Analyze 10-Ks for value investing
The poster is experimenting with an LLM agent that reads 10-Ks and handles normalized earnings, ROIC, owner earnings and a rough reverse DCF. Task: Suggested implementation: An LLM agent reads 10-K filings to calculate normalized earnings, ROIC, owner earnings, and reverse DCF. The workflow involves extracting data from EDGAR/XBRL Companyfacts and handling custom tags or footnotes. Human analysts must verify extracted structures and passage locations to prevent hallucinations and ensure data integrity.; tools: custom-built or unnamed agent. Human role: Source evidence is unavailable; the provided text does not specify human roles or responsibilities.; limitations: Prerequisites include access to EDGAR/XBRL data and robust handling of custom tags. Constraints involve context window limits and the risk of false confidence from incomplete extraction. Operating costs may rise with complex parsing logic. Human approval is required for all financial outputs before use. Source reliability is low as the original link returned a 403 error..
Tools
custom-built or unnamed agent
Published on Agent Work Atlas:
Overview
The poster is experimenting with an LLM agent that reads 10-Ks and handles normalized earnings, ROIC, owner earnings and a rough reverse DCF.
Workflow
Suggested implementation: An LLM agent reads 10-K filings to calculate normalized earnings, ROIC, owner earnings, and reverse DCF. The workflow involves extracting data from EDGAR/XBRL Companyfacts and handling custom tags or footnotes. Human analysts must verify extracted structures and passage locations to prevent hallucinations and ensure data integrity.
Reported result
No performance results are reported in the source. Known issues include context limit failures when processing full filing histories, evidence loss during chunking, and unrealistic backtest outcomes due to data leakage. A measurable acceptance check is proposed: compare LLM-extracted financial metrics against manual review for accuracy on a sample of 10 filings.
Human role
Source evidence is unavailable; the provided text does not specify human roles or responsibilities.
Limitations
Prerequisites include access to EDGAR/XBRL data and robust handling of custom tags. Constraints involve context window limits and the risk of false confidence from incomplete extraction. Operating costs may rise with complex parsing logic. Human approval is required for all financial outputs before use. Source reliability is low as the original link returned a 403 error.
Try it
Act as a financial data analyst. I need to build a pilot workflow for extracting value investing metrics from 10-K filings. Inputs: [Replace with specific company tickers, e.g., AAPL, MSFT] and [Replace with source URL or local file path for 10-Ks]. Steps: 1. Retrieve the latest 10-K for each ticker using EDGAR/XBRL Companyfacts. 2. Use an LLM to extract normalized earnings, ROIC, and owner earnings. Ensure the model cites specific passage locations for each figure. 3. Handle custom tags and footnotes by flagging them for manual review rather than guessing. 4. Perform a dry run on 3 filings. Output a JSON object for each with fields: 'metric_name', 'value', 'source_passage', 'confidence_score'. 5. Stop and ask for my confirmation before processing the full batch. Deliverables: A JSON file with extracted metrics and a markdown report listing any custom tags or footnotes that required human intervention. Quality Check: Verify that no data is invented. If context limits are hit, split the document logically and re-verify cross-references. Do not proceed to full batch processing until I approve the dry run results.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.