Improving data efficiency for analyzing global exchange rate fluctuations based on nonlinear causal network-based clustering.

This study used information theory and network theory to predict the fluctuations of currency values of the machine learning model. For experiments, we calculate the causal relationships between currencies using loarithmic return (log-return) and entropic value-at-risk (EVaR) values of gold price per troy ounce in 48 currencies over 25 years. To quantify the causal relationships, we used the concept of transfer entropy. After quantifying their information flow, we modeled and analyzed those nonlinear causal relationships as a network. The network analysis results confirmed that information flow-based nonlinear causal relationships differed from the commonly-known key currency order. Then, we classified currencies using hierarchical clustering methods based on the configured networks. We predicted fluctuations in currency values using machine learning algorithms based on network topology-based information. As a result, we show that using the data columns in the same communities based on statistically significant nonlinear causal relationships can improve most machine-learning-based fluctuations of currency values for various countries from the perspective of data efficiency.