The remarkable achievements and rapid advancements of Large Language Models
(LLMs) such as ChatGPT and GPT-4 have showcased their immense potential in
quantitative investment. Traders can effectively leverage these LLMs to analyze
financial news and predict stock returns accurately. However, integrating LLMs
into existing quantitative models presents two primary challenges: the
insufficient utilization of semantic information embedded within LLMs and the
difficulties in aligning the latent information within LLMs with pre-existing
quantitative stock features. We propose a novel framework consisting of two
components to surmount these challenges. The first component, the Local-Global
(LG) model, introduces three distinct strategies for modeling global
information. These approaches are grounded respectively on stock features, the
capabilities of LLMs, and a hybrid method combining the two paradigms. The
second component, Self-Correlated Reinforcement Learning (SCRL), focuses on
aligning the embeddings of financial news generated by LLMs with stock features
within the same semantic space. By implementing our framework, we have
demonstrated superior performance in Rank Information Coefficient and returns,
particularly compared to models relying only on stock features in the China
A-share market.

通过引入 LG 模型和 SCRL 方法，我们提出了一个用于量化投资的创新框架，能够有效将 Large Language Models 中包含的语义信息与现有的量化股票特征相结合，以提高金融新闻分析和股票收益预测的性能。在中国 A 股市场中，我们的框架相较于仅依赖于股票特征的模型，表现出卓越的排名信息系数和收益表现。