Araştırma Makalesi
BibTex RIS Kaynak Göster

Kripto Para Piyasasının Minimum Kapsayan Ağaç ile Sektörel Analizi

Yıl 2025, Cilt: 40 Sayı: 2, 400 - 409, 04.06.2025
https://doi.org/10.24988/ije.1514118

Öz

Bu çalışma, Binance’de işlem gören 67 kripto paranın günlük kapanış fiyatları kullanılarak yapılan MST (Minimum Spanning Tree) analiziyle, kripto paraların etkileşim dinamiklerini ve kümelenme özelliklerini incelemiştir. Çalışma, oyun sektöründen 10 kripto parayı ve piyasa hacmi yüksek 56 kripto parayı analiz kapsamına almıştır. Literatürde MST analiziyle kripto paralar çeşitli veçhelerle incelenmiş ancak sektörel bir analiz ihmal edilmiştir. Bu çalışma kripto paraların kategorizasyonuna ve doğrudan kripto oyunlara odaklanmıştır. MST analizine göre, kripto oyunlar ve merkeziyetsiz borsalar kendi aralarında belirgin bir kümelenme sergilemiştir. Ethereum, ağ üzerinde en fazla bağlantıya sahip kripto para olarak öne çıkmıştır. ENJ, kripto oyunlar içinde merkezi bir konumda yer almış ve kripto oyunların çoğu kendi içinde bir kümelenme göstermiştir. İçsel öngörü Bitcoin’in kripto para piyasasında domine bir güce sahip olarak daha fazla bağlantıya sahip olacağıdır. Ancak analize göre Ethereum kripto para sektöründe merkezi bir konumda yer almaktadır.

Kaynakça

  • Binance (2023). Zones. Erişim adresi https://www.binance.com/tr/markets/zones
  • Bonanno, G., Lillo, F., & Mantegna, R.N. (2001). High-frequency cross-correlation in a set of stocks. Quantitative Finance, 1: 96-104.
  • Bonanno, G., Vandewalle, N, & Mantegna, R. N. (2000). Taxonomy of stock market indices. Physical Review E, 62(6): R7615.
  • Briola, A. & Aste, T. (2022). Dependency structures in cryptocurrency market from high to low frequency. Entropy, 24(11): 1548.
  • Chaudhari, H. & Crane, M. (2020). Cross-correlation dynamics and community structures of cryptocurrencies. Journal of Computational Science, 44: 101-130.
  • Dönmez, C. Ç., Dereli, A. F., Horosan, B. M., & Yıldız, C. (2020). An analysis of evolutionary cryptocurrency market dynamics. Elektronik Sosyal Bilimler Dergisi, 19(74): 611-629.
  • Dönmez, C. C., Sen, D., Dereli, A. F., Horasan, M. B., Yildiz, C., & Kaplan Donmez, N. F. (2021). An investigation of fiat characterization and evolutionary dynamics of the cryptocurrency market. SAGE Open, 11(1) : 2158244021994809.
  • Francés, C. J., Carles, P. G., & Arellano, D. J. (2018). The cryptocurrency market: A network analysis. Esic Market Economics and Business Journal, 49(3): 569-583.
  • Freeman, L. (1977). A set of measures of centrality based on betweenness. Sociometry, 40 (1): 35–41.
  • Giudici, P. & Polinesi, G. (2021). Crypto price discovery through correlation networks. Annals of Operations Research, Springer,299(1): 443-457.
  • Ho, K. H., Chiu, W. H., & Li, C. (2020). A network analysis of the cryptocurrency market. 2020 IEEE Symposium Series on Computational Intelligence (SSCI). 2178-2185.
  • Hong, M. Y. & Yoon, J. W. (2022). The impact of COVID-19 on cryptocurrency markets: A network analysis based on mutual information. Plos One, 17(2): e0259869.
  • Katsiampa, P., Yarovaya, L., & Zięba, D. (2022). High-frequency connectedness between bitcoin and other top-traded crypto assets during the COVID-19 crisis. Journal of International Financial Markets, Institutions and Money, 79: 101578.
  • Kitanovski, D., Mirchev, M., Chorbev, I., & Mishkovski, I. (2022). Cryptocurrency portfolio diversification using network community detection. 2022 30th Telecommunications Forum (TELFOR). 1-4.
  • Kwapień, J., Wątorek, M., & Drożdż, S. (2021). Cryptocurrency market consolidation in 2020–2021. Entropy, 23(12): 1674.
  • Mantegna, R. N. (1999), Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 11(1): 193-197.
  • Mirzaei, M. & Hosseini, S. M. P. (2019). Measuring stock market connectedness among palm oil buyers: Do sustainability standards matter? Journal of Cleaner Production, 240: 118266.
  • Prim, R. C. (1957). Shortest connection networks and some generalizations. The Bell System Technical Journal, 36(6): 1389-1401.
  • Şensoy, A., Silva, T. C., Corbet, S., & Tabak, B. M. (2021). High-frequency return and volatility spillovers among cryptocurrencies. Applied Economics, 53(37): 4310-4328.
  • Song, J. Y., Chang, W., & Song, J. W. (2019). Cluster analysis on the structure of the cryptocurrency market via bitcoin–ethereum filtering. Physica A: Statistical Mechanics and its Applications, 527: 121339.
  • Stosic, D., Stosic, D., Ludermir, T. B., & Stosic, T. (2018). Collective behavior of cryptocurrency price changes. Physica A: Statistical Mechanics and its Applications, 507: 499-509.
  • Vandewalle, N., Brisbois, F., & Tordoir, X. (2001). Self-organized critical topology of stock markets. Quantitative Finance, 1: 372–375.
  • Zięba, D., Kokoszczyński, R., & Śledziewska, K. (2019). Shock transmission in the cryptocurrency market. Is bitcoin the most influential? International Review of Financial Analysis, 64: 102-125.

