Augmented release built on Bose345/sp500_earnings_transcripts (same transcript calendar span as that collection: 2005–2025). Static tabular data for supervised learning or RL-style experiments on earnings-call episodes. Each row is one company–quarter call, keyed by a stable episode_id, with long-form text (full earnings transcript, SEC press materials), pre-earnings price context, OHLCV anchors, SEC XBRL fundamentals (xbrl_* columns), and post-earnings return labels.

Companion report: sweetviz_episodes.html — a Sweetviz profile of episodes.parquet, shipped in this dataset repo. View on the Hub or download the raw file and open it locally in a browser (distributions, missingness, associations).

What’s in this folder

These files are the materialized outputs of the build pipeline (upstream Hugging Face transcripts → Yahoo Finance prices → SEC EDGAR 8-K press text → feature engineering → merge → optional XBRL join). Intermediate download caches usually live under data/cache/ locally and are not required for analysis if you only use the parquet files below.

File	Role
`episodes.parquet`	Primary dataset — one row per episode with identity, text, features, OHLCV anchors, SEC XBRL fundamentals (`xbrl_*`), and labels (see Schema).
`episodes_press_release_8k.parquet`	Subset of `episodes.parquet`: only rows where `press_release_8k_body` is not null (same schema; fewer rows — on the order of ~16k after a full pipeline run). Browse on the Hub. Produced locally with `uv run python pipeline/filter_episodes_press_release_8k.py`.
`sweetviz_episodes.html`	Exploratory HTML report (Sweetviz) for `episodes.parquet`; same folder on the Hub as the parquet files (see below).
`raw_hf.parquet`	Base transcript metadata and structured content source fields from the upstream Hugging Face dataset (see Provenance).
`raw_prices.parquet`	Per-episode OHLCV anchors, sector, and price-derived fields from market data.
`raw_press_releases.parquet`	SEC 8-K body and exhibit text (e.g. EX-99.1 / EX-99.2) aligned to each episode.
`features.parquet`	Formatted earnings transcript, text flags, momentum/volume features, and label columns produced in the feature stage.

Rough scale (after a full pipeline run): on the order of ~33k rows in episodes.parquet and ~16k rows in episodes_press_release_8k.parquet, and hundreds of tickers (in line with upstream transcript coverage), 2005–2025 span — confirm row and symbol counts on your copy with len(pd.read_parquet("episodes.parquet")) and ep["symbol"].nunique().

Schema (`episodes.parquet`)

Columns follow this order in the merged export:

Identity: episode_id, symbol, company_name, company_id, year, quarter, date, earnings_date, sector

Text (observation): earnings_transcript, press_release_8k_body, press_release_ex991, press_release_ex992, press_release_sources

Text flags: guidance_mentioned, beat_mentioned

Pre-call price features: price_momentum_30d, price_momentum_90d, pct_from_52w_high_pt, avg_volume_20d

OHLCV anchors (grading / simulation): d_minus_1_*, d_plus_1_*, d_plus_30_*, next_qtr_d_minus_1_* (open, high, low, close, volume as listed in the table)

Labels / targets: sentiment_label, move_1d, move_30d, move_next_qtr, move_1d_direction, gap_open_d1, volume_surge_d1

Audit / quality: next_qtr_date

XBRL (SEC EDGAR companyfacts, 2009+): Per-episode numeric facts from the SEC company facts JSON API (data.sec.gov/api/xbrl/companyfacts/CIK{cik}.json), documented under SEC EDGAR APIs. Facts use us-gaap concepts only. Episodes with year < 2009 have nulls in all xbrl_* columns (no companyfacts match is attempted for those rows).

How it is joined: each episode’s ticker maps to a CIK via the same SEC ticker map used elsewhere in the pipeline (data/cache/edgar/cik_map.json, built during EDGAR steps or with uv run python pipeline/build_cik_map.py). If no CIK is found, companyfacts are not fetched for that row. After the merged table exists, run:

uv run python pipeline/06_xbrl.py

That step fills xbrl_* on episodes.parquet and refreshes episodes_press_release_8k.parquet with the same columns. Requests respect SEC rate limits (under 10 requests per second). When you run the pipeline locally, gaps and reasons are appended to reports/failures_xbrl.csv (not required to use the Hub parquet).

Matching logic: each metric tries several GAAP local names in priority order (e.g. revenue tries Revenues, then revenue-from-contract variants, then net sales) so more cells populate despite issuer tag choice; see pipeline/06_xbrl.py for the exact chains.

Provenance (string): for each value column there is a sibling *_tag column (e.g. xbrl_revenue_tag) with the winning local GAAP name, or null if the value is null.

