English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

In this paper, the cross-correlations of cryptocurrency returns are analysed.The paper examines one years worth of data for 146 cryptocurrencies from theperiod January 1 2019 to December 31 2019. The cross-correlations of thesereturns are firstly analysed by comparing eigenvalues and eigenvectorcomponents of the cross-correlation matrix C with Random Matrix Theory (RMT)assumptions. Results show that C deviates from these assumptions indicatingthat C contains genuine information about the correlations between thedifferent cryptocurrencies. From here, Louvain community detection method isapplied as a clustering mechanism and 15 community groupings are detected.Finally, PCA is completed on the standardised returns of each of these clustersto create a portfolio of cryptocurrencies for investment. This method selects aportfolio which contains a number of high value coins when compared backagainst their market ranking in the same year. In the interest of assessingcontinuity of the initial results, the method is also applied to a smallerdataset of the top 50 cryptocurrencies across three time periods of T = 125days, which produces similar results. The results obtained in this paper showthat these methods could be useful for constructing a portfolio of optimallyperforming cryptocurrencies.

Community Detection in Cryptocurrencies with Potential Applications to Portfolio Diversification