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Deep Learning and Machine Learning Insights Into the Global Economic Drivers of the Bitcoin Price

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  • Nezir Köse
  • Yunus Emre Gür
  • Emre Ünal

Abstract

This study examines the connection between Bitcoin and global factors, including the VIX, the oil price, the US dollar index, the gold price, and interest rates estimated using the Federal funds rate and treasury securities rate, for forecasting analysis. Deep learning methodologies, including LSTM, GRU, CNN, and TFT, with machine learning algorithms such as XGBoost, LightGBM, and SVR, were employed to identify the optimal prediction model for the Bitcoin price. The findings indicate that the TFT model is the most successful predictive approach, with the gold price identified as the most relevant component in determining the Bitcoin price. After the gold indicator, the US dollar index was a substantial factor in the explanation of the Bitcoin price. The TFT model also included regulatory decisions and global events. It was estimated that the Bitcoin price was significantly influenced by the COVID‐19 pandemic. After that, global climate events and China mining ban strongly affected the Bitcoin price. These findings indicate that regulatory decisions and global events determine the Bitcoin price in addition to macroeconomic factors. The VAR analysis was employed as a robustness check. The results indicate that gold and oil prices have a strong negative influence on Bitcoin, particularly in the long term. The paper has significant policy implications for investors, portfolio managers, and scholars.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:jforec:v:44:y:2025:i:5:p:1666-1698
    DOI: 10.1002/for.3258
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    Cited by:

    1. Olfa El Aoun, 2026. "Market-specific connectedness behaviors across quantiles and frequencies connectedness patterns among G7 markets, commodities, bitcoin, and interest rate spread," Digital Finance, Springer, vol. 8(1), pages 1-45, March.
    2. 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.
    3. Nezir Köse & Emre Ünal & Savas Gayaker, 2026. "The role of global factors in Bitcoin dynamics: Evidence from the TVP-VAR-SV model," Empirical Economics, Springer, vol. 70(3), pages 1-28, March.
    4. Maxime L. D. Nicolas & Franc{c}ois Sicard & Marion Laboure & Zixin Sun & Anah'i Rodr'iguez-Mart'inez, 2026. "Is Bitcoin A Hedge Against Central Banking? Evidence from AI-Driven Monetary Policy Expectations," Papers 2604.08825, arXiv.org.

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