International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 2 March-April 2024 Submit your research before last 3 days of April to publish your research paper in the issue of March-April.

Analyzing Cryptocurrency Prices through Artificial Intelligence

Author(s) P.Sathi Reddy, T. Ashish, B. Sankar, P. Srinivas Rao, P. Evan Theodar, P. Gayatri
Country India
Abstract Cryptocurrency is reshaping the financial landscape, gaining popularity and acceptance among merchants. Despite the increasing investments in cryptocurrency, its dynamic features, uncertainties, and predictability remain largely unknown, posing significant investment risks. This study aims to understand the factors influencing cryptocurrency value formation. Leveraging advanced artificial intelligence frameworks like fully connected Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) Recurrent Neural Network, we analyze the price dynamics of Bitcoin, Ethereum, and Ripple. Our findings indicate that ANN relies more on long-term history, while LSTM focuses on short-term dynamics, suggesting LSTM's superior efficiency in utilizing historical information. However, with sufficient historical data, ANN can achieve comparable accuracy to LSTM. This study underscores the predictability of cryptocurrency market prices, though the explanation may vary depending on the machine-learning model employed.
Keywords Cryptocurrency, Artificial Intelligence, ANN, Price Analysis
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-03-23
Cite This Analyzing Cryptocurrency Prices through Artificial Intelligence - P.Sathi Reddy, T. Ashish, B. Sankar, P. Srinivas Rao, P. Evan Theodar, P. Gayatri - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.15600
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.15600
Short DOI https://doi.org/gtn3vn

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