Datasets:

OpenSourceAP
/

CrossSection

Describes the data, quick downloads for the latest release, and how to load
each archived release by branch (revision=).

Claude-Session: https://claude.ai/code/session_019jhbPVKzWGSubG2HoKReUR

Files changed (1) hide show

README.md +270 -0

README.md ADDED Viewed

	@@ -0,0 +1,270 @@

+---
+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
+---
+# Open Source Cross-Sectional Asset Pricing (data releases)
+Stock-level signals (firm characteristics) and portfolio returns from
+[Chen and Zimmermann (2022), "Open Source Cross-Sectional Asset Pricing"](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3604626),
+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).
+**Each data release is its own branch of this repo.** The `main` branch holds only this
+README. The latest release is on branch `Data-Release-2026.10`.
+The data are licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). If you
+use them, please cite [Chen and Zimmermann (2022)](#citation).
+## Quick download (latest release, 2026.10)
+### Predictor Portfolios
+Click a link to download, or pass it to any parquet reader. No account or token is needed.
+| 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 |
+| Placebo portfolio returns (long)                                             | [PlaceboPortsFull.parquet](https://huggingface.co/datasets/OpenSourceAP/CrossSection/resolve/Data-Release-2026.10/Portfolios/Full%20Sets%20OP/PlaceboPortsFull.parquet)                        | 7.9 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/resolve/Data-Release-2026.10/Release%20Notes%202026.10.md)                                                | 17 KB   |
+For example, in Python:
+```python
+import pandas as pd
+ports = pd.read_parquet(
+    "hf://datasets/OpenSourceAP/CrossSection@Data-Release-2026.10/Portfolios/Full Sets OP/PredictorPortsFull.parquet"
+)
+```
+### 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)
+## Files (2025.10 and later)
+| Path                                                           | Contents                                                                                                                                     |
+| -------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
+| `SignalDoc.parquet`, `SignalDoc.csv`                           | One row per signal: category, original paper, sign, portfolio construction settings                                                          |
+| `Firm Level Characteristics/Predictors.parquet`                | Wide panel: `permno`, `yyyymm`, one column per predictor (209 predictors)                                                                    |
+| `Firm Level Characteristics/signed_predictors_dl_wide.parquet` | Same, with each predictor multiplied by its `Sign`, so that higher means higher expected return                                              |
+| `Firm Level Characteristics/Placebos.parquet`                  | Wide panel of placebos (113 "not predictors" and "indirect evidence" signals)                                                                |
+| `Portfolios/Full Sets OP/PredictorPortsFull.parquet`           | Monthly portfolio returns using the original papers' construction. Long: `signalname`, `port`, `date`, `ret`, `signallag`, `Nlong`, `Nshort` |
+| `Portfolios/Full Sets OP/PredictorLSretWide.parquet`           | Long-short returns, wide: `date`, one column per predictor                                                                                   |
+| `Portfolios/Full Sets OP/PlaceboPortsFull.parquet`             | Same as `PredictorPortsFull`, for placebos                                                                                                   |
+| `Portfolios/Full Sets OP/*Summary.xlsx`                        | Summary statistics by signal                                                                                                                 |
+| `Portfolios/Full Sets Alt/*.parquet`                           | Alternative implementations: deciles, quintiles, EW/VW, holding periods, liquidity screens, FF93 style                                       |
+| `DailyPortfolios/*.parquet`                                    | Daily portfolio returns. Long: `signalname`, `port`, `date`, `ret`                                                                           |
+| `Results/`                                                     | Figures and tables from the paper                                                                                                            |
+For firm-level signals, `yyyymm` identifies the month-end when the signal is available
+and valid. Use signals from month-end $t$ to predict returns in month $t+1$.
+For monthly portfolio returns, `date` identifies the return month: the return runs
+from the last trading day's closing price in the preceding month to the last trading
+day's closing price in that month. Returns are in percent.
+`port` is text: `"01"`–`"10"` for quantiles, `"LS"` for long-short.
+Firm-level signals exclude Price, Size and STreversal (CRSP-only), and BidAskTAQ (TAQ),
+which cannot be redistributed. The R and Python packages above retrieve the three
+CRSP signals using your WRDS account. To construct BidAskTAQ, use the required TAQ
+data with the
+[CrossSection code](https://github.com/OpenSourceAP/CrossSection).
+Older releases may have slightly different file structure.
+## Releases
+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}
+}
+```