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

Haohan Zhang, Fengrui Hua, Chengjin Xu, Hao Kong, Ruiting Zuo +1 more
6/25/2023
cs.CLcs.AIq-fin.ST

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Abstract

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.

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Cite this paper

@article{zhang2023unveiling,
  title  = {Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?},
  author = {Haohan Zhang and Fengrui Hua and Chengjin Xu and Hao Kong and Ruiting Zuo and Jian Guo},
  year   = {2023},
  eprint = {2306.14222},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.14222v2}
}

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