Research Article

A Swarm Intelligence Optimization Algorithm for Cryptocurrency Portfolio Optimization

Volume: 10 Number: 1 April 30, 2022
TR EN

A Swarm Intelligence Optimization Algorithm for Cryptocurrency Portfolio Optimization

Abstract

In recent years, cryptocurrency has been widely adopted and seen as an alternative investment tool for investors. However, which cryptocurrency to invest in and how much to invest becomes a problem. Since there is a conflict of multiple criteria, portfolio optimization (PO) is needed to solve the problem. In this study, an Artificial Bee Colony (ABC) algorithm has been developed based on Markowitz's mean-variance model (M-MVM). With this method, the portfolio of cryptocurrencies has been tried to be optimized. Hourly data of 12 cryptocurrencies between 01.09.2020 and 01.04.2021 were used as data. It has been observed that the ABC algorithm achieves good results in the solution of the problem in a reasonable time. In addition, the method was tested with different parameter values and different risk-averse coefficient values (λ).

Keywords

References

  1. Akyer, H., Kalaycı, C. B., & Aygören, H. (2018). Ortalama-Varyans portföy optimizasyonu için parçacık sürü optimizasyonu algoritması: Bir Borsa İstanbul uygulaması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 24(1), 124-129.
  2. Alpago, H. (2018). Bitcoin’den Selfcoin’e kripto para. Uluslararası Bilimsel Araştırmalar Dergisi (IBAD), 3(2), 411-428.
  3. Avadhani, V., H. (2008). Securities Anaylsis and Portfolio Management. USA: Himalaya Publishing House.
  4. Bonabeau, E., Theraulaz, G., & Dorigo, M (1999). Swarm intelligence: from natural to artificial intelligence. NY: Oxford University Press, NewYork. Google Scholar Google Scholar Digital Library Digital Library.
  5. Brauneis, A., & Mestel, R. (2019). Cryptocurrency-portfolios in a mean-variance framework. Finance Research Letters, 28, 259-264.
  6. Briere, M., Oosterlinck, K., & Szafarz, A. (2015). Virtual currency, tangible return: Portfolio diversification with bitcoin. Journal of Asset Management, 16(6), 365-373.
  7. Cai, T. T., Hu, J., Li, Y., & Zheng, X. (2020). High-dimensional minimum variance portfolio estimation based on high-frequency data. Journal of Econometrics, 214(2), 482-494. Charles, A., & Darné, O. (2019). Volatility estimation for Bitcoin: Replication and robustness. International Economics, 157, 23-32.
  8. Chen, W. (2015). Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem. Physica A: Statistical Mechanics and its Applications, 429, 125-139. Eisl, A., Gasser, S., & Weinmayer, K. (2015). Caveat emptor: Does Bitcoin improve portfolio diversification?. Available at SSRN 2408997.

Details

Primary Language

English

Subjects

-

Journal Section

Research Article

Publication Date

April 30, 2022

Submission Date

July 28, 2021

Acceptance Date

February 8, 2022

Published in Issue

Year 2022 Volume: 10 Number: 1

APA
Yurtsal, A., Karaömer, Y., & Benzer, A. İ. (2022). A Swarm Intelligence Optimization Algorithm for Cryptocurrency Portfolio Optimization. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 10(1), 347-363. https://doi.org/10.18506/anemon.975505