ANALYSIS OF THE WEEKLY CLOSING PRICE OF BITCOIN: INFLUENCING FACTORS AND TRADER'S FORECASTS

Keywords: cryptocurrency, bitcoin, MACD histogram, forecasting, trader

Abstract

The study focuses on the degree of correlation between the MACD histogram and the closing price of bitcoin on a weekly timeframe, which is an important factor in the formation of traders' forecasts within the framework of technical analysis. The main objectives of the study were to find out whether the minimum price of bitcoin increased or decreased compared to the previous week with similar changes in the MACD, as well as to determine the average and maximum series for the weekly closing price. The study found that in 54.45% of cases, a trader can expect the closing price of bitcoin to increase this week if the MACD histogram showed an increase in the previous week; or to decrease this week if the MACD histogram showed a decrease in the previous week. A trader can expect the closing price of bitcoin to continue its direction of movement in the second week 48.02% of the time. A trader can expect the closing price movement in one direction to end after the 4th week 95.48% of the time.

References

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Appel G. Technical analysis: power tools for the active investors. 2005. 241 р.

Bâra A., Oprea S.V. An ensemble learning method for Bitcoin price prediction based on volatility indicators and trend. Engineering Applications of Artificial Intelligence. 2024. №133 (А). DOI: https://doi.org/10.1016/j.engappai.2024.107991

Chen K.S., Yang J.J. Price dynamics and volatility jumps in bitcoin options. Financial Innovation. 2024. №10. Р.132. DOI: https://doi.org/10.1186/s40854-024-00653-z

Ignatenko A., Dokiienko L. Practical use of the maximum price of bitcoin dynamics and the MACD histogram to formulate trader's forecasts. Інвестиції: практика та досвід. 2025. №1. С.120-126. DOI: https://doi.org/10.32702/2306-6814.2025.1.120

Ignatenko A., Dokiienko L. Generating trader forecasts based on the dynamics of the minimum bitcoin price and the MACD histogram. International Scientific Journal "Internauka". Series: "Economic Sciences". 2025. №1. DOI: https://doi.org/10.25313/2520-2294-2025-1-10610

Maleki N., Nikoubin A., Rabbani M., Zeinali Y. Bitcoin price prediction based on other cryptocurrencies using machine learning and time series analysis. Scientia Iranica. 2023. №30(1). Р.285-301. DOI: https://doi.org/10.24200/sci.2020.55034.4040

Rudd M., Porter D. Forecasting Bitcoin price trajectories using a supply and demand framework. 2024. URL: https://ssrn.com/abstract=5059523

Samizadeh I. Enhanced forecasting of bitcoin price dynamics using empirical evaluation of the time series forecasting model with integrated technical indicators and market repressor’s. 2024. DOI: https://doi.org/10.13140/RG.2.2.10560.34566

Shahzad A., Anwar Y., Nadeem M., Shair W. Cryptocurrency price dynamics: unveiling Bitcoin’s predictors. Journal of Policy Research. 2024. №10(3). Р.459–469. DOI: https://doi.org/10.61506/02.00364

Uras N, Marchesi L, Marchesi M, Tonelli R. Forecasting Bitcoin closing price series using linear regression and neural networks models. PeerJ Computer Science. 2020. DOI: https://doi.org/10.7717/peerj-cs.279

Zeba A., Jinan F., Ahmer S., Mahpar A. Bitcoin Price Prediction using ARIMA Model. DOI: https://doi.org/10.36227/techrxiv.12098067.v1.

MACD Bitcoin data: weekly prices and histogram from 2018 to 2024. URL: https://math-bitcoin-predictions.com/research/macd-bitcoin/

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Published
2025-01-27
How to Cite
Ignatenko, A., & Dokiienko, L. (2025). ANALYSIS OF THE WEEKLY CLOSING PRICE OF BITCOIN: INFLUENCING FACTORS AND TRADER’S FORECASTS. Economy and Society, (71). https://doi.org/10.32782/2524-0072/2025-71-17
Section
FINANCE, BANKING AND INSURANCE