# Sentiment-driven prediction of financial returns: a Bayesian-enhanced FinBERT approach

arXiv: https://arxiv.org/abs/2403.04427 · HTML: https://mlsanity.com/paper/2403.04427/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [Sentiment Analysis of Twitter Data for Predicting Stock Market Movements](https://mlsanity.com/paper/1610.09225/) (2016)
- [Bayesian Optimization for Machine Learning : A Practical Guidebook](https://mlsanity.com/paper/1612.04858/) (2016)
- [Hawkes-based cryptocurrency forecasting via Limit Order Book data](https://mlsanity.com/paper/2312.16190/) (2023)

## Abstract (arXiv)

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.