Enhancing Trading Performance Through Sentiment Analysis with Large Language Models: Evidence from the S&P 500
Enhancing Trading Performance Through Sentiment Analysis with Large Language Models: Evidence from the S&P 500 ArXiv ID: 2507.09739 “View on arXiv” Authors: Haojie Liu, Zihan Lin, Randall R. Rojas Abstract 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. ...