  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.
