This study integrates real-time sentiment analysis from financial news, GPT-2 and FinBERT, with technical indicators and time-series models like ARIMA and ETS to optimize S&P 500 trading strategies. By merging sentiment data with momentum and trend-based metrics, including a benchmark buy-and-hold and sentiment-based approach, is evaluated through assets values and returns. Results show that combining sentiment-driven insights with traditional models improves trading performance, offering a more dynamic approach to stock trading that adapts to market changes in volatile environments.
@article{arxiv.2507.09739,
title = {Enhancing Trading Performance Through Sentiment Analysis with Large Language Models: Evidence from the S&P 500},
author = {Haojie Liu and Zihan Lin and Randall R. Rojas},
journal= {arXiv preprint arXiv:2507.09739},
year = {2025}
}