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<p>This study explores multi-scale interactions between geo-astronomical activity and financial market dynamics from a complex systems perspective. Using high-frequency data from the EUR/USD exchange rate and S&amp;P&#xa0;500 trading volume, we examine whether geomagnetic fluctuations and planetary motion indices contain statistically meaningful information for short-term market forecasting. A redundant tight wavelet frame decomposition is employed to capture localized time–frequency structures and to reduce sensitivity to noise. This shift-invariant representation enables the detection of coherent low-frequency patterns that may reflect underlying systemic responses to external perturbations. Following rigorous preprocessing and temporal alignment of financial and geophysical datasets, model performance is evaluated using both in-sample and out-of-sample experiments with RMSE, MAE, and <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(R^2_{\textrm{out}}\)</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mi>R</mi> <mtext>out</mtext> <mn>2</mn> </msubsup> </math></EquationSource> </InlineEquation> as accuracy metrics. The results indicate that several geomagnetic indicators, particularly those associated with low-frequency components, exhibit persistent statistical association with market variables and provide measurable predictive utility under strict out-of-sample testing. While no causal inference is implied, the findings highlight reproducible statistical dependencies linking environmental and financial dynamics, suggesting potential applications in early-warning systems and stress prediction within complex economic environments.</p>

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A Wavelet-Based Econophysical Analysis of Multi-scale Coupling Between Geomagnetic Activity and Market Dynamics

  • 期刊论文

  • 发表日期：

    2026年4月14日

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  • Mutaz Mohammad,
  • Mohyeedden Sweidan,
  • Alexander Trounev

摘要

This study explores multi-scale interactions between geo-astronomical activity and financial market dynamics from a complex systems perspective. Using high-frequency data from the EUR/USD exchange rate and S&P 500 trading volume, we examine whether geomagnetic fluctuations and planetary motion indices contain statistically meaningful information for short-term market forecasting. A redundant tight wavelet frame decomposition is employed to capture localized time–frequency structures and to reduce sensitivity to noise. This shift-invariant representation enables the detection of coherent low-frequency patterns that may reflect underlying systemic responses to external perturbations. Following rigorous preprocessing and temporal alignment of financial and geophysical datasets, model performance is evaluated using both in-sample and out-of-sample experiments with RMSE, MAE, and \(R^2_{\textrm{out}}\) R out 2 as accuracy metrics. The results indicate that several geomagnetic indicators, particularly those associated with low-frequency components, exhibit persistent statistical association with market variables and provide measurable predictive utility under strict out-of-sample testing. While no causal inference is implied, the findings highlight reproducible statistical dependencies linking environmental and financial dynamics, suggesting potential applications in early-warning systems and stress prediction within complex economic environments.