S&P 500 Earnings Transcripts
A comprehensive corpus of quarterly financial transcripts from S&P 500 companies over 20 years, ideal for semantic, temporal, and strategic analysis.
Description
The dataset S&P 500 Earnings Transcripts brings together more than 33,000 quarterly financial call transcripts from 685 American S&P 500 companies, covering the period 2005—2025. Each call is structured by speaker, thus offering a rich basis for NLP work applied to finance and the analysis of economic dialogues.
What is this dataset for?
- Analyzing market sentiment through executive statements
- Train automatic summary models for long economic content
- Building specialized models for financial question answering or the detection of weak signals
Can it be enriched or improved?
Yes, this corpus can be enriched with tone annotations, thematic tags (finance, growth, risk) or links to stock market data for correlation. It is also possible to couple transcripts with voice models for audio-text alignment.
🔎 In summary
🧠 Recommended for
- NLP analysts
- Quantitative finance researchers
- Automatic summary projects
🔧 Compatible tools
- LangChain
- SpacY
- LlamaIndex
- Hugging Face Transformers
- Haystack
💡 Tip
Use the stakeholder structure to train a role classification model (CEO, CFO, analyst).
Frequently Asked Questions
Does this dataset only cover S&P 500 companies?
No, it also includes other large American capitalizations in addition to S&P 500 companies.
Is the content suitable for an automatic summary template?
Yes, the richness of the text and the clear structure per speaker make it ideal for training financial summary models.
Can this dataset be cross-referenced with stock market data?
Yes, each file contains time and business information that makes it easy to join with other databases.