Sectoral Analysis of the Cryptocurrency Market Using Minimum Spanning Tree

Yıl 2025, Cilt: 40 Sayı: 2, 400 - 409, 04.06.2025
https://doi.org/10.24988/ije.1514118

Öz

This study examined the interaction dynamics and clustering characteristics of cryptocurrencies using MST (Minimum Spanning Tree) analysis based on the daily closing prices of 67 cryptocurrencies traded on Binance. The study included ten cryptocurrencies from the gaming sector and 56 high-market-cap cryptocurrencies. While cryptocurrencies have been analysed from various perspectives using MST in the literature, sectoral analysis has been neglected. This study focused on the categorization of cryptocurrencies and directly on crypto games. According to the MST analysis, crypto games and decentralized exchanges exhibited significant clustering within themselves. Ethereum emerged as the cryptocurrency with the most connections on the network. ENJ held a central position within crypto games, and most crypto games showed clustering among themselves. The intuitive expectation was that Bitcoin would have more connections, dominating the cryptocurrency market. However, the analysis indicated that Ethereum occupies a central position in the cryptocurrency sector.

Kaynakça

  • Binance (2023). Zones. Erişim adresi https://www.binance.com/tr/markets/zones
  • Bonanno, G., Lillo, F., & Mantegna, R.N. (2001). High-frequency cross-correlation in a set of stocks. Quantitative Finance, 1: 96-104.
  • Bonanno, G., Vandewalle, N, & Mantegna, R. N. (2000). Taxonomy of stock market indices. Physical Review E, 62(6): R7615.
  • Briola, A. & Aste, T. (2022). Dependency structures in cryptocurrency market from high to low frequency. Entropy, 24(11): 1548.
  • Chaudhari, H. & Crane, M. (2020). Cross-correlation dynamics and community structures of cryptocurrencies. Journal of Computational Science, 44: 101-130.
  • Dönmez, C. Ç., Dereli, A. F., Horosan, B. M., & Yıldız, C. (2020). An analysis of evolutionary cryptocurrency market dynamics. Elektronik Sosyal Bilimler Dergisi, 19(74): 611-629.
  • Dönmez, C. C., Sen, D., Dereli, A. F., Horasan, M. B., Yildiz, C., & Kaplan Donmez, N. F. (2021). An investigation of fiat characterization and evolutionary dynamics of the cryptocurrency market. SAGE Open, 11(1) : 2158244021994809.
  • Francés, C. J., Carles, P. G., & Arellano, D. J. (2018). The cryptocurrency market: A network analysis. Esic Market Economics and Business Journal, 49(3): 569-583.
  • Freeman, L. (1977). A set of measures of centrality based on betweenness. Sociometry, 40 (1): 35–41.
  • Giudici, P. & Polinesi, G. (2021). Crypto price discovery through correlation networks. Annals of Operations Research, Springer,299(1): 443-457.
  • Ho, K. H., Chiu, W. H., & Li, C. (2020). A network analysis of the cryptocurrency market. 2020 IEEE Symposium Series on Computational Intelligence (SSCI). 2178-2185.
