Datasets:

CID99
/

FinCAC40

license: etalab-2.0
language:
  - fr
tags:
  - finance
  - regulatory
  - esg
  - csrd
  - esrs
  - sustainability
  - temporal-robustness
  - french
task_categories:
  - text-classification
size_categories:
  - 100K<n<1M
pretty_name: FinCAC40 — French AMF Regulatory Corpus, CAC 40 Issuers (2010–2026)
configs:
  - config_name: corpus
    data_files:
      - split: train
        path: corpus/*.parquet
  - config_name: gold
    data_files:
      - split: test
        path: gold/*.parquet

FinCAC40 — French AMF Regulatory Corpus, CAC 40 Issuers (2010–2026)

FinCAC40 contains 313,898 paragraphs extracted from 23,144 documents filed with the Autorité des Marchés Financiers (AMF, the French financial markets regulator) by CAC 40 issuers between 2010 and 2026, together with a Gold Standard of 140 paragraphs annotated under the ESRS taxonomy (CSRD).

The corpus was built to study the temporal robustness of language models on French regulatory text. It spans four successive regulatory regimes, from the absence of structured ESG obligations (2010–2014) to the entry into force of the CSRD (2023–2026).

Note on language: the corpus content is entirely in French. This card is in English for accessibility; the accompanying paper is written in French.

⚠️ Read before use

The Gold Standard (140 examples) is a preliminary evaluation set. It is too small and too imbalanced (69.3% none class, five ESRS categories entirely absent) to serve as a training set or to validate a compliance system in production.
Public release means future contamination. Now that the Gold Standard is public, it may appear in the pretraining corpora of future models. Any evaluation carried out after this release should account for that.
Survivorship bias. The CAC 40 filter retains issuers that are currently index members, then collects their documents over 2010–2026. Companies that left the index are not represented.
This corpus is not regulatory advice. Annotations reflect the judgment of a single annotator and are no substitute for a qualified auditor's opinion.
paragraph_id is not a primary key. It is a SHA256 hash of the paragraph content, so two paragraphs with strictly identical text share the same identifier. Roughly 18% of the corpus consists of repeated paragraphs — disclaimers, legal notices, standardised clauses recurring across documents and across years. This is a genuine property of regulatory filings, not an extraction defect. Use row_id as the unique identifier, and paragraph_id to detect or remove identical content:
```
# Corpus deduplicated by content
unique = corpus.to_pandas().drop_duplicates(subset=["paragraph_id"])
```
Depending on your use case, duplicates should be kept (the same text at two different dates is meaningful information for a temporal study) or removed (model training, where they over-weight boilerplate).

Structure

Config `corpus` — 313,898 paragraphs

Field	Type	Description
`row_id`	int	Unique row identifier
`paragraph_id`	string	SHA256 hash of the content — not unique (see above)
`document_id`	string	Source AMF document identifier
`content`	string	Paragraph text (20–340 words)
`date_envoi`	date	Certified AMF filing date
`emetteur`	string	Issuing company (CAC 40)
`type_document`	string	Regulatory document type
`epoch`	string	Derived regulatory regime (see below)

Config `gold` — 140 annotated paragraphs

Same fields as above, plus:

Field	Type	Description
`csrd_category`	string	ESRS category, or `none` (12 possible values)
`esrs_subcategory`	string	ESRS subcategory, where applicable
`chain_of_thought`	string	Expert's written reasoning behind the label (see below)

Expert reasoning (`chain_of_thought`)

Each annotation carries the annotator's written justification, structured in three steps: (1) nature of the disclosed information, (2) materiality analysis under the CSRD double materiality framework, (3) assessment of market surprise. Reasonings average roughly 620 characters.

This field is released because expert-written justifications on regulatory text are scarce. It supports uses the labels alone do not: auditing why a paragraph was classified a given way, studying where human and model reasoning diverge, chain-of-thought distillation, or retraining an annotator on the same protocol.

It is a single annotator's reasoning, with no inter-annotator agreement measure — treat it as documented judgment, not ground truth.

Regulatory regimes (`epoch`)

Epoch	Dominant characteristic	n (Gold)
`2010-2014`	No systematic ESG obligation; informal vocabulary	33
`2015-2019`	Paris Agreement; French DPEF mandatory (2017)	30
`2020-2022`	EU Taxonomy, SFDR; "double materiality" emerging	33
`2023-2026`	CSRD in force, ESRS Set 1, standardised vocabulary	42

Gold Standard label distribution

none 97 · E1 18 · ESRS2 17 · E5 5 · E2 1 · S1 1 · S4 1 · absent: E3, E4, S2, S3, G1

Construction

Source. Public portal info-financiere.gouv.fr, full metadata export (524,589 entries).

Filtering. CAC 40 → French language → 2010–2026 period → valid URL, yielding 23,753 documents. Text extracted with pdfplumber; 97.44% of documents extracted successfully.

Paragraph reconstruction. Raw PDF extraction produces text fragmented line by line (3 to 8 words per line). A naive length filter rejected 95% of extracts. Paragraphs are therefore reconstructed in two stages (boundary detection, then merging of wrapped lines), followed by structural, not semantic filtering: 20–340 words, ≥ 2 sentences, alphabetic ratio ≥ 0.55, unique-word ratio ≥ 0.30.

Gold Standard sampling. Proportional stratification along three axes — epoch, document type, and CSRD keyword-density quartile — designed to avoid any filtering on the variable of interest. A CSRD keyword filter would have mechanically biased the corpus toward recent documents, where that vocabulary is standardised, and under-represented older documents carrying the same content expressed differently.

Usage

from datasets import load_dataset

# Full corpus
corpus = load_dataset("YOUR_USERNAME/FinCAC40", "corpus", split="train")

# Annotated Gold Standard
gold = load_dataset("YOUR_USERNAME/FinCAC40", "gold", split="test")

# Filter by regulatory epoch
older = corpus.filter(lambda x: x["epoch"] == "2010-2014")

License and compliance

Source AMF documents are published under the Licence Ouverte / Open Licence 2.0 (Etalab), which permits reuse and redistribution subject to attribution. The derived corpus is released under the same licence.

The corpus contains only public regulatory documents from listed companies. It holds no personal data within the meaning of the GDPR, other than the names of executives already appearing in official filings.

Citation

@misc{dieng2026fincac40,
  title  = {FinCAC40 : un corpus réglementaire français (2010--2026) pour l'évaluation
            de la robustesse temporelle des LLM en classification de durabilité},
  author = {Dieng, Cheikh Ibra},
  year   = {2026},
  note   = {Preprint}
}

Contributing

The Gold Standard would benefit from extension, prioritising:

the absent ESRS categories (E3, E4, S2, S3, G1)
the earlier epochs (2010–2014, 2015–2019), where the number of genuinely CSRD-relevant paragraphs is lowest (3 and 4 respectively)
an inter-annotator agreement measure, which this release lacks

The full annotation protocol is provided in the companion code repository.