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Journals / Journal of Business Economics and Finance / 2023 / Cilt: 12 Sayı: 3

THE VALIDITY OF TECHNICAL ANALYSIS IN THE CRYPTOCURRENCY MARKET: EVIDENCE FROM MACHINE LEARNING METHODS

Ersin KANAT
Pages
102–109
Publication Date
2023-09-30
DOI
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AI Summary & Key Takeaways

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

Conclusion- In conclusion, this study delves into the potency of technical analysis techniques employed by investors in cryptocurrency markets.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Purpose- This study aims to assess the effectiveness of technical analysis indicators used by investors in the cryptocurrency market for making informed decisions.
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Key Findings & Data:Emphasizing the importance of accurate decision-making methods in financial markets, this research particularly focuses on the cryptocurrency market, which has gained significant attention among investors in recent years.
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Conclusion & Impact:Conclusion- In conclusion, this study delves into the potency of technical analysis techniques employed by investors in cryptocurrency markets.
🔬Design:Modeling & Simulation
📍Scope:2017 Verileri
⚙️Variables:Cryptocurrency · technical analysis · machine-learning

Abstract

Purpose- This study aims to assess the effectiveness of technical analysis indicators used by investors in the cryptocurrency market for making informed decisions. Emphasizing the importance of accurate decision-making methods in financial markets, this research particularly focuses on the cryptocurrency market, which has gained significant attention among investors in recent years. Methodology- The study specifically examines technical analysis, a widely employed method in various financial markets, with a focus on its predictive capabilities concerning Bitcoin price forecasts. Leveraging advanced technologies, such as big data analysis and machine learning, the research utilizes daily trading data from January 1, 2017, to June 30, 2022, presenting technical indicators and their associated error margins. Findings- The study highlights the significance of using Weighted Moving Average (WMA) and Stochastic Oscillator (STO) indicators in combination, demonstrating that multiple indicators outperform individual ones. This research underscores the effectiveness of technical analysis methods in the cryptocurrency market, aiding the development of enhanced investment strategies. Conclusion- In conclusion, this study delves into the potency of technical analysis techniques employed by investors in cryptocurrency markets. The insights indicate that combining indicators and technical analysis methods holds promise for future investment strategies. It is essential to note that even the best method can lead to losses, as evidenced by the presence of error margins, and absolute profitability cannot be guaranteed through technical analysis methods.

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Cite This Article

KANAT, E. (2023). THE VALIDITY OF TECHNICAL ANALYSIS IN THE CRYPTOCURRENCY MARKET: EVIDENCE FROM MACHINE LEARNING METHODS. Journal of Business Economics and Finance, Cilt: 12 Sayı: 3, 102–109. https://ajindex.com/articles/965558

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References (10)

  1. Abuselidze, G. D., Slobodianyk, A. N. (2021), Value Assessment of Shares of Corporate Issuers by Applying the Methods of Fundamental Analysis in the Stock Exchange Market. In A. V. Bogoviz (Ed.), The Challenge of Sustainability in Agricultural Systems. Cham: Springer, 25-39, ISBN: 978-3-030-72109-1.
  2. Agrawal, M., Khan, A. U., Shukla, P. K. (2019). Stock price prediction using technical indicators: a predictive model using optimal deep learning. International Journal of Recent Technology and Engineering, 8(2), 2297-2305.
  3. Anghel, D. G. (2021). A reality check on trading rule performance in the cryptocurrency market: Machine learning vs. technical analysis. Finance Research Letters, 39(C), 1-8. BlockChain. (2022, September 9). Retrieved from https://www.blockchain.com/charts/trade-volume
  4. Breiman, L., Friedman, J., Olshen, R. A., Stone, C. J. (1984). Classification and Regression Trees. Florida: Chapman and Hall/CRC, ISBN 978-0-412-04841-8.
  5. Bustos, O., Pomares, A., Gonzales, E. (2017). A Comparison between SVM and Multilayer Perceptron in Predicting an Emerging Financial Market: Colombian Stock Market. Congreso Internacional de Innovaciony Tendencias en Ingenieria (CONIITI 2017), Colombia: Institute of Electrical and Electronics Engineers Inc,. 1-6.
  6. Chai, J., Du, J., Lai, K., Lee, Y. (2015). A hybrid least square support vector machine model with parameters optimization for stock forecasting. Mathematical Problems in Engineering, 20(S), 1-7.
  7. Chakraborty P., Pria U., Rony M., Majumdar M. (2018). Predicting Stock Movement Using Sentiment Analysis of Twitter Feed. 2017 6th International Conference on Informatics, Electronics and Vision And 2017 7th International Symposium in Computational Medical and Health Technology, Icıev-Iscmht 2017, Institute of Electrical and Electronics Engineers Inc. (2018), Himeji: IEEE, 1-6.
  8. Chou, C. C., Lin, K. S. (2019). A fuzzy neural network combined with technical indicators and its application to Baltic Dry Index forecasting. Journal of Marine Engineering & Technology, 18(2), 82-91.
  9. Coyne S., Madiraju P., Coelho J. (2018). Forecasting Stock Prices Using Social Media Analysis. Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, Institute of Electrical and Electronics Engineers Inc. (2018), Orlando: IEEE, 1031-1038.
  10. Dang M., Duong D. (2016). Improvement Methods for Stock Market Prediction Using Financial News Articles. NICS 2016 - Proceedings of 2016 3rd National Foundation for Science