Global Stock Market Network Indices with Multi-Perspective Metrics, 2017-2022
by Andy Domínguez-Monterroza·Updated 24d ago
9.5 KB1files
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Description
A dataset containing daily data from major global stock indices from 2017 to 2022, used to analyze market interdependence and systemic stability. It was created by Andy Domínguez-Monterroza and published on figshare under a CC-BY-4.0 license. The data is structured to support a multi-perspective framework integrating random matrix theory, Ricci curvature, and Euler characteristic for analyzing market synchronization, fragility, and structural cohesion.
Use Cases
Modeling collective market behavior based on the maximum eigenvalue metric described.
Quantifying local network fragility using Ricci curvature measures mentioned in the description.
Analyzing global topological cohesion through Euler characteristic calculations.
Studying structural transitions during crisis periods like the COVID-19 pandemic.
Detecting secondary structural reconfigurations aligned with geopolitical shocks.
Strengths
Covers a 5-year time range from 2017 to 2022.
Applies a multi-perspective analytical framework integrating spectral, geometric, and topological descriptors.
Explicitly identifies structural transitions during the COVID-19 pandemic and a reconfiguration in early 2022.
Limitations
The dataset is very small at 9.5 KB, indicating limited scope.
Column-level documentation is absent; field semantics must be inferred after download.
Row count is unknown, which may limit suitability assessment.
Provenance
Source
figshare
Collection Method
Likely compiled from daily data of major global stock indices.
Time Range
2017 to 2022
Freshness
Last updated 2026-05-12 17:43:47; freshness should be verified.