Social turbulence can affect people financial decisions, causing changes in spending and saving. During a global turbulence as significant as the COVID-19 pandemic, such changes are inevitable. Here we examine how the effects of COVID-19 on various jurisdictions influenced the global price of Bitcoin. We hypothesize that lock downs and expectations of economic recession erode people trust in fiat (government-issued) currencies, thus elevating cryptocurrencies. Hence, we expect to identify a causal relation between the turbulence caused by the pandemic, demand for Bitcoin, and ultimately its price. To test the hypothesis, we merged datasets of Bitcoin prices and COVID-19 cases and deaths. We also engineered extra features and applied statistical and machine learning (ML) models. We applied a Random Forest model (RF) to identify and rank the feature importance, and ran a Long Short-Term Memory (LSTM) model on Bitcoin prices data set twice: with and without accounting for COVID-19 related features. We find that adding COVID-19 data into the LSTM model improved prediction of Bitcoin prices.

翻译：社会动荡会影响人们的财务决策，导致支出与储蓄行为发生变化。在COVID-19疫情这类全球性重大动荡期间，此类变化不可避免。本文研究了COVID-19对不同司法管辖区的影响如何作用于比特币的全球价格。我们假设封锁措施与经济衰退预期会削弱人们对法定（政府发行）货币的信任，从而推高加密货币价格。因此，我们预期能够识别疫情引发的动荡、比特币需求及其最终价格之间的因果关系。为验证这一假设，我们整合了比特币价格数据集与COVID-19病例及死亡数据，同时构建了额外特征，并应用统计与机器学习（ML）模型。采用随机森林（RF）模型识别并排序特征重要性，并对比特币价格数据集运行长短期记忆（LSTM）模型两次：分别考虑与不考虑COVID-19相关特征。研究发现，将COVID-19数据纳入LSTM模型后，比特币价格预测效果得到改善。