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What Drives Bitcoin? An Approach from Continuous Local Transfer Entropy and Deep Learning Classification Models.

Andrés García-Medina1,2, Toan Luu Duc Huynh3,4,5

  • 1Unidad Monterrey, Centro de Investigación en Matemáticas, A.C. Av. Alianza Centro 502, PIIT, Apodaca 66628, Mexico.

Entropy (Basel, Switzerland)
|December 24, 2021
PubMed
Summary
This summary is machine-generated.

This study reveals Bitcoin

Keywords:
Bitcoinlocal transfer entropylong-short-term-memory

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Area of Science:

  • Quantitative Finance
  • Computational Economics
  • Machine Learning Applications

Background:

  • Bitcoin exhibits unpredictable price volatility, including significant jumps and crashes.
  • Understanding Bitcoin's price dynamics is crucial for market participants.
  • Existing models struggle to consistently predict short-term Bitcoin price movements.

Purpose of the Study:

  • To identify key determinants influencing Bitcoin's price direction using advanced feature selection.
  • To develop a deep learning model for predicting Bitcoin price movements.
  • To assess the role of external market drivers during different economic periods.

Main Methods:

  • Utilized continuous transfer entropy for feature selection to identify significant price determinants.
  • Employed a deep learning classification model for Bitcoin price direction prediction.
  • Validated the model's predictive power across different market scenarios, including the pandemic period.

Main Results:

  • The transfer entropy approach identified statistically significant drivers, excluding NASDAQ and Tesla.
  • The predictive accuracy improved in the post-pandemic period (July 2020-January 2021) even without external drivers.
  • Bitcoin demonstrated a self-regulating capacity during high volatility periods.

Conclusions:

  • Bitcoin's price direction can be predicted with higher accuracy during specific post-pandemic periods without relying on traditional market drivers.
  • The findings suggest that Bitcoin may exhibit intrinsic self-regulation mechanisms, especially during volatile times.
  • The study highlights the potential of transfer entropy and deep learning in analyzing cryptocurrency market dynamics.