Sentiment-driven prediction of financial returns: a Bayesian-enhanced FinBERT approach
This work addresses financial prediction for investors by improving accuracy with sentiment analysis, though it is incremental as it builds on existing methods like FinBERT.
The study tackled the challenge of predicting financial returns by integrating sentiment from tweets using FinBERT and Bayesian-optimized feature selection, achieving an F1-score over 70% and higher cumulative profits in backtested trading.
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.