Araştırma Makalesi
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A Swarm Intelligence Optimization Algorithm for Cryptocurrency Portfolio Optimization

Yıl 2022, Cilt: 10 Sayı: 1, 347 - 363, 30.04.2022
https://doi.org/10.18506/anemon.975505

Öz

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).

Kaynakça

  • 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.
  • Alpago, H. (2018). Bitcoin’den Selfcoin’e kripto para. Uluslararası Bilimsel Araştırmalar Dergisi (IBAD), 3(2), 411-428.
  • Avadhani, V., H. (2008). Securities Anaylsis and Portfolio Management. USA: Himalaya Publishing House.
  • 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.
  • Brauneis, A., & Mestel, R. (2019). Cryptocurrency-portfolios in a mean-variance framework. Finance Research Letters, 28, 259-264.
  • Briere, M., Oosterlinck, K., & Szafarz, A. (2015). Virtual currency, tangible return: Portfolio diversification with bitcoin. Journal of Asset Management, 16(6), 365-373.
  • 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.
  • 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.
  • Eren, B. S., Erek, M. S., & Buyruk Akbaba, A. N. (2020). Kripto Para Kavramı ve Muhasebeleştirilmesi. Itobiad: Journal of the Human & Social Science Researches, 9(2).
  • Fisher, Donald, E. ve Jordan, Ronald, J. (1987). Security Analysis and Portfolio Management. Amerika: New Jersey.
  • Ge, M. (2014, August). Artificial bee colony algorithm for portfolio optimization. In Fifth International Conference on Intelligent Control and Information Processing (pp. 449-453). IEEE.
  • Guesmi, K., Saadi, S., Abid, I., & Ftiti, Z. (2019). Portfolio diversification with virtual currency: Evidence from bitcoin. International Review of Financial Analysis, 63, 431-437.
  • Hrytsiuk, P., Babych, T., & Bachyshyna, L. (2019, September). Cryptocurrency portfolio optimization using Value-at-Risk measure. In 6th International Conference on Strategies, Models and Technologies of Economic Systems Management (SMTESM 2019) (pp. 385-389). Atlantis Press.
  • Hsu, C. M. (2014). An integrated portfolio optimisation procedure based on data envelopment analysis, artificial bee colony algorithm and genetic programming. International Journal of Systems Science, 45(12), 2645-2664.
  • Hüseyinov, İ., & Uluçay, S. (2019). Application of Genetic and Particle Swarm Optimization Algorithms to Portfolio Optimization Problem: Borsa İstanbul and Crypto Money Exchange. In 2019 4th International Conference on Computer Science and Engineering (UBMK) (pp. 189-194). IEEE.
  • Inci, A. C., & Lagasse, R. (2019). Cryptocurrencies: applications and investment opportunities. Journal of Capital Markets Studies.
  • Johnson, R. S. (2014). Equity markets and portfolio analysis (Vol. 618). John Wiley & Sons.
  • Kajtazi, A., & Moro, A. (2019). The role of bitcoin in well diversified portfolios: A comparative global study. International Review of Financial Analysis, 61, 143-157.
  • Kalayci, C. B., Ertenlice, O., Akyer, H., & Aygoren, H. (2017). An artificial bee colony algorithm with feasibility enforcement and infeasibility toleration procedures for cardinality constrained portfolio optimization. Expert Systems with Applications, 85, 61-75.
  • Kalayci, C. B., Polat, O., & Akbay, M. A. (2020). An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization. Swarm and Evolutionary Computation, 54, 100662.
  • Karaboga, D. (2005) An idea based on honey bee swarm for numerical optimization. Technical Report TR06. Computer Engineering Department, Engineering Faculty, Erciyes University.
  • Karaşin, Gültekin A. (1987). Sermaye Piyasası Analizleri. Ankara: Sermaye Piyasası Kurulu.
  • Katrancı, A., & Kundakcı, N. (2020). Bulanık CODAS Yöntemi ile Kripto Para Yatırım Alternatiflerinin Değerlendirilmesi. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 22(4), 958-973.
  • Katsiampa, P., Corbet, S., & Lucey, B. (2019). Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis. Finance Research Letters, 29, 68-74.
  • Kumar, D., & Mishra, K. K. (2017). Portfolio optimization using novel co-variance guided Artificial Bee Colony algorithm. Swarm and evolutionary computation, 33, 119-130. Markowitz, H (1952). Portfolio Selection. The Journal of Finance, 7 (1), 77-91.
  • Mazanec, J. (2021). Portfolio Optimalization on Digital Currency Market. Journal of Risk and Financial Management, 14(4), 160.
  • Mba, J. C., Pindza, E., & Koumba, U. (2018). A differential evolution copula-based approach for a multi-period cryptocurrency portfolio optimization. Financial Markets and Portfolio Management, 32(4), 399-418.
  • Millonas MM (1994) Swarms, phase transitions, and collective intelligence. In: Artificial life III. Addison-Wesley, Reading, pp 417–445.
  • Petukhina, A., Trimborn, S., Härdle, W.K., Elendner, H., (2018). Investing with cryptocurrencies– Evaluating the potential of portfolio allocation strategies. SSRN Electron. J. 49, 1–41.
  • Rahmani, M., Khalili Eraqi, M., & Nikoomaram, H. (2019). Portfolio Optimization by Means of Meta Heuristic Algorithms. Advances in Mathematical Finance and Applications, 4(4), 83-97.
  • Satchell, S. (2003). Advances in portfolio construction and implementation. Elsevier.
  • Schellinger, B. (2020). Optimization of special cryptocurrency portfolios. The Journal of Risk Finance. Vol. 21 No. 2, pp. 127-157.
  • Strumberger, I., Tuba, E., Bacanin, N., Beko, M., & Tuba, M. (2018, July). Hybridized artificial bee colony algorithm for constrained portfolio optimization problem. In 2018 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE.
  • Suthiwong, D., & Sodanil, M. (2016, December). Cardinality-constrained portfolio optimization using an improved quick artificial bee colony algorithm. In 2016 International Computer Science and Engineering Conference (ICSEC) (pp. 1-4). IEEE.
  • Symitsi, E., & Chalvatzis, K. J. (2019). The economic value of Bitcoin: A portfolio analysis of currencies, gold, oil and stocks. Research in International Business and Finance, 48, 97-110.
  • Tuba, M., & Bacanin, N. (2014). Artificial bee colony algorithm hybridized with firefly algorithm for cardinality constrained mean-variance portfolio selection problem. Applied Mathematics & Information Sciences, 8(6), 2831.
  • Weigand, R. A. (2014). Applied equity analysis and portfolio management: tools to analyze and manage your stock portfolio. John Wiley & Sons.
  • Wu, C.Y. & Pandey, V.K. (2014), The Value of Bitcoin in Enhancing the Efficiency of an Investor’s Portfolio Executive Summary, pp. 44-53.
  • Yakut, E., & Çankal, A. (2016). Çok Amaçli Genetik Algoritma ve Hedef Programlama Metotlarini Kullanarak Hisse Senedi Portföy Optimizasyonu: BIST-30'da Bir Uygulama/Portfolio Optimzation Using of Metods Multi Objective Genetic Algorithm and Goal Programming: An Application in BIST-30. Business and Economics Research Journal, 7(2), 43.

