Datasetpublic

artefactory/ledger-market-sentiment

LEDGER Market Sentiment Prediction Data Data used for the market sentiment prediction case study in the LEDGER paper, linking CEO-letter rhetoric to EPS surprises and post-publication market reactions. Dataset Description This dataset supports research on whether the rhetoric in corporate annual report CEO letters carries signal about future fundamentals and market reaction. It covers six highly liquid industries (specialty chemicals, auto parts, packaged foods… See the full description on the dataset page: https://huggingface.co/datasets/artefactory/ledger-market-sentiment.

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LEDGER Market Sentiment Prediction Data

Data used for the market sentiment prediction case study in the LEDGER paper, linking CEO-letter rhetoric to EPS surprises and post-publication market reactions.

Dataset Description

This dataset supports research on whether the rhetoric in corporate annual report CEO letters carries signal about future fundamentals and market reaction. It covers six highly liquid industries (specialty chemicals, auto parts, packaged foods, oil & gas E&P, oil & gas equipment & services, and mortgage REITs) spanning fiscal years 2017–2022.

Configs

Config	Rows	Description
`letters`	464	CEO/chairman letters extracted from annual reports, with sentiment labels
`eps_surprise`	13,489	Earnings per share surprise data (consensus vs. reported)
`stock_prices`	422,888	Daily stock prices with rolling return windows (t-90 to t+90)
`industry_indicators`	12,525	Aggregate industry-level daily indicators

Schema

`letters`

Column	Type	Description
`ticker`	string	Stock ticker symbol
`exchange`	string	Stock exchange (NYSE, NASDAQ, LSE, AMEX)
`industry`	string	Industry classification
`year`	int	Fiscal year of the annual report
`letter_number`	int	Letter index within the report (1-based)
`title`	string	Letter heading (e.g. "Chairman's Statement")
`start_page`	int	First page in the source report
`end_page`	int	Last page in the source report
`sentiment`	string	Overall sentiment label: `positive`, `negative`, or null
`text`	string	Full letter text (Markdown)
`source_report`	string	Path to the source OCR'd report

`eps_surprise`

Column	Type	Description
`ticker`	string	Stock ticker symbol
`earnings_date`	datetime	Earnings announcement date (UTC)
`eps_estimate`	float	Consensus analyst EPS estimate
`reported_eps`	float	Actual reported EPS (null for future dates)
`surprise_pct`	float	Surprise percentage: (actual - estimate) / estimate × 100

`stock_prices`

Column	Type	Description
`ticker`	string	Stock ticker symbol
`Date`	date	Trading date
`Open`	float	Opening price
`High`	float	Daily high
`Low`	float	Daily low
`Close`	float	Closing price
`Volume`	float	Trading volume
`Volume_ATS`	float	Alternative trading system volume (normalized)
`returns`	float	Daily return
`Volatility`	float	Rolling volatility
`return_t-90` … `return_t90`	float	Rolling returns from t-90 to t+90 trading days

`industry_indicators`

Column	Type	Description
`industry`	string	Industry name
`Date`	date	Trading date
`returns`	float	Equal-weighted industry return
`volumes`	float	Normalized aggregate volume
`volatility`	float	Industry volatility
`returns_vw`	float	Value-weighted industry return
`volumes_vw`	float	Value-weighted aggregate volume
`volatility_vw`	float	Value-weighted volatility
`return_t-90_vw` … `return_t42_vw`	float	Value-weighted rolling returns (t-90 to t+42)

Usage

python

from datasets import load_dataset

# Load CEO letters with sentiment
letters = load_dataset("artefactory/LEDGER-market-sentiment", "letters")

# Load EPS surprises
eps = load_dataset("artefactory/LEDGER-market-sentiment", "eps_surprise")

# Load daily stock prices
prices = load_dataset("artefactory/LEDGER-market-sentiment", "stock_prices")

# Load industry indicators
indicators = load_dataset("artefactory/LEDGER-market-sentiment", "industry_indicators")

# Example: filter to positive-sentiment letters
positive_letters = letters["train"].filter(lambda x: x["sentiment"] == "positive")

Industries Covered

1.Basic Materials / Specialty Chemicals
2.Consumer Cyclical / Auto Parts
3.Consumer Defensive / Packaged Foods
4.Energy / Oil & Gas E&P
5.Energy / Oil & Gas Equipment & Services
6.Real Estate / REIT - Mortgage

Citation key

If you use LEDGER datasets and / or code ressources, please consider citing our work with:

@misc{moslonka2026ledgerlongcontextbenchmarkcorporate,
      title={LEDGER: A Long-Context Benchmark of Corporate Annual Reports for Grounded Financial Retrieval and Extraction}, 
      author={Charles Moslonka and Amaury de Vitry and Arthur Garnier and Hicham Randrianarivo and Emmanuel Malherbe},
      year={2026},
      eprint={2606.13100},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2606.13100}, 
}

License

Code: MIT | Data: CC-BY-4.0