Predictive Power of An Ensemble Model for Cryptocurrency Forecasting

Abstract

Cryptocurrencies have received much attention amongst investors and policymakers due to the innovative features and simplicity. However, prices of the cryptocurrencies are nonlinear and volatile, which creates challenges for the investors to forecast the cryptocurrency prices. The present study takes the price data of two important cryptocurrencies, i.e., Bitcoin and Ripple, for 2013 to 2020. The study presents the forecasting accuracy of statistical models such as random walk (RW) and autoregressive integrated moving average (ARIMA), and machine learning models such as artificial neural network (ANN) and ensemble model. The study develops the ensemble of RW, ARIMA, and ANN. The study compares the predictive power of all the models and demonstrates that the forecasting accuracy of the ensemble model is better than all the component models, i.e., RW, ARIMA, and ANN. The results of the study have several implications for investors, traders, and policymakers.