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Optimizing Cryptocurrency Returns: A Quantitative Study on Factor-Based Investing

Author

Listed:
  • Phumudzo Lloyd Seabe

    (Department of Mathematics and Applied Mathematics, Sefako Makgatho Health Sciences University, Pretoria 0204, South Africa)

  • Claude Rodrigue Bambe Moutsinga

    (Department of Mathematics and Applied Mathematics, Sefako Makgatho Health Sciences University, Pretoria 0204, South Africa)

  • Edson Pindza

    (College of Economic and Management Sciences, Department of Decision Sciences, University of South Africa, Pretoria 0002, South Africa)

Abstract

This study explores cryptocurrency investment strategies by adapting the robust framework of factor investing, traditionally applied in equity markets, to the distinctive landscape of cryptocurrency assets. It conducts an in-depth examination of 31 prominent cryptocurrencies from December 2017 to December 2023, employing the Fama–MacBeth regression method and portfolio regressions to assess the predictive capabilities of market, size, value, and momentum factors, adjusted for the unique characteristics of the cryptocurrency market. These characteristics include high volatility and continuous trading, which differ markedly from those of traditional financial markets. To address the challenges posed by the perpetual operation of cryptocurrency trading, this study introduces an innovative rebalancing strategy that involves weekly adjustments to accommodate the market’s constant fluctuations. Additionally, to mitigate issues like autocorrelation and heteroskedasticity in financial time series data, this research applies the Newey–West standard error approach, enhancing the robustness of regression analyses. The empirical results highlight the significant predictive power of momentum and value factors in forecasting cryptocurrency returns, underscoring the importance of tailoring conventional investment frameworks to the cryptocurrency context. This study not only investigates the applicability of factor investing in the rapidly evolving cryptocurrency market, but also enriches the financial literature by demonstrating the effectiveness of combining Fama–MacBeth cross-sectional analysis with portfolio regressions, supported by Newey–West standard errors, in mastering the complexities of digital asset investments.

Suggested Citation

  • Phumudzo Lloyd Seabe & Claude Rodrigue Bambe Moutsinga & Edson Pindza, 2024. "Optimizing Cryptocurrency Returns: A Quantitative Study on Factor-Based Investing," Mathematics, MDPI, vol. 12(9), pages 1-28, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1351-:d:1385677
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    References listed on IDEAS

    as
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