Guide
Parsing Earnings Calls with GPT-4o
Stop trying to build complex NLP pipelines. Here is the exact Python script and prompt structure to extract forward-looking statements from raw transcript text.
The Problem with Traditional NLP
Older sentiment analysis tools (like VADER) fall apart on financial text. "Debt was slashed" is positive, but "slashed" is traditionally a negative word. GPT-4o understands financial context out of the box.
The Architecture
- Fetch the transcript via an API (e.g., Financial Modeling Prep).
- Chunk the text if it exceeds the context window (though 128k context makes this less necessary).
- Pass the transcript to the OpenAI API with a strict system prompt.
- Force JSON output.
The System Prompt
You are a senior equity analyst.
Read the following earnings transcript and extract:
1. Guidance revisions (Up/Down/Unchanged)
2. Mentions of supply chain issues (Boolean)
3. Capex plans (Extract numbers)
4. Overall tone score (1-10, 10 being highly optimistic)
You must respond in valid JSON format ONLY.
Structure: { "guidance": "", "supply_chain_issues": true/false, "capex_notes": "", "tone_score": 0 }
Common Mistakes
- Ignoring JSON Mode: Always use `response_format={ "type": "json_object" }` in the API call. Otherwise, you'll spend hours writing regex to clean up markdown blocks.
- Temperature: Set temperature to `0`. You want deterministic extraction, not creative writing.