A Swarm Intelligence Optimization Algorithm for Cryptocurrency Portfolio Optimization

Yıl 2022, Cilt: 10 Sayı: 1, 347 - 363, 30.04.2022
https://doi.org/10.18506/anemon.975505

Öz

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).

Kaynakça

  • 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.
  • Alpago, H. (2018). Bitcoin’den Selfcoin’e kripto para. Uluslararası Bilimsel Araştırmalar Dergisi (IBAD), 3(2), 411-428.
  • Avadhani, V., H. (2008). Securities Anaylsis and Portfolio Management. USA: Himalaya Publishing House.
  • 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.
  • Brauneis, A., & Mestel, R. (2019). Cryptocurrency-portfolios in a mean-variance framework. Finance Research Letters, 28, 259-264.
  • Briere, M., Oosterlinck, K., & Szafarz, A. (2015). Virtual currency, tangible return: Portfolio diversification with bitcoin. Journal of Asset Management, 16(6), 365-373.
  • 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.
  • 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.
  • Eren, B. S., Erek, M. S., & Buyruk Akbaba, A. N. (2020). Kripto Para Kavramı ve Muhasebeleştirilmesi. Itobiad: Journal of the Human & Social Science Researches, 9(2).
  • Fisher, Donald, E. ve Jordan, Ronald, J. (1987). Security Analysis and Portfolio Management. Amerika: New Jersey.
  • Ge, M. (2014, August). Artificial bee colony algorithm for portfolio optimization. In Fifth International Conference on Intelligent Control and Information Processing (pp. 449-453). IEEE.
  • Guesmi, K., Saadi, S., Abid, I., & Ftiti, Z. (2019). Portfolio diversification with virtual currency: Evidence from bitcoin. International Review of Financial Analysis, 63, 431-437.
  • Hrytsiuk, P., Babych, T., & Bachyshyna, L. (2019, September). Cryptocurrency portfolio optimization using Value-at-Risk measure. In 6th International Conference on Strategies, Models and Technologies of Economic Systems Management (SMTESM 2019) (pp. 385-389). Atlantis Press.
  • Hsu, C. M. (2014). An integrated portfolio optimisation procedure based on data envelopment analysis, artificial bee colony algorithm and genetic programming. International Journal of Systems Science, 45(12), 2645-2664.
  • Hüseyinov, İ., & Uluçay, S. (2019). Application of Genetic and Particle Swarm Optimization Algorithms to Portfolio Optimization Problem: Borsa İstanbul and Crypto Money Exchange. In 2019 4th International Conference on Computer Science and Engineering (UBMK) (pp. 189-194). IEEE.
  • Inci, A. C., & Lagasse, R. (2019). Cryptocurrencies: applications and investment opportunities. Journal of Capital Markets Studies.
  • Johnson, R. S. (2014). Equity markets and portfolio analysis (Vol. 618). John Wiley & Sons.
  • Kajtazi, A., & Moro, A. (2019). The role of bitcoin in well diversified portfolios: A comparative global study. International Review of Financial Analysis, 61, 143-157.
  • Kalayci, C. B., Ertenlice, O., Akyer, H., & Aygoren, H. (2017). An artificial bee colony algorithm with feasibility enforcement and infeasibility toleration procedures for cardinality constrained portfolio optimization. Expert Systems with Applications, 85, 61-75.
  • Kalayci, C. B., Polat, O., & Akbay, M. A. (2020). An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization. Swarm and Evolutionary Computation, 54, 100662.
  • Karaboga, D. (2005) An idea based on honey bee swarm for numerical optimization. Technical Report TR06. Computer Engineering Department, Engineering Faculty, Erciyes University.
  • Karaşin, Gültekin A. (1987). Sermaye Piyasası Analizleri. Ankara: Sermaye Piyasası Kurulu.
