Forecasting Cryptocurrency Prices Using ARIMA and Neural Network: A Comparative Study
Keywords:ARIMA, Neural Network, Forecasting, Cryptocurrency, Market Efficiency
AbstractThe prices of cryptocurrencies are very volatile and forecasting them is a challenging task for the researchers across the world. The present study examines the accuracy of forecasted returns of the two most popular cryptocurrencies (Bitcoin and Ethereum) for the sample period spanning from October 1, 2013, to November 30, 2018. Auto-regressive integrated moving average (ARIMA) and Neural Network models have been used to forecast the returns of the cryptocurrencies. The forecasting results for different time-horizons indicate that for a shorter time-horizon, ARIMA model is better for forecasting the returns of cryptocurrencies, whereas, for a longer time-horizon, Neural Network model is better for forecasting the returns of cryptocurrencies. These results have implications for traders, investors, regulators, policymakers and academia.
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