  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.
