  Predicting financial returns accurately poses a significant challenge due to
the inherent uncertainty in financial time series data. Enhancing prediction
models' performance hinges on effectively capturing both social and financial
sentiment. In this study, we showcase the efficacy of leveraging sentiment
information extracted from tweets using the FinBERT large language model. By
meticulously curating an optimal feature set through correlation analysis and
employing Bayesian-optimized Recursive Feature Elimination for automatic
feature selection, we surpass existing methodologies, achieving an F1-score
exceeding 70% on the test set. This success translates into demonstrably higher
cumulative profits during backtested trading. Our investigation focuses on
real-world SPY ETF data alongside corresponding tweets sourced from the
StockTwits platform.
