International Journal on Science and Technology

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Enhancing Financial Market Prediction: A Comparative Analysis of RNN and LSTM Models for Cryptocurrency Price Forecasting

Author(s) Sadanand Sundaray
Country United States
Abstract Advances in deep learning models such asRecur-rent Neural Networks (RNNs) and Long Short-TermMemory (LSTM) networks have shown promise in forecastingfinancial markets, yet their application to cryptocurrencyremains un-derexplored. A detailed evaluation of both RNNand LSTM models is conducted, with a focus on Ethereumprice prediction using ten years of historical data. The studycompares model performance in capturing the intricate, non-linear dynamics of highly volatile cryptocurrency markets.Results indicate that LSTMs outperform RNNs in learninglong-term dependencies, but both models face limitations innoisy, non-stationary market conditions. A hybrid approachintegrating traditional technical indicators—Moving Averages,Relative Strength Index (RSI), and Bollinger Bands—demonstrates improved prediction accuracy. The researchhighlights the potential for real-time learning and dynamicfeature selection in enhancing model adaptability to suddenmarket changes, offering new insights into the capabilities andchallenges of applying deep learning to cryptocurrency trading.
Keywords Recurrent Neural Networks, Long SHort-Term Memory, Cryptocurrency, Crypto market
Field Mathematics > Economy / Commerce
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-04-22
Cite This Enhancing Financial Market Prediction: A Comparative Analysis of RNN and LSTM Models for Cryptocurrency Price Forecasting - Sadanand Sundaray - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3828
DOI https://doi.org/10.71097/IJSAT.v16.i2.3828
Short DOI https://doi.org/g9gdth
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