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Related Concept Videos

Random Error01:04

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
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The coefficient of variation measures the dispersion of the data points or distribution around the mean. Using the coefficient of variation, we can compare two data series with drastically different means or different units of measurement. The coefficient of variation for a sample and a population is expressed as a percentage of the ratio of standard deviation to the mean.
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Updated: Sep 3, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Regularity in Stock Market Indices within Turbulence Periods: The Sample Entropy Approach.

Joanna Olbryś1, Elżbieta Majewska2

  • 1Faculty of Computer Science, Bialystok University of Technology, Wiejska 45a, 15-351 Białystok, Poland.

Entropy (Basel, Switzerland)
|July 27, 2022
PubMed
Summary

Stock market regularity, measured by Sample Entropy (SampEn), increases during economic turbulence like the 2008 financial crisis and the COVID-19 pandemic. This suggests stock market predictability rises in times of crisis.

Keywords:
COVID-19Global Financial CrisisSample Entropy (SampEn)predictabilityregularityrolling-windowstock market index

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Area of Science:

  • Quantitative Finance
  • Financial Market Analysis
  • Time Series Analysis

Background:

  • Major economic turbulence, such as the Global Financial Crisis (2007-2009) and the COVID-19 pandemic (2020-2021), significantly impacts financial markets.
  • Understanding stock market behavior during these periods is crucial for investors and policymakers.

Purpose of the Study:

  • To assess and compare changes in stock market index regularity across European and U.S. markets during periods of significant economic turbulence.
  • To test the hypothesis that market entropy decreases, implying increased regularity and predictability, during crises.

Main Methods:

  • Utilized the Sample Entropy (SampEn) algorithm to quantify sequential regularity in daily stock market index time series.
  • Compared SampEn values before and during the Global Financial Crisis and the COVID-19 pandemic.
  • Employed a rolling-window procedure to analyze the evolution of SampEn over time.

Main Results:

  • Empirical findings unambiguously support the research hypothesis: Sample Entropy decreases during turbulence periods.
  • Increased regularity and predictability were observed in stock market indices during both the Global Financial Crisis and the COVID-19 pandemic.
  • SampEn results showed similarity across developed and emerging European economies.

Conclusions:

  • The study confirms that stock market indices exhibit increased regularity and predictability during periods of major economic turbulence.
  • The findings hold consistently across different economic conditions and geographical markets (European and U.S.).
  • Sample Entropy is a robust measure for detecting changes in market dynamics during crises.