Financial News Sentiment Analysis and Market Sentiment Prediction Based on Large Language Models
DOI:
https://doi.org/10.71222/y0vdp428Keywords:
financial sentiment analysis, large language models, market prediction, multimodal learning, investor behavior, risk managementAbstract
This study investigates the application of large language models (LLMs) to financial news sentiment analysis and market sentiment prediction. By integrating textual signals from financial news with structured market data, the research constructs a methodological framework combining sentiment classification, multimodal learning, and time-series forecasting. Experiments demonstrate that LLMs, particularly domain-specific models such as FinBERT, outperform traditional machine learning and deep learning approaches in capturing nuanced financial sentiment. Moreover, incorporating sentiment variables into market prediction models enhances forecasting accuracy, as illustrated by improvements in RMSE, MAE, and R² metrics. Although the analysis relies on simulated data for demonstration, the results align with existing empirical studies, underscoring the potential of LLMs to bridge qualitative sentiment extraction and quantitative market forecasting. This study highlights opportunities for improving investment decision-making, risk monitoring, and policy evaluation while also addressing challenges related to data quality, interpretability, scalability, and ethical concerns.
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Copyright (c) 2025 Ruoyu Qi (Author)
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