Nowcasting the Bitcoin Market with Twitter Signals

TL;DR

This study investigates the relationship between Twitter emotional signals and Bitcoin market indicators over 104 days, finding that Twitter sentiment reflects rather than predicts market movements, with trading volume influencing emotional signals.

Contribution

It provides an empirical analysis of Twitter emotional signals and their correlation with Bitcoin market data, highlighting that Twitter reflects market dynamics more than it predicts them.

Findings

Positive correlation between emotional tweets and Bitcoin prices and volume

No significant causal effect of tweets on Bitcoin market values

Trading volume influences emotional signals within 24-72 hours

Abstract

This paper analyzes correlations and causalities between Bitcoin market indicators and Twitter posts containing emotional signals on Bitcoin. Within a timeframe of 104 days (November 23rd 2013 - March 7th 2014), about 160,000 Twitter posts containing "bitcoin" and a positive, negative or uncertainty related term were collected and further analyzed. For instance, the terms "happy", "love", "fun", "good", "bad", "sad" and "unhappy" represent positive and negative emotional signals, while "hope", "fear" and "worry" are considered as indicators of uncertainty. The static (daily) Pearson correlation results show a significant positive correlation between emotional tweets and the close price, trading volume and intraday price spread of Bitcoin. However, a dynamic Granger causality analysis does not confirm a statistically significant effect of emotional Tweets on Bitcoin market values. To the contrary, the analyzed data shows that a higher Bitcoin trading volume Granger causes more signals of uncertainty within a 24 to 72-hour timeframe. This result leads to the interpretation that emotional sentiments rather mirror the market than that they make it predictable. Finally, the conclusion of this paper is that the microblogging platform Twitter is Bitcoin's virtual trading floor, emotionally reflecting its trading dynamics.