Network Topology of Renewable Energy Companies: Minimal Spanning Tree and Sub-Dominant Ultrametric for the American Stock

Kazemilari, Mansooreh; Mohamadi, Ali; Mardani, Abbas; Štreimikis, Justas

doi:10.3846/tede.2019.7686

Cited by 8 publications

(8 citation statements)

References 51 publications

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“…Particularly, it reduces the amount of redundant information since it only keeps the

most important edges, i.e.,

shortest edges that are well connected. MST stems from graph theory and is applied widely to different fields [ 4 , 87 , 88 ], especially in financial markets [ 89 , 90 , 91 ]. To exploit the useability of MST, the dynamics of community structures in the stock market are observed by Huang et al [ 92 ] with the dataset split into consecutive smaller periods and a MST constructed at each of them.…”

Section: Research Methodologymentioning

confidence: 99%

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

Nguyen

Tan

Bezbradica

et al. 2022

Entropy

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We analyze the correlation between different assets in the cryptocurrency market throughout different phases, specifically bearish and bullish periods. Taking advantage of a fine-grained dataset comprising 34 historical cryptocurrency price time series collected tick-by-tick on the HitBTC exchange, we observe the changes in interactions among these cryptocurrencies from two aspects: time and level of granularity. Moreover, the investment decisions of investors during turbulent times caused by the COVID-19 pandemic are assessed by looking at the cryptocurrency community structure using various community detection algorithms. We found that finer-grain time series describes clearer the correlations between cryptocurrencies. Notably, a noise and trend removal scheme is applied to the original correlations thanks to the theory of random matrices and the concept of Market Component, which has never been considered in existing studies in quantitative finance. To this end, we recognized that investment decisions of cryptocurrency traders vary between bearish and bullish markets. The results of our work can help scholars, especially investors, better understand the operation of the cryptocurrency market, thereby building up an appropriate investment strategy suitable to the prevailing certain economic situation.

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“…Particularly, it reduces the amount of redundant information since it only keeps the

most important edges, i.e.,

Section: Research Methodologymentioning

confidence: 99%

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

Nguyen

Tan

Bezbradica

et al. 2022

Entropy

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“…In addition to highlighting important nodes of the network, they also found significant role of these nodes for the development of renewable energy market. In addition, (Kazemilari et al, 2019) also investigated sectorial behavior pattern of 60 renewable energy companies listed in American stock market between July 2015 and January 2018.…”

Section: Literature Reviewmentioning

confidence: 99%

Examining Network Structures and Dynamics of World Energy Companies in Stock Markets: A Complex Network Approach

Memon

Tahir

2021

IJEEP

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The energy sector occupies a mainstay role in overall growth in the modern worldwide economy. Therefore, it is essential to examine network structures and dynamics of leading energy companies of the world through complex network methods. Because, complex network methods are significant tools of studying the static and dynamics properties of the stock market, which allows us to better comprehend the stock market. We use daily prices of 147 energy stocks belonging to 34 countries of the world from 2006-2019. In addition to the overall sample, we explore networks for two sub-periods to examine the topological evolution during global recession of 2008, and energy and European debt crisis of 2011. Our results show substantial clustering of energy companies based on their geographic position during overall sample period. However, the crisis periods lead to a break in Asian and European clusters and only one prominent cluster appears in all the periods belonging to North American energy companies. We also observe few top US and European based companies occupying important and great global influence positions in the networks. In addition, time-varying topological measures indicate contraction of networks during crisis time, and an expansion in the recovery periods. More implications are also discussed.

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“…Shahzad (2019) [2] adopted the bivariate cross-quantitative graph method to analyze the spillover network structure of the stock market from the perspectives of a bear market, normal market, and bull market, and determined the direction of the spillover risk effect. Memon (2019), Kazemilari (2019) and Cao (2018) [3][4][5] built a correlation network for shares based on the threshold method, adopted the stochastic matrix theory to remove market trends, and studied the difference between the network topology structure and the traditional network topology structure during financial crises and Chinese stock market turbulence. These studies simply analyzed the changes in the stock market network topology caused by emergencies from the perspective of network topology.…”

Section: Introductionmentioning

confidence: 99%

Research on Stock Market Risk Contagion of Major Debt Crises Based on Complex Network Models—The Case of Evergrande in China

Liang,

Li,

Zhang

et al. 2024

Mathematics

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After a major debt crisis occurs in a listed company, the stock prices of related enterprises may also fluctuate sharply, resulting in the spread of debt risk to more enterprises. Taking the stocks of listed companies as network nodes, we constructed a complex stock market network over three periods of time through the logarithmic return rate of stocks for the three periods of prophase, metaphase, and anaphase of the debt crisis. We studied the topological characteristics of the network and destructiveness over the three periods. Finally, the minimum spanning tree was used to construct a network and the community structure of the network. The empirical analysis took the debt crisis of the China Evergrande Company as an example to analyze the impact of its major debt crisis on the Chinese stock market. The research findings were as follows: First, the debt crisis increased the inter-industry connections within the network, that is, the correlations between enterprises in different industries was enhanced. Second, the closeness of the cross-industry connections increased the connectivity efficiency of the network, but compared with the other two periods, the debt crisis in the metaphase was less stable. Third, community research showed that in the metaphase of the debt crisis, the enterprises became the core nodes of the network.

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Network Topology of Renewable Energy Companies: Minimal Spanning Tree and Sub-Dominant Ultrametric for the American Stock

Cited by 8 publications

References 51 publications

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

Examining Network Structures and Dynamics of World Energy Companies in Stock Markets: A Complex Network Approach

Research on Stock Market Risk Contagion of Major Debt Crises Based on Complex Network Models—The Case of Evergrande in China

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