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Exploring Entropy-Based Portfolio Strategies: Empirical Analysis and Cryptocurrency Impact

Author

Listed:
  • Nicolò Giunta

    (Department of Business Studies, Roma Tre University, 00145 Roma, Italy)

  • Giuseppe Orlando

    (Department of Economics and Finance, University of Bari, 70124 Bari, Italy)

  • Alessandra Carleo

    (Department of Business Studies, Roma Tre University, 00145 Roma, Italy)

  • Jacopo Maria Ricci

    (Department of Business Studies, Roma Tre University, 00145 Roma, Italy)

Abstract

This study addresses market concentration among major corporations, highlighting the utility of relative entropy for understanding diversification strategies. It introduces entropic value at risk (EVaR) as a coherent risk measure, which is an upper bound to the conditional value at risk (CVaR), and explores its generalization, relativistic value at risk (RLVaR), rooted in Kaniadakis entropy. Through extensive empirical analysis on both developed (i.e., S&P 500 and Euro Stoxx 50) and developing markets (i.e., BIST 100 and Bovespa), the study evaluates entropy-based criteria in portfolio selection, investigates model behavior across different market types, and assesses the impact of cryptocurrency introduction on portfolio performance and diversification. The key finding indicates that entropy measures effectively identify optimal portfolios, particularly in scenarios of heightened risk and increased concentration, crucial for mitigating negative net performances during low returns or high turnover. Bitcoin is primarily used for diversification and performance enhancement in the BIST 100 index, while its allocation in other markets remains minimal or non-existent, confirming the extreme concentration observed in stock markets dominated by a few leading stocks.

Suggested Citation

  • Nicolò Giunta & Giuseppe Orlando & Alessandra Carleo & Jacopo Maria Ricci, 2024. "Exploring Entropy-Based Portfolio Strategies: Empirical Analysis and Cryptocurrency Impact," Risks, MDPI, vol. 12(5), pages 1-26, May.
    • Handle: RePEc:gam:jrisks:v:12:y:2024:i:5:p:78-:d:1392817
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    References listed on IDEAS

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    1. Hossein Varamini & Svetlana Kalash, 2008. "Testing Market Efficiency for Different Market Capitalization Funds," American Journal of Business, Emerald Group Publishing, vol. 23(2), pages 17-26.
    2. Gambarelli, Luca & Marchi, Gianluca & Muzzioli, Silvia, 2023. "Hedging effectiveness of cryptocurrencies in the European stock market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    3. Bruni, Renato & Cesarone, Francesco & Scozzari, Andrea & Tardella, Fabio, 2017. "On exact and approximate stochastic dominance strategies for portfolio selection," European Journal of Operational Research, Elsevier, vol. 259(1), pages 322-329.
    4. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    5. Hossein Varamini & Svetlana Kalash, 2008. "Testing Market Efficiency for Different Market Capitalization Funds," American Journal of Business, Emerald Group Publishing Limited, vol. 23(2), pages 17-28, October.
    6. Jui-Cheng Hung & Yen-Hsien Lee & Tung-Yueh Pai, 2009. "Examining market efficiency for large- and small-capitalization of TOPIX and FTSE stock indices," Applied Financial Economics, Taylor & Francis Journals, vol. 19(9), pages 735-744.
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