Social media engagement and cryptocurrency performance

Abstract

Cryptocurrencies are highly speculative assets with large price volatility. If one could forecast their behavior, this would make them more attractive to investors. In this work we study the problem of predicting the future performance of cryptocurrencies using social media data. We propose a new model to measure the engagement of users with topics discussed on social media based on interactions with social media posts. This model overcomes the limitations of previous volume and sentiment based approaches. We use this model to estimate engagement coefficients for 48 cryptocurrencies created between 2019 and 2021 using data from Twitter from the first month of the cryptocurrencies’ existence. We find that the future returns of the cryptocurrencies are dependent on the engagement coefficients. Cryptocurrencies whose engagement coefficients have extreme values have lower returns. Low engagement coefficients signal a lack of interest, while high engagement coefficients signal artificial activity which is likely from automated accounts known as bots. We measure the amount of bot posts for the cryptocurrencies and find that generally, cryptocurrencies with more bot posts have lower future returns. While future returns are dependent on both the bot activity and engagement coefficient, the dependence is strongest for the engagement coefficient, especially for short-term returns. We show that simple investment strategies which select cryptocurrencies with engagement coefficients exceeding a fixed threshold perform well for holding times of a few months.

Efficient Market Hypothesis on the blockchain: A social‐media‐based index for cryptocurrency efficiency

“…Hence, other researchers suggest that further scrutiny is required, since sentiment analysis alone might not have enough descriptive power to capture the information exchange in online platforms (Banerjee, 2022;Evangelopoulos et al, 2012;Kalamara et al, 2022). Qureshi and Zaman (2023) suggest using user engagement with social media posts and, employing a sample of 48 cryptocurrencies, find that this measure can help predict returns.…”