arXiv 2306.142222024-05-04cs.CLcs.AIq-fin.ST

揭示情感的潜力：大型语言模型能否预测中国股票价格走势？

Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?

International Digital Economy Academy (IDEA)（国际数字经济学院（IDEA））；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Data Science and Analytics Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）数据科学与分析学域）；Financial Technology Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）金融科技学域）

Haohan Zhang, Fengrui Hua, Chengjin Xu, Hao Kong, Ruiting Zuo, Jian Guo

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AI总结：

本文通过标准化实验流程评估三种大型语言模型从中文新闻摘要中提取情感因子并构建量化交易策略的表现，以探究LLMs预测中国股票价格走势的潜力。

摘要：

大型语言模型（LLMs）的快速发展引发了关于其增强量化交易策略潜力的讨论。LLMs在分析财经新闻中关于上市公司的情感方面表现出色，为交易决策提供了关键洞察。然而，由于LLMs的固有特性，其在该任务中的表现差异显著。本文介绍了一套标准化的实验流程，用于进行全面评估。我们详细阐述了使用三种不同LLMs的方法，每种模型都体现了独特的性能增强途径，并专门应用于从大量中文新闻摘要中提取情感因子的任务。随后，我们利用这些情感因子开发量化交易策略，并在现实场景中进行回测。我们的结果将为大型语言模型应用于从中文新闻文本中提取情感的表现提供视角。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。