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pretty_name: Open Source Cross-Sectional Asset Pricing
license: cc-by-4.0
language:
- en
tags:
- finance
- asset-pricing
- stock-returns
- anomalies
size_categories:
- 100M<n<1B
# dataset viewer tables (files on main, which mirrors the latest release)
configs:
- config_name: long_short_returns
data_files: "Portfolios/Full Sets OP/PredictorLSretWide.parquet"
default: true
- config_name: predictor_portfolios
data_files: "Portfolios/Full Sets OP/PredictorPortsFull.parquet"
- config_name: placebo_portfolios
data_files: "Portfolios/Full Sets OP/PlaceboPortsFull.parquet"
- config_name: signal_doc
data_files: "SignalDoc.parquet"
- config_name: predictors
data_files: "Firm Level Characteristics/Predictors.parquet"
---
# Open Source Cross-Sectional Asset Pricing (data releases)
Stock-level signals (firm characteristics) and portfolio returns from
[Chen and Zimmermann (2022)](#paper), produced by the code at
[github.com/OpenSourceAP/CrossSection](https://github.com/OpenSourceAP/CrossSection).
The official data page and FAQ are at [openassetpricing.com](https://www.openassetpricing.com), which
includes a [browser for signal descriptions](https://openassetpricing.com/SignalDoc-Browser.html).
**All past releases are available.** This page shows the latest, 2026.10. See
[Releases](#releases) for older ones, including 2021.03, which matches the published paper.
## Paper
The paper for this dataset is
[Chen and Zimmermann (2022), "Open Source Cross-Sectional Asset Pricing," *Critical Finance Review* 11(2): 207–264](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3604626).
The data are licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). If you
use them, please cite the paper ([BibTeX](#citation)).
## Quick download (latest release, 2026.10)
### Core predictor portfolios and documentation
Click a link to download.
| Data | File | Size |
| ---------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| Signal documentation | [SignalDoc.csv](https://huggingface.co/datasets/OpenSourceAP/CrossSection/resolve/Data-Release-2026.10/SignalDoc.csv) | 0.2 MB |
| Long-short returns, original papers' construction (wide) | [PredictorLSretWide.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/resolve/Data-Release-2026.10/Portfolios/Full%20Sets%20OP/PredictorLSretWide.parquet) | 1.4 MB |
| All portfolio returns, original papers' construction (long) | [PredictorPortsFull.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/resolve/Data-Release-2026.10/Portfolios/Full%20Sets%20OP/PredictorPortsFull.parquet) | 18.7 MB |
| Value-weighted decile portfolio returns (long) | [PredictorAltPorts_DecilesVW.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/resolve/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PredictorAltPorts_DecilesVW.parquet) | 25.7 MB |
| Daily value-weighted portfolio returns, original papers' construction (long) | [PredictorVW.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/resolve/Data-Release-2026.10/DailyPortfolios/PredictorVW.parquet) | 252 MB |
| Release notes | [Release Notes 2026.10.md](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Release%20Notes%202026.10.md) | 12 KB |
Or use a parquet reader, e.g.:
```python
import pandas as pd
ports = pd.read_parquet(
"hf://datasets/OpenSourceAP/CrossSection@Data-Release-2026.10/Portfolios/Full Sets OP/PredictorPortsFull.parquet"
)
```
We offer many other portfolio implementations too. See the [File Directory](#file-directory-202610).
### Accessing Specific Predictor Signals (Firm Level Characteristics)
For most predictors, you can quickly download them via the `columns` argument in `pd.read_parquet()`.
```python
# only the columns you list are downloaded (here 3 of the 211 in the 1.9 GB file)
bm = pd.read_parquet(
"hf://datasets/OpenSourceAP/CrossSection@Data-Release-2026.10/Firm Level Characteristics/Predictors.parquet",
columns=["permno", "yyyymm", "BM"],
)
```
Check [SignalDoc.csv](https://huggingface.co/datasets/OpenSourceAP/CrossSection/resolve/Data-Release-2026.10/SignalDoc.csv) for the column name definitions, including the sign of each signal (e.g. higher BM implies higher mean return).
