Alternative data datasets
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Quarterly conference calls are processed to detect revisions in guidance and shifts in executive language.
A static tabular extension of the Bose345/sp500_earnings_transcripts collection covering the same 2005 to 2025 calendar of S&P 500 earnings events, where each row represents a single company-quarter call identified by a stable episode_id and bundles the full transcript along with related SEC press materials and pre-event inputs suitable for supervised learning or reinforcement-style experimentation.
Quarterly Earnings Call Transcripts for 10 NASDAQ companies from 2016-2020
An English-language NLP corpus of cleaned earnings call transcripts gathered from public investor-relations pages, sized between 10,000 and 100,000 documents for financial analysis and LLM work.
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.
View source information and access options.
Earnings Call Transcripts of META (formerly Facebook) 2018-2025
EarningsSignal: Earnings Call Tone & Guidance Classification Dataset
CSV file contains over 2000 rows of detailed commentary on AI mentions.
18,755 earnings call transcripts scraped from Motley Fool
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.
A private collection of cleaned earnings-call transcript segments derived from a Motley Fool Kaggle release, split into 135,306 training rows tagged with ticker, exchange, date, and chunk text.
The awinml/earnings_calls_transcripts dataset on the Hugging Face Hub packages a small collection of earnings-call transcript segments formatted as chat-style messages for fine-tuning language models. It is distributed as a parquet file and is tagged for text modality with libraries including datasets, pandas, mlcroissant, and polars.
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.
S&P 500 earnings-call transcripts preprocessed into optimized Parquet, tens of thousands of calls (MIT). Ready for sentiment, QA and summarization research on the large-cap earnings season.
Earnings-call transcripts from an IBM research project, tens of thousands of records with finance tags, in CC0. A filings-adjacent corpus for call-tone and question-and-answer analysis.
Earnings Call LLM Insights 📚 Read the Full Story: For a deep dive into the methodology, the wildest moments we found, and key takeaways, check out the blog post:KnowTrend.ai: Auto-Grading Ten Years of Earnings Calls for Prescience and Delusion This dataset contains LLM-generated analysis of ~70,000+ earnings call transcripts. The analysis was performed using Kimi k2-0905-preview, focusing on extra
Earnings-call transcripts for S&P 500 companies, tens of thousands of calls in English with timestamps, in Parquet (MIT). Direct input for call-tone and question-and-answer sentiment analysis across the large-cap earnings cycle.