Suppr超能文献

金融网络层级结构的变化:对孟加拉国一个新兴市场的企业的研究。

Change in hierarchy of the financial networks: A study on firms of an emerging market in Bangladesh.

机构信息

Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Sonapur, Noakhali, Bangladesh.

Department of Computer Science and Engineering, Daffodil International University, Ashulia, Dhaka, Bangladesh.

出版信息

PLoS One. 2024 May 31;19(5):e0301725. doi: 10.1371/journal.pone.0301725. eCollection 2024.

Abstract

We investigate the hierarchical structure of Dhaka stocks' financial networks, known as an emerging market, from 2008 to 2020. To do so, we determine correlations from the returns of the firms over a one-year time window. Then, we construct a minimum spanning tree (MST) from correlations and calculate the hierarchy of the tree using the hierarchical path. We find that during the unprecedented crisis in 2010-11, the hierarchy of this emerging market did not sharply increase like in developed markets, implying the absence of a compact cluster in the center of the tree. Noticeably, the hierarchy fell before the big crashes in the Bangladeshi local market, and the lowest value was found in 2010, just before the 2011 Bangladesh market scam. We also observe a lower hierarchical MST during COVID-19, which implies that the network is fragile and vulnerable to financial crises not seen in developed markets. Moreover, the volatility in the topological indicators of the MST indicates that the network is adequately responding to crises and that the firms that play an important role in the market during our analysis periods are financial, particularly the insurance companies. We notice that the largest degrees are minimal compared to the total number of nodes in the tree, implying that the network nodes are somewhat locally compact rather than globally centrally coupled. For this random structure of the emerging market, the network properties do not properly reflect the hierarchy, especially during crises. Identifying hierarchies, topological indicators, and significant firms will be useful for understanding the movement of an emerging market like Dhaka Stock exchange (DSE), which will be useful for policymakers to develop the market.

摘要

我们研究了达卡股票金融网络的层次结构,该网络是一个新兴市场,时间范围为 2008 年至 2020 年。为此,我们确定了企业在一年时间窗口内的收益相关系数。然后,我们从相关系数构建了最小生成树(MST),并使用层次路径计算树的层次结构。我们发现,在 2010-11 年这场前所未有的危机中,这个新兴市场的层次结构并没有像发达市场那样急剧增加,这意味着树的中心没有紧凑的集群。值得注意的是,该层次结构在孟加拉国本地市场的大崩溃之前下降,并且在 2010 年,即 2011 年孟加拉国市场骗局之前达到了最低值。我们还观察到 COVID-19 期间 MST 的层次结构较低,这意味着网络脆弱,容易受到发达市场未出现的金融危机的影响。此外,MST 的拓扑指标的波动表明,网络对危机有足够的反应,并且在我们分析期间在市场中发挥重要作用的公司是金融公司,特别是保险公司。我们注意到,最大度数与树中节点的总数相比是最小的,这意味着网络节点在某种程度上是局部紧凑的,而不是全局集中耦合的。对于新兴市场的这种随机结构,网络属性不能很好地反映层次结构,尤其是在危机期间。确定层次结构、拓扑指标和重要公司将有助于理解像达卡证券交易所(DSE)这样的新兴市场的动向,这将有助于政策制定者发展市场。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6fec/11142525/b5b3304d8676/pone.0301725.g001.jpg

相似文献

1
Change in hierarchy of the financial networks: A study on firms of an emerging market in Bangladesh.
PLoS One. 2024 May 31;19(5):e0301725. doi: 10.1371/journal.pone.0301725. eCollection 2024.
2
Stock market comovements among Asian emerging economies: A wavelet-based approach.
PLoS One. 2020 Oct 12;15(10):e0240472. doi: 10.1371/journal.pone.0240472. eCollection 2020.
3
Cross-correlation asymmetries and causal relationships between stock and market risk.
PLoS One. 2014 Aug 27;9(8):e105874. doi: 10.1371/journal.pone.0105874. eCollection 2014.
4
Impact of stock market structure on intertrade time and price dynamics.
PLoS One. 2014 Apr 3;9(4):e92885. doi: 10.1371/journal.pone.0092885. eCollection 2014.
5
Identifying states of a financial market.
Sci Rep. 2012;2:644. doi: 10.1038/srep00644. Epub 2012 Sep 10.
6
Is this time really different? Flight-to-safety and the COVID-19 crisis.
PLoS One. 2021 May 26;16(5):e0251752. doi: 10.1371/journal.pone.0251752. eCollection 2021.
7
Dynamic evolution of cross-correlations in the Chinese stock market.
PLoS One. 2014 May 27;9(5):e97711. doi: 10.1371/journal.pone.0097711. eCollection 2014.
8
Impact persistence of stock market risks in commodity markets: Evidence from China.
PLoS One. 2021 Nov 8;16(11):e0259308. doi: 10.1371/journal.pone.0259308. eCollection 2021.
9
Scaling and volatility of breakouts and breakdowns in stock price dynamics.
PLoS One. 2013 Dec 23;8(12):e82771. doi: 10.1371/journal.pone.0082771. eCollection 2013.
10
The source of financial contagion and spillovers: An evaluation of the covid-19 pandemic and the global financial crisis.
PLoS One. 2022 Jan 14;17(1):e0261835. doi: 10.1371/journal.pone.0261835. eCollection 2022.

本文引用的文献

1
Feature ranking and network analysis of global financial indices.
PLoS One. 2022 Jun 3;17(6):e0269483. doi: 10.1371/journal.pone.0269483. eCollection 2022.
2
Structure and dynamics of financial networks by feature ranking method.
Sci Rep. 2021 Sep 2;11(1):17618. doi: 10.1038/s41598-021-97100-1.
3
Synergistic Information Transfer in the Global System of Financial Markets.
Entropy (Basel). 2020 Sep 8;22(9):1000. doi: 10.3390/e22091000.
5
Systemic risk and hierarchical transitions of financial networks.
Chaos. 2017 Jun;27(6):063107. doi: 10.1063/1.4978925.
6
Networks in financial markets based on the mutual information rate.
Phys Rev E Stat Nonlin Soft Matter Phys. 2014 May;89(5):052801. doi: 10.1103/PhysRevE.89.052801. Epub 2014 May 1.
7
Hierarchicality of trade flow networks reveals complexity of products.
PLoS One. 2014 Jun 6;9(6):e98247. doi: 10.1371/journal.pone.0098247. eCollection 2014.
8
Carbon-dioxide emissions trading and hierarchical structure in worldwide finance and commodities markets.
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Jan;87(1):012814. doi: 10.1103/PhysRevE.87.012814. Epub 2013 Jan 29.
9
Correlation and network analysis of global financial indices.
Phys Rev E Stat Nonlin Soft Matter Phys. 2012 Aug;86(2 Pt 2):026101. doi: 10.1103/PhysRevE.86.026101. Epub 2012 Aug 2.
10
Structure and response in the world trade network.
Phys Rev Lett. 2010 Nov 5;105(19):198701. doi: 10.1103/PhysRevLett.105.198701.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验