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  3. Information Theory Quantifiers In Cryptocurrency Time Series Analysis.
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  3. Information Theory Quantifiers In Cryptocurrency Time Series Analysis.

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Information Theory Quantifiers in Cryptocurrency Time Series Analysis.

Micaela Suriano1,2, Leonidas Facundo Caram2, Cesar Caiafa3

  • 1Departamento de Hidráulica, Facultad de Ingeniería, Universidad de Buenos Aires, Av. Las Heras 2214, Buenos Aires C1127AAR, Argentina.

Entropy (Basel, Switzerland)
|April 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Cryptocurrency time series show chaotic behavior in shorter datasets (under two years) and stochastic behavior in longer ones. Project narratives in white papers do not significantly influence market dynamics, suggesting a focus on real-time metrics for investment.

Keywords:
cryptocurrencypermutation entropystatistical complexity

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Area of Science:

  • Quantitative Finance
  • Complexity Science
  • Data Science

Background:

  • Cryptocurrency markets exhibit complex temporal dynamics.
  • Understanding the randomness and chaos in financial time series is crucial for market analysis.
  • Existing research often overlooks the interplay between project narratives and market behavior.

Purpose of the Study:

  • To investigate the temporal evolution of cryptocurrency time series using information-theoretic measures.
  • To differentiate between chaotic and stochastic behaviors in cryptocurrency price data.
  • To assess the influence of white paper content on cryptocurrency market dynamics.

Main Methods:

  • Applied information measures like complexity, entropy, and Fisher information to 176 daily cryptocurrency closing price time series.
  • Utilized Complexity-Entropy Causality Plane (CECP) analysis to classify time series behavior.
  • Employed Natural Language Processing (NLP) for white paper analysis and clustering, followed by time series dynamics comparison.

    • Main Results:

    • Cryptocurrency time series under two years show chaotic behavior; series longer than two years exhibit stochastic behavior, often resembling colored noise (k between 0 and 2).
    • NLP analysis revealed four distinct clusters based on white paper content.
    • No significant correlation was found between white paper clusters and the time series dynamics.

    Conclusions:

    • Cryptocurrency market behavior transitions from chaotic to stochastic as data length increases.
    • Project narratives, as reflected in white papers, do not appear to dictate short-to-medium term market dynamics.
    • Investment strategies should prioritize real-time informational metrics over static white paper analysis.