You cannot access the Predictors `Price`, `Size`, `STreversal` this way. To access these signals, you need a WRDS account.
### The Full Panel of Firm Level Predictors (signals)
If you have a WRDS account, you can download the full dataset of predictor signals with one call using
the `openassetpricing` (Python) or `OpenSourceAP.DownloadR` (R) package.
Both examples return signed signals by default: each predictor is multiplied by its
`Sign` so that higher values imply higher expected returns. The `Predictors.parquet`
example above returns unsigned signals.
Python:
```python
# pip install openassetpricing
import openassetpricing as oap
openap = oap.OpenAP() # latest release
signals = openap.dl_all_signals("pandas") # or "polars", defaults to signed signals
```
R:
```r
# install.packages("OpenSourceAP.DownloadR")
library(OpenSourceAP.DownloadR)
openap <- OpenAP$new() # latest release
signals <- openap$dl_all_signals() # defaults to signed signals
```
Package documentation:
- Python: [openassetpricing](https://github.com/mk0417/open-asset-pricing-download)
- R: [OpenSourceAP.DownloadR](https://github.com/tomz23/OpenSourceAP.DownloadR)
### Placebos
Placebos are 114 signals whose original papers did not show clear, statistically significant
predictability in long-short portfolios. This includes 14 "not-predictors," which were clearly
insignificant, and 100 "indirect signals," which had only suggestive evidence or were modifications of
another signal. Most come from [Hou, Xue, and Zhang (2020)](https://doi.org/10.1093/rfs/hhy131),
who count them among their 452 "anomalies." Chen and Zimmermann (2022) include them so that
the dataset nests Hou, Xue, and Zhang, and helps reconcile the disparate replication rates
(Hou, Xue, and Zhang's "failed replications" are mostly tests that were never shown to be
predictive previously).
The placebos may still be useful. Many of them seem to have predictive power and true
statistical predictability is plentiful, historically (Chen 2026, "Most Claimed Statistical Findings in Cross-Sectional Return
Predictability Are Likely True," *JF Insights*; Chen and Dim 2023, "High-Throughput Asset Pricing").
Firm-level placebo signals are in [Placebos.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Firm%20Level%20Characteristics/Placebos.parquet),
and their portfolio returns are in
[PlaceboPortsFull.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20OP/PlaceboPortsFull.parquet).
`BidAskTAQ` is not included. To build it with your own TAQ, ISSM and CRSP data, run the
[TAQ prep scripts](https://github.com/OpenSourceAP/CrossSection/tree/master/Signals/pyCode/PrepScripts/taq-chen-velikov)
and then [BidAskTAQ.py](https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Placebos/BidAskTAQ.py)
from the CrossSection repo.
## File Directory (2026.10)
Click a file or folder to open its page on the hub. Then click "Download" to download.
- [SignalDoc.csv](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/SignalDoc.csv) (0.2 MB) — one row per signal: category, original paper, sign, portfolio settings
- [SignalDoc.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/SignalDoc.parquet) (64 KB) — same, as parquet
- [Release Notes 2026.10.md](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Release%20Notes%202026.10.md) (12 KB)
- **[Firm Level Characteristics/](https://huggingface.co/datasets/OpenSourceAP/CrossSection/tree/Data-Release-2026.10/Firm%20Level%20Characteristics)** — monthly panels: `permno`, `yyyymm`, one column per signal
- [Predictors.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Firm%20Level%20Characteristics/Predictors.parquet) (1.9 GB) — 209 predictors; omits `Price`, `Size` and `STreversal` ([how to get them](#the-full-panel-of-firm-level-predictors-signals))
- [signed_predictors_dl_wide.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Firm%20Level%20Characteristics/signed_predictors_dl_wide.parquet) (1.9 GB) — same, each multiplied by its `Sign` so that higher signal means higher in-sample mean return; omits `Price`, `Size` and `STreversal` ([how to get them](#the-full-panel-of-firm-level-predictors-signals))
- [Placebos.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Firm%20Level%20Characteristics/Placebos.parquet) (1.0 GB) — 113 placebos (not-predictors and indirect signals); omits `BidAskTAQ` ([how to get it](#placebos))
- **[Portfolios/](https://huggingface.co/datasets/OpenSourceAP/CrossSection/tree/Data-Release-2026.10/Portfolios)**
