Browsing by Subject "bitcoin"

In little over a decade, cryptocurrencies have become a highly speculative asset class in global financial markets, with Bitcoin leading the way. Throughout its relatively brief history, the price of bitcoin has gone through multiple cycles of growth and decline. As a consequence, Bitcoin has become a widely discussed – and polarizing – topic on Twitter. This work studies whether the sentiment of popular Bitcoin-related tweets can be used to predict the future price movements of bitcoin. In total, seven different algorithms are evaluated: Vector Autoregression, Vector Autoregression Moving-Average, Random Forest, XGBoost, LightGBM, Long Short-Term Memory, and Gated Recurrent Unit. By applying lexicon-based sentiment analysis, and heuristic filtering of tweets, it was discovered that sentiment-based features of popular tweets improve the prediction accuracy over baseline features (open-high-low-close data) in five of the seven algorithms tested. The tree-based algorithms (Random Forest, XGBoost, LightGBM) generally had the lowest prediction errors, while the neural network algorithms (Light Short-Term Memory and Gated Recurrent Unit) had the poorest performance. The findings suggest that the sentiment of popular Bitcoin-related tweets can be an important feature in predicting the future price movements of bitcoin.