| Autor: |
Nurun Najwa Bahari, Hafizah Bahaludin, Munira Ismail, Fatimah Abdul Razak |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Data Science in Finance and Economics, Vol 4, Iss 3, Pp 362-387 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2769-2140 |
| DOI: |
10.3934/DSFE.2024016?viewType=HTML |
| Popis: |
COVID-19 triggered a worldwide economic decline and raised concerns regarding its economic consequences on stock markets across the globe, notably on the Malaysian stock market. We examined how COVID-19 impacted Malaysia's financial market using correlation and network analysis. We found a rise in correlations between stocks during the pandemic, suggesting greater interdependence. To visualize this, we created networks for pre-pandemic, during-pandemic, and post-pandemic periods. Additionally, we built a network for the during-pandemic period with a specific threshold corresponding to pre- and post-pandemic network density. The networks during the pandemic showed increased connectivity and only contained positive correlations, reflecting synchronized stock movements. Last, we analyzed the networks' modularity, revealing highest modularity during the pandemic, which suggests stronger yet risk-prone communities. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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