Should you buy when the market is bullish? How does social media discussion affect the price of Bitcoin?

Foreign media analyzed Twitter data from 2017 to the present to verify whether social media discussions have predictive power over Bitcoin prices and can be a reliable price forecasting indicator.

With Bitcoin's (BTC) outstanding performance from the beginning of the year to date, data such as the number of Twitter tweets and Google search volume mentioning Bitcoin have reached new highs in two years. These external factors, especially social media indicators such as the number of tweets mentioning specific cryptocurrencies, daily search volumes, or the frequency of terms like "Lambos," "To The Moon," are considered potential factors affecting market prices.

To verify the impact of social media on the market, foreign media Cointelegraph used Twitter data from 2017 to the present for sentiment analysis, aiming to clarify the relationship between social media data and Bitcoin prices, and whether social media data can help investors predict returns, making it a novel investment indicator.

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Analyzing Tweets and Bitcoin Prices from 2017 to 2019

Looking at the daily number of tweets mentioning Bitcoin in 2017, there was a significant surge in tweet volume due to the bull market that year. On December 7, 2017, the number of tweets mentioning Bitcoin hit a historical high of 155,600. Following this peak, the average number of tweets sharply declined throughout 2018 and 2019.

The mid-term trend between tweet volume and Bitcoin price shown in the graph indicates a correlation between these two variables. The research further confirms that in 2017, the correlation between price and tweet volume was the highest at 0.86. Despite the bear market in 2018, the relationship between price and tweets remained high with a correlation coefficient of 0.74. Although Bitcoin saw a significant price increase from March to June in 2019, the correlation was relatively low that year, standing at only 0.12.

The following graph illustrates the annual correlation between tweet volume and trading volume, where a correlation coefficient of 1 indicates a complete positive correlation between Bitcoin and tweet volume, while -1 indicates a complete negative correlation. A correlation of 0 signifies no correlation between the two.

Tweets as Predictors of Bitcoin Trading Volume

To explore whether social media indicators can serve as reliable predictors of future Bitcoin prices, Cointelegraph conducted an annual analysis and found a significant correlation between tweet volume and trading volume. The results show that in 2017, the social media indicators had a higher correlation with Bitcoin compared to 2018 or 2019. In 2017, a 1% increase in tweet volume was likely to result in a 2.6% increase in Bitcoin trading volume on the same day, while the growth in trading volume in 2018 and 2019 was below 1%.

Further comparison of social media indicators with trading volume the next day revealed that in 2017, a 1% increase in tweet volume led to a 2.5% increase in Bitcoin trading volume the following day, compared to 0.64% in 2018 and 0.72% in 2019.

Can Tweets Accurately Predict Bitcoin Prices?

Any indicator that can predict future returns is the holy grail sought after by all markets. After studying historical data, Cointelegraph found that unlike the significant correlation with trading volume, there was no significant predictive power between tweet volume and Bitcoin returns (unless one considers a 1% increase in tweet volume leading to a 0.036% increase in return on the same day as significant).

Declining Yearly Relevance of Social Media Discussions in the Market

While the research indicates a high correlation between social media indicators and trading volume, the correlation between the two has gradually diminished over time. From the beginning of 2020 until now, the correlation is only at 0.423%. Although 2017 marked the largest bull market in the cryptocurrency market's history, it is undeniable that the market was not mature at that time. Investors were easily influenced by FOMO or FUD, thus the high correlation between social media index and trading volume was an expected result. However, as the market matures, it is possible that investors have become wiser and are no longer easily swayed by external opinions, leading to a significant decrease in correlation. From this perspective, the decreasing correlation between social media indicators and the market may be seen as a positive development.

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