Research Article

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

Volume: 12 Number: 3 September 30, 2023
EN

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

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.

Keywords

References

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Details

Primary Language

English

Subjects

Finance, Business Administration

Journal Section

Research Article

Publication Date

September 30, 2023

Submission Date

August 3, 2023

Acceptance Date

September 22, 2023

Published in Issue

Year 2023 Volume: 12 Number: 3

APA
Kanat, E. (2023). THE VALIDITY OF TECHNICAL ANALYSIS IN THE CRYPTOCURRENCY MARKET: EVIDENCE FROM MACHINE LEARNING METHODS. Journal of Business Economics and Finance, 12(3), 102-109. https://doi.org/10.17261/Pressacademia.2023.1821
AMA
1.Kanat E. THE VALIDITY OF TECHNICAL ANALYSIS IN THE CRYPTOCURRENCY MARKET: EVIDENCE FROM MACHINE LEARNING METHODS. JBEF. 2023;12(3):102-109. doi:10.17261/Pressacademia.2023.1821
Chicago
Kanat, Ersin. 2023. “THE VALIDITY OF TECHNICAL ANALYSIS IN THE CRYPTOCURRENCY MARKET: EVIDENCE FROM MACHINE LEARNING METHODS”. Journal of Business Economics and Finance 12 (3): 102-9. https://doi.org/10.17261/Pressacademia.2023.1821.
EndNote
Kanat E (September 1, 2023) THE VALIDITY OF TECHNICAL ANALYSIS IN THE CRYPTOCURRENCY MARKET: EVIDENCE FROM MACHINE LEARNING METHODS. Journal of Business Economics and Finance 12 3 102–109.
IEEE
[1]E. Kanat, “THE VALIDITY OF TECHNICAL ANALYSIS IN THE CRYPTOCURRENCY MARKET: EVIDENCE FROM MACHINE LEARNING METHODS”, JBEF, vol. 12, no. 3, pp. 102–109, Sept. 2023, doi: 10.17261/Pressacademia.2023.1821.
ISNAD
Kanat, Ersin. “THE VALIDITY OF TECHNICAL ANALYSIS IN THE CRYPTOCURRENCY MARKET: EVIDENCE FROM MACHINE LEARNING METHODS”. Journal of Business Economics and Finance 12/3 (September 1, 2023): 102-109. https://doi.org/10.17261/Pressacademia.2023.1821.
JAMA
1.Kanat E. THE VALIDITY OF TECHNICAL ANALYSIS IN THE CRYPTOCURRENCY MARKET: EVIDENCE FROM MACHINE LEARNING METHODS. JBEF. 2023;12:102–109.
MLA
Kanat, Ersin. “THE VALIDITY OF TECHNICAL ANALYSIS IN THE CRYPTOCURRENCY MARKET: EVIDENCE FROM MACHINE LEARNING METHODS”. Journal of Business Economics and Finance, vol. 12, no. 3, Sept. 2023, pp. 102-9, doi:10.17261/Pressacademia.2023.1821.
Vancouver
1.Ersin Kanat. THE VALIDITY OF TECHNICAL ANALYSIS IN THE CRYPTOCURRENCY MARKET: EVIDENCE FROM MACHINE LEARNING METHODS. JBEF. 2023 Sep. 1;12(3):102-9. doi:10.17261/Pressacademia.2023.1821

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