评估用于中国股票交易的 LLM 情绪因子
文章 arXiv papers · 作者: Haohan Zhang et al.
总结
本文提出一个实验框架，用于检验大型语言模型能否从中文财经新闻中提取有用的情绪信号。研究将三种模型应用于公司相关新闻摘要，这些模型分别代表了提升模型性能的不同方法。研究将得到的情绪指标视为候选量化因子，把文本分析与投资组合决策联系起来，而不只是评估语言输出本身。
随后，研究基于这些因子构建交易策略，并通过旨在反映现实交易条件的回测进行评估。根据本文描述，其核心贡献是提供一套比较模型及其后续交易应用的标准化流程。摘录未提供模型名称、回测时期、基准、数值结果，也未详细说明如何防范数据泄漏和处理交易成本，因此无法据此判断哪种模型或策略表现最佳。研究将发现表述为对潜力的评估，而非持久预测能力的证明。
核心观点
- 可评估大型语言模型从中文财经新闻摘要中提取情绪的能力。
- 研究比较了三种代表不同性能提升方法的模型。
- 提取出的情绪被用于构建量化因子和交易策略。
- 回测将语言模型输出与投资表现评估联系起来。
- 摘录没有提供判断稳健性和预测持久性所需的数值结果或细节。
标签
全文
# Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements? # Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements? The rapid advancement of Large Language Models (LLMs) has spurred discussions about their potential to enhance quantitative trading strategies. LLMs excel in analyzing sentiments about listed companies from financial news, providing critical insights for trading decisions. However, the performance of LLMs in this task varies substantially due to their inherent characteristics. This paper introduces a standardized experimental procedure for comprehensive evaluations. We detail the methodology using three distinct LLMs, each embodying a unique approach to performance enhancement, applied specifically to the task of sentiment factor extraction from large volumes of Chinese news summaries. Subsequently, we develop quantitative trading strategies using these sentiment factors and conduct back-tests in realistic scenarios. Our results will offer perspectives about the performances of Large Language Models applied to extracting sentiments from Chinese news texts.
在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0
此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。