Functional Hypergraphs of Stock Markets.

Jerry Jones David, Narayan G Sabhahit, Sebastiano Stramaglia, T Di Matteo, Stefano Boccaletti, Sarika Jalan

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Wavelet-Decoupled Spatiotemporal Network for Stock Return Prediction.Entropy (Basel, Switzerland) · 2026
Article

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jerry Jones DavidComplex Systems Lab, Department of Physics, Indian Institute of Technology Indore, Khandwa Road, Indore 453552, India.

Narayan G SabhahitNetwork Science Institute, Northeastern University, Boston, MA 02115, USA.

Sebastiano StramagliaDipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro and INFN, 70125 Bari, Italy.ORCID 0000-0002-5873-8564

T Di MatteoDepartment of Mathematics, King's College London, Strand, London WC2R 2LS, UK.

Stefano BoccalettiCNR-Institute of Complex Systems, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy.

Sarika JalanComplex Systems Lab, Department of Physics, Indian Institute of Technology Indore, Khandwa Road, Indore 453552, India.

Funding

Science and Engineering Research Board SERB Power grant SPF/2021/000136.

6 · The paper itself

Abstract

In stock markets, nonlinear interdependencies between various companies result in nontrivial time-varying patterns in stock prices. A network representation of these interdependencies has been successful in identifying and understanding hidden signals before major events like stock market crashes. However, these studies have revolved around the assumption that correlations are mediated in a pairwise manner, whereas, in a system as intricate as this, the interactions need not be limited to pairwise only. Here, we introduce a general methodology using information-theoretic tools to construct a higher-order representation of the stock market data, which we call

Indexed as

complex systemshypergraphsstock markets

Identifiers

PMID39451925

DOI10.3390/e26100848

PMCPMC11507234

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.