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arXiv · 2510.10877

Quantifying USA tariffs effect: machine learning, entropy and fractal insights into the stock markets

Mridul Patel
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Abstract

This study presents a multiscale econometric framework to evaluate the impact of the USA tariff announcement of 2 April 2025 on the S&P 500 (USA) and S&P/ASX 200 (AUS) stock indices. We employ normalized permutation entropy (PE) to characterise the evolution of ordinal-pattern complexity and compute fractal-dimension estimates (Higuchi, Katz, Sevcik) to assess geometric scaling behaviour across different time-windows. Post-event PE remains uniformly high across windows, with values ranging from 0.666 to 0.913 for the USA and 0.690 to 0.923 for AUS, implying stable distributional entropy and no significant alteration of underlying symbolic dynamics. Fractal dimension estimates exhibit small but systematic changes: the Higuchi dimension increases (USA: 1.507 to 1.561; AUS: 1.524 to 1.533), indicating a marginal rise in high-frequency roughness, while Katz and Sevcik dimensions decline (USA Sevcik: 1.337 to 1.229), consistent with smoother medium-scale structure. Across all measures, the shock does not generate a statistically meaningful structural break. Additionally, machine-learning models (kNN, SVR, Random Forest, XGBoost, Neural Network) demonstrate strong cross-market predictability, with ensemble methods achieving out-of-sample R2>0.98. Overall, the results suggest that both markets absorbed the tariff shock rapidly, exhibiting stable multiscale dynamics despite heightened geopolitical uncertainty. A model-agnostic XAI framework combining SHAP attribution and permutation-based diagnostics is used to isolate robust and independent information content.

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Mridul Patel. 2026-07-02. Quantifying USA tariffs effect: machine learning, entropy and fractal insights into the stock markets. https://doi.org/10.1007/s11135-026-02581-9

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