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Updated: Aug 13, 2025

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The dynamical relation between price changes and trading volume: A multidimensional clustering analysis.

Emiliano Alvarez1, Gabriel Brida1, Leonardo Moreno1

  • 1IESTA, Departamento de Métodos Cuantitativos, Facultad de Ciencias Económicas y de Administración, Universidad de la República, Montevideo, Uruguay.

Quality & Quantity
|January 23, 2023
PubMed
Summary

This study introduces a novel wavelet-based method for analyzing multidimensional time series, revealing distinct firm clusters in the Nasdaq-100 and their varied responses to macroeconomic events during the pandemic.

Keywords:
Clustering financeMultivariate times seriesNasdaq-100 stock marketPortfolios constructionWavelets

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Area of Science:

  • Financial econometrics
  • Network analysis
  • Time series analysis

Background:

  • Traditional methods struggle with complex financial market dynamics.
  • Understanding asset returns and trading volume interactions is crucial.
  • The COVID-19 pandemic significantly impacted global financial markets.

Purpose of the Study:

  • To introduce a new wavelet-based methodology for multidimensional time series analysis.
  • To investigate the structure and dynamics of the Nasdaq-100 stock market using asset returns and trading volume.
  • To identify firm clusters and analyze their differential responses to macroeconomic events.

Main Methods:

  • Functional random variable modeling of time series.
  • Wavelet analysis for multidimensional data.
  • Application of an efficient clustering algorithm to financial data.
  • Network analysis of asset returns and trading volume.

Main Results:

  • Detection of four distinct clusters of firms within the Nasdaq-100 index.
  • Identification of nonlinear relationships between asset returns and trading volume.
  • Demonstration that macroeconomic events impacted clusters with varying intensity.
  • The new algorithm showed superior performance compared to existing clustering methods.

Conclusions:

  • The wavelet methodology effectively captures complex financial market dynamics.
  • Firm clusters exhibit heterogeneous responses to macroeconomic shocks.
  • Findings highlight the need for policy analysis to mitigate systemic risks and market bubbles.