Entropic signatures of market response under concentrated policy communication

2026-03-12

AI Summary

This study investigates the response characteristics of financial markets under intense policy communication, explicitly distinguishing between volatility magnitude and informational complexity. Employing an information-theoretic framework that integrates standard deviation with a sliding-window cumulative entropy measure, the analysis examines the reactions of major global equity indices to concentrated policy shocks during the first hundred days preceding Trump’s anticipated second term in 2025. The findings reveal a decoupling between information entropy and volatility, where entropy captures outcome diversity rather than mere price fluctuations. Cumulative entropy proves effective in identifying high-information-density, policy-driven extreme events. Moreover, markets exhibit a short-term response pattern characterized by “global synchronization with regional modulation.” This approach offers a novel perspective for quantifying market complexity under policy-induced uncertainty.

Technology Category

• Game Theory and Economic Paradigms: Imperfect Information
• Machine Learning: Information Theory
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Content-based information diffusion

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.

Problem

• market response
• policy communication
• entropy
• volatility
• information complexity

Innovation

• entropy
• cumulative entropy
• policy communication
• market response
• information theory