Dataset
earnings-call-data
S&P 500 earnings call episodes from 2005 to 2025 are released as static tabular rows, each identified by a company-quarter episode identifier and containing full transcripts along with SEC press materials, suited to supervised or reinforcement-style experiments.
Data preview
Dataset card
The earnings-call-data dataset on Hugging Face, published by user RudrakshNanavaty, merges S&P 500 earnings transcripts with SEC 8-K press-release text and price-history features for episodes spanning roughly 2005 to 2025. It is distributed as parquet files under an MIT-licensed dataset card, though the publisher notes that users remain responsible for the upstream terms of the source transcript dataset, Yahoo Finance data, and SEC redistribution.
What this dataset contains
The dataset is built from upstream Hugging Face earnings-call transcripts, Yahoo Finance price history, and SEC EDGAR 8-K filings, then merged into per-quarter episodes. The publisher reports roughly 33,000 rows in episodes.parquet and roughly 16,000 rows in episodes_press_release_8k.parquet, covering hundreds of S&P 500 tickers from 2005 through 2025. Optional SEC XBRL companyfacts (us-gaap) fields can be joined for episodes from 2009 onward.
Structure and fields
Each row in episodes.parquet is keyed by episode_id (for example, A_2006_Q1) and carries identifying fields such as symbol, company_name, company_id, year, quarter, date, earnings_date, and sector. Text content is stored in earnings_transcript, press_release_8k_body, press_release_ex991, press_release_ex992, and press_release_sources. Engineered and price-derived fields include boolean flags guidance_mentioned and beat_mentioned, longer-horizon price features like price_momentum_30d, price_momentum_90d, pct_from_52w_high_pt, and avg_volume_20d, plus D-1 and D+1 open/high/low/close/volume columns sourced from yfinance.
How analysts use it
Quant teams can join the transcript text in earnings_transcript with the boolean flags guidance_mentioned and beat_mentioned to train text-classification or summarization models that score management tone around earnings. Event-driven funds may combine the D-1 and D+1 open/high/low/close/volume fields with price_momentum_30d and pct_from_52w_high_pt to study post-earnings drift and reaction windows, while ESG or sector-rotation strategies can aggregate by the sector field across year and quarter. The Hugging Face listing also tags the data for feature-extraction, text-retrieval, and reinforcement-learning tasks, suggesting it can serve as a training corpus for NLP pipelines that consume SEC and earnings materials.
Documentation & references 1
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SEC EDGAR 10-K & 10-Q financial statements for 2,600+ NASDAQ companies.
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