An overview of renewable energy companies in stock exchange: Evidence from minimal spanning tree approach

Kazemilari, Mansooreh; Mardani, Abbas; Štreimikienė, Dalia; Zavadskas, Edmundas Kazimieras

doi:10.1016/j.renene.2016.10.029

Cited by 50 publications

(24 citation statements)

References 65 publications

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“…The outcomes showed extensive stocks within the network that played significant roles in renewable energy development in terms of market capitals. Moreover, degree, closeness and betweenness centralities provided similar results [32]. The closeness centrality was applied to measure the 2008 market crash of tensor financial network in Thailand, and it was claimed that the closeness centrality algorithm is the best tool to detect market crash [33].…”

Section: Graph Centrality Metricsmentioning

confidence: 81%

Centrality Metrics’ Performance Comparisons on Stock Market Datasets

Hua

Huang

2019

Symmetry

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The stock market is an essential sub-sector in the financial area. Both understanding and evaluating the mountains of collected stock data has become a challenge in relevant fields. Data visualisation techniques can offer a practical and engaging method to show the processed data in a meaningful way, with centrality measurements representing the significant variables in a network, through exploring the aspects of the exact definition of the metric. Here, in this study, we conducted an approach that combines data processing, graph visualisation and social network analysis methods, to develop deeper insights of complex stock data, with the ultimate aim of drawing the correct conclusions with the finalised graph models. We addressed the performance of centrality metrics methods such as betweenness, closeness, eigenvector, PageRank and weighted degree measurements, drawing comparisons between the experiments’ results and the actual top 300 shares in the Australian Stock Market. The outcomes showed consistent results. Although, in our experiments, the results of the top 300 stocks from those five centrality measurements’ rankings did not match the top 300 shares given by the ASX (Australian Securities Exchange) entirely, in which the weighted degree and PageRank metrics performed better than other three measurements such as betweenness, closeness and eigenvector. Potential reasons may include that we did not take into account the factor of stock’s market capitalisation in the methodology. This study only considers the stock price’s changing rates among every two shares and provides a relevant static pattern at this stage. Further research will include looking at cycles and symmetry in the stock market over chosen trading days, and these may assist stakeholder in grasping deep insights of those stocks.

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Section: Graph Centrality Metricsmentioning

confidence: 81%

Centrality Metrics’ Performance Comparisons on Stock Market Datasets

Hua

Huang

2019

Symmetry

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“…Previous study is also confirmed our result. Kazemilari, Mardani, Streimikiene, and Zavadskas (2017) This figure describes the use of the box plot chart to illustrate the distances of the sectors. It provides a useful way to visualize the range of distances for each sector.…”

Section: Network Analysis Of Sectors: Evidence From Mst and Sdumentioning

confidence: 99%

“…It is also important to note that General Cable Corporation (BGC) was detected as a central hub in a priori research by Kazemilari et al (2017) and Canadian Solar Inc. (CSIQ) was at the periphery systematically with worst performance. However, in this paper, BGC from Solar & Wind sector was dominated by Pattern Energy Group Inc. (PEGI) in the same sector and Canadian Solar Inc. (CSIQ) from Solar sector hub.…”

Section: Network Analysis Of Sectors: Evidence From Mst and Sdumentioning

confidence: 99%

“…However, in this paper, BGC from Solar & Wind sector was dominated by Pattern Energy Group Inc. (PEGI) in the same sector and Canadian Solar Inc. (CSIQ) from Solar sector hub. Some reasons of this observa- Kwapień and Drożdż (2012) and Kazemilari et al (2017) has been carried out for stocks in the world; however in this research only American companies have been involved. During the period, as shown in Figure 5, the performance of BGC is dominated by PEGI in the same sector.…”

Section: Network Analysis Of Sectors: Evidence From Mst and Sdumentioning

confidence: 99%

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

Kazemilari

Mohamadi

Mardani

et al. 2019

Technological and Economic Development of Economy

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Renewable energy has become a significant market player after the turn of the millennium. Wind, solar, smart grid and further renewable energy stocks have experienced both serious up and down trends since that time. In this paper, computed the Minimal Spanning Tree (MST) and Sub-Dominant Ultrametric (SDU) for topological properties of what has been driving the price of renewable energy stock markets and sectors. In this regard, the main object is to define the similarity among sectors in financial market, which is statistically a multivariate time series. The principal mathematical tool to do macro analysis is multivariate vector correlation where multi-dimensional data is considered as a complex system. Furthermore, the base approach for filtering the significant information in a financial system is similarity network analysis. In this paper, the behavior of economic sectors of renewable energy played during 30th July 2015 – 1th January 2018 in America. Results of this study found that, solar sector in renewable energy is confirmed as the dominant sector in America during this period. In addition, results demonstrated that, the leader sector is Solar and the central hubs are Canadian Solar Inc. (CSIQ)from Solar and then Pattern Energy Group Inc. (PEGI)from Solar-Wind sectors.

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“…Additionally, some previous studies have reviewed the application of various methods such as DEA, structural equation modeling and multiple criteria decision-making (MCDM) techniques in different areas [26][27][28][29][30][31][32][33][34][35][36][37][38][39]. For example, a review of ranking methods (Adler et al [40]), research in efficiency and productivity (Emrouznejad et al [41]; Emrouznejad and Yang [42]), fuzzy DEA (Hatami-Marbini et al [43]), energy and environmental studies (Zhou et al [44]), operation research (Liu et al [45], Cook and Seiford [46], Kuah et al [47]), measuring efficiency in the context of higher education (Johnes [48]), performance measurement and evaluation(Cooper et al [49]), environmental efficiency evaluation (Song et al [50]), network DEA (Kao [51]), or energy efficiency (Mardani et al [1]).…”

Section: Introductionmentioning

confidence: 99%

Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends

et al. 2018

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Measurement of environmental and energy economics presents an analytical foundation for environmental decision making and policy analysis. Applications of data envelopment analysis (DEA) models in the assessment of environmental and energy economics are increasing notably. The main objective of this review paper is to provide the comprehensive overview of the application of DEA models in the fields of environmental and energy economics. In this regard, a total 145 articles published in the high-quality international journals extracted from two important databases (Web of Science and Scopus) were selected for review. The 145 selected articles are reviewed and classified based on different criteria including author(s), application scheme, different DEA models, application fields, the name of journals and year of publication. This review article provided insights into the methodological and conceptualization study in the application of DEA models in the environmental and energy economics fields. This study should enable scholars and practitioners to understand the state of art of input and output indicators of DEA in the fields of environmental and energy economics.

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An overview of renewable energy companies in stock exchange: Evidence from minimal spanning tree approach

Cited by 50 publications

References 65 publications

Centrality Metrics’ Performance Comparisons on Stock Market Datasets

Centrality Metrics’ Performance Comparisons on Stock Market Datasets

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

Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends

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