Community Detection in Cryptocurrencies with Potential Applications to Portfolio Diversification

Jenna Gavin and Martin Crane

Chapter 4 in FinTech Research and Applications:Challenges and Opportunities, 2023, pp 177-202 from World Scientific Publishing Co. Pte. Ltd.

Abstract: In this chapter, the cross-correlations of cryptocurrency returns are analyzed. The chapter examines one years worth of data for 146 cryptocurrencies from the period 01/01/2019 to 31/12/2019. The cross-correlations of these returns are firstly analyzed by comparing eigenvalues and eigenvector components of the cross-correlation matrix C with random matrix theory (RMT) assumptions. Results show that C deviates from these assumptions indicating that C contains genuine information about the correlations between the different cryptocurrencies. From here, Louvain community detection method is applied as a clustering mechanism and 15 community groupings are detected. Finally, Principal Component analysis (PCA) is completed on the standardized returns of each of these clusters to create a portfolio of cryptocurrencies for investment. This method selects a portfolio which contains a number of high value coins when compared back against their market ranking in the same year. In the interest of assessing continuity of the initial results, the method is also applied to a smaller dataset of the top 50 cryptocurrencies across three time periods of T = 125 days, which produces similar results. The results obtained in this chapter show that these methods could be useful for constructing a portfolio of optimally performing cryptocurrencies.

Keywords: FinTech; FinTech Regulation; Artificial Intelligence; Machine Learning; Cryptocurrencies; Smart Contracts; Financial Fraud Detection; FinTech in Financial Services (search for similar items in EconPapers)
JEL-codes: G2 O3 O33 (search for similar items in EconPapers)
Date: 2023
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