The Role of Commodities and Crypto Assets In Systematic Risk Reduction During The COVID-19 Process: An Application On BIST50 with Fuzzy Logic

Öz

With the impact of the COVID-19 pandemic on the global economy, financial markets have entered a period of uncertainty and volatility, causing investors to reassess their investment preferences. In addition to traditional investment instruments such as stocks and bonds, safe-haven assets such as gold and crypto assets have become alternatives for investors. Using weekly data from 11 March 2020 to 31 May 2022, this study covers equities in the BIST50 index, 10 commodities and 10 crypto assets with the highest trading volume during the analysis period. The research examines the role of commodities and crypto assets in mitigating systematic risk during COVID-19. According to the findings, the portfolio containing commodities and crypto assets has the highest return, while the lowest risk is observed in the portfolio consisting of BIST50 equities. The Sharpe index and coefficient of variation results reveal that the optimal portfolio of equities outperforms. Although the inclusion of commodities and crypto assets in the portfolio contributed to reduce systematic risk, the high volatility of these assets led to relatively lower portfolio performance. In this context, investors' diversification of their portfolios by considering the risk-return balance is emphasized as an effective strategy to reduce systematic risk

Anahtar Kelimeler

• Fuzzy Logic
• Portfolio Optimization
• Systematic Risk

Proje Numarası

1919B012206174

COVID-19 Sürecinde Sistematik Riskin Azaltılmasında Emtia ve Kripto Varlıkların Rolü: Bulanık Mantıkla BIST50 Üzerine Bir Uygulama

Öz

COVID-19 pandemisinin küresel ekonomi üzerindeki etkisiyle beraber finansal piyasalar, belirsizlik ve volatilitenin hâkim olduğu bir sürece girmiş, bu durum yatırımcıların yatırım tercihlerini yeniden değerlendirmesine neden olmuştur. Geleneksel yatırım araçları olan pay senetleri ve tahvillerin yanı sıra altın gibi güvenli liman varlıkları ve kripto varlıklar, yatırımcılar için alternatifler arasında yer almıştır. Bu çalışma, BIST50 endeksinde yer alan pay senetleri ve 10 emtia ile analiz dönemindeki en yüksek işlem hacmine sahip 10 kripto varlığın 11 Mart 2020 ile 31 Mayıs 2022 tarihleri arasındaki haftalık verilerini kapsamaktadır. Çalışma COVID-19 sürecinde portföyün sistematik riskinin azaltılmasında emtiaların ve kripto varlıkların oynadığı rolü incelemektedir. Emtia ve kripto varlıkların portföye dâhil edilmesi, sistematik riski azaltma yönünde katkı sunsa da bu varlıkların yüksek volatilitesi, portföy performansında görece daha düşük sonuçlar elde edilmesine neden olmuştur. Elde edilen bulgulara göre en yüksek getiriyi sağlayan portföy, emtia ve kripto varlıkları içeren portföy olurken en düşük risk ise BIST50 pay senetlerinden oluşan portföyde gözlemlenmiştir. Bu bağlamda yatırımcıların risk-getiri dengesini gözeterek portföylerini çeşitlendirmeleri, sistematik riskin azaltılmasında etkili bir strateji olarak vurgulanmaktadır.

Anahtar Kelimeler

• Bulanık Mantık
• Portföy Optimizasyonu
• Sistematik Risk

Destekleyen Kurum

TUBİTAK

Proje Numarası

1919B012206174

Kaynakça

1. Abdelmalek, W. (2024), Cryptocurrencies and portfolio diversification before and during COVID-19, EuroMed Journal of Business, 19(4), 1084-1120.
2. Aliu, F., Bajra, U. and Preniqi, N. (2022), Analysis of diversification benefits for cryptocurrency portfolios before and during the COVID-19 pandemic, Studies in Economics and Finance, 39(3), 444 457.
3. Baykal, N. and Beyan, T. (2004), Bulanık mantık ilke ve temelleri, Bıçaklar Kitabevi, Ankara.
4. Brealey, R.A., Myers, S.C. and Marcus, A.J. (2001), Fundamentals of corporate finance (3rd ed.), The McGraw-Hill Companies, New York.
5. Briere, M., Oosterlinck, K. and Szafarz, A. (2015), Virtual currency, tangible return: Portfolio diversification with bitcoin, Journal of Asset Management, 16, 365-373.
6. Carrick, J. (2016), Bitcoin as a complement to emerging market currencies, Emerging Markets Finance and Trade, 52(10), 2321-2334.
7. Colombo, J., Cruz, F., Paese, L. and Cortes, R. (2021), The diversification benefits of cryptocurrencies in multi-asset portfolios: Cross-country evidence, Available at SSRN 3776260.
8. Çevik, O. ve Yıldırım, Y. (2010), Bulanık doğrusal programlama ile süt ürünleri optimum ürün formülü oluşturma, Ekonomik Araştırmalar Dergisi, 12(18), 15-26.

