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Predicting Bitcoin Price with the LSTM Model


 
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1. Title Title of document Predicting Bitcoin Price with the LSTM Model
 
2. Creator Author's name, affiliation, country Osman Gazi Polat; İstanbul Ticaret Üniversitesi; Turkey
 
2. Creator Author's name, affiliation, country Ayben Koy; İstanbul Ticaret Üniversitesi; Turkey
 
3. Subject Discipline(s) behavioral economics
 
3. Subject Keyword(s) LSTM, Bitcoin, Cryptocurrency, Neural Network, Prediction
 
3. Subject Subject classification behavioral finance
 
4. Description Abstract

Forecasting techniques and models play a pivotal role in guiding individuals and organizations towards informed decision-making and prudent investments. The accuracy of the forecast enables successful decisions and allows investors to maximize utility. The developments in finance and finance-related technologies around the world, along with innovative financial instruments, have piqued the interest of investors. Undoubtedly, the most prominent among these advancements is Bitcoin, a product of blockchain technology. In this study, future predictions will be generated using the LSTM model, relying on the historical data of Bitcoin and crucial market predictors. Specifically, three distinct datasets will be employed, each drawing one indicator from four different indicator types. With these datasets in hand, the study aims to predict the next ten data points pertaining to Bitcoin's performance, employing the relevant methodology. This forecast serves as a guiding model for investors and as a risk reduction study, given that it encompasses 3 different management scenarios for Bitcoin.

 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s)
 
7. Date (YYYY-MM-DD) 23-05-2024
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type Neural Network
 
9. Format File format PDF
 
10. Identifier Uniform Resource Identifier https://www.jital.org/index.php/jital/article/view/452
 
11. Source Title; vol., no. (year) Journal of International Trade, Logistics and Law; Volume 10, Issue 1 (2024): June
 
12. Language English=en en
 
13. Relation Supp. Files
 
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.)
 
15. Rights Copyright and permissions Copyright (c) 2024 Journal of International Trade, Logistics and Law