Cryptocurrency Prediction

Abstract

Cryptocurrency price prediction has become crucial for informed trading decisions due to the volatile nature of assets like Bitcoin, Ethereum, Ripple, and Litecoin. Traditional methods like ARIMA and GARCH struggle with this volatility, while modern approaches such as machine learning and deep learning provide better accuracy. This study evaluates advanced models, including LSTM, GRU, and Light GBM, to predict cryptocurrency prices and assess trading strategies before and after the COVID-19 pandemic. GRU and LSTM excel at identifying patterns in price data, with GRU performing best for Ripple. Ensemble methods like Light GBM proved highly accurate for Bitcoin and Ethereum across time periods. Simpler models like RNN were sufficient for Ripple and Litecoin. The COVID-19 pandemic significantly impacted market dynamics, emphasizing the importance of precise predictions. Trading strategies based on model predictions showed that ensemble methods like Light GBM yielded the highest profitability post-pandemic. The findings highlight the need to tailor models to specific cryptocurrencies and market conditions. Improved deep learning tools can enhance trading efficiency and provide actionable insights for investors and policymakers. Future research could focus on predicting multiple cryptocurrencies simultaneously and optimizing portfolio-based trading strategies. Key Words: LSTM, ARIMA, GARCH, RNN