol. 51(C).
    11. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
    12. 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.
    13. Duc Huynh, Toan Luu & Burggraf, Tobias & Wang, Mei, 2020. "Gold, platinum, and expected Bitcoin returns," Journal of Multinational Financial Management, Elsevier, vol. 56(C).
    14. 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.
    15. Fang, Tong & Su, Zhi & Yin, Libo, 2020. "Economic fundamentals or investor perceptions? The role of uncertainty in predicting long-term cryptocurrency volatility," International Review of Financial Analysis, Elsevier, vol. 71(C).
    16. Mokni, Khaled & Ajmi, Ahdi Noomen & Bouri, Elie & Vo, Xuan Vinh, 2020. "Economic policy uncertainty and the Bitcoin-US stock nexus," Journal of Multinational Financial Management, Elsevier, vol. 57.
    17. Walther, Thomas & Klein, Tony & Bouri, Elie, 2019. "Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    18. Yang, Boyu & Sun, Yuying & Wang, Shouyang, 2020. "A novel two-stage approach for cryptocurrency analysis," International Review of Financial Analysis, Elsevier, vol. 72(C).
    19. 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.
    20. Khanh Hoang & Cuong C. Nguyen & Kongchheng Poch & Thang X. Nguyen, 2020. "Does Bitcoin Hedge Commodity Uncertainty?," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 13(6), pages 1-14, June.

    More about this item

    Keywords

    Bitcoin; forecasting; machine learning; U.S. – China trade war;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pre:wpaper:201980. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Rangan Gupta). General contact details of provider: http://edirc.repec.org/data/decupza.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.