用大模型预测选举日股市波动，提升市场预测准确性
From Votes to Volatility Predicting the Stock Market on Election Day
- 融合大语言模型与政治经济分析代理，捕捉选举影响
- 在选举日使标普500预测准确率显著提升
- 适合关注政治事件对金融市场影响的研究者
股市预测一直是广泛研究的课题，旨在为投资者提供更高回报的股票建议。近年来，随着深度学习模型的广泛应用，该领域受到更多关注。尽管这些模型在预测股市行为方面取得了优异表现，但针对特定场景的适配愈发重要。选举日即为关键场景之一，其特征是市场波动加剧，因胜选者政策将显著影响多个经济部门和企业。为此，我们提出选举日股市预测模型（EDSMF）。该方法利用大语言模型的上下文理解能力，并结合专门设计的智能体分析选举的政治经济后果。基于先进架构，我们证明了EDSMF在这一特殊高波动日提升了标普500的预测性能。
原文摘要 · Abstract (English)
Stock market forecasting has been a topic of extensive research, aiming to provide investors with optimal stock recommendations for higher returns. In recent years, this field has gained even more attention due to the widespread adoption of deep learning models. While these models have achieved impressive accuracy in predicting stock behavior, tailoring them to specific scenarios has become increasingly important. Election Day represents one such critical scenario, characterized by intensified market volatility, as the winning candidate's policies significantly impact various economic sectors and companies. To address this challenge, we propose the Election Day Stock Market Forecasting (EDSMF) Model. Our approach leverages the contextual capabilities of large language models alongside specialized agents designed to analyze the political and economic consequences of elections. By building on a state-of-the-art architecture, we demonstrate that EDSMF improves the predictive performance of the S&P 500 during this uniquely volatile day.
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