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金融网络中具有动态存量相互依存性的生存能力恢复力特征分析。

Characterisation of survivability resilience with dynamic stock interdependence in financial networks.

作者信息

Tang Junqing, Khoja Layla, Heinimann Hans R

机构信息

ETH Zurich, Future Resilient Systems, Singapore-ETH Centre, 1 CREATE Way, CREATE Tower, Singapore, 138602 Singapore.

出版信息

Appl Netw Sci. 2018;3(1):23. doi: 10.1007/s41109-018-0086-z. Epub 2018 Jul 31.

DOI:10.1007/s41109-018-0086-z
PMID:30839745
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6214309/
Abstract

This paper examines the dynamic evolutionary process in the London Stock Exchange and uses network statistical measures to model the resilience of stock. A large historical dataset of companies was collected over 40 years (1977-2017) and conceptualised into weighted, temporally evolving and signed networks using correlation-based interdependences. Our results revealed a "fission-fusion" market growth in network topologies, which indicated the dynamic and complex characteristics of its evolutionary process. In addition, our regression and modelling results offer insights for construction a "characterisation tool" which can be used to predict stocks that have delisted and continuing performance relatively well, but were less adequate for stocks with normal performance. Moreover, the analysis of deviance suggested that the survivability resilience could be described and approximated by degree-related centrality measures. This study introduces a novel alternative for looking at the bankruptcy in the stock market and is potentially helpful for shareholders, decision- and policy-makers.

摘要

本文考察了伦敦证券交易所的动态演化过程，并使用网络统计方法对股票的弹性进行建模。收集了40年（1977 - 2017年）间大量公司的历史数据集，并利用基于相关性的相互依存关系将其概念化为加权、随时间演化且带符号的网络。我们的结果揭示了网络拓扑结构中“裂变 - 融合”的市场增长模式，这表明了其演化过程的动态和复杂特征。此外，我们的回归和建模结果为构建一种“特征工具”提供了见解，该工具可用于预测已退市但仍表现相对良好的股票，但对于表现正常的股票则不太适用。而且，偏差分析表明，生存弹性可以通过与度相关的中心性度量来描述和近似。本研究为审视股票市场中的破产问题引入了一种新颖的方法，可能对股东、决策者和政策制定者有所帮助。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15aa/6214309/5212988bf2e4/41109_2018_86_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15aa/6214309/e6114a8cc62a/41109_2018_86_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15aa/6214309/063ccbcdb441/41109_2018_86_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15aa/6214309/06a8e50e9ada/41109_2018_86_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15aa/6214309/7cd3d9281fab/41109_2018_86_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15aa/6214309/5212988bf2e4/41109_2018_86_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15aa/6214309/e6114a8cc62a/41109_2018_86_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15aa/6214309/d5460348ad82/41109_2018_86_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15aa/6214309/2cca928219ef/41109_2018_86_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15aa/6214309/063ccbcdb441/41109_2018_86_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15aa/6214309/06a8e50e9ada/41109_2018_86_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15aa/6214309/7cd3d9281fab/41109_2018_86_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15aa/6214309/5212988bf2e4/41109_2018_86_Fig8_HTML.jpg

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本文引用的文献

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Topological Characteristics of the Hong Kong Stock Market: A Test-based P-threshold Approach to Understanding Network Complexity.香港股票市场的拓扑特征：基于测试的 P-阈值方法理解网络复杂性。
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