Paper: arXiv 2603.12040

Authors: Ewa A. Drzazga-Szczȩśniak, Rishabh Gupta, Adam Z. Kaczmarek, Jakub T. Gnyp, Marcin W. Jarosik, Róża Waligóra, Marta Kielak, Shivam Gupta, Agata Gurzyńska, Johann Gil, Piotr Szczepanik, Józefa Kielak, Dominik Szczȩśniak

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.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents an interesting applied framework using Shannon entropy and cumulative sliding-window entropy to capture market dynamics during a specific high-volatility event. While the mathematical formulations rely on standard information theory metrics, the empirical application across global stock indices is structured well.

Research Flowchart

  flowchart TD
    A["Research Question: How do stock markets respond to concentrated policy communication?"] --> B["Data: Major stock indices from Americas, Europe, Asia & Oceania (Jan-Apr 2025)"]
    B --> C["Compute Standard Deviation (Dispersion) & Information Entropy (Complexity)"]
    B --> D["Apply Sliding Window Cumulative Entropy"]
    C & D --> E["Decoupling & Regional Event Localization Analysis"]
    E --> F["Findings: Entropy reflects narrative constraints, not just amplitude; captures policy-driven regional market impacts"]