IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v45y2026i3p1245-1260.html

When Are Statistical Forecast Gains Economically Relevant? Evidence From Bitcoin Returns

Author

Listed:
  • Rehan Arain
  • Stephen Snudden

Abstract

We study how statistical forecast gains for Bitcoin translate into trading profits. Using real‐time out‐of‐sample forecasts from daily bivariate VARs from October 2021 to February 2024, we show that Bitcoin returns are forecastable and that seven predictive indices yield significant gains in directional accuracy (DA). However, mean‐squared forecast error is largely uninformative, and mean DA alone is insufficient to explain trading profitability. To understand this puzzle, we introduce a conditional DA measure based on the magnitude of price movements and a threshold‐based trading strategy. Profits arise only when DA remains stable during large market swings. Mean DA obscures breakdowns precisely when accurate forecasts are most valuable. Under a formal excess‐profitability test, the USD index and the Shanghai Stock Exchange deliver statistically significant profits, challenging the efficient market hypothesis.

Suggested Citation

  • Rehan Arain & Stephen Snudden, 2026. "When Are Statistical Forecast Gains Economically Relevant? Evidence From Bitcoin Returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(3), pages 1245-1260, April.
    • Handle: RePEc:wly:jforec:v:45:y:2026:i:3:p:1245-1260
    DOI: 10.1002/for.70077
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.70077
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.70077?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. Oluwadamilare Omole & David Enke, 2024. "Deep learning for Bitcoin price direction prediction: models and trading strategies empirically compared," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-26, December.
    2. Moiz Qureshi & Hasnain Iftikhar & Paulo Canas Rodrigues & Mohd Ziaur Rehman & S. A. Atif Salar, 2024. "Statistical Modeling to Improve Time Series Forecasting Using Machine Learning, Time Series, and Hybrid Models: A Case Study of Bitcoin Price Forecasting," Mathematics, MDPI, vol. 12(23), pages 1-15, November.
    3. Muhammad Ali Nasir & Toan Luu Duc Huynh & Sang Phu Nguyen & Duy Duong, 2019. "Forecasting cryptocurrency returns and volume using search engines," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-13, December.
    4. Junwei Chen, 2023. "Analysis of Bitcoin Price Prediction Using Machine Learning," JRFM, MDPI, vol. 16(1), pages 1-25, January.
    5. Abderraouf Ben Ahmed Mtiraoui & Nadia Slimene & Leila Chemli, 2025. "The Bitcoin Price Prediction by Vector Auto-Regression (VAR) Model," Post-Print hal-05253337, HAL.
    6. Nils Bundi & Marc Wildi, 2019. "Bitcoin and market-(in)efficiency: a systematic time series approach," Digital Finance, Springer, vol. 1(1), pages 47-65, November.
    7. Imad Moosa & John Vaz, 2015. "Directional accuracy, forecasting error and the profitability of currency trading: model-based evidence," Applied Economics, Taylor & Francis Journals, vol. 47(57), pages 6191-6199, December.
    8. Bouri, Elie & Gupta, Rangan & Tiwari, Aviral Kumar & Roubaud, David, 2017. "Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions," Finance Research Letters, Elsevier, vol. 23(C), pages 87-95.
    9. Peter F. Christoffersen & Francis X. Diebold, 2006. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," Management Science, INFORMS, vol. 52(8), pages 1273-1287, August.
    10. Cohen, Gil & Aiche, Avishay, 2025. "Intelligent forecasting in bitcoin markets," Finance Research Letters, Elsevier, vol. 71(C).
    11. John M. Griffin & Amin Shams, 2020. "Is Bitcoin Really Untethered?," Journal of Finance, American Finance Association, vol. 75(4), pages 1913-1964, August.
    12. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
    13. Carlos Trucíos & James W. Taylor, 2023. "A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 989-1007, July.
    14. Peter F. Christoffersen & Francis X. Diebold, 2000. "How Relevant is Volatility Forecasting for Financial Risk Management?," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 12-22, February.
    15. Cheung, Yin-Wong & Chinn, Menzie D. & Pascual, Antonio Garcia, 2005. "Empirical exchange rate models of the nineties: Are any fit to survive?," Journal of International Money and Finance, Elsevier, vol. 24(7), pages 1150-1175, November.
    16. Nicola Uras & Lodovica Marchesi & Michele Marchesi & Roberto Tonelli, 2020. "Forecasting Bitcoin closing price series using linear regression and neural networks models," Papers 2001.01127, arXiv.org.
    17. Anatolyev, Stanislav & Gerko, Alexander, 2005. "A Trading Approach to Testing for Predictability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 455-461, October.
    18. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    19. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
    20. Hui Xiao & Yiguo Sun, 2020. "Forecasting the Returns of Cryptocurrency: A Model Averaging Approach," JRFM, MDPI, vol. 13(11), pages 1-15, November.
    21. Leitch, Gordon & Tanner, J Ernest, 1991. "Economic Forecast Evaluation: Profits versus the Conventional Error Measures," American Economic Review, American Economic Association, vol. 81(3), pages 580-590, June.
    22. Yuze Li & Shangrong Jiang & Xuerong Li & Shouyang Wang, 2022. "Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.
    23. Nezir Köse & Yunus Emre Gür & Emre Ünal, 2025. "Deep Learning and Machine Learning Insights Into the Global Economic Drivers of the Bitcoin Price," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(5), pages 1666-1698, August.
