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Related Concept Videos

Entropy02:39

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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
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In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Scientists refer to the measure of randomness or disorder within a system as entropy. High entropy means high disorder and low energy. To better understand entropy, think of a student’s bedroom. If no energy or work were put into it, the room would quickly become messy. It would exist in a very disordered state, one of high entropy. Energy must be...
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The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
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Tsallis Entropy for Cross-Shareholding Network Configurations.

Roy Cerqueti1,2, Giulia Rotundo3, Marcel Ausloos4,5,6

  • 1Department of Social and Economic Sciences, Sapienza University of Rome, p.le A. Moro 5, 00185 Roma, Italy.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

This study uses Tsallis entropy to analyze Italian stock market cross-shareholding networks, revealing how company integration and diversification impact market structure and response to shocks. The findings offer insights for policymakers on market polarization and fairness.

Keywords:
Tsallis entropycopula functionscross-shareholding networkfinance

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Area of Science:

  • * Network Science
  • * Financial Economics
  • * Statistical Physics

Background:

  • * Cross-shareholding networks are crucial for understanding corporate structures.
  • * Company diversification (out-degree) and integration (in-degree) define industrial organization.
  • * Modeling the stochastic dependence between these factors is key to market analysis.

Purpose of the Study:

  • * To apply Tsallis entropy to analyze cross-shareholding networks on the Italian stock market.
  • * To model the stochastic dependence between company diversification and integration using copulas.
  • * To investigate how this dependence relates to market structure, polarization, and fairness under external shocks.

Main Methods:

  • * Development of a Tsallis entropy approach for network analysis.
  • * Utilizing copulas to model the joint distribution of diversification and integration.
  • * Empirical validation on a large dataset of Italian companies.

Main Results:

  • * Tsallis entropy provides insights into market structure reactions to external shocks.
  • * The dependence structure between in-degree and out-degree correlates with market polarization and fairness.
  • * The Tsallis entropy parameter offers guidance for policy interventions.

Conclusions:

  • * The Tsallis entropy framework effectively analyzes financial networks.
  • * Understanding diversification-integration dependence is crucial for market stability and policy.
  • * The study validates the theoretical model with empirical data from the Italian stock market.