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The cryptocurrency market in transition before and after COVID-19: an opportunity for investors?

Nguyen, An Pham Ngoc, Mai, Tai Tan ORCID: 0000-0001-6657-0872, Bezbradica, Marija ORCID: 0000-0001-9366-5113 and Crane, Martin ORCID: 0000-0001-7598-3126 (2022) The cryptocurrency market in transition before and after COVID-19: an opportunity for investors? Entropy, 24 (9). pp. 1-28. ISSN 1099-4300

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Abstract

We analyze the correlation between different assets in the cryptocurrency market throughout different phases, specifically bearish and bullish periods. Taking advantage of a fine-grained dataset comprising 34 historical cryptocurrency price time series collected tick-by-tick on the HitBTC exchange, we observe the changes in interactions among these cryptocurrencies from two aspects: time and level of granularity. Moreover, the investment decisions of investors during turbulent times caused by the COVID-19 pandemic are assessed by looking at the cryptocurrency community structure using various community detection algorithms. We found that finer-grain time series describes clearer the correlations between cryptocurrencies. Notably, a noise and trend removal scheme is applied to the original correlations thanks to the theory of random matrices and the concept of Market Component, which hasnever been considered in existing studies in quantitative finance. To this end, we recognized that investment decisions of cryptocurrency traders vary between bearish and bullish markets. The results of our work can help scholars, especially investors, better understand the operation of the cryptocurrency market, thereby building up an appropriate investment strategy suitable to the prevailing certain economic situation.

Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Cryptocurrencies; Noise and Trend Effects; Tick-by-Tick data; Network Structures; Community Detection; COVID-19; Complex Systems
Subjects:Business > Finance
Computer Science > Artificial intelligence
Computer Science > Machine learning
Physical Sciences > Statistical physics
Mathematics > Mathematical models
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Publisher:MDPI
Official URL:https://doi.org/10.3390/e24091317
Copyright Information:© 2022 The Authors. Open Access (CC-BY 4.0)
Funders:Science Foundation Ireland Centre for Research Training in Artificial Intelligence under grant number 18/CRT/6223 (APNN), DCU University Research Committee, Science Foundation Ireland under Grant Agreement No. 13/RC/2106_P2 at the ADAPT SFI Research Centre at DCU (MC,MB,TTM)
ID Code:27757
Deposited On:21 Sep 2022 09:24 by Martin Crane . Last Modified 21 Sep 2022 09:24

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