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A network-based strategy of price correlations for optimal cryptocurrency portfolios

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  • Ruixue Jing
  • Luis Enrique Correa Rocha

Abstract

A cryptocurrency is a digital asset maintained by a decentralised system using cryptography. Investors in this emerging digital market are exploring the profitability potential of portfolios in place of single coins. Portfolios are particularly useful given that price forecasting in such a volatile market is challenging. The crypto market is a self-organised complex system where the complex inter-dependencies between the cryptocurrencies may be exploited to understand the market dynamics and build efficient portfolios. In this letter, we use network methods to identify highly decorrelated cryptocurrencies to create diversified portfolios using the Markowitz Portfolio Theory agnostic to future market behaviour. The performance of our network-based portfolios is optimal with 46 coins and superior to benchmarks up to an investment horizon of 14 days, reaching up to 1,066% average expected return within 1 day, with reasonable associated risks. We also show that popular cryptocurrencies are typically not included in the optimal portfolios. Past price correlations reduce risk and may improve the performance of crypto portfolios in comparison to methodologies based exclusively on price auto-correlations. Short-term crypto investments may be competitive to traditional high-risk investments such as the stock market or commodity market but call for caution given the high variability of prices.

Suggested Citation

  • Ruixue Jing & Luis Enrique Correa Rocha, 2023. "A network-based strategy of price correlations for optimal cryptocurrency portfolios," Papers 2304.02362, arXiv.org.
  • Handle: RePEc:arx:papers:2304.02362
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    1. da Gama Silva, Paulo Vitor Jordão & Klotzle, Marcelo Cabus & Pinto, Antonio Carlos Figueiredo & Gomes, Leonardo Lima, 2019. "Herding behavior and contagion in the cryptocurrency market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 22(C), pages 41-50.
    2. Lieberman, Marvin B, 1987. "Excess Capacity as a Barrier to Entry: An Empirical Appraisal," Journal of Industrial Economics, Wiley Blackwell, vol. 35(4), pages 607-627, June.
    3. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    4. Dedhy Sulistiawan & Felizia Arni Rudiawarni, 2017. "Do accrual minimise (maximise) stock risk (return)?: evidence from Indonesia," International Journal of Globalisation and Small Business, Inderscience Enterprises Ltd, vol. 9(1), pages 20-28.
    5. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    6. Devenow, Andrea & Welch, Ivo, 1996. "Rational herding in financial economics," European Economic Review, Elsevier, vol. 40(3-5), pages 603-615, April.
    7. Chi-Wei Su & Meng Qin & Ran Tao & Xiaoyan Zhang, 2020. "Is the status of gold threatened by Bitcoin?," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 33(1), pages 420-437, January.
    8. Broadstock, David C. & Cao, Hong & Zhang, Dayong, 2012. "Oil shocks and their impact on energy related stocks in China," Energy Economics, Elsevier, vol. 34(6), pages 1888-1895.
    9. Jalan, Akanksha & Matkovskyy, Roman, 2023. "Systemic risks in the cryptocurrency market: Evidence from the FTX collapse," Finance Research Letters, Elsevier, vol. 53(C).
    10. 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.
    11. Kurosaki, Tetsuo & Kim, Young Shin, 2022. "Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk," Finance Research Letters, Elsevier, vol. 45(C).
    12. John L. Evans & Stephen H. Archer, 1968. "Diversification And The Reduction Of Dispersion: An Empirical Analysis," Journal of Finance, American Finance Association, vol. 23(5), pages 761-767, December.
    13. Balcilar, Mehmet & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2017. "Can volume predict Bitcoin returns and volatility? A quantiles-based approach," Economic Modelling, Elsevier, vol. 64(C), pages 74-81.
    14. Brauneis, Alexander & Mestel, Roland, 2019. "Cryptocurrency-portfolios in a mean-variance framework," Finance Research Letters, Elsevier, vol. 28(C), pages 259-264.
    15. Mi Yeon Hong & Ji Won Yoon, 2022. "The impact of COVID-19 on cryptocurrency markets: A network analysis based on mutual information," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-24, February.
    16. Kate Murray & Andrea Rossi & Diego Carraro & Andrea Visentin, 2023. "On Forecasting Cryptocurrency Prices: A Comparison of Machine Learning, Deep Learning, and Ensembles," Forecasting, MDPI, vol. 5(1), pages 1-14, January.
    17. Myles E. Mangram, 2013. "A Simplified Perspective Of The Markowitz Portfolio Theory," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 7(1), pages 59-70.
    18. Coelho, Ricardo & Gilmore, Claire G. & Lucey, Brian & Richmond, Peter & Hutzler, Stefan, 2007. "The evolution of interdependence in world equity markets—Evidence from minimum spanning trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 455-466.
    19. Tran, Vu Le & Leirvik, Thomas, 2020. "Efficiency in the markets of crypto-currencies," Finance Research Letters, Elsevier, vol. 35(C).
    20. Meng Qin & Tong Wu & Ran Tao & Chi-Wei Su & Stefea Petru, 2022. "The inevitable role of bilateral relation: a fresh insight into the bitcoin market," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 4260-4279, December.
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