Practical Applications DOI: https://doi.org/10.3905/pa.2022.pa495

     Abstract

In Analysis and Comparison of Natural Language Processing Algorithms as Applied to Bitcoin Conversations on Social Media, from the February 2022 issue of The Journal of Investing, authors Benjamin McMillan, Joshua Myers (both of IDX Digital Assets), An Nguyen (of Johns Hopkins University), Don Robinson, and Mark Kennard (both of Palladiem) studied how natural language processing (NLP) algorithms capture investor sentiment online and whether changes in sentiment can predict investment returns. The authors used four different NLP algorithms to measure trends in sentiment toward bitcoin on two Reddit message boards, r/bitcoin (which focuses on bitcoin) and r/investing (which covers general investments). The authors used sentiment analysis to determine whether positive or negative posts about bitcoin correlated with bitcoin’s short-term performance.

     Practical Applications

• Clusters of negative posts on the r/bitcoin subreddit tend to occur when bitcoin’s returns hit a local bottom, meaning they rise thereafter. Positive posts on r/bitcoin and r/investing do not have a statistically significant correlation with bitcoin’s performance.
   
• Negative posts on message boards hosting bitcoin-focused online communities may be predictive of bitcoin’s performance, while posts on boards hosting general investment discussions may not be. More research is necessary to determine which online communities best reflect the sentiments of bitcoin investors.
   
• Bitcoin investors may wish to explore the use of NLP algorithms and sentiment analysis to identify conditions when negative online posts indicate bitcoin is oversold. However, more research is necessary to determine which NLP algorithms best capture bitcoin-related sentiment online.