Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction
This work addresses challenges in quantitative investment by improving stock prediction accuracy, though it appears incremental as it builds on existing LLM and quantitative modeling approaches.
The paper tackled the problem of integrating large language models (LLMs) with quantitative stock features for enhanced stock return prediction, proposing a framework with a Local-Global model and Self-Correlated Reinforcement Learning that demonstrated superior performance in Rank Information Coefficient and returns in the China A-share market.
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.