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 and Dongming Han

Papers from arXiv.org

Abstract: 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.

Date: 2023-10
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp, nep-fmk and nep-ifn
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in International Joint Conferences on Artificial Intelligence,2023

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2310.05627

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