- **[Full Sets OP/](https://huggingface.co/datasets/OpenSourceAP/CrossSection/tree/Data-Release-2026.10/Portfolios/Full%20Sets%20OP)** — monthly returns, original papers' construction
- [PredictorLSretWide.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20OP/PredictorLSretWide.parquet) (1.4 MB) — long-short returns, one column per predictor
- [PredictorPortsFull.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20OP/PredictorPortsFull.parquet) (18.7 MB) — all portfolios, long format
- [PlaceboPortsFull.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20OP/PlaceboPortsFull.parquet) (7.9 MB) — same, for placebos
- [PredictorSummary.xlsx](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20OP/PredictorSummary.xlsx) (1.1 MB) — summary statistics by predictor
- [PlaceboSummary.xlsx](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20OP/PlaceboSummary.xlsx) (0.4 MB) — summary statistics by placebo
- **[Full Sets Alt/](https://huggingface.co/datasets/OpenSourceAP/CrossSection/tree/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt)** — monthly returns, alternative constructions
- [PredictorAltPorts_Deciles.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PredictorAltPorts_Deciles.parquet) (25.1 MB)
- [PredictorAltPorts_DecilesEW.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PredictorAltPorts_DecilesEW.parquet) (25.1 MB)
- [PredictorAltPorts_DecilesVW.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PredictorAltPorts_DecilesVW.parquet) (25.7 MB)
- [PredictorAltPorts_Quintiles.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PredictorAltPorts_Quintiles.parquet) (13.7 MB)
- [PredictorAltPorts_QuintilesEW.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PredictorAltPorts_QuintilesEW.parquet) (13.7 MB)
- [PredictorAltPorts_QuintilesVW.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PredictorAltPorts_QuintilesVW.parquet) (13.9 MB)
- [PredictorAltPorts_HoldPer_1.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PredictorAltPorts_HoldPer_1.parquet) (18.8 MB)
- [PredictorAltPorts_HoldPer_3.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PredictorAltPorts_HoldPer_3.parquet) (19.0 MB)
- [PredictorAltPorts_HoldPer_6.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PredictorAltPorts_HoldPer_6.parquet) (18.9 MB)
- [PredictorAltPorts_HoldPer_12.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PredictorAltPorts_HoldPer_12.parquet) (18.8 MB)
- [PredictorAltPorts_LiqScreen_ME_gt_NYSE20pct.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PredictorAltPorts_LiqScreen_ME_gt_NYSE20pct.parquet) (18.8 MB)
- [PredictorAltPorts_LiqScreen_NYSEonly.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PredictorAltPorts_LiqScreen_NYSEonly.parquet) (18.2 MB)
- [PredictorAltPorts_LiqScreen_Price_gt_5.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PredictorAltPorts_LiqScreen_Price_gt_5.parquet) (18.9 MB)
- [PredictorAltPorts_LiqScreen_VWforce.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PredictorAltPorts_LiqScreen_VWforce.parquet) (19.0 MB)
- [PredictorAltPorts_FF93style.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PredictorAltPorts_FF93style.parquet) (16.2 MB)
- [PlaceboPortsFull.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/Portfolios/Full%20Sets%20Alt/PlaceboPortsFull.parquet) (7.9 MB)
- **[DailyPortfolios/](https://huggingface.co/datasets/OpenSourceAP/CrossSection/tree/Data-Release-2026.10/DailyPortfolios)** — daily returns, long format
- [Predictor.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/DailyPortfolios/Predictor.parquet) (252 MB)
- [PredictorVW.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/DailyPortfolios/PredictorVW.parquet) (252 MB)
- [CtsPredictorDecile.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/DailyPortfolios/CtsPredictorDecile.parquet) (324 MB)
- [CtsPredictorDecileVW.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/DailyPortfolios/CtsPredictorDecileVW.parquet) (324 MB)
- [CtsPredictorQuintile.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/DailyPortfolios/CtsPredictorQuintile.parquet) (182 MB)
- [CtsPredictorQuintileVW.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/DailyPortfolios/CtsPredictorQuintileVW.parquet) (182 MB)
- [DailyPortSummary.xlsx](https://huggingface.co/datasets/OpenSourceAP/CrossSection/blob/Data-Release-2026.10/DailyPortfolios/DailyPortSummary.xlsx) (8 KB)
- **[Results/](https://huggingface.co/datasets/OpenSourceAP/CrossSection/tree/Data-Release-2026.10/Results)** — figures and tables from the paper
Older releases may have slightly different file structure.
