Dataset
2024-earnings-call-transcript
The 2024 Earnings Call Transcript dataset, published by yeong-hwan on Hugging Face, aggregates quarterly earnings call dialogue for individual stock tickers. It is distributed as a JSON table with 1,904 rows in a single train split.
Data preview
Dataset card
What this dataset contains
The dataset is a tabular-text corpus of 2024 earnings call transcripts organized by ticker, fiscal year, and quarter. It falls into the 1K–10K row size category and is tagged for English language content. Per the provided sample, row identifiers are minimally masked to single-letter strings, so the actual coverage of named companies is not documented.
Structure and fields
Each row carries a string ticker, an int64 year, an int64 quarter (q), and a conversations field that nests speaker and content objects representing the call dialogue. The dataset is formatted as JSON and is loadable with pandas, polars, or the Hugging Face datasets library. A single train split contains 1,904 rows; no separate validation or test splits are defined.
How analysts use it
Investment analysts can iterate row by row to read prepared remarks and analyst Q&A for a given ticker and quarter, supporting transcript-based NLP work such as topic extraction or tone scoring. Because every row is keyed by ticker, year, and quarter, the file can be filtered to build a panel of calls for one company across 2024 or compared across peers. The absence of a stated license and the anonymized sample rows mean downstream use should be treated cautiously until redistribution terms are confirmed.
Documentation & references 1
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SEC EDGAR 10-K & 10-Q financial statements for 2,600+ NASDAQ companies.
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