Income statement: xbrl_revenue, xbrl_cost_of_revenue, xbrl_gross_profit, xbrl_operating_income, xbrl_net_income, xbrl_eps_basic, xbrl_eps_diluted — plus xbrl_revenue_tag, …, xbrl_eps_diluted_tag
Balance sheet: xbrl_cash_and_cash_equivalents, xbrl_total_assets, xbrl_total_liabilities — plus xbrl_cash_and_cash_equivalents_tag, xbrl_total_assets_tag, xbrl_total_liabilities_tag
Cash flow: xbrl_net_cash_operating_activities, xbrl_capital_expenditures — plus xbrl_net_cash_operating_activities_tag, xbrl_capital_expenditures_tag

Treat these fields as best-effort fundamentals aligned to the earnings quarter, not audited restatements; expect sparse cells where filings, tags, or timing do not yield a match.

sentiment_label is derived from move_1d using fixed percentage bands (very bearish through very bullish). Treat labels as historical hindsight for research, not investment advice.

Sweetviz HTML

The Sweetviz report is an exploratory companion to episodes.parquet only. It summarizes column types, missingness, numeric distributions, and target associations without loading the full frame in a notebook.

On this Hub repo the file lives next to the parquet exports:

Filename: sweetviz_episodes.html
Browse: dataset files → sweetviz_episodes.html
Direct download: .../resolve/main/sweetviz_episodes.html

Download with Python (huggingface_hub):

from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="RudrakshNanavaty/earnings-call-data",
    filename="sweetviz_episodes.html",
    repo_type="dataset",
)
print(path)  # open this path in a browser

Regenerate locally (from the pipeline repo that produced these files):

uv run python pipeline/sweetviz_report.py data/episodes.parquet -o reports/sweetviz_episodes.html

Sweetviz is a third-party tool; report content reflects the table at generation time.

Provenance

Transcripts / call metadata: same underlying universe and years as Bose345/sp500_earnings_transcripts (this release augments those transcripts with market, SEC, and label columns; respect that dataset’s license and terms when redistributing derived work).
Market data: via yfinance (subject to Yahoo / vendor terms of use).
Filings: U.S. SEC EDGAR public data (comply with SEC fair access and rate-limiting expectations when re-fetching).
XBRL fundamentals: derived from SEC company facts (same public data policy as above); re-fetch only with a proper User-Agent and polite throughput.

This package is a processed merge for research; it is not an official SEC or exchange product.

Loading examples

pandas / PyArrow

import pandas as pd

ep = pd.read_parquet("episodes.parquet")
print(ep.shape, ep.columns[:5].tolist())

# Optional: only episodes with SEC 8-K body text populated
ep_8k = pd.read_parquet("episodes_press_release_8k.parquet")
print(ep_8k.shape)

# Optional: rows with at least headline XBRL (example)
ep_xbrl = ep.dropna(subset=["xbrl_revenue", "xbrl_net_income"])
print(ep_xbrl.shape)

Hugging Face datasets (if you upload parquet to a Hub dataset repo)

from datasets import Dataset

ds = Dataset.from_parquet("episodes.parquet")  # or hf://datasets/<user>/<name>/path.parquet
print(ds)

Use cases

Train or evaluate models on text + tabular market context with aligned forward returns and optional reported fundamentals (xbrl_*).
Build RL environments where observations include call text and pre-earnings features and rewards depend on realized moves (subject to your own leakage and causality checks).
Reproduce or extend the pipeline using the sibling repository that emits these files.

Limitations

Rows may contain nulls where a source (e.g. a filing or price window) was missing; use the audit columns and null summaries in the Sweetviz report or your own QC.
xbrl_* columns are intentionally sparse: many episodes will have nulls (no CIK, no matching GAAP fact for the quarter, or year < 2009). Do not assume complete fundamentals coverage.
Survivorship and sample bias follow the upstream universe and filters.
Non-stationarity: financial regimes change; test generalization across time and sectors.

Citation

If you use this dataset, cite the upstream transcript dataset as its authors request, plus a citation or link to this Hub dataset. Example BibTeX skeleton (fill in author as appropriate):

@misc{earnings_episodes_2026,
  title        = {S\&P 500 Earnings Episodes (merged transcripts, prices, SEC, labels)},
  author       = {YOUR NAME OR ORG},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/RudrakshNanavaty/earnings-call-data}},
  note         = {Augments Bose345/sp500\_earnings\_transcripts (2005--2025); adds yfinance, SEC EDGAR-derived fields, and optional SEC XBRL companyfacts (us-gaap) on episodes from 2009+.}
}

License

This dataset card specifies MIT (license: mit in the frontmatter). You remain responsible for upstream terms (e.g. the Hugging Face transcript dataset, Yahoo/yfinance, SEC redistribution) when publishing or redistributing derived data.

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S&P 500 earnings episodes (2005–2025)