Automate Strategy Finding with LLM in Quant investment

• URL: http://arxiv.org/abs/2409.06289v1
• Date: Tue, 10 Sep 2024 07:42:28 GMT
• Title: Automate Strategy Finding with LLM in Quant investment
• Authors: Zhizhuo Kou, Holam Yu, Jingshu Peng, Lei Chen,
• Abstract summary: We propose a novel framework for quantitative stock investment in portfolio management and alpha mining. This paper proposes a framework where large language models (LLMs) mine alpha factors from multimodal financial data. Experiments on the Chinese stock markets demonstrate that this framework significantly outperforms state-of-the-art baselines.
• Score: 4.46212317245124
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite significant progress in deep learning for financial trading, existing models often face instability and high uncertainty, hindering their practical application. Leveraging advancements in Large Language Models (LLMs) and multi-agent architectures, we propose a novel framework for quantitative stock investment in portfolio management and alpha mining. Our framework addresses these issues by integrating LLMs to generate diversified alphas and employing a multi-agent approach to dynamically evaluate market conditions. This paper proposes a framework where large language models (LLMs) mine alpha factors from multimodal financial data, ensuring a comprehensive understanding of market dynamics. The first module extracts predictive signals by integrating numerical data, research papers, and visual charts. The second module uses ensemble learning to construct a diverse pool of trading agents with varying risk preferences, enhancing strategy performance through a broader market analysis. In the third module, a dynamic weight-gating mechanism selects and assigns weights to the most relevant agents based on real-time market conditions, enabling the creation of an adaptive and context-aware composite alpha formula. Extensive experiments on the Chinese stock markets demonstrate that this framework significantly outperforms state-of-the-art baselines across multiple financial metrics. The results underscore the efficacy of combining LLM-generated alphas with a multi-agent architecture to achieve superior trading performance and stability. This work highlights the potential of AI-driven approaches in enhancing quantitative investment strategies and sets a new benchmark for integrating advanced machine learning techniques in financial trading can also be applied on diverse markets.

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