Extreme Events and Stock Market Efficiency: The Modified Shannon Entropy Approach

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.