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Daniel Svogun

Personal Details

First Name:Daniel
Middle Name:
Last Name:Svogun
Suffix:
RePEc Short-ID:psv56
[This author has chosen not to make the email address public]

Affiliation

School of Business and Economics
Catholic University of America

Washington, District of Columbia (United States)
http://business.cua.edu/
RePEc:edi:decuaus (more details at EDIRC)

Research output

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Jump to: Articles

Articles

  1. Valentinas Rudys & Daniel Svogun, 2025. "Investing in cryptocurrencies with information costs," Applied Economics Letters, Taylor & Francis Journals, vol. 32(3), pages 365-368, February.
  2. Daniel Svogun & Valentinas Rudys, 2024. "Adjustable-band moving average learning strategies for technical analysis: evidence from the Dow Jones Industrial Average," Applied Economics, Taylor & Francis Journals, vol. 56(50), pages 6221-6230, October.
  3. Valentinas Rudys & Daniel Svogun, 2023. "Optimal cryptocurrency portfolio allocation over the life cycle," Applied Economics, Taylor & Francis Journals, vol. 55(46), pages 5419-5433, October.
  4. Bazán-Palomino, Walter & Svogun, Daniel, 2023. "On the drivers of technical analysis profits in cryptocurrency markets: A Distributed Lag approach," International Review of Financial Analysis, Elsevier, vol. 86(C).
  5. Svogun, Daniel & Bazán-Palomino, Walter, 2022. "Technical analysis in cryptocurrency markets: Do transaction costs and bubbles matter?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. Bazán-Palomino, Walter & Svogun, Daniel, 2023. "On the drivers of technical analysis profits in cryptocurrency markets: A Distributed Lag approach," International Review of Financial Analysis, Elsevier, vol. 86(C).

    Cited by:

    1. Mercik, Aleksander & Słoński, Tomasz & Karaś, Marta, 2024. "Understanding crypto-asset exposure: An investigation of its impact on performance and stock sensitivity among listed companies," International Review of Financial Analysis, Elsevier, vol. 92(C).
    2. Assaf, Ata & Demir, Ender & Ersan, Oguz, 2024. "Detecting and date-stamping bubbles in fan tokens," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 98-113.
    3. Peng, Yaohao & de Moraes Souza, João Gabriel, 2024. "Chaos, overfitting and equilibrium: To what extent can machine learning beat the financial market?," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    4. Day, Min-Yuh & Ni, Yensen, 2023. "Be greedy when others are fearful: Evidence from a two-decade assessment of the NDX 100 and S&P 500 indexes," International Review of Financial Analysis, Elsevier, vol. 90(C).
    5. María José Ayala & Nicolás Gonzálvez-Gallego & Rocío Arteaga-Sánchez, 2024. "Google search volume index and investor attention in stock market: a systematic review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-29, December.

  2. Svogun, Daniel & Bazán-Palomino, Walter, 2022. "Technical analysis in cryptocurrency markets: Do transaction costs and bubbles matter?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).

    Cited by:

    1. Kevin Rink, 2023. "The predictive ability of technical trading rules: an empirical analysis of developed and emerging equity markets," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(4), pages 403-456, December.
    2. Deprez, Niek & Frömmel, Michael, 2024. "Are simple technical trading rules profitable in bitcoin markets?," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 858-874.
    3. Bazán-Palomino, Walter & Svogun, Daniel, 2023. "On the drivers of technical analysis profits in cryptocurrency markets: A Distributed Lag approach," International Review of Financial Analysis, Elsevier, vol. 86(C).

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