## Data Format
**Firm-level signals** (`Firm Level Characteristics/`)
- One row per stock-month. `permno` is the CRSP stock identifier, `yyyymm` is the month as an
integer (e.g. `198601`), and every other column is one signal, named by its acronym in
`SignalDoc.csv`.
- Timing: signals are available and relevant for trading (based on the original paper) at the
end of the month `yyyymm`. Use signals with `yyyymm = 202511` to predict the return between
end of November 2025 and some future date (e.g. end of December 2025).
- Note: Many of the original papers imply that signals are valid long after the fiscal year end.
For example, Fama-French 1992 assume that book equity is available 6 months after the fiscal
year end, and is relevant for trading for another 12 months after that.
**Monthly portfolio returns** (`Portfolios/`)
- Long format (`PredictorPortsFull`, `PlaceboPortsFull` and the `Full Sets Alt` files): one row
per signal, portfolio and month, with columns
- `signalname`: the signal's acronym, as in `SignalDoc.csv`
- `port`: text, `"01"`–`"10"` for quantile portfolios and `"LS"` for long-short
- `date`: the return month, dated the last trading day of the month
- `ret`: return, in percent
- `signallag`: the average lagged signal of the stocks in the portfolio
- `Nlong`, `Nshort`: the number of stocks in the long and short legs (for quantile
portfolios, `Nlong` is the number of stocks and `Nshort` is 0)
- Wide format (`PredictorLSretWide`): long-short returns only, one row per month, with `date`
and one column per predictor.
- The return for `date` runs from the closing price on the last trading day of the preceding
month to the closing price on the last trading day of that month.
- Long-short portfolios are signed using the original papers' results, so the long leg is
the one the original paper found to have higher returns.
**Daily portfolio returns** (`DailyPortfolios/`)
- Long format with `signalname`, `port`, `date` (the trading day) and `ret` (in percent).
## Releases
Each data release is its own branch of this repo. The latest release is on branch
`Data-Release-2026.10`, and the `main` branch holds a copy of it (plus this README), so
downloads that don't name a branch (no `revision=` in Python) get the latest data.
For another release, replace `Data-Release-2026.10` in the link with that release's branch
from the table below.
| Release | Branch (`revision=`) | Notes |
| ------- | ------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 2026.10 | `"Data-Release-2026.10"` | 212 predictors, 114 placebos; data through 2025-12; CRSP v2 (CIZ); parquet only. See `Release Notes 2026.10.md` |
| 2025.10 | `"2025.10"` | 212 predictors, 114 placebos; data through 2024-12; code translated from Stata to Python (v2.0.0) |
| 2024.10 | `"2024.10"` | 212 predictors, 114 placebos; patch of 2024.08 fixing look-ahead bias in AnnouncementReturn |
| 2024.08 | `"2024.08"` | 212 predictors, 114 placebos; data through 2023-12; OptionMetrics signals end 2022-12 |
| 2023.08 | `"2023.08"` | 212 predictors, 114 placebos; data through 2022-12; 5 new predictors; BM follows Stattman (1980) |
| 2022.04 | `"2022.04"` | 207 predictors, 114 placebos; SignalDoc corrections; no daily portfolios or `Results/` |
| 2022.03 | `"2022.03"` | 207 predictors, 114 placebos; data through 2021-12; `SignalDoc.csv` replaces the xlsx; FF93-style portfolios added |
| 2021.04 | `"2021.04"` | 205 predictors, 114 placebos; adds value-weighted and daily portfolios; docs in `SignalDocumentation.xlsx` |
| 2021.03 | `"2021.03"` | 205 predictors, 114 placebos; **code v1.0.0, matches the CFR publication**; portfolios in `Full_Sets_Main/` and `Full_Sets_Extended/`; `signed_predictors_dl_wide` lacks KZ and ZScore |
| 2020.07 | `"2020.07"` | 210 base and 104 additional signals; adds trading-cost portfolios (`PortCosts/`); original layout |
| 2020.05 | `"2020.05"` | 210 base and 104 additional signals; first release; original layout (`signalbase`, `portbase`, ...) |
Counts include Price, Size, STreversal and BidAskTAQ, which are not in the firm-level files.
