Abstract
For more than a decade, as the number and value of cryptocurrencies exploded, more and more investors flocked to the cryptocurrency market with the expectation of positive returns. The price of cryptocurrencies, on the other hand, is extremely volatile. As a result, there is a great need to develop an accurate price prediction model to assist investors in making decisions and profit. This paper focuses on developing an LSTM-based prediction model for Bitcoin, Ethereum, EOS, and Solana cryptocurrency price prediction and calculating their RMSE and MAPE. Furthermore, four models are compared using this calculated MAPE. Based on the comparison results, the impact of cryptocurrency volatility, liquidity, and technology level on the accuracy of the LSTM prediction model is also examined. The paper concludes that the LSTM model can predict the price of Bitcoin more accurately because Bitcoin has the least volatility, the most liquidity and uses the oldest but most secure consensus mechanism.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Advances in Economics, Management and Political Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
