Surveying the prediction of risks in cryptocurrency investments using recurrent neural networks

Abstract

Abstract Decentralized cryptocurrencies have received much attention over the last few years. Bitcoin (BTC) has enabled straight online expenditures without the need for centralized financial institutions. Cryptocurrencies are used not only for online payments but are also increasingly used as financial assets. With the rise in the number of cryptocurrencies, including BTC, Ethereum (ETH), and Ripple (XRP), and the millions of daily trades through different exchange services, cryptocurrency trading is prone to challenges similar to those seen in the traditional financial industry, such as price and trend forecasting, volatility forecasting, portfolio building, and fraud detection. This study examines the use of Recurrent neural networks (RNNs) for predicting BTC, ETH, and XRP prices. Accurate price prediction is essential for investors and traders in this volatile market. Machine learning techniques, including RNNs, Long-Short-Term Memory (LSTM), and convolutional neural networks, have been employed to forecast cryptocurrency prices with varying degrees of success. The aim of this study is to evaluate the effectiveness of RNNs in predicting cryptocurrency prices and compare their performance with other established methods. The results indicate that RNNs, particularly LSTMs and Gated Recurrent Units, demonstrate excellent capabilities in accurately predicting currency prices and providing insights to investors and traders in the cryptocurrency market.