  • Katrancı, A., & Kundakcı, N. (2020). Bulanık CODAS Yöntemi ile Kripto Para Yatırım Alternatiflerinin Değerlendirilmesi. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 22(4), 958-973.
  • Katsiampa, P., Corbet, S., & Lucey, B. (2019). Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis. Finance Research Letters, 29, 68-74.
  • Kumar, D., & Mishra, K. K. (2017). Portfolio optimization using novel co-variance guided Artificial Bee Colony algorithm. Swarm and evolutionary computation, 33, 119-130. Markowitz, H (1952). Portfolio Selection. The Journal of Finance, 7 (1), 77-91.
  • Mazanec, J. (2021). Portfolio Optimalization on Digital Currency Market. Journal of Risk and Financial Management, 14(4), 160.
  • Mba, J. C., Pindza, E., & Koumba, U. (2018). A differential evolution copula-based approach for a multi-period cryptocurrency portfolio optimization. Financial Markets and Portfolio Management, 32(4), 399-418.
  • Millonas MM (1994) Swarms, phase transitions, and collective intelligence. In: Artificial life III. Addison-Wesley, Reading, pp 417–445.
  • Petukhina, A., Trimborn, S., Härdle, W.K., Elendner, H., (2018). Investing with cryptocurrencies– Evaluating the potential of portfolio allocation strategies. SSRN Electron. J. 49, 1–41.
  • Rahmani, M., Khalili Eraqi, M., & Nikoomaram, H. (2019). Portfolio Optimization by Means of Meta Heuristic Algorithms. Advances in Mathematical Finance and Applications, 4(4), 83-97.
  • Satchell, S. (2003). Advances in portfolio construction and implementation. Elsevier.
  • Schellinger, B. (2020). Optimization of special cryptocurrency portfolios. The Journal of Risk Finance. Vol. 21 No. 2, pp. 127-157.
  • Strumberger, I., Tuba, E., Bacanin, N., Beko, M., & Tuba, M. (2018, July). Hybridized artificial bee colony algorithm for constrained portfolio optimization problem. In 2018 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE.
  • Suthiwong, D., & Sodanil, M. (2016, December). Cardinality-constrained portfolio optimization using an improved quick artificial bee colony algorithm. In 2016 International Computer Science and Engineering Conference (ICSEC) (pp. 1-4). IEEE.
  • Symitsi, E., & Chalvatzis, K. J. (2019). The economic value of Bitcoin: A portfolio analysis of currencies, gold, oil and stocks. Research in International Business and Finance, 48, 97-110.
  • Tuba, M., & Bacanin, N. (2014). Artificial bee colony algorithm hybridized with firefly algorithm for cardinality constrained mean-variance portfolio selection problem. Applied Mathematics & Information Sciences, 8(6), 2831.
  • Weigand, R. A. (2014). Applied equity analysis and portfolio management: tools to analyze and manage your stock portfolio. John Wiley & Sons.
  • Wu, C.Y. & Pandey, V.K. (2014), The Value of Bitcoin in Enhancing the Efficiency of an Investor’s Portfolio Executive Summary, pp. 44-53.
  • Yakut, E., & Çankal, A. (2016). Çok Amaçli Genetik Algoritma ve Hedef Programlama Metotlarini Kullanarak Hisse Senedi Portföy Optimizasyonu: BIST-30'da Bir Uygulama/Portfolio Optimzation Using of Metods Multi Objective Genetic Algorithm and Goal Programming: An Application in BIST-30. Business and Economics Research Journal, 7(2), 43.
Toplam 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makalesi
Yazarlar

Ahmet Yurtsal 0000-0003-0523-3519

Yunus Karaömer 0000-0002-6377-1326

Ali İhsan Benzer 0000-0002-5032-7058

Yayımlanma Tarihi 30 Nisan 2022
Kabul Tarihi 8 Şubat 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 10 Sayı: 1

Kaynak Göster

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

Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.