Determination of Systemically Important Companies with Cross-Shareholding Network Analysis: A Case Study from an Emerging Market

Dastkhan, Hossein; Gharneh, Naser Shams

doi:10.3390/ijfs4030013

Cited by 18 publications

(21 citation statements)

References 29 publications

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“…We also demonstrate that in the special case where flows split uniformly at random, the centrality measures are identical to the result of the original work (that assumed flow by transfers only) we extend [18]. We demonstrate the versatility of this generalized split-and-transfer entropic centrality model by applying it to several networks, particularly a Maine airport dataset [19], a company cross-shareholding dataset [20] and a Bitcoin network dataset [22]. The case studies demonstrate one important property of the proposed entropic centrality approach, namely, it captures well the transitive aspect of influence or spread.…”

Section: Discussionsupporting

confidence: 59%

“…We next consider 131 companies from the Tehran Stock Market, based on the dataset from [20] 3 . They form the vertex set of the next graph, a cross-shareholding network of companies which have shares of other companies.…”

Section: Organizational Cross-shareholding In Tehran Stock Marketmentioning

confidence: 99%

“…This flow based characterization of network [5,18] provides the foundations for the thesis, but we build upon these initial interpretations of flow, and add a further concept of non-atomic (but volume conserved) flows. In this context, we design general purpose information flow based graph algorithms (particularly for node centrality and graph clustering), and demonstrate their applicability by studying a wide range of real world networks, including but not limited to (i) a network derived from wire-tapped communication of cocaine dealers [6]; (ii) a network of Maine airports and the flow of passengers through them [19]; (iii) a network representing cross-shareholding among companies listed in Tehran stock exchange [20]; and (iv) sub-networks [21][22][23][24] induced by Bitcoin [16] transactions.…”

Section: Introduction 11 Motivation and Scope Of This Thesismentioning

confidence: 99%

“…Figure 3.7), namely 55 and 57. Neither of 55 nor 57 has a high centrality individually, but they together provide node 65 a conduit to a larger subgraph than the individual transit nodes themselves do, illustrating the secondary e↵ects of flow propagation.The cross-shareholding network of Teheran Stock Market was analyzed in[20], where a closeness centrality ranking is shown to be almost identical to the degree based centrality one. The entropic centrality ranking in contrast manages to capture a di↵erent dynamics, by involving the spread of influence via flow propagation, together with a quantitative edge di↵erentiation.…”

mentioning

confidence: 99%

“…20 Fitting a power law for synthetic network: on the left, the in-degree distribution with ↵ in = 1.7033, x in min = 1, and on the right, the out-degree distribution ↵ out = 1.6807, x out min = 1. . .…”

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Graph analysis techniques and applications to bitcoin forensics

Phetsouvanh¹

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I would like to express my greatest gratitude to my advisor Assoc. Prof. Anwitaman Datta for his continuous support during my Ph.D. studies. His patient guidance, encouragement, and immense knowledge are precious to me and beyond what words can express. I really appreciate having a supervisor who cares so much about my work, who always responded to my queries promptly, and who did not give up on me despite my slow progress. I would also like to thank all my thesis advisory committee members, especially Assoc. Prof. Frédérique Oggier who guided me with the same care as a co-supervisor. Completing my thesis would have been all the more di cult were it not for her support, encouragement, and for her patiently explaining to me the mathematics and theory on which the ideas of this thesis are founded. I will also like to thank Dr. Alwen Tiu for his guidance and collaboration during the early phase of my PhD. Even though I eventually decided to pursue a di↵erent line of research for my thesis, the experience gained from working with him is invaluable. Finally, I would like to thank the School of Computer Science and Engineering at Nanyang Technological University Singapore for providing me an opportunity to take this study under NTU research scholarship, and for providing financial support to attend a conference and present some of my works.

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Section: Discussionsupporting

confidence: 59%

Section: Organizational Cross-shareholding In Tehran Stock Marketmentioning

confidence: 99%

Section: Introduction 11 Motivation and Scope Of This Thesismentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Graph analysis techniques and applications to bitcoin forensics

Phetsouvanh¹

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Network‐based early warning system to predict financial crisis

Dastkhan

2019

Int. J Fin Econ

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In recent years, early warning systems have been developed as a significant tool to prevent financial and economic crises. In this paper, we applied the forward‐looking conditional value at risk (COVaR) as a market‐based systemic risk measure to analyse the systemic risk in an emerging market. A network representation of asset exposures is presented based on the values of ΔCoVaR. According to the exposure networks, two new network‐based indices are introduced. Furthermore, regarding the dependence of the new indices on the values of forward‐looking CoVaR, they are applied as the indicators for the proposed early warning system to predict downturns and crises in the level of firms and the whole market. The results of the early warning system for the market return show that the indicators based on both proposed models have a good ability to predict the crises in the market. In other words, the indicators issued a warning signal up to seven periods before the real crisis in the market. Similarly, the results of the early warning system to predict downturns at the level of firms also indicate the ability of indicators to issue the true warning signals. The results of the proposed system can help policymakers and authorities to determine the appropriate microprudential and macroprudential policies to avoid systemic risks.

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Interdependence and portfolio optimisation of bank equity returns from developed and emerging Europe

2020

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We examine the spillovers, portfolio allocation, and diversification potential of bank equity portfolios from developed and emerging countries in Europe (henceforth, developed and emerging Europe) using a directional spillover index and nonlinear portfolio optimisation methods. Empirical results indicate that in developed Europe the largest spillover receivers and transmitters are the banks from France and Spain, whereas in emerging Europe are those from Greece and Poland. Cross-country spillover transmission and reception is larger across banks from developed Europe than across banks from emerging Europe. Banks from emerging Europe offer greater diversification potential and have a lower risk for financial resource allocation.

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Determination of Systemically Important Companies with Cross-Shareholding Network Analysis: A Case Study from an Emerging Market

Cited by 18 publications

References 29 publications

Graph analysis techniques and applications to bitcoin forensics

Graph analysis techniques and applications to bitcoin forensics

Network‐based early warning system to predict financial crisis

Interdependence and portfolio optimisation of bank equity returns from developed and emerging Europe

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