Journals / Fenerbahçe Üniversitesi Sosyal Bilimler Dergisi / 2022 / Cilt: 2 - Sayı: 2

Examining The Performance Of Technical Analysis Trading Strategies Applied To Cryptocurrency Markets

Kripto Para Piyasalarına Uygulanan Teknik Analiz Yöntemlerinin Performanslarının Analizi

Pages
258–272
Publication Date
2022-12-31
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The Sharpe ratio and Sortino ratio are applied in order to evaluate the trading performances of the related trading strategies, whereas the MDD (Maximum drawdown, MDD) is used in order to measure the downside risk of the related trading strategies.The results show that moving averages trading strategies offer high positive average rates of return and at least 90% of them outperform the "buy and hold" strategy.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study examines the performance of 30 different moving averages trading strategies on Bitcoin and Ethereum during the COVID-19 pandemic. 10-, 20-, 50-, 100- and 200-day simple and exponential moving averages trading strategies are applied in the analysis.
📊
Key Findings & Data:The performances of the moving averages trading strategies are also compared with the performance of the “buy and hold” strategy.
💡
Conclusion & Impact:The Sharpe ratio and Sortino ratio are applied in order to evaluate the trading performances of the related trading strategies, whereas the MDD (Maximum drawdown, MDD) is used in order to measure the downside risk of the related trading strategies.The results show that moving averages trading strategies offer high positive average rates of return and at least 90% of them outperform the "buy and hold" strategy.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Teknik analiz · Hareketli ortalama · Kripto para piyasaları

Abstract

This study examines the performance of 30 different moving averages trading strategies on Bitcoin and Ethereum during the COVID-19 pandemic. 10-, 20-, 50-, 100- and 200-day simple and exponential moving averages trading strategies are applied in the analysis. The performances of the moving averages trading strategies are also compared with the performance of the “buy and hold” strategy. The Sharpe ratio and Sortino ratio are applied in order to evaluate the trading performances of the related trading strategies, whereas the MDD (Maximum drawdown, MDD) is used in order to measure the downside risk of the related trading strategies.The results show that moving averages trading strategies offer high positive average rates of return and at least 90% of them outperform the "buy and hold" strategy. Additionally, findings indicate that especially the best performing moving averages trading strategies provide high significant downward protection compared to the buy and hold strategy because MDD of moving averages trading strategies are significantly less than that of the buy and hold strategy. Finally, the findings reveal that cryptocurrency markets are not weak-form efficient.

Özet

Bu çalışmada Covid-19 dönemi dikkate alınarak önemli bir teknik analiz yöntemi olan hareketli ortalama yöntemine dayalı 30 farklı yatırım stratejisinin performansı Bitcoin ile Ethereum için incelenmiştir. Analizlerde 10 günlük, 20 günlük, 50 günlük, 100 günlük ve 200 günlük basit ve üssel hareketli ortalama yöntemlerinden yararlanılmıştır. İlgili teknik analiz yöntemine dayalı yatırım stratejilerinin performansları “Satın al ve beklet (Buy and hold)” yatırım stratejisinin performansı ile de karşılaştırılmıştır. Yatırım stratejilerinin performanslarının analizinde Sharpe rasyosu ile Sortino rasyosundan, finansal risk düzeylerinin ölçümünde ise MDD (Maximum drawdown, MDD) yönteminden yararlanılmıştır. Çalışma bulguları ilgili teknik analiz yöntemlerine dayalı yatırım stratejilerinin yüksek pozitif ortalama getiri oranları sunduklarını ve en az %90’ının “satın al ve beklet” yatırım stratejisine göre daha iyi performans sergilediklerini göstermektedir. Bulgular ayrıca en iyi performansı sergileyen teknik analiz yöntemlerinin aşağı yönlü sert fiyat hareketlerinin yol açabileceği finansal risklere karşı da yüksek oranlı koruma sağladıkları sonucuna işaret etmektedir. Son olarak da bulgular kripto para piyasalarının zayıf formda etkin olmadığını göstermektedir.

🔖

Cite This Article

BÜBERKÖKÜ, Ö. (2022). Examining The Performance Of Technical Analysis Trading Strategies Applied To Cryptocurrency Markets. Fenerbahçe Üniversitesi Sosyal Bilimler Dergisi, Cilt: 2 - Sayı: 2, 258–272. https://ajindex.com/articles/873402

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. Corbet, S., Eraslan,V., Lucey, B., & Sensoy, A. (2019). The Effectiveness of Technical Trading Rules in Crypocurrency Markets. Finance Research Letters, 31,32-37.
  2. Çelik, O. (2019). Implementation of Technical Analysis on Selected Cryptocurrencies (Basılmamış Yüksek Lisans Tezi). Marmara Üniversitesi Sosyal Bilimler Enstitüsü İşletme Anabilim Dalı Muhasebe ve Finansman (ing) Bili Dalı, Ankara.
  3. Deniz, E. (2022). Teknik Analiz Nedir ? Teknik Analiz Nasıl Yapılır ?. GCM Yatırım Eğitim Makaleleri, https://www.gcmyatirim.com.tr/egitim/makaleler/teknik-analiz-nedir-teknik-analiz-nasil-yapilir.
  4. Detzel, A. L., Liu, H., Strauss, J., Zhou, G., & Zhu, Y. (2020). Learning and Predictability via Technical Analysis: Evidence from Bitcoin and Stocks with Hard-to-Value Fundamentals. SSRN,24,1-51.
  5. Fama, E.F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
  6. Gedik Forex (2018). Forex Rehberi. Erişim Adresi: https://gedik-cdn.foreks.com/forex/assets/ ckeditor attachment /000/001/ 361/original/forex_rehber.pdf. Erişim Tarihi: 04.04.2022.
  7. Grobys, K., Ahmed, S., & Sapkota, N. (2020). Technical Trading Rules in the Cryptocurrency Market. Finance Research Letters,32,1-7.
  8. Ha, S., & Moon, B.R. (2018).Finding Attractive Technical Patterns in Cryptocurrency Markets. Memetic Computing, 10, 301–306.
  9. Hiremath, G., & Kumari, J. (2014). Stock Returns Predictability and the Adaptive Market Hypothesis İn Emerging Markets: Evidence From India. Springerplus, 3(428), 1-14.
  10. Hudson, R., & Urquhart, A. (2021). Technical Trading And Cryptocurrencies. Analysis of Operations Research, 297, 191-220.
  11. Jain, R., Bhardwaj, P.,& Soni, P. (2022). Can The Market of Cryptocurrency Be Followed with the Technical Analysis. International Journal for Research in Applied Science & Engineering Technology, 10(IV), 2425-2445.
  12. Li, Z., & Meng, Q. (2022). Time and Frequency Connectedness and Portfolio Diversification Between Cryptocurencies and Renewable Energy Stock Markets During COVID-19. North American Journal of Economics and Finance, 59,1-15.
  13. Liberto, D. (2021). Adaptive Market Hypothesis (AMH), Investopedia,1-2. Erişim Adresi: https://www.investopedia.com/terms/a/adaptive-market-hypothesis.asp. Erişim Tarihi: 12.04.2022.
  14. Lo, A.W. (2004).The Adaptive Market Hypothesis: Market Efficiency from an Evolutionary Perspective. The Journal of Portfolio Management 30th Anniversary Issue, 30 (5) 15-29.
  15. Marot, E. (2017). 10 Market Timing Strategies, Compared. S