    24. Pesaran, M. Hashem & Timmermann, Allan, 2009. "Testing Dependence Among Serially Correlated Multicategory Variables," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 325-337.
    25. Daniele Bianchi & Massimo Guidolin & Manuela Pedio, 2023. "The dynamics of returns predictability in cryptocurrency markets," The European Journal of Finance, Taylor & Francis Journals, vol. 29(6), pages 583-611, April.
    26. David Zhao & Alessandro Rinaldo & Christopher Brookins, 2019. "Cryptocurrency Price Prediction and Trading Strategies Using Support Vector Machines," Papers 1911.11819, arXiv.org, revised Nov 2019.
    27. Mingzhe Wei & Georgios Sermpinis & Charalampos Stasinakis, 2023. "Forecasting and trading Bitcoin with machine learning techniques and a hybrid volatility/sentiment leverage," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 852-871, July.
    28. Chan, Wing Hong & Le, Minh & Wu, Yan Wendy, 2019. "Holding Bitcoin longer: The dynamic hedging abilities of Bitcoin," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 107-113.
    29. Theo Berger & Jana Koubová, 2024. "Forecasting Bitcoin returns: Econometric time series analysis vs. machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2904-2916, November.
    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. Dal Bianco, Marcos & Camacho, Maximo & Perez Quiros, Gabriel, 2012. "Short-run forecasting of the euro-dollar exchange rate with economic fundamentals," Journal of International Money and Finance, Elsevier, vol. 31(2), pages 377-396.
    2. Dick, Christian D. & MacDonald, Ronald & Menkhoff, Lukas, 2015. "Exchange rate forecasts and expected fundamentals," Journal of International Money and Finance, Elsevier, vol. 53(C), pages 235-256.
    3. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 3, pages 99-134, Elsevier.
    4. Moosa, Imad & Burns, Kelly, 2014. "The unbeatable random walk in exchange rate forecasting: Reality or myth?," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 69-81.
    5. Pablo Pincheira, 2006. "Shrinkage Based Tests of the Martingale Difference Hypothesis," Working Papers Central Bank of Chile 376, Central Bank of Chile.
    6. Imad Moosa & Kelly Burns, 2016. "The random walk as a forecasting benchmark: drift or no drift?," Applied Economics, Taylor & Francis Journals, vol. 48(43), pages 4131-4142, September.
    7. Liu, Jiadong & Papailias, Fotis & Quinn, Barry, 2021. "Direction-of-change forecasting in commodity futures markets," International Review of Financial Analysis, Elsevier, vol. 74(C).
    8. Dick, Christian D. & MacDonald, Ronald & Menkhoff, Lukas, 2011. "Individual exchange rate forecasts and expected fundamentals," ZEW Discussion Papers 11-062, ZEW - Leibniz Centre for European Economic Research.
    9. Breen, John David & Hu, Liang, 2021. "The predictive content of oil price and volatility: New evidence on exchange rate forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    10. Martin McCarthy, Stephen Snudden, 2024. "Forecasts of Period-Average Exchange Rates: New Insights from Real-Time Daily Data," LCERPA Working Papers jc0148, Laurier Centre for Economic Research and Policy Analysis, revised Oct 2024.
    11. Garratt, Anthony & Lee, Kevin, 2010. "Investing under model uncertainty: Decision based evaluation of exchange rate forecasts in the US, UK and Japan," Journal of International Money and Finance, Elsevier, vol. 29(3), pages 403-422, April.
    12. Constantin Bürgi & Dorine Boumans, 2020. "Categorical Forecasts and Non-Categorical Loss Functions," CESifo Working Paper Series 8266, CESifo.
    13. Pablo Pincheira, 2008. "Combining Tests of Predictive Ability Theory and Evidence for Chilean and Canadian Exchange Rates," Working Papers Central Bank of Chile 459, Central Bank of Chile.
    14. Nicolás Magner & Nicolás Hardy, 2022. "Cryptocurrency Forecasting: More Evidence of the Meese-Rogoff Puzzle," Mathematics, MDPI, vol. 10(13), pages 1-27, July.
    15. Cerra, Valerie & Saxena, Sweta Chaman, 2010. "The monetary model strikes back: Evidence from the world," Journal of International Economics, Elsevier, vol. 81(2), pages 184-196, July.
    16. Cheung, Yin-Wong & Chinn, Menzie D. & Pascual, Antonio Garcia, 2005. "Empirical exchange rate models of the nineties: Are any fit to survive?," Journal of International Money and Finance, Elsevier, vol. 24(7), pages 1150-1175, November.
    17. Feng, Wenjun & Zhang, Zhengjun, 2023. "Currency exchange rate predictability: The new power of Bitcoin prices," Journal of International Money and Finance, Elsevier, vol. 132(C).
    18. Gaglianone, Wagner Piazza & Marins, Jaqueline Terra Moura, 2017. "Evaluation of exchange rate point and density forecasts: An application to Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 707-728.
    19. Hambuckers, J. & Ulm, M., 2023. "On the role of interest rate differentials in the dynamic asymmetry of exchange rates," Economic Modelling, Elsevier, vol. 129(C).
    20. Eric Hillebrand & Jakob Mikkelsen & Lars Spreng & Giovanni Urga, 2020. "Exchange Rates and Macroeconomic Fundamentals: Evidence of Instabilities from Time-Varying Factor Loadings," CREATES Research Papers 2020-19, Department of Economics and Business Economics, Aarhus University.

    More about this item

    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:wly:jforec:v:45:y:2026:i:3:p:1245-1260. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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.