Evolution of Cohesion between USA Financial Sector Companies before, during, and Post-Economic Crisis: Complex Networks Approach
Various mathematical frameworks play an essential role in understanding the economic systems and the emergence of crises in them. Understanding the relation between the structure of connections between the system’s constituents and the emergence of a crisis is of great importance. In this paper, we...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/7/1005 |
| _version_ | 1797406481521311744 |
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| author | Vojin Stević Marija Rašajski Marija Mitrović Dankulov |
| author_facet | Vojin Stević Marija Rašajski Marija Mitrović Dankulov |
| author_sort | Vojin Stević |
| collection | DOAJ |
| description | Various mathematical frameworks play an essential role in understanding the economic systems and the emergence of crises in them. Understanding the relation between the structure of connections between the system’s constituents and the emergence of a crisis is of great importance. In this paper, we propose a novel method for the inference of economic systems’ structures based on complex networks theory utilizing the time series of prices. Our network is obtained from the correlation matrix between the time series of companies’ prices by imposing a threshold on the values of the correlation coefficients. The optimal value of the threshold is determined by comparing the spectral properties of the threshold network and the correlation matrix. We analyze the community structure of the obtained networks and the relation between communities’ inter and intra-connectivity as indicators of systemic risk. Our results show how an economic system’s behavior is related to its structure and how the crisis is reflected in changes in the structure. We show how regulation and deregulation affect the structure of the system. We demonstrate that our method can identify high systemic risks and measure the impact of the actions taken to increase the system’s stability. |
| first_indexed | 2024-03-09T03:26:07Z |
| format | Article |
| id | doaj.art-1a64a6670d6145dd839b06f53cc45251 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T03:26:07Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-1a64a6670d6145dd839b06f53cc452512023-12-03T15:01:24ZengMDPI AGEntropy1099-43002022-07-01247100510.3390/e24071005Evolution of Cohesion between USA Financial Sector Companies before, during, and Post-Economic Crisis: Complex Networks ApproachVojin Stević0Marija Rašajski1Marija Mitrović Dankulov2University of Belgrade-School of Electrical Engineering, Bulevar Kralja Aleksandra 73, 11120 Belgrade, SerbiaUniversity of Belgrade-School of Electrical Engineering, Bulevar Kralja Aleksandra 73, 11120 Belgrade, SerbiaInstitute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, SerbiaVarious mathematical frameworks play an essential role in understanding the economic systems and the emergence of crises in them. Understanding the relation between the structure of connections between the system’s constituents and the emergence of a crisis is of great importance. In this paper, we propose a novel method for the inference of economic systems’ structures based on complex networks theory utilizing the time series of prices. Our network is obtained from the correlation matrix between the time series of companies’ prices by imposing a threshold on the values of the correlation coefficients. The optimal value of the threshold is determined by comparing the spectral properties of the threshold network and the correlation matrix. We analyze the community structure of the obtained networks and the relation between communities’ inter and intra-connectivity as indicators of systemic risk. Our results show how an economic system’s behavior is related to its structure and how the crisis is reflected in changes in the structure. We show how regulation and deregulation affect the structure of the system. We demonstrate that our method can identify high systemic risks and measure the impact of the actions taken to increase the system’s stability.https://www.mdpi.com/1099-4300/24/7/1005complex networkstime serieseconomic systemsevolution of community structure |
| spellingShingle | Vojin Stević Marija Rašajski Marija Mitrović Dankulov Evolution of Cohesion between USA Financial Sector Companies before, during, and Post-Economic Crisis: Complex Networks Approach Entropy complex networks time series economic systems evolution of community structure |
| title | Evolution of Cohesion between USA Financial Sector Companies before, during, and Post-Economic Crisis: Complex Networks Approach |
| title_full | Evolution of Cohesion between USA Financial Sector Companies before, during, and Post-Economic Crisis: Complex Networks Approach |
| title_fullStr | Evolution of Cohesion between USA Financial Sector Companies before, during, and Post-Economic Crisis: Complex Networks Approach |
| title_full_unstemmed | Evolution of Cohesion between USA Financial Sector Companies before, during, and Post-Economic Crisis: Complex Networks Approach |
| title_short | Evolution of Cohesion between USA Financial Sector Companies before, during, and Post-Economic Crisis: Complex Networks Approach |
| title_sort | evolution of cohesion between usa financial sector companies before during and post economic crisis complex networks approach |
| topic | complex networks time series economic systems evolution of community structure |
| url | https://www.mdpi.com/1099-4300/24/7/1005 |
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