  • Hong, M. Y. & Yoon, J. W. (2022). The impact of COVID-19 on cryptocurrency markets: A network analysis based on mutual information. Plos One, 17(2): e0259869.
  • Katsiampa, P., Yarovaya, L., & Zięba, D. (2022). High-frequency connectedness between bitcoin and other top-traded crypto assets during the COVID-19 crisis. Journal of International Financial Markets, Institutions and Money, 79: 101578.
  • Kitanovski, D., Mirchev, M., Chorbev, I., & Mishkovski, I. (2022). Cryptocurrency portfolio diversification using network community detection. 2022 30th Telecommunications Forum (TELFOR). 1-4.
  • Kwapień, J., Wątorek, M., & Drożdż, S. (2021). Cryptocurrency market consolidation in 2020–2021. Entropy, 23(12): 1674.
  • Mantegna, R. N. (1999), Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 11(1): 193-197.
  • Mirzaei, M. & Hosseini, S. M. P. (2019). Measuring stock market connectedness among palm oil buyers: Do sustainability standards matter? Journal of Cleaner Production, 240: 118266.
  • Prim, R. C. (1957). Shortest connection networks and some generalizations. The Bell System Technical Journal, 36(6): 1389-1401.
  • Şensoy, A., Silva, T. C., Corbet, S., & Tabak, B. M. (2021). High-frequency return and volatility spillovers among cryptocurrencies. Applied Economics, 53(37): 4310-4328.
  • Song, J. Y., Chang, W., & Song, J. W. (2019). Cluster analysis on the structure of the cryptocurrency market via bitcoin–ethereum filtering. Physica A: Statistical Mechanics and its Applications, 527: 121339.
  • Stosic, D., Stosic, D., Ludermir, T. B., & Stosic, T. (2018). Collective behavior of cryptocurrency price changes. Physica A: Statistical Mechanics and its Applications, 507: 499-509.
  • Vandewalle, N., Brisbois, F., & Tordoir, X. (2001). Self-organized critical topology of stock markets. Quantitative Finance, 1: 372–375.
  • Zięba, D., Kokoszczyński, R., & Śledziewska, K. (2019). Shock transmission in the cryptocurrency market. Is bitcoin the most influential? International Review of Financial Analysis, 64: 102-125.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Finansal Ekonomi
Bölüm Araştırma Makalesi
Yazarlar

Emircan Yıldırım 0000-0001-5020-7788

Kerim Eser Afşar 0000-0002-9853-0186

Ahmet Aydın Arı 0000-0002-7177-5116

Gönderilme Tarihi 10 Temmuz 2024
Kabul Tarihi 5 Eylül 2024
Erken Görünüm Tarihi 23 Mayıs 2025
Yayımlanma Tarihi 4 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 40 Sayı: 2

Kaynak Göster

APA Yıldırım, E., Afşar, K. E., & Arı, A. A. (2025). Sectoral Analysis of the Cryptocurrency Market Using Minimum Spanning Tree. İzmir İktisat Dergisi, 40(2), 400-409. https://doi.org/10.24988/ije.1514118

İzmir İktisat Dergisi
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İZMİR İKTİSAT DERGİSİ 2022 yılı 37. cilt 1. sayı ile birlikte sadece elektronik olarak yayınlanmaya başlamıştır.