9. Eisl, A., Gasser, S.M. and Weinmayer, K. (2015), Caveat emptor: Does Bitcoin improve portfolio diversification?, SSRN 2408997.
10. Gangwal, S. (2017), Analyzing the effects of adding Bitcoin to portfolio, International Journal of Economics and Management Engineering, 10(10), 3519-3532.
11. Gülcan, B. (2012), Bulanık doğrusal programlama ve bir bisküvi işletmesinde uygulama, Yüksek Lisans, Karamanoğlu Mehmetbey Üniversitesi Sosyal Bilimler Enstitüsü, Karaman.
12. Hansen, K. B. (1996), Fuzzy logic and linear programming find optimal solutions for meteorological problems, Applied Mathematics and Computation, 139, 383-399.
13. Kocadağlı, O. (2006), Bulanık matematiksel programlama ve portföy analizi uygulaması, Yüksek Lisans, Hacettepe Üniversitesi Fen Bilimleri Enstitüsü, Ankara.
14. Liu, X., Shehzad, K., Kocak, E. and Zaman, U. (2022), Dynamic correlations and portfolio implications across stock and commodity markets before and during the COVID-19 era: A key role of gold, Resources Policy, 79, 102985.
15. Ma, Y., Ferhana, A., Miao, L. and Zilong, W. (2020), Portfolio optimization in the era of digital financialization using cryptocurrencies, Technological Forecasting and Social Change, 161, 1-11.
16. Markowitz, H. (1952), Portfolio selection, The Journal of Finance, 7(1), 77-91.
17. Mezghani, T., Ben Hamadou, F. and Boujelbène Abbes, M. (2021), The dynamic network connectedness and hedging strategies across stock markets and commodities: COVID-19 pandemic effect, Asia-Pacific Journal of Business Administration, 13(4), 520-552.
18. Mokni, K.H., Youssef, M. and Ajmi, A.N. (2022), COVID-19 pandemic and economic policy uncertainty: The first test on the hedging and safe haven properties of cryptocurrencies, Research in International Business and Finance, 60, 101573.
19. Nasreen, S., Tiwari, A.K. and Yoon, S.M. (2021), Dynamic connectedness and portfolio diversification during the coronavirus disease 2019 pandemic: Evidence from the cryptocurrency market, Sustainability, 13(14), 7672.
20. Petukhina, A.A., Reule, C.G. and Härdle, W.K. (2021), Rise of the machines? Intraday high frequency trading patterns of cryptocurrencies, The European Journal of Finance, 27(1), 8-30.
21. Pinudom, B., Tungpisansampun, W., Tansuchat, R. and Maneejuk, P. (2018), Could Bitcoin enhance the portfolio performance?, In Journal of Physics: Conference Series (Vol. 1053, No. 1, p. 012113). IOP Publishing.
22. Rubbaniy, G., Khalid, A.A., Syriopoulos, K. and Samitas, A. (2022), Safe-haven properties of soft commodities during times of COVID-19, Journal of Commodity Markets, 27, 100223.
23. Symitsi, E. and 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.
24. Tuncel, S. Ö. (1997), Bulanık doğrusal programlama, Yüksek Lisans, Mimar Sinan Güzel Sanatlar Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
25. Trichilli, Y. and Boujelbène Abbes, M. (2023), The impact of COVID-19 on the portfolio optimization, EuroMed Journal of Business, 18(2), 207-228.
26. Trimborn, S., Li, M. and Härdle, W.K. (2020), Investing with cryptocurrencies–a liquidity constrained investment approach, Journal of Financial Econometrics, 18(2), 280-306.
27. Watorek, M., Drozdz, S., Kwapien, J., Minati, L., Oswiecimka, P. and Stanuszek, M. (2021), Multiscale characteristics of the emerging global cryptocurrency market, Physics Reports, 901, 1-82.
28. Werners, B. (1987), An interactive fuzzy programming system, Fuzzy Sets and Systems, 23, 131-147.
29. Wu, C.Y., Pandey, V.K. and Dba, C. (2014), The value of Bitcoin in enhancing the efficiency of an investor’s portfolio, Journal of Financial Planning, 27(9), 44-52.
30. Zadeh, L. A. (1965), Fuzzy sets, Information and Control, 8(3), 338-353.
31. Zhang, D., Hu, M. and Ji, Q. (2020), Financial markets under the global pandemic of COVID-19, Finance Research Letters, 36, 101528.