A Perspective on Correlation-Based Financial Networks and Entropy Measures

In this mini-review, we critically examine the recent work done on correlation-based networks in financial systems. The structure of empirical correlation matrices constructed from the financial market data changes as the individual stock prices fluctuate with time, showing interesting evolutionary patterns, especially during critical events such as market crashes, bubbles, etc. We show that the study ofcorrelation-based networksand their evolution with time is useful for extracting important information of the underlying market dynamics. Also, we present our perspective on the use of recently-developed entropy measures, such asstructural entropyandeigen-entropy, for continuous monitoring of correlation-based networks.