Abstract
In this study, we predicted the log returns of the top 10 cryptocurrencies based on market cap, using univariate and multivariate machine learning methods such as recurrent neural networks, deep learning neural networks, Holt’s exponential smoothing, autoregressive integrated moving average, ForecastX, and long short-term memory networks. The multivariate long short-term memory networks performed better than the univariate machine learning methods in terms of the prediction error measures.
Highlights
Cryptocurrencies are virtual currencies used to buy goods and services; there is no need for financial institutions such as central authorities or clearing houses in transactions involving cryptocurrency
When looking at the prediction error measures such as Root-mean-square (prediction) error (RMSE) in Table 2 and mean absolute error deviation (MAD) in Table 3, the multivariate long short-term memory networks (LSTMs) time-series model seems to have consistently lower numbers for the price log returns of cryptocurrencies compared to the univariate machine learning methods, except for the case of BTC, because BTC is a major cryptocurrency that influences the prices of all other Altcoins
We proved from this research that the multivariate LSTM machine learning method can handle covariate time-series data for 10 cryptocurrencies without experiencing computational difficulties
Summary
Cryptocurrencies are virtual currencies used to buy goods and services; there is no need for financial institutions such as central authorities or clearing houses in transactions involving cryptocurrency. Katsiampa (2017) studied volatility estimation for Bitcoin by comparing generalized autoregressive conditional heteroskedasticity (GARCH) models. Mostafa et al (2021) implemented GJR-GARCH over the GARCH model to estimate the volatility of 10 popular cryptocurrencies based on market capitalization: Bitcoin, Bitcoin Cash, Bitcoin SV, Chainlink, EOS, Ethereum, Litecoin, TETHER, Tezos, and XRP. Kim et al (2021) used stochastic volatility and GARCH models on cryptocurrencies that they selected for the study, and learned that the stochastic volatility method has better forecasting results compared to the GARCH method. We apply univariate and multivariate machine learning methods such as recurrent neural networks (RNNs), deep learning networks (DNNs), long short-term memory networks (LSTMs), Holt’s exponential smoothing, autoregressive integrated moving average, and ForecastX to predict the log returns of cryptocurrencies.
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