Financial News Corpora for Sentiment and Return Research
Summary
This reference describes two financial news corpora used for sentiment analysis, text-feature development, and experiments linking news to returns. FNSPID connects headlines with stock tickers and covers a broad historical span; a smaller sample is available alongside the full collection. The Bloomberg archive contains news text and structured financial series over a shorter period and is presented as a secondary source for sentiment, topic, and cross-dataset robustness work. Both are distributed through a public dataset hub, and the document outlines their basic contents and loading options.
The guide also explains important practical limits. FNSPID requires attribution under its listed license, while the Bloomberg text is restricted to research use and cannot be commercially redistributed. The Bloomberg structured data is handled separately from its news loader, and the guide recommends FNSPID for new pipelines. These corpora provide research inputs rather than evidence that news features predict returns; users still need to define and validate their own signals, timestamps, and evaluation design.
Key ideas
- FNSPID links financial headlines to stock tickers and supports news sentiment and return-attribution research.
- The Bloomberg archive combines news text with structured financial data for secondary experiments.
- The corpora differ in coverage, scale, and the way their text and structured data are loaded.
- FNSPID has an attribution requirement, while the Bloomberg archive is limited to research use.
- The datasets supply research material but do not by themselves establish that news predicts returns.
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Full text
# Financial News Archives
# Financial News Archives
Two news corpora used for sentiment analysis, text-signal engineering,
and news-return experiments. Both distributed via HuggingFace Hub.
| Dataset | Records | Coverage | Disk | Source |
| ----------------------- | -------------------------------------------- | ---------- | ------ | --------------------------------------------------- |
| FNSPID | 15.7M headlines, 4,775 S&P 500 companies | 1999-2023 | ~50 MB (1M sample); ~3 GB full | HuggingFace `Zihan1004/FNSPID` |
| Bloomberg news archive | ~470k news records + structured fin-data | 2006-2013 | ~940 MB | HuggingFace (mirrored archive) |
Both downloads are HuggingFace public datasets — **no API key required**,
but a free HuggingFace account (`huggingface-cli login`) makes downloads
faster and more reliable.
## FNSPID
Large-scale financial news dataset linking headlines to stock tickers.
Used in Ch10 for news-sentiment features and return-attribution
experiments.
- **Source / citation**: Zhao et al., "FNSPID: A Comprehensive Financial
News Dataset in Time Series," *arXiv:2402.06698* (2024).
GitHub: https://github.com/Zdong104/FNSPID_Financial_News_Dataset.
- **License**: the HuggingFace dataset page lists `cc-by-4.0` — free for
any use (including commercial) with attribution to the FNSPID paper.
- **Size on disk**: 1M sample ~50 MB (default); full ~3 GB.
- **Runtime**: ~30 seconds for 1M sample; ~10 minutes for the full pull.
### Download
```bash
# 1M sample (default, recommended — ~50 MB)
uv run python data/alternative/news/fnspid_download.py
# Larger samples
uv run python data/alternative/news/fnspid_download.py --sample 2000000
uv run python data/alternative/news/fnspid_download.py --sample 0 # full ~15.7M rows
# Preview
uv run python data/alternative/news/fnspid_download.py --dry-run
```
Output under `$ML4T_DATA_PATH/alternative/news/fnspid/`:
```
fnspid_1000k.parquet # 1M sample (default)
fnspid_2000k.parquet # when --sample 2000000 is used
fnspid_full.parquet # --sample 0
```
### Loading
```python
from data import load_fnspid
news = load_fnspid() # newest sample on disk
aapl = load_fnspid(symbols=["AAPL", "MSFT"],
start_date="2020-01-01", end_date="2023-12-31")
```
Schema: `symbol`, `timestamp`, `title`, `body`, `source`, `url`.
### Consumers
- **Ch10**: `07_news_return_signals.py`, `08_text_feature_evaluation.py`.
## Bloomberg News Archive
Bloomberg news headlines and bodies combined with structured financial
data series, distributed via a HuggingFace-mirrored archive. Used as a
secondary corpus for sentiment / topic experiments and cross-dataset
robustness checks.
- **Source**: HuggingFace mirrored Bloomberg archive.
- **License**: Bloomberg owns the underlying text; the mirrored archive
is distributed under HuggingFace terms **for research use only**.
Commercial redistribution is not permitted. See the dataset card on
HuggingFace before using for anything beyond personal study.
- **Size on disk**: ~940 MB total (news ~460 MB, structured ~480 MB).
- **Runtime**: ~3-5 minutes.
### Download
```bash
uv run python data/alternative/news/bloomberg_download.py
```
Output under `$ML4T_DATA_PATH/alternative/news/bloomberg/`:
```
bloomberg_news.parquet # headline + body corpus (~460 MB)
bloomberg_financial_data.parquet.gzip # structured financial fields (~480 MB)
.cache/huggingface/download/ # HF download staging (safe to wipe)
```
### Loading
```python
from data import load_bloomberg_news
df = load_bloomberg_news(start_date="2010-01-01", end_date="2013-12-31")
```
The loader covers ``bloomberg_news.parquet`` (headline + body corpus) and
returns the article publication time as canonical ``timestamp``.
The structured-financials file is still read directly when needed —
Bloomberg corpora are secondary to FNSPID; prefer ``load_fnspid()``
when building new pipelines.
### Consumers
- **Ch22**: ESG RAG-vs-fine-tune comparison (``06_esg_rag_vs_finetune.py``).Shown in full with attribution under the source's licence. Licence: MIT
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.