Given that such opinions may and do sway potential investors’ interest, it is desirable to investigate whether better methods could be developed to support investors. Despite the fact that it and its underlying blockchain technology have been declared ‘dead’ on a number of occations, Footnote 1 recent all-time high prices Footnote 2 and research indicates otherwise (Ellul 2021). The main research contribution from this paper is that we demonstrate that not only can price direction prediction be made but the magnitude in price change can be predicted with relative accuracy ( 63%).īitcoin (Nakamoto 2009) has, since its introduction in 2008/9, attracted extensive attention for various reasons from different stakeholders with varying opinions regarding its utility and potential. It is shown that this model yields more reliable predictions when used alongside a price trend prediction model. An additional model is presented to predict the magnitude of change, which is framed as a multi-class classification problem. Two different neural network models are explored and evaluated, one based on recurrent nets and one based on convolutional networks.

We present results from experiments exploring the relation between sentiment and future price at different temporal granularities, with the goal of discovering the optimal time interval at which the sentiment expressed becomes a reliable indicator of price change.

We utilise not only sentiment extracted from tweets, but also the volume of tweets. In this paper, we seek to build on the state-of-the-art to not only predict the direction yet to also predict the magnitude of increase/decrease. Yet the state-of-the-art is limited to predicting the price direction and not the magnitude of increase/decrease. Twitter sentiment has been shown to be useful in predicting whether Bitcoin’s price will increase or decrease.

Google trends sentiment analysis