A statistical physics perspective on criticality in financial markets

Abstract

Stock markets are complex systems exhibiting collective phenomena and particular features such as synchronization, fluctuations distributed as power-laws, non-random structures and similarity to neural networks. Such specific properties suggest that markets operate at a very special point. Financial markets are believed to be critical by analogy to physical systems but few statistically founded evidence have been given. Through a data-based methodology and comparison to simulations inspired by statistical physics of complex systems, we show that the Dow Jones and indices sets are not rigorously critical. However, financial systems are closer to the criticality in the crash neighborhood.

23 pages, 19 figures

References in corpus (5)

• Power-law distributions in empirical data
• Scale-free brain functional networks
• On sampling and modeling complex systems
• Market structure explained by pairwise interactions
• Statistical pairwise interaction model of stock market

Cited by in corpus (5)

• Inverse statistical problems: from the inverse Ising problem to data science
• Network reconstruction via the minimum description length principle
• Matrix H-theory approach to stock market fluctuations
• Inverse Ising inference from high-temperature re-weighting of observations
• Mapping Inter-City Trade Networks to Maximum Entropy Models using Electronic Invoice Data