Large Language Models and Stock Investing: Is the Human Factor Required?
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摘要
This working paper evaluates four state-of-the-art LLMs (ChatGPT, Gemini, DeepSeek, Perplexity) on IBEX-35 stock prediction tasks and finds that LLMs can deliver economically meaningful outperformance only when combined with structured prompts and active human oversight; recurring failures (data retrieval, financial misinterpretation, arithmetic and meta-reasoning) limit autonomous deployment, while grounding recommendations in regulatory filings substantially improves accuracy and risk-adjusted returns [page::0][page::26]
