Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?
This work addresses the challenge of enhancing quantitative trading strategies for investors by applying LLMs to sentiment analysis in Chinese financial news, though it appears incremental as it focuses on standardized evaluation rather than a novel breakthrough.
The paper tackled the problem of predicting Chinese stock price movements by evaluating the performance of three distinct Large Language Models (LLMs) in extracting sentiment factors from Chinese news summaries and using them in quantitative trading strategies, with results offering perspectives on their effectiveness through back-tests in realistic scenarios.
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.