From SEC Filings to Financial Signals: Evaluating LLMs on Stock Market Reactions and Analyst Forecasts
Benchmarks for financial filings commonly evaluate question answering or numerical reasoning, leaving unclear whether outputs from large language models (LLMs) align with market and analyst outcomes.
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Key points
- Form 10-Q MD&A sections combine long-form management narrative with accounting and forward-looking information.
- We evaluate prompted, zero-shot outputs from 13 general-purpose and finance-specialized LLMs on 45,964 Form 10-Q filing events from 2001 to 2024, linking MD&A text to cross-sectional fundamental ranks, pre-filing analyst forecasts, and realized abnormal returns.
- Relative to disclosure alone, adding cross-sectional fundamental ranks raises mean effective-date rank correlation (RankIC) by 0.0089 for effective-event-date returns and 0.0098 for following-day returns, whereas analyst-summary context provides no comparable average gain.
- Across the four disclosure-containing views, finance-specialized models show an effective-event-date group advantage but no statistically reliable following-day advantage, while general-purpose Qwen models produce the strongest individual return-ranking results.
Sources (1)
- [1]From SEC Filings to Financial Signals: Evaluating LLMs on Stock Market Reactions and Analyst ForecastsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 09:54 PM
Benchmarks for financial filings commonly evaluate question answering or numerical reasoning, leaving unclear whether outputs from large language models (LLMs) align with market and analyst outcomes.
Form 10-Q MD&A sections combine long-form management narrative with accounting and forward-looking information.
Extractive summary: sentences quoted from the sources.
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