Network-based diversification of stock and cryptocurrency portfolios
Abstract Maintaining a balance between returns and volatility is a common strategy for portfolio diversification, whether investing in traditional equities or digital assets like cryptocurrencies. One approach for diversification is the application of community detection or clustering, using a netwo...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-06-01
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| Series: | Applied Network Science |
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| Online Access: | https://doi.org/10.1007/s41109-025-00708-9 |
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| author | Dimitar Kitanovski Igor Mishkovski Viktor Stojkoski Miroslav Mirchev |
| author_facet | Dimitar Kitanovski Igor Mishkovski Viktor Stojkoski Miroslav Mirchev |
| author_sort | Dimitar Kitanovski |
| collection | DOAJ |
| description | Abstract Maintaining a balance between returns and volatility is a common strategy for portfolio diversification, whether investing in traditional equities or digital assets like cryptocurrencies. One approach for diversification is the application of community detection or clustering, using a network representing the relationships between assets. We examine two network representations, one based on a standard distance matrix based on correlation, and another based on mutual information. The Louvain and Affinity propagation algorithms were employed for finding the network communities (clusters) based on annual data. Furthermore, we examine building assets’ co-occurrence networks, where communities are detected for each month throughout a whole year, and then the links represent how often assets belong to the same community. Portfolios are then constructed by selecting several assets from each community based on local properties (degree centrality), global properties (closeness centrality), or explained variance (Principal component analysis), with three value ranges (max, med, min), calculated on a minimal spanning tree or a fully connected community sub-graph. We explored these various strategies on data from the S&P 500 and the Top 203 cryptocurrencies with a market cap above 2 M USD in the period from Jan 2019 to Sep 2022. Moreover, we study in more detail the periods of the beginning of the COVID-19 outbreak and the start of the war in Ukraine. The results confirm some of the previous findings already known for traditional stock markets and provide some further insights, while they reveal an opposing trend in the crypto-assets market. |
| format | Article |
| id | doaj-art-8c1c0ac5aee94042908904aa486ef7fe |
| institution | Kabale University |
| issn | 2364-8228 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Network Science |
| spelling | doaj-art-8c1c0ac5aee94042908904aa486ef7fe2025-08-20T03:45:57ZengSpringerOpenApplied Network Science2364-82282025-06-0110114110.1007/s41109-025-00708-9Network-based diversification of stock and cryptocurrency portfoliosDimitar Kitanovski0Igor Mishkovski1Viktor Stojkoski2Miroslav Mirchev3Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in SkopjeFaculty of Computer Science and Engineering, Ss. Cyril and Methodius University in SkopjeFaculty of Economics, Ss. Cyril and Methodius University in SkopjeFaculty of Computer Science and Engineering, Ss. Cyril and Methodius University in SkopjeAbstract Maintaining a balance between returns and volatility is a common strategy for portfolio diversification, whether investing in traditional equities or digital assets like cryptocurrencies. One approach for diversification is the application of community detection or clustering, using a network representing the relationships between assets. We examine two network representations, one based on a standard distance matrix based on correlation, and another based on mutual information. The Louvain and Affinity propagation algorithms were employed for finding the network communities (clusters) based on annual data. Furthermore, we examine building assets’ co-occurrence networks, where communities are detected for each month throughout a whole year, and then the links represent how often assets belong to the same community. Portfolios are then constructed by selecting several assets from each community based on local properties (degree centrality), global properties (closeness centrality), or explained variance (Principal component analysis), with three value ranges (max, med, min), calculated on a minimal spanning tree or a fully connected community sub-graph. We explored these various strategies on data from the S&P 500 and the Top 203 cryptocurrencies with a market cap above 2 M USD in the period from Jan 2019 to Sep 2022. Moreover, we study in more detail the periods of the beginning of the COVID-19 outbreak and the start of the war in Ukraine. The results confirm some of the previous findings already known for traditional stock markets and provide some further insights, while they reveal an opposing trend in the crypto-assets market.https://doi.org/10.1007/s41109-025-00708-9Portfolio diversificationFinancial marketsNetwork science |
| spellingShingle | Dimitar Kitanovski Igor Mishkovski Viktor Stojkoski Miroslav Mirchev Network-based diversification of stock and cryptocurrency portfolios Applied Network Science Portfolio diversification Financial markets Network science |
| title | Network-based diversification of stock and cryptocurrency portfolios |
| title_full | Network-based diversification of stock and cryptocurrency portfolios |
| title_fullStr | Network-based diversification of stock and cryptocurrency portfolios |
| title_full_unstemmed | Network-based diversification of stock and cryptocurrency portfolios |
| title_short | Network-based diversification of stock and cryptocurrency portfolios |
| title_sort | network based diversification of stock and cryptocurrency portfolios |
| topic | Portfolio diversification Financial markets Network science |
| url | https://doi.org/10.1007/s41109-025-00708-9 |
| work_keys_str_mv | AT dimitarkitanovski networkbaseddiversificationofstockandcryptocurrencyportfolios AT igormishkovski networkbaseddiversificationofstockandcryptocurrencyportfolios AT viktorstojkoski networkbaseddiversificationofstockandcryptocurrencyportfolios AT miroslavmirchev networkbaseddiversificationofstockandcryptocurrencyportfolios |