ArticleEntropy (Basel, Switzerland)2024
Entropy as a Tool for the Analysis of Stock Market Efficiency During Periods of Crisis.
Article in Entropy (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Entropy-Driven Volatility Prediction for ETF Quantitative Investment in the Chinese Stock Market: A Machine Learning Framework for Barbell Strategy Optimization.Entropy (Basel, Switzerland) · 2026Article
- Entropy-Augmented Forecasting and Portfolio Construction at the Industry-Group Level: A Causal Machine-Learning Approach Using Gradient-Boosted Decision Trees.Entropy (Basel, Switzerland) · 2026Article
- Flexible Target Prediction for Quantitative Trading in the American Stock Market: A Hybrid Framework Integrating Ensemble Models, Fusion Models and Transfer Learning.Entropy (Basel, Switzerland) · 2026Article
- Entropy of Volatility Changes: Novel Method for Assessment of Regularity in Volatility Time Series.Entropy (Basel, Switzerland) · 2025Article
- Analysis of the Spatiotemporal Heterogeneity and Influencing Factors of Regional Economic Resilience in China.Entropy (Basel, Switzerland) · 2024Article
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Authors and funding
2 authors.
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Abstract
In the article, we analyse the problem of the efficiency market hypothesis using entropy in moments of transition from a normal economic situation to crises or slowdowns in European, Asian and US stock markets and the economy in the years 2007-2023 (2008-2009, U.S. financial sector crises; 2020-2021, Pandemic period; and the 2022-2023 period of Russia's attack on Ukraine). The following hypothesis is put forward in the article: In periods of economic slowdown and economic crises, the entropy of prices and return rates decreases. According to the principles of physics, in an isolated system, entropy increases and decreases at the moment of external intervention, similar to finance, where during crises and economic slowdowns, there is interference from governments introducing new regulations and intervening in financial markets. The article uses the Shannon entropy method. This measure, as a statistical measure, does not require the assumption of stationarity of time series or a known probability distribution, unlike classical statistical methods. Our results confirm decreased entropy in stock markets during crisis.
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