Amaanaush/earnings-call-data

S&P 500 earnings episodes (2005–2025)

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.

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.