Bitcoin price prediction in the time of COVID-19
Jiayun Luo
Abstract
Based on the Bitcoin exchange data, COVID-19 data, and Twitter data from January 2020 to July 2020, this paper compares the performance of four different machine learning models on predicting the Bitcoin return rate and price trend. Data are formulated to four input feature sets, including: (1) Historical Bitcoin exchange data; (2) Historical Bitcoin exchange data + COVID-19 data (recovery, confirmed, death); (3) Historical Bitcoin exchange data + Twitter data; (4) Historical Bitcoin exchange data + COVID-19 data (recovery, confirmed, death) + Twitter data. The four machine learning models implemented are: (1) Random forest; (2) Decision tree; (3) AdaBoost; (4) Support vector machine. We found that: (1) Twitter data can improve the performance of models; (2) People consider information within 5 days when they make decisions on investments; (3) Support vector machine does not perform well in predicting Bitcoin return rate or price trend; (4) COVID-19 data does not help improve the prediction. However, we have very limited COVID-19 data, so future research with more COVID-19 data may help confirm if the last statement is correct or not.