Complex forecasting and investment strategy optimization via chain-of-thought of large language models

This paper pioneers the application of chain-of-thought (CoT) prompting in large language models (LLMs) for financial forecasting and portfolio optimization. Leveraging anonymized financial statements from 608 Chinese A-share listed companies (2010–2023), we benchmark ChatGPT 4.0 against human analysts in earnings direction prediction. The CoT-enhanced model achieved superior accuracy (64.35% vs. 58.37%) and generated a 17.14% alpha with a Sharpe ratio of 1.5959 in backtests, demonstrating LLMs’ capacity to automate financial reasoning while reducing reliance on specialized expertise. Our findings bridge AI and quantitative finance by validating LLMs’ cross-domain adaptability using purely numerical data, offering practical implications for AI-driven investment decision systems.