Dataset

sp500_earnings_transcripts

S&P 500 earnings-call transcripts in Parquet under MIT, tens of thousands of calls. Independently compiled and comparable to other public transcript corpora for sentiment and tone work.

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The sp500_earnings_transcripts dataset aggregates earnings call transcripts for S&P 500 companies and other US large-caps from 2005 to 2025. It is published on the Hugging Face Hub by Bose345 in Parquet format with a single train split of 33,362 rows.

What this dataset contains

The dataset holds earnings call transcripts for S&P 500 constituents and primary US large-cap equity listings, covering all 11 GICS sectors. Coverage expands noticeably after 2007 and, as of May 2025, includes transcripts from major index companies such as Agilent Technologies (A), Abbott Laboratories (ABT), Exxon Mobil Corporation (XOM) and Yum! Brands (YUM). Only the primary share class is generally included for companies with multiple listings.

Structure and fields

Each row is keyed by a company record with the string columns symbol, company_name, date and content, paired with the int64 columns quarter and year and a float64 company_id. A separate structured_content field captures the call as ordered segments, each carrying a speaker label and text payload, allowing the operator introduction, management remarks, Q&A and closing to be analyzed independently. The file is delivered as Parquet and is loadable through the datasets and pandas libraries.

How analysts use it

Analysts can filter transcripts by ticker, year and quarter, for example isolating Apple (AAPL) transcripts from 2022, to build text-based features for fundamental research. The structured_content segmentation supports speaker-attribution studies, sentiment scoring of prepared remarks versus analyst Q&A, and NLP modeling around forward-looking guidance. Because the dataset spans two decades of index constituents, it is also suited to backtesting event-driven signals around earnings releases across sectors.

Documentation & references 1