Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?
Haohan Zhang, Fengrui Hua, Chengjin Xu, Hao Kong, Ruiting Zuo, Jian Guo
TL;DR
The paper tackles whether large language models can reliably extract sentiment from Chinese financial news to predict stock price movements. It introduces a standardized experimental procedure and applies three distinct LLMs, each with a different enhancement strategy, to extract sentiment factors from vast Chinese news summaries. Quantitative trading strategies are built on these sentiment factors and back-tested under realistic conditions to assess practical viability. The results offer guidance on the relative strengths and limitations of LLM-based sentiment extraction for Chinese market signals and emphasize the importance of procedural standardization for fair comparison.
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.