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Symbolic Encoding Methods with Entropy-Based Applications to Financial Time Series Analyses.

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This study reveals that symbolic encoding with quantile thresholds effectively analyzes stock market efficiency. Market informational efficiency decreases during extreme events like the COVID-19 pandemic and the Ukraine war.

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Area of Science:

  • Quantitative Finance
  • Information Theory
  • Financial Econometrics

Background:

  • Symbolic encoding is fundamental to information theory and relates to capital market efficiency.
  • Assessing information processing by equity market participants is crucial for understanding market dynamics.
  • Financial time series analysis often requires methods robust to extreme market events.

Purpose of the Study:

  • To compare symbolic coding methods with modified Shannon entropy for stock market efficiency analysis.
  • To investigate market efficiency during turbulent periods, specifically the COVID-19 pandemic and the war in Ukraine.
  • To identify the most effective symbolic encoding method for financial time series analysis.

Main Methods:

  • Symbolic encoding using thresholds (5% and 95% quantiles).
  • Modified Shannon entropy calculation for information efficiency assessment.
  • Analysis of European equity market indices during extreme events.

Main Results:

  • The 5%/95% quantile threshold encoding method proved most effective for recognizing dynamic patterns.
  • Shannon entropy analysis using this method yielded homogenous results across markets.
  • Market informational efficiency, measured by entropy of index returns, decreased during extreme event periods.

Conclusions:

  • The symbolic threshold encoding method is a precise tool for financial time series analysis.
  • Shannon entropy confirms reduced market informational efficiency during crises.
  • The STSA (Symbolic Threshold Symbolic Aggregate) method is recommended for financial time series analysis during extreme events.