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A Darwinian Approach via ML to the Analysis of Cryptocurrencies’ Returns

Author

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  • Federico Cini
  • Annalisa Ferrari

Abstract

This study adopts a Darwinian approach leveraging machine learning (ML) to analyze cryptocurrency returns and their interactions with traditional financial markets. Using a daily dataset from 2018 to 2023, the Random Forest model proved particularly effective in identifying key factors influencing cryptocurrency returns, including technology stock indices (NASDAQ), global equity indices (S&P500, Eurostoxx600), commodity prices (gold, crude oil), and market sentiment (Google Trends). The analysis reveals consistent positive relationships between market sentiment and cryptocurrency returns, highlighting the crucial role of public interest in shaping long-term outcomes. Cryptocurrencies emerge as a distinct asset class with specific correlations to traditional markets and investor sentiment. The study provides strategic insights into understanding cryptocurrency behavior and integrating these dynamics into informed portfolio strategies. It emphasizes the importance of monitoring both traditional financial indices and market sentiment for investment decisions across various time horizons. Â JEL classification numbers: C58, G11, G15.

Suggested Citation

  • Federico Cini & Annalisa Ferrari, 2024. "A Darwinian Approach via ML to the Analysis of Cryptocurrencies’ Returns," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 14(6), pages 1-6.
    • Handle: RePEc:spt:apfiba:v:14:y:2024:i:6:f:14_6_6
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    References listed on IDEAS

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    1. Salisu, Afees A. & Akanni, Lateef & Raheem, Ibrahim, 2020. "The COVID-19 global fear index and the predictability of commodity price returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    2. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    3. Aniruddha Dutta & Saket Kumar & Meheli Basu, 2020. "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," JRFM, MDPI, vol. 13(2), pages 1-16, February.
    4. Daniel Traian Pele & Niels Wesselhöfft & Wolfgang Karl Härdle & Michalis Kolossiatis & Yannis G. Yatracos, 2023. "Are cryptos becoming alternative assets?," The European Journal of Finance, Taylor & Francis Journals, vol. 29(10), pages 1064-1105, July.
    5. Urquhart, Andrew, 2018. "What causes the attention of Bitcoin?," Economics Letters, Elsevier, vol. 166(C), pages 40-44.
    6. Sabrina T Howell & Marina Niessner & David Yermack & Jiang Wei, 2020. "Initial Coin Offerings: Financing Growth with Cryptocurrency Token Sales," The Review of Financial Studies, Society for Financial Studies, vol. 33(9), pages 3925-3974.
    7. Brauneis, Alexander & Mestel, Roland & Riordan, Ryan & Theissen, Erik, 2021. "How to measure the liquidity of cryptocurrency markets?," Journal of Banking & Finance, Elsevier, vol. 124(C).
    8. Tarchella, Salma & Khalfaoui, Rabeh & Hammoudeh, Shawkat, 2024. "The safe haven, hedging, and diversification properties of oil, gold, and cryptocurrency for the G7 equity markets: Evidence from the pre- and post-COVID-19 periods," Research in International Business and Finance, Elsevier, vol. 67(PB).
    9. Phillip, Andrew & Chan, Jennifer & Peiris, Shelton, 2019. "On long memory effects in the volatility measure of Cryptocurrencies," Finance Research Letters, Elsevier, vol. 28(C), pages 95-100.
    10. Bouri, Elie & Molnár, Peter & Azzi, Georges & Roubaud, David & Hagfors, Lars Ivar, 2017. "On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?," Finance Research Letters, Elsevier, vol. 20(C), pages 192-198.
    11. 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.
    12. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    13. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    14. Jean-Marc Figuet, 2016. "Bitcoin et blockchain : quelles opportunités ?," Revue d'économie financière, Association d'économie financière, vol. 0(3), pages 325-338.
    15. Wenjun Feng & Yiming Wang & Zhengjun Zhang, 2018. "Can cryptocurrencies be a safe haven: a tail risk perspective analysis," Applied Economics, Taylor & Francis Journals, vol. 50(44), pages 4745-4762, September.
    16. Serda Selin Ozturk, 2020. "Dynamic Connectedness between Bitcoin, Gold, and Crude Oil Volatilities and Returns," JRFM, MDPI, vol. 13(11), pages 1-14, November.
    17. Okorie, David Iheke & Lin, Boqiang, 2020. "Crude oil price and cryptocurrencies: Evidence of volatility connectedness and hedging strategy," Energy Economics, Elsevier, vol. 87(C).
    18. Zeng, Ting & Yang, Mengying & Shen, Yifan, 2020. "Fancy Bitcoin and conventional financial assets: Measuring market integration based on connectedness networks," Economic Modelling, Elsevier, vol. 90(C), pages 209-220.
    19. Achraf Ghorbel & Ahmed Jeribi, 2021. "Investigating the relationship between volatilities of cryptocurrencies and other financial assets," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 817-843, December.
    20. José Antonio Núñez & Mario I Contreras-Valdez & Carlos A Franco-Ruiz, 2019. "Statistical analysis of bitcoin during explosive behavior periods," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-22, March.
    21. Yukun Liu & Aleh Tsyvinski, 2021. "Risks and Returns of Cryptocurrency," The Review of Financial Studies, Society for Financial Studies, vol. 34(6), pages 2689-2727.
    22. Baur, Dirk G. & Hong, KiHoon & Lee, Adrian D., 2018. "Bitcoin: Medium of exchange or speculative assets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 54(C), pages 177-189.
    23. Mensi, Walid & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2020. "Spillovers and co-movements between precious metals and energy markets: Implications on portfolio management," Resources Policy, Elsevier, vol. 69(C).
    24. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & David Martinez-Regoband & Fan Wu, 2020. "Cryptocurrency Trading: A Comprehensive Survey," Papers 2003.11352, arXiv.org, revised Jan 2022.
    25. Amine Lahiani & Ahmed Jeribi & Nabila Boukef Jlassi, 2021. "Nonlinear tail dependence in cryptocurrency-stock market returns: The role of Bitcoin futures," Post-Print hal-03573206, HAL.
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    More about this item

    Keywords

    Crypto Assets; Bitcoin; Machine Learning; Investor Decisions.;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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