Enhancing Trading Performance Through Sentiment Analysis with Large Language Models: Evidence from the S&P 500

Haojie Liu, Zihan Lin and Randall R. Rojas

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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.

Date: 2025-07
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