IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2304.02362.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2304.02362
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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. Adekoya, Oluwasegun B. & Akinseye, Ademola B. & Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Gabauer, David & Oliyide, Johnson, 2022. "Crude oil and Islamic sectoral stocks: Asymmetric TVP-VAR connectedness and investment strategies," Resources Policy, Elsevier, vol. 78(C).
    3. 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.
    4. 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.
    5. 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.
    6. Lorenzo Lucchini & Laura Alessandretti & Bruno Lepri & Angela Gallo & Andrea Baronchelli, 2020. "From code to market: Network of developers and correlated returns of cryptocurrencies," Papers 2004.07290, arXiv.org, revised Dec 2020.
    7. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    8. Devenow, Andrea & Welch, Ivo, 1996. "Rational herding in financial economics," European Economic Review, Elsevier, vol. 40(3-5), pages 603-615, April.
    9. Junwei Chen, 2023. "Analysis of Bitcoin Price Prediction Using Machine Learning," JRFM, MDPI, vol. 16(1), pages 1-25, January.
    10. 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.
    11. 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.
    12. Jalan, Akanksha & Matkovskyy, Roman, 2023. "Systemic risks in the cryptocurrency market: Evidence from the FTX collapse," Finance Research Letters, Elsevier, vol. 53(C).
    13. 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.
    14. 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).
    15. 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.
    16. 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.
    17. Antonakakis, Nikolaos & Cunado, Juncal & Filis, George & Gabauer, David & de Gracia, Fernando Perez, 2020. "Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness," Energy Economics, Elsevier, vol. 91(C).
    18. Brauneis, Alexander & Mestel, Roland, 2019. "Cryptocurrency-portfolios in a mean-variance framework," Finance Research Letters, Elsevier, vol. 28(C), pages 259-264.
    19. 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.
    20. Sayan Chatterjee & Birger Wernerfelt, 1991. "The link between resources and type of diversification: Theory and evidence," Strategic Management Journal, Wiley Blackwell, vol. 12(1), pages 33-48, January.
    21. 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.
    22. 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.
    23. 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.
    24. Tran, Vu Le & Leirvik, Thomas, 2020. "Efficiency in the markets of crypto-currencies," Finance Research Letters, Elsevier, vol. 35(C).
    25. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu, Xiaoling & Cifuentes-Faura, Javier, 2024. "Information spillover among cryptocurrency and traditional financial assets: Evidence from complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 646(C).
    2. Fakhfekh, Mohamed & Bejaoui, Azza & Bariviera, Aurelio F. & Jeribi, Ahmed, 2024. "Dependence structure between NFT, DeFi and cryptocurrencies in turbulent times: An Archimax copula approach," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    3. Zhao, Yingxiu & Goodell, John W. & Shen, Dehua, 2024. "Spillover effects according to classification of cryptocurrency," Finance Research Letters, Elsevier, vol. 65(C).
    4. Nichanan Sakolvieng, 2024. "Optimizing Cryptocurrency Portfolios: A Comparative Study of Rebalancing Strategies," GATR Journals jfbr220, Global Academy of Training and Research (GATR) Enterprise.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cynthia Weiyi Cai & Rui Xue & Bi Zhou, 2023. "Cryptocurrency puzzles: a comprehensive review and re-introduction," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 46(1), pages 26-50, June.
    2. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    3. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    4. Luis Lorenzo & Javier Arroyo, 2022. "Analysis of the cryptocurrency market using different prototype-based clustering techniques," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-46, December.
    5. Fan, Di & Liang, Tianheng & Yeung, Andy C.L. & Zhang, Haomin, 2020. "The impact of capacity-reduction initiatives on the stock market value of Chinese manufacturing firms," International Journal of Production Economics, Elsevier, vol. 223(C).
    6. Cui, Jinxin & Alshater, Muneer M. & Mensi, Walid, 2023. "Higher-order moment risk spillovers and optimal portfolio strategies in global oil markets," Resources Policy, Elsevier, vol. 86(PA).
    7. Aggarwal, Divya & Chandrasekaran, Shabana & Annamalai, Balamurugan, 2020. "A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    8. Ozkan Haykir & Ibrahim Yagli, 2022. "Speculative bubbles and herding in cryptocurrencies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-33, December.
    9. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
    10. Lehlohonolo Letho & Grieve Chelwa & Abdul Latif Alhassan, 2022. "Cryptocurrencies and portfolio diversification in an emerging market," China Finance Review International, Emerald Group Publishing Limited, vol. 12(1), pages 20-50, January.
    11. Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    12. Jiang, Yonghong & Nie, He & Ruan, Weihua, 2018. "Time-varying long-term memory in Bitcoin market," Finance Research Letters, Elsevier, vol. 25(C), pages 280-284.
    13. Trancoso, Tiago, 2014. "Emerging markets in the global economic network: Real(ly) decoupling?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 499-510.
    14. Chen, Yong & Kelly, Bryan & Wu, Wei, 2020. "Sophisticated investors and market efficiency: Evidence from a natural experiment," Journal of Financial Economics, Elsevier, vol. 138(2), pages 316-341.
    15. Carlos León & Javier Miguélez, 2020. "Interbank relationship lending in Colombia," Borradores de Economia 1118, Banco de la Republica de Colombia.
    16. Djauhari, Maman Abdurachman & Gan, Siew Lee, 2015. "Optimality problem of network topology in stocks market analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 108-114.
    17. Sandoval, Leonidas, 2014. "To lag or not to lag? How to compare indices of stock markets that operate on different times," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 227-243.
    18. Dang, Tam Hoang Nhat & Balli, Faruk & Balli, Hatice Ozer & Gabauer, David & Nguyen, Thi Thu Ha, 2024. "Sectoral uncertainty spillovers in emerging markets: A quantile time–frequency connectedness approach," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 121-139.
    19. Aggarwal, Divya, 2019. "Do bitcoins follow a random walk model?," Research in Economics, Elsevier, vol. 73(1), pages 15-22.
    20. Carmen López-Martín & Sonia Benito Muela & Raquel Arguedas, 2021. "Efficiency in cryptocurrency markets: new evidence," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 403-431, September.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2304.02362. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.