Enhancing Trading Performance Through Sentiment Analysis with Large Language Models: Evidence from the S&P 500
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913939710803968 |
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| author | Liu, Haojie Lin, Zihan Rojas, Randall R. |
| author_facet | Liu, Haojie Lin, Zihan Rojas, Randall R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09739 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Trading Performance Through Sentiment Analysis with Large Language Models: Evidence from the S&P 500 Liu, Haojie Lin, Zihan Rojas, Randall R. Computational Finance Trading and Market Microstructure 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. |
| title | Enhancing Trading Performance Through Sentiment Analysis with Large Language Models: Evidence from the S&P 500 |
| topic | Computational Finance Trading and Market Microstructure |
| url | https://arxiv.org/abs/2507.09739 |