Entropic signatures of market response under concentrated policy communication

TL;DR

This paper introduces an entropic analysis framework to study market responses to concentrated policy actions during Trump's second term, revealing how entropy and dispersion capture market complexity and regional impacts.

Contribution

It presents a novel application of information theory, especially entropy measures, to analyze and localize market responses to policy events across global regions.

Findings

Entropy reflects diversity of market outcomes beyond volatility.

Cumulative entropy signals regional policy-driven market events.

Market impacts are globally coupled but regionally modulated.

Abstract

The first 100 days of Donald Trump second presidential term (January 20th - April 30th, 2025) featured policy actions with potential market repercussions, constituting a well-suited case study of a concentrated policy scenario. Here, we provide a first look at this period, rooted in the information theory, by analyzing major stock indices across the Americas, Europe as well as Asia and Oceania. Our approach jointly examines dispersion (standard deviation) and information complexity (entropy), but also employs a sliding window cumulative entropy to localize extreme events. We find a notable decoupling between the first two measures, indicating that entropy is not merely a proxy for amplitude but reflects the diversity of populated outcomes. As such, they allow us to capture both market volatility and narrative constraints, signaling large and coherent moves driven by policy changes. In turn, the cumulative entropy is found to notably increase during regional episodes with high information density, providing effective signatures of such events. We argue that the obtained results indicate short-term globally coupled, yet regionally modulated, market impacts with clear connection to introduced policies. In what follows, the presented entropic framework emerges as an efficient complement to standard methods for characterizing markets under turbulent conditions, with potential to enhance forecasting strategies such as the stochastic modeling.