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.
Keywords: Sentiment analysis, GPT-2, FinBERT, ARIMA, Technical indicators
Complexity vs Empirical Score
- Math Complexity: 4.5/10
- Empirical Rigor: 7.5/10
- Quadrant: Street Traders
- Why: The paper uses established time-series models (ARIMA, ETS) and basic technical indicators, but lacks advanced mathematical derivations, focusing instead on practical implementation. Empirical rigor is high due to real-time data integration, benchmarking against buy-and-hold, and evaluation of trading performance on S&P 500.
flowchart TD
A["Research Goal: Enhance S&P 500 Trading Performance"] --> B["Data Acquisition & Sentiment Analysis"]
B --> C{"Data Processing"}
C --> D["Computational Models & Integration"]
D --> E["Backtesting & Benchmarking"]
E --> F["Key Findings & Outcomes"]
B --> B1["Financial News Data"]
B --> B2["GPT-2 & FinBERT Models"]
C --> C1["Technical Indicators"]
C --> C2["Real-time Sentiment Scores"]
D --> D1["ARIMA & ETS Time-Series Models"]
D --> D2["Momentum/Trend Metrics"]
E --> E1["Buy-and-Hold Benchmark"]
E --> E2["Sentiment-Based Strategy"]
F --> F1["Improved Trading Performance"]
F --> F2["Dynamic Market Adaptation"]
F --> F3["Superior Volatility Handling"]