Integrating Stock Features and Global Information via Large Language
  Models for Enhanced Stock Return Prediction
Yujie Ding Shuai Jia Tianyi Ma Bingcheng Mao Xiuze Zhou Liuliu Li Dongming Han
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.