 Extreme Events and Stock Market Efficiency: The Modified Shannon Entropy ApproachChapter Chapter 6 in Applied Economic Research and Trends, 2024, pp 77-89 from Springer Abstract: Abstract The aim of this chapter is to investigate whether extreme events influence stock markets’ informational efficiency measured by entropy. As an example, the 2-year period from February 24, 2021 to February 24, 2023 is explored. It comprises 1 year before the war in Ukraine and 1 year including the period of the war. The selected European equity markets are analyzed. As the data sample is not long, the modified Shannon entropy based on symbolic encoding with thresholds is utilized. This approach is especially useful in assessing stock market efficiency during extreme events as it allows us to capture extreme changes in market returns. The symbol-sequence histograms are obtained. The empirical results of this dynamic approach indicate that the influence of the war in Ukraine on daily returns of stock market indices was not very significant, but overall market efficiency measured by entropy decreased during this turbulence period. Keywords: Extreme event; Stock market; Informational efficiency; Symbolic time series analysis (STSA); Shannon entropy (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-49105-4_6 Ordering information: This item can be ordered from DOI: 10.1007/978-3-031-49105-4_6 Access Statistics for this chapter More chapters in Springer Proceedings in Business and Economics from Springer |
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