Anonymized panel of ~1.2M stock-month observations with 46 firm characteristics and forward returns, spanning 1967-2016. Built from the replication archive of Chen, Pelger, and Zhu (2020), Deep Learning in Asset Pricing. Used throughout the book for ML-based asset-pricing examples where a standard, reproducible benchmark matters more than symbol-level interpretation.

Dataset

Source: GitHub replication repo (https://github.com/jasonzy121/Deep_Learning_Asset_Pricing), which itself ships the published dataset via Google Drive
Coverage: 1967-01 → 2016-12, monthly observations, ~1.2M rows
Features: 46 firm characteristics (accounting ratios, price-based measures, momentum variants), 178 macro indicators, forward returns
Size on disk: ~1.1 GB raw CSV; converted to ~500 MB parquet
Access: Public, no API key required
Canonical schema: symbol (anonymous integer id), timestamp (monthly Date), 46 characteristic columns, return, split

Pre-defined Splits

The dataset ships with deterministic train/valid/test splits aligned to the original paper:

Split	Period	Share
train	1967-1989	~70%
valid	1990-1999	~15%
test	2000-2016	~15%

Download

This is the largest free dataset in the book: ~1.5 GB pulled from the authors' Google Drive folder (RetChar.csv alone is ~1.1 GB), followed by a CSV → parquet conversion. How long it takes depends on your bandwidth and disk speed — usually a few minutes, but treat that as indicative, not a guarantee. Per-file progress is printed as it runs. A single command downloads and converts — no separate --convert pass is needed.

# Download the ~1.5 GB folder and convert to parquet in one step
uv run python data/equities/firm_characteristics/download.py

# Verify what's already on disk, do not refetch
uv run python data/equities/firm_characteristics/download.py --check

# Force a re-download even if files exist
uv run python data/equities/firm_characteristics/download.py --force

# Re-run only the CSV → parquet conversion (files already downloaded)
uv run python data/equities/firm_characteristics/download.py --convert

It is also fetched automatically as part of data/download_all.py. To skip it there (e.g. on a metered or space-constrained connection), pass --skip-firm-characteristics.

Output layout under $ML4T_DATA_PATH/equities/firm_characteristics/:

firm_characteristics_all.parquet      # full panel
firm_characteristics_train.parquet    # 1967-1989
firm_characteristics_test.parquet     # 2000-2016
dl_asset_pricing/                     # raw CSV + NPZ staging
    RetChar.csv
    Macro.csv
    char/Char_{train,valid,test}.npz
    macro/macro_{train,valid,test}.npz
    RF/RF_{train,valid,test}_normalized_task_1.npz

The staging directory is kept so experiments that need the pre-split NPZ arrays (the original Chen-Pelger-Zhu format) can read them directly.

Loading

from data import load_firm_characteristics

df = load_firm_characteristics()                        # full panel
df = load_firm_characteristics(split="train")
df = load_firm_characteristics(split="test", include_macro=True)

If the canonical parquets are missing, the loader raises DataNotFoundError pointing at the download command.

Consumers

Chapters 10-16 — standard benchmark for linear and nonlinear asset-pricing models.
case_studies/us_firm_characteristics/ — Chen-Pelger-Zhu replication pipeline (CV, GBM, latent factor, deep models).