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

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  • 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.

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  • Haojie Liu & Zihan Lin & Randall R. Rojas, 2025. "Enhancing Trading Performance Through Sentiment Analysis with Large Language Models: Evidence from the S&P 500," Papers 2507.09739, arXiv.org.
    • Handle: RePEc:arx:papers:2507.09739
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    File URL: http://arxiv.org/pdf/2507.09739
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