  Bitcoin, with its ever-growing popularity, has demonstrated extreme price
volatility since its origin. This volatility, together with its decentralised
nature, make Bitcoin highly subjective to speculative trading as compared to
more traditional assets. In this paper, we propose a multimodal model for
predicting extreme price fluctuations. This model takes as input a variety of
correlated assets, technical indicators, as well as Twitter content. In an
in-depth study, we explore whether social media discussions from the general
public on Bitcoin have predictive power for extreme price movements. A dataset
of 5,000 tweets per day containing the keyword `Bitcoin' was collected from
2015 to 2021. This dataset, called PreBit, is made available online. In our
hybrid model, we use sentence-level FinBERT embeddings, pretrained on financial
lexicons, so as to capture the full contents of the tweets and feed it to the
model in an understandable way. By combining these embeddings with a
Convolutional Neural Network, we built a predictive model for significant
market movements. The final multimodal ensemble model includes this NLP model
together with a model based on candlestick data, technical indicators and
correlated asset prices. In an ablation study, we explore the contribution of
the individual modalities. Finally, we propose and backtest a trading strategy
based on the predictions of our models with varying prediction threshold and
show that it can used to build a profitable trading strategy with a reduced
risk over a `hold' or moving average strategy.
