Detecting and explaining changes in various assets' relationships in financial markets

TL;DR

This paper introduces a novel method combining network analysis and entropy measures to detect and interpret relationship changes among financial assets, aiding fund managers in decision-making.

Contribution

It adapts bioinformatics differential network analysis to financial markets, providing a new approach for visualizing and understanding asset relationship dynamics.

Findings

Accurately detected market relationship changes aligned with real events

Produced interpretable network visualizations for asset interactions

Demonstrated usefulness for fund managers in decision support

Abstract

We study the method for detecting relationship changes in financial markets and providing human-interpretable network visualization to support the decision-making of fund managers dealing with multi-assets. First, we construct co-occurrence networks with each asset as a node and a pair with a strong relationship in price change as an edge at each time step. Second, we calculate Graph-Based Entropy to represent the variety of price changes based on the network. Third, we apply the Differential Network to finance, which is traditionally used in the field of bioinformatics. By the method described above, we can visualize when and what kind of changes are occurring in the financial market, and which assets play a central role in changes in financial markets. Experiments with multi-asset time-series data showed results that were well fit with actual events while maintaining high interpretability. It is suggested that this approach is useful for fund managers to use as a new option for decision making.