Cryptocurrency Market Dynamics: Analyzing Trends And Patterns In Bitcoin
DOI:
https://doi.org/10.38035/dijemss.v5i4.2553Keywords:
Cryptocurrency, Trends, Patterns, Bitcoin.Abstract
Consumer demand for speed, comfort, and security in financial transactions is rising along with the globalization of the business. As a result, we require a payment method that is dependable and simple for bank clients. A payment system is a set of arrangements that facilitates the exchange of value between individuals and financial institutions on a national and international level in order to deliver payments. A literature review, or literature review, is the research design used here. A literature review is an explanation of the hypotheses, conclusions, and other research materials that are gleaned from reference works and used as the foundation for further study. The literature review includes summaries, reviews, and the author's observations about a variety of literature sources (books, papers, slides, online material, and so forth) that address the subject under discussion. As it stands, Bitcoin is still the most popular cryptocurrency in terms of user base, market value, and popularity. Though certain altcoins are supported due to their better or more sophisticated features than Bitcoin, virtual currencies like Ethereum and Ripple, which are more commonly utilized as other enterprise solutions, are currently growing in popularity. Based on the current trajectory, cryptocurrencies are here to stay, but as time goes on, only a select handful will stand out as leaders in the face of growing competition and more visibility. The world of cryptocurrency may prove to be quite promising in the future as long as the bitcoin options market continues to gain traction, even with all the drawbacks that accompany it.
References
Aliyu, I., Um, T. W., Lee, S. J., Lim, C. G., & Kim, J. (2023). Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings. Computers, Materials and Continua, 75(3), 5947–5964. https://doi.org/10.32604/cmc.2023.037202
Islam, R., Fujiwara, Y., Kawata, S., & Yoon, H. (2019). Analyzing outliers activity from the time-series transaction pattern of bitcoin blockchain. Evolutionary and Institutional Economics Review, 16(1), 239–257. https://doi.org/10.1007/s40844-018-0107-8
Khan, Z. A., Xia, Y., Ali, S., Khan, J. A., Askar, S. S., Abouhawwash, M., & El-Rashidy, N. (2023). Identifying Hot Topic Trends in Streaming Text Data Using News Sequential Evolution Model Based on Distributed Representations. IEEE Access, 11(August), 98787–98804. https://doi.org/10.1109/ACCESS.2023.3312764
Nair, M., Marie, M. I., & Abd-Elmegid, L. A. (2023). Prediction of Cryptocurrency Price using Time Series Data and Deep Learning Algorithms. International Journal of Advanced Computer Science and Applications, 14(8), 338–347. https://doi.org/10.14569/IJACSA.2023.0140837
Özbay Karaku?, M. (2023). Impact of climatic factors on the prediction of hydroelectric power generation: a deep CNN-SVR approach. Geocarto International, 38(1). https://doi.org/10.1080/10106049.2023.2253203
Parente, M., Rizzuti, L., & Trerotola, M. (2024). A profitable trading algorithm for cryptocurrencies using a Neural Network model. Expert Systems with Applications, 238(PA), 121806. https://doi.org/10.1016/j.eswa.2023.121806
Park, S., & Yang, J. S. (2023). Intelligent cryptocurrency trading system using integrated AdaBoost-LSTM with market turbulence knowledge[Formula presented]. Applied Soft Computing, 145(November). https://doi.org/10.1016/j.asoc.2023.110568 (scopus)
Rahaman, A., Bitto, A. K., Biplob, K. B. M. B., Bijoy, M. H. I., Jahan, N., & Mahmud, I. (2023). Bitcoin trading indicator: a machine learning driven real time bitcoin trading indicator for the crypto market. Bulletin of Electrical Engineering and Informatics, 12(3), 1762–1772. https://doi.org/10.11591/eei.v12i3.4486
Rashid, N. A., & Ismail, M. T. (2023). Modelling and Forecasting the Trend in Cryptocurrency Prices. Journal of Information and Communication Technology, 22(3), 449–501. https://doi.org/10.32890/jict2023.22.3.6
Raza, S. A. (2021). Google Trends and Cryptocurrencies: A Nonparametric Causality-In-Quantiles Analysis Abstract: Paper Knowledge . Toward a Media History of Documents, 3(2), 6.
Rico-Peña, J. J., Arguedas-Sanz, R., & López-Martin, C. (2023). Models used to characterise blockchain features. A systematic literature review and bibliometric analysis. Technovation, 123(February). https://doi.org/10.1016/j.technovation.2023.102711
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Hugo Prasetyo Winotoatmojo, Antonius Ary Setyawan, Akbar Ramadhan Hendraningrat, Jovita Grace Setiawati
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish their manuscripts in this journal agree to the following conditions:
- The copyright on each article belongs to the author(s).
- The author acknowledges that the Dinasti International Journal of Education Management and Social Science (DIJEMSS) has the right to be the first to publish with a Creative Commons Attribution 4.0 International license (Attribution 4.0 International (CC BY 4.0).
- Authors can submit articles separately, arrange for the non-exclusive distribution of manuscripts that have been published in this journal into other versions (e.g., sent to the author's institutional repository, publication into books, etc.), by acknowledging that the manuscript has been published for the first time in the Dinasti International Journal of Education Management and Social Science (DIJEMSS).
