**7. Conclusions**

This research aims at answering three questions related to cross-correlations in the cryptocurrency market: Firstly, how do noise and trends in cryptocurrencies influence their cross-correlations and then the corresponding network structure? Secondly, what level of granularity should we use? Lastly, is the dramatic change in the cryptocurrency network structure during the pandemic caused by investors' investment strategy? We firstly analyze the effect of noise and trend in cryptocurrencies on their cross-correlations and then remove these factors thanks to Random Matrix Theory and Market Component. Four sub-datasets with different levels of granularity including 30 min, 6 h, 12 h and 1 day are created from the original tick-by-tick data to examine the importance of choosing the right frequency resolution. Then, we use MST to construct a correlation-based network and detect different potential communities by using Louvain and Girvan–Newman algorithms. We found that the correlations between cryptocurrencies are mainly caused by noise and trend effects, which might lead to a big problem for the traders' investment strategy because investors might be fooled by looking at the counterfeit relationship. It is necessary to analyze and explore real interactions between cryptocurrencies so that the evolution of the cryptocurrency market can be learned properly and thus investors can choose a good strategy for their investment. Moreover, the frequency resolution of our data plays an important role in the performance of correlation matrix and also community detection. Specifically, the finer the data, the more precise the community structure. Thus, we use a 30 min dataset, which is the finest available timescale in this study. The dramatic change in the community structures between bearish and bullish markets reveals a change in the investment decisions of investors. In particular, investors makes their own investment decisions based on their personal market analysis and experience during normal times. Eventually, this causes a diversification in the cryptocurrencies chosen to invest in, since not only high- but also low-ranking cryptocurrencies are added in the portfolios. On the other hand, investors tend to only trade cryptocurrencies with high market capitalization during turbulent times, while smaller cryptocurrencies are mainly used for other purposes, such as transaction fees, smart contracts tokens or simply used to run a digital platform.

**Author Contributions:** A.P.N.N.: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing—original draft, Visualization. T.T.M.: Methodology, Writing—review and editing, Supervision. M.B.: Conceptualization, Methodology, Data curation, Writing—review and editing, Supervision. M.C.: Conceptualization, Methodology, Data curation, Writing—review and editing, Supervision. All authors have read and agreed to the published version of the manuscript.

**Funding:** The APC was funded by Science Foundation Ireland Centre for Research Training in Artificial Intelligence under grant number 18/CRT/6223.

**Institutional Review Board Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors Martin Crane, Marija Bezbradica, Tai Tan Mai, wish to acknowledge the support, in part, from the Science Foundation Ireland under Grant Agreement No. 13/RC/2106\_P2 at the ADAPT SFI Research Centre at DCU. ADAPT, the SFI Research Centre for AI-Driven Digital Content Technology, is funded by the Science Foundation Ireland through the SFI Research Centres Programme. The author An P. N. Nguyen wishes to acknowledge the support from Dublin City University's Research Committee and research grants from Science Foundation Ireland Centre for Research Training in Artificial Intelligence under grant number 18/CRT/6223.

**Conflicts of Interest:** The authors declare no conflict of interest.
