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Stock markets reconstruction via entropy maximization driven by fitness and density

Overview of attention for article published in arXiv, September 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

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20 tweeters
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1 Google+ user

Citations

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4 Dimensions

Readers on

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17 Mendeley
Title
Stock markets reconstruction via entropy maximization driven by fitness and density
Published in
arXiv, September 2017
DOI 10.1103/physreve.96.032315
Pubmed ID
Authors

Tiziano Squartini, Guido Caldarelli, Giulio Cimini, Assaf Almog, Iman van Lelyveld, Diego Garlaschelli

Abstract

Reconstructing patterns of interconnections from partial information is one of the most important issues in the statistical physics of complex networks. A paramount example is provided by financial networks. In fact, the spreading and amplification of financial distress in capital markets are strongly affected by the interconnections among financial institutions. Yet, while the aggregate balance sheets of institutions are publicly disclosed, information on single positions is mostly confidential and, as such, unavailable. Standard approaches to reconstruct the network of financial interconnection produce unrealistically dense topologies, leading to a biased estimation of systemic risk. Moreover, reconstruction techniques are generally designed for monopartite networks of bilateral exposures between financial institutions, thus failing in reproducing bipartite networks of security holdings (e.g., investment portfolios). Here we propose a reconstruction method based on constrained entropy maximization, tailored for bipartite financial networks. Such a procedure enhances the traditional capital-asset pricing model (CAPM) and allows us to reproduce the correct topology of the network. We test this enhanced CAPM (ECAPM) method on a dataset, collected by the European Central Bank, of detailed security holdings of European institutional sectors over a period of six years (2009-2015). Our approach outperforms the traditional CAPM and the recently proposed maximum-entropy CAPM both in reproducing the network topology and in estimating systemic risk due to fire sales spillovers. In general, ECAPM can be applied to the whole class of weighted bipartite networks described by the fitness model.

Twitter Demographics

The data shown below were collected from the profiles of 20 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 29%
Researcher 4 24%
Professor 1 6%
Lecturer > Senior Lecturer 1 6%
Lecturer 1 6%
Other 4 24%
Unknown 1 6%
Readers by discipline Count As %
Economics, Econometrics and Finance 5 29%
Unspecified 4 24%
Physics and Astronomy 3 18%
Computer Science 2 12%
Mathematics 1 6%
Other 1 6%
Unknown 1 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 May 2017.
All research outputs
#1,053,698
of 11,206,229 outputs
Outputs from arXiv
#21,023
of 553,973 outputs
Outputs of similar age
#38,349
of 269,365 outputs
Outputs of similar age from arXiv
#1,096
of 24,724 outputs
Altmetric has tracked 11,206,229 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 553,973 research outputs from this source. They receive a mean Attention Score of 2.9. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 269,365 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 24,724 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.