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, 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

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

License

CC-BY-4.0