Older releases were originally distributed as csv/zip files on Google Drive. On this hub, every
csv (zipped or not) is stored as parquet at the same path. The other files (pdf, tex, xlsx, docx)
are kept as they were. The layout of each release follows its original Drive folder, so file paths
can differ between releases. To see what a release contains, list its files:
```python
from huggingface_hub import list_repo_files
list_repo_files("OpenSourceAP/CrossSection", repo_type="dataset", revision="Data-Release-2026.10")
```
## Loading a release
Python, one file at a time (cached locally after the first download):
```python
import pandas as pd
from huggingface_hub import hf_hub_download
release = "Data-Release-2026.10" # any branch in the table above
path = hf_hub_download(
repo_id="OpenSourceAP/CrossSection",
repo_type="dataset",
filename="Firm Level Characteristics/signed_predictors_dl_wide.parquet",
revision=release,
)
signals = pd.read_parquet(path)
```
One line per release, for the main portfolio returns:
```python
from huggingface_hub import hf_hub_download
f = "Portfolios/Full Sets OP/PredictorPortsFull.parquet"
kw = dict(repo_id="OpenSourceAP/CrossSection", repo_type="dataset", filename=f)
hf_hub_download(**kw, revision="Data-Release-2026.10")
hf_hub_download(**kw, revision="2025.10")
hf_hub_download(**kw, revision="2024.10")
hf_hub_download(**kw, revision="2024.08")
hf_hub_download(**kw, revision="2023.08")
hf_hub_download(**kw, revision="2022.04")
hf_hub_download(**kw, revision="2022.03")
hf_hub_download(**kw, revision="2021.04")
```
(2021.03 keeps this file under `Portfolios/Full_Sets_Main/`. The 2020 releases use the original
layout, so call `list_repo_files` first.)
With 🤗 `datasets`:
```python
from datasets import load_dataset
ports = load_dataset(
"OpenSourceAP/CrossSection",
data_files="Portfolios/Full Sets OP/PredictorPortsFull.parquet",
revision="Data-Release-2026.10",
split="train",
)
```
pandas can read straight from the hub (via `huggingface_hub`). Put the release after `@`:
```python
import pandas as pd
ports = pd.read_parquet(
"hf://datasets/OpenSourceAP/CrossSection@Data-Release-2026.10/Portfolios/Full Sets OP/PredictorPortsFull.parquet"
)
# only the columns you list are downloaded (here 3 of the 211 in the 1.9 GB file)
bm = pd.read_parquet(
"hf://datasets/OpenSourceAP/CrossSection@Data-Release-2026.10/Firm Level Characteristics/Predictors.parquet",
columns=["permno", "yyyymm", "BM"],
)
```
`columns=` saves downloading only with `hf://` paths. With an `https://` link, pandas downloads
the whole file first.
R (download, then read with `arrow`):
```r
release <- "Data-Release-2026.10"
url <- paste0(
"https://huggingface.co/datasets/OpenSourceAP/CrossSection/resolve/", release,
"/Portfolios/Full%20Sets%20OP/PredictorPortsFull.parquet"
)
download.file(url, f <- tempfile(fileext = ".parquet"), mode = "wb")
ports <- arrow::read_parquet(f)
```
## License
The data are licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Please cite the paper below.
## Citation
```bibtex
@article{ChenZimmermann2022,
title = {Open Source Cross Sectional Asset Pricing},
author = {Chen, Andrew Y. and Zimmermann, Tom},
journal = {Critical Finance Review},
year = {2022},
volume = {11},
number = {2},
pages = {207--264}
}
```
|