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Evaluation of the importance of criteria for the selection of cryptocurrencies

Author

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  • Natalia A. Van Heerden
  • Juan B. Cabral
  • Nadia Luczywo

Abstract

In recent years, cryptocurrencies have gone from an obscure niche to a prominent place, with investment in these assets becoming increasingly popular. However, cryptocurrencies carry a high risk due to their high volatility. In this paper, criteria based on historical cryptocurrency data are defined in order to characterize returns and risks in different ways, in short time windows (7 and 15 days); then, the importance of criteria is analyzed by various methods and their impact is evaluated. Finally, the future plan is projected to use the knowledge obtained for the selection of investment portfolios by applying multi-criteria methods.

Suggested Citation

  • Natalia A. Van Heerden & Juan B. Cabral & Nadia Luczywo, 2021. "Evaluation of the importance of criteria for the selection of cryptocurrencies," Papers 2109.00130, arXiv.org.
    • Handle: RePEc:arx:papers:2109.00130
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    References listed on IDEAS

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    1. Michel Rauchs & Garrick Hileman, 2017. "Global Cryptocurrency Benchmarking Study," Cambridge Centre for Alternative Finance Reports 201704-gcbs, Cambridge Centre for Alternative Finance, Cambridge Judge Business School, University of Cambridge.
    2. Bariviera, Aurelio F., 2017. "The inefficiency of Bitcoin revisited: A dynamic approach," Economics Letters, Elsevier, vol. 161(C), pages 1-4.
    3. Walther, Thomas & Klein, Tony & Bouri, Elie, 2019. "Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    4. Abeer ElBahrawy & Laura Alessandretti & Anne Kandler & Romualdo Pastor-Satorras & Andrea Baronchelli, 2017. "Evolutionary dynamics of the cryptocurrency market," Papers 1705.05334, arXiv.org, revised Nov 2017.
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    Cited by:

    1. Maghsoodi, Abtin Ijadi, 2023. "Cryptocurrency portfolio allocation using a novel hybrid and predictive big data decision support system," Omega, Elsevier, vol. 115(C).

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