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Analysis of weakly correlated nodes in market network

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Abstract

The aim of the article is to analyze graphs of weakly correlated stocks. Characteristics of these graphs such as number of edges, histogram of vertices degrees, degrees distribution, hubs and cliques are investigated. Pearson correlation and Kendall correlation are used to construct these graphs. Graphs constructed by the traditional procedure and by Holm procedure are compared. Obtained results are exemplified on the data of French stock market. In particular it is shown that reliable maximum cliques contain very few nodes despite the large number of edges in the graph of weakly correlated stocks.

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Acknowledgements

The results of the Sects. 2–4 of the article were prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE). The results of the Sects. 5, 1 of the article were obtained with the support of the RSF grant N 22-11-00073.

Funding

Russian Science Foundation Grant (No. 22-11-00073) the Basic Research Program at the National Research University Higher School of Economics (HSE).

Author information

Author notes

  1. Alexander Koldanov, Petr Koldanov have contributed equally to this work.

Authors and Affiliations

  1. Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics, 136 Rodionova Street, Nizhny Novgorod, Russia, 603093

    Dmitry Semenov, Alexander Koldanov & Petr Koldanov

Authors
  1. Dmitry Semenov
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  2. Alexander Koldanov
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  3. Petr Koldanov
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Contributions

All authors contributed to the article in equal parts to each Section.

Corresponding author

Correspondence to Dmitry Semenov.

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The authors declare no competing interests.

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Appendix

Appendix

1.1 Graphs of weakly correlated stocks for another thresholds

See Figs. 12, 13, 14, 15, 16, 17, 18, 19, 20 and 21.

Fig. 12
figure 12

Degree rank plots and degree histograms of the graphs of weakly correlated stocks obtained by the traditional procedure with Kendall correlation. Thresholds \(-\)0.05 (left) and 0.05 (right)

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Fig. 13
figure 13

Degree rank plots and degree histograms of the graphs of weakly correlated stocks obtained by the traditional procedure with Kendall correlation. Threshold 0.15

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Fig. 14
figure 14

Degree rank plots and degree histograms of the graphs of weakly correlated stocks obtained by the traditional procedure with Pearson correlation. Thresholds \(-\)0.05 (left) and 0.05 (right)

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Fig. 15
figure 15

Degree rank plot and degree histogram of the graph of weakly correlated stocks obtained by the traditional procedure with Pearson correlation. Threshold 0.15

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Fig. 16
figure 16

Degree rank plots and degree histograms of the graphs of weakly correlated stocks obtained by the Holm procedure with Kendall correlation. Thresholds \(-\)0.05 (left) and 0.05 (right)

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Fig. 17
figure 17

Degree rank plot and degree histogram of the graph of weakly correlated stocks obtained by the Holm procedure with Kendall correlation. Threshold 0.15

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Fig. 18
figure 18

Maximum cliques in the graph constructed using the traditional procedure with Kendall correlation (5). Reliable edges - solid line, unreliable edges - dotted line. Threshold \(-\)0.05

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Fig. 19
figure 19

Maximum cliques in the graph constructed using the traditional procedure with Kendall correlation (5). Reliable edges - solid line, unreliable edges - dotted line. Threshold 0.05

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Fig. 20
figure 20

Maximum cliques in the graph constructed using the traditional procedure with Kendall correlation (5). Reliable edges - solid line, unreliable edges - dotted line. Threshold 0.15

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Fig. 21
figure 21

Maximum cliques in the graph constructed using the traditional procedure with Kendall correlation (5). Reliable edges - solid line, unreliable edges - dotted line. Threshold 0.15

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1.2 List of tickers of French stock market

Ticker

Company name

Ticker

Company name

ABCA.PA

ABC Arbitrage SA

CGM.PA

Cegedim SA

AC.PA

Accor

CO.PA

Casino, Guichard-Perrachon S.A.

ACA.PA

Crédit Agricole

COH.PA

Coheris Société Anonyme

ACAN.PA

Acanthe Developpement

COX.PA

Nicox SA

AF.PA

Air France-KLM

CRI.PA

Chargeurs SA

AI.PA

Air Liquide

CS.PA

AXA Group

AIR.PA

AIRBUS GROUP

DBG.PA

Derichebourg

ASY.PA

Assystem SA

DEC.PA

JCDecaux SA

ATE.PA

Alten SA

DG.PA

VINCI S.A.

ATO.PA

Atos SE

DIM.PA

Sartorius Stedim Biotech S.A.

AUB.PA

Aubay Société Anonyme

DPT.PA

S.T.Dupont Société Anonyme

AURE.PA

Aurea SA

DSY.PA

Dassault Systemes SA

AURS.PA

Aures Technologies S.A.

EN.PA

Bouygues

AVT.PA

Avenir Telecom S.A.

EO.PA

Faurecia S.A.

BB.PA

Societe BIC SA

EOS.PA

ACTEOS S.A.

BEN.PA

Bénéteau S.A.

ERA.PA

Eramet SA

BIG.PA

BigBen Interactive

ERF.PA

Eurofins Scientific SA

BLC.PA

Bastide le Confort Medical SA

ES.PA

Esso S.A.F.

BN.PA

Danone

ESI.PA

ESI Group SA

BNP.PA

BNP Paribas

EUR.PA

Euro Ressources SA

BOI.PA

Boiron SA

EXE.PA

Exel Industries Société Anonyme

BOL.PA

Bollore

FGR.PA

Eiffage SA

BON.PA

Bonduelle SA

FII.PA

Lisi SA

BSD.PA

Bourse Direct Société Anonyme

FPG.PA

UTI Group S.A.

BUI.PA

Barbara Bui

GBT.PA

Guerbet SA

CA.PA

Carrefour

GEA.PA

GEA Company

CAP.PA

Capgemini

GFC.PA

Gecina SA

CDA.PA

Compagnie des Alpes SA

GLE.PA

Societe Generale Group

CEN.PA

Groupe CRIT SA

GLO.PA

GL Events

CGG.PA

CGG

  

The dataset generated during and/or analysed during the current study can be downloaded for free from open sources using list of tickers.

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Semenov, D., Koldanov, A. & Koldanov, P. Analysis of weakly correlated nodes in market network. Comput Manag Sci 21, 18 (2024). https://doi.org/10.1007/s10287-023-00499-3

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