Global Collective Dynamics of Financial Market Efficiency Using Attention Entropy with Hierarchical Clustering
The efficient market hypothesis (EMH) assumes that all available information in an efficient financial market is ideally fully reflected in the price of an asset. However, whether the reality that asset prices are not informational efficient is an opportunity for profit or a systemic risk of the fin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/6/10/562 |
| _version_ | 1797473251585163264 |
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| author | Poongjin Cho Kyungwon Kim |
| author_facet | Poongjin Cho Kyungwon Kim |
| author_sort | Poongjin Cho |
| collection | DOAJ |
| description | The efficient market hypothesis (EMH) assumes that all available information in an efficient financial market is ideally fully reflected in the price of an asset. However, whether the reality that asset prices are not informational efficient is an opportunity for profit or a systemic risk of the financial system that needs to be corrected is still a ubiquitous concept, so many economic participants and research scholars have conducted related studies in order to understand the phenomenon of the financial market. This research employed attention entropy of the log-returns of 27 global assets to analyze the time-varying informational efficiency. International markets could be classified hierarchically into groups with similar long-term efficiency trends; however, at the same time, the ranks and clusters were found to remain stable only for a short period of time in terms of short-term efficiency. Therefore, a complex network representation analysis was performed to express whether the short-term efficiency patterns have interacted with each other over time as a coherent picture. It was confirmed that the network of 27 international markets was fully connected, strongly globalized and entangled. In addition, the complex network was composed of two modular structures grouped together with similar efficiency dynamics. As a result, although the informational efficiency of financial markets may be globalized to a high-efficiency state, it shows a collective dynamics pattern in which the global system may fall into risk due to the spread of systemic risk. |
| first_indexed | 2024-03-09T20:12:06Z |
| format | Article |
| id | doaj.art-d98d0e798b7e40dca295eaa36cc98944 |
| institution | Directory Open Access Journal |
| issn | 2504-3110 |
| language | English |
| last_indexed | 2024-03-09T20:12:06Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj.art-d98d0e798b7e40dca295eaa36cc989442023-11-24T00:11:39ZengMDPI AGFractal and Fractional2504-31102022-10-0161056210.3390/fractalfract6100562Global Collective Dynamics of Financial Market Efficiency Using Attention Entropy with Hierarchical ClusteringPoongjin Cho0Kyungwon Kim1Department of Industrial Engineering, Hanyang University, Seoul 04763, KoreaSchool of International Trade and Business, Incheon National University, Incheon 22012, KoreaThe efficient market hypothesis (EMH) assumes that all available information in an efficient financial market is ideally fully reflected in the price of an asset. However, whether the reality that asset prices are not informational efficient is an opportunity for profit or a systemic risk of the financial system that needs to be corrected is still a ubiquitous concept, so many economic participants and research scholars have conducted related studies in order to understand the phenomenon of the financial market. This research employed attention entropy of the log-returns of 27 global assets to analyze the time-varying informational efficiency. International markets could be classified hierarchically into groups with similar long-term efficiency trends; however, at the same time, the ranks and clusters were found to remain stable only for a short period of time in terms of short-term efficiency. Therefore, a complex network representation analysis was performed to express whether the short-term efficiency patterns have interacted with each other over time as a coherent picture. It was confirmed that the network of 27 international markets was fully connected, strongly globalized and entangled. In addition, the complex network was composed of two modular structures grouped together with similar efficiency dynamics. As a result, although the informational efficiency of financial markets may be globalized to a high-efficiency state, it shows a collective dynamics pattern in which the global system may fall into risk due to the spread of systemic risk.https://www.mdpi.com/2504-3110/6/10/562attention entropyinternational financial marketsmarket efficiencyadaptive market hypothesisclustering in machine learningcollective dynamics |
| spellingShingle | Poongjin Cho Kyungwon Kim Global Collective Dynamics of Financial Market Efficiency Using Attention Entropy with Hierarchical Clustering Fractal and Fractional attention entropy international financial markets market efficiency adaptive market hypothesis clustering in machine learning collective dynamics |
| title | Global Collective Dynamics of Financial Market Efficiency Using Attention Entropy with Hierarchical Clustering |
| title_full | Global Collective Dynamics of Financial Market Efficiency Using Attention Entropy with Hierarchical Clustering |
| title_fullStr | Global Collective Dynamics of Financial Market Efficiency Using Attention Entropy with Hierarchical Clustering |
| title_full_unstemmed | Global Collective Dynamics of Financial Market Efficiency Using Attention Entropy with Hierarchical Clustering |
| title_short | Global Collective Dynamics of Financial Market Efficiency Using Attention Entropy with Hierarchical Clustering |
| title_sort | global collective dynamics of financial market efficiency using attention entropy with hierarchical clustering |
| topic | attention entropy international financial markets market efficiency adaptive market hypothesis clustering in machine learning collective dynamics |
| url | https://www.mdpi.com/2504-3110/6/10/562 |
| work_keys_str_mv | AT poongjincho globalcollectivedynamicsoffinancialmarketefficiencyusingattentionentropywithhierarchicalclustering AT kyungwonkim globalcollectivedynamicsoffinancialmarketefficiencyusingattentionentropywithhierarchicalclustering |