README.md

---
license: apache-2.0
language:
  - en
pretty_name: US Industry Classification — free sectors for SEC filers, with dates
size_categories:
  - 100K<n<1M
task_categories:
  - tabular-classification
tags:
  - finance
  - sector
  - industry
  - classification
  - sic
  - naics
  - sec
  - edgar
  - point-in-time
  - backtesting
  - ziplime
configs:
  - config_name: classification
    data_files:
      - split: train
        path: "data/classification/*.parquet"
  - config_name: by_ticker
    data_files:
      - split: train
        path: "data/by_ticker/*.parquet"
  - config_name: spans
    data_files:
      - split: train
        path: "data/spans/*.parquet"
  - config_name: observations
    data_files:
      - split: train
        path: "data/observations/*.parquet"
  - config_name: sic_codes
    data_files:
      - split: train
        path: "data/sic_codes/*.parquet"
  - config_name: naics_map
    data_files:
      - split: train
        path: "data/naics_map/*.parquet"
  - config_name: taxonomy
    data_files:
      - split: train
        path: "data/taxonomy/*.parquet"
---

# US Industry Classification

What business a listed company is in — and what business it was in then.

**22 270 filers · 11 334 live tickers · 23 835 dated classification spans ·
441 485 statements · 15 sectors, 90 industries · 2009 to 2026**

Free to use, free to redistribute. Built entirely from US government works:
the SIC code the SEC assigns to every filer, and the Census Bureau's bridge to
NAICS. The rollup from those codes to sectors is ZipLime's own, published
under Apache-2.0 in [`recipe/taxonomy.py`](recipe/taxonomy.py).

The pipeline lives in [`recipe/`](recipe/) at the same revision as the data.
See [PIPELINE.md](PIPELINE.md) for the method and its limits.

## This is not GICS and not ICB

It cannot be. GICS belongs to MSCI and S&P Dow Jones Indices, ICB to FTSE
Russell, and neither can be redistributed — which is the whole reason this
exists. Nothing licensed from either was used or consulted.

So expect disagreements, and know where they are:

* **Alphabet and Meta are Technology here, Communication Services in GICS.**
  The SEC files both under 7370, alongside systems integrators, and nothing in
  a filing separates an advertising platform from a consultancy.
* **Disney is Consumer Cyclical here, Communication Services in GICS.** Its
  assigned code is 7990, amusement and recreation. The code is the SEC's.
* **Blank cheque companies get a sector of their own.** GICS has no bucket for
  one. 2 181 filers here have been one, and anyone building a universe wants
  to drop them by name rather than find them scattered through Financials.

If you have a GICS or ICB licence, the `sic` column is here to join against.

## The point: it is dated

A company's industry is not a constant. **2 320 filers have been reclassified
at least once** — most often a blank cheque that became an operating company.

```python
import polars as pl

history = pl.read_parquet("data/by_ticker/*.parquet")
history.filter(pl.col("ticker") == "LCID").select(
    "valid_from", "valid_to", "sic", "sector", "industry"
)
# 2021-07-27 … 2026-08-04 | 3711 | Consumer Cyclical | Automobiles
```

Churchill Capital IV was SIC 6770, a blank cheque, until it became Lucid. A
dataset that states only today's code calls it a car maker in 2020, and a
sector-neutral backtest then shorts the automotive basket against an empty
shell.

## Getting a sector for a ticker

```python
import polars as pl

live = (
    pl.read_parquet("data/by_ticker/*.parquet")
      .filter(pl.col("is_current"))
      .select("ticker", "cik", "sector_code", "sector", "industry", "sic")
)
```

`is_current` marks exactly one row per live symbol: the filer's ticker now,
under the newest code it has been seen under. For a date in the past, filter
on the window instead:

```python
on = pl.date(2015, 6, 30)
history.filter((pl.col("valid_from") <= on) & (on <= pl.col("valid_to")))
```

Join on `cik` wherever you have it. It is stable, and it is the key the rest
of this dataset family uses.

## Configs

| Config | Rows | What one row is |
|---|---|---|
| `classification` | 22 270 | one filer: the sector it is in now, fully resolved |
| `by_ticker` | 30 820 | one ticker over one window, with the sector it was in then |
| `spans` | 23 835 | one run of filings that all stated the same SIC code |
| `observations` | 441 485 | one dated statement — the evidence the spans are built from |
| `sic_codes` | 444 | one EDGAR SIC code: hierarchy, sector, NAICS bridge, usage |
| `naics_map` | 1 854 | one 1987 SIC to 1997 NAICS pair from the Census concordance |
| `taxonomy` | 90 | one industry of the taxonomy and the SIC ranges it covers |

`observations` is published so that anyone who disagrees with how the spans
were cut can cut their own. It is the only honest way to ship a judgement.

## The taxonomy

Fifteen sectors. Thirteen are what you would expect; three exist because the
SEC's universe is not just operating companies.

| Sector | Live filers | Note |
|---|---|---|
| Health Care | 1 426 | includes Managed Health Care — Centene, Cigna, Elevance |
| Industrials | 1 149 | |
| Technology | 1 048 | includes Internet Software & Services |
| Financials | 1 054 | |
| Consumer Cyclical | 766 | |
| Funds & Trusts | 591 | closed-end funds, ETFs, securitisation vehicles |
| Materials | 506 | |
| Shell & Blank Check | 444 | SPACs and non-operating establishments |
| Unclassified | 417 | almost all depositary receipt programmes |
| Real Estate | 336 | |
| Energy | 272 | |
| Consumer Defensive | 267 | |
| Telecom & Media | 220 | |
| Utilities | 182 | |
| Government | — | foreign sovereigns and public administration |

**No sector holds more than 17% of the live universe**, which is the test the
SIC divisions fail: Division D, Manufacturing, is a third of it on its own.

Read the taxonomy without reading Python:

```python
pl.read_parquet("data/taxonomy/*.parquet").select(
    "sector", "industry", "sic_ranges"
)
```

## Where it comes from

| Source | What it gives | Rows |
|---|---|---|
| XBRL filing headers, via [ZipLime/company-fundamentals](https://huggingface.co/datasets/ZipLime/company-fundamentals) | the code the SEC had a filer under, on the day it filed — the only free source of SIC **history** | 437 619 |
| EDGAR's submissions archive | today's code for filers that never filed XBRL: delisted before 2009, funds, foreign issuers on paper | 3 866 |
| EDGAR's submissions archive, read for forms | what kind of entity a filer with no code is — N-CSR means a registered investment company, F-6 a depositary receipt programme | 1 476 |
| Ticker windows, via [ZipLime/security-master](https://huggingface.co/datasets/ZipLime/security-master) | which symbol belonged to which filer, when | 29 584 |
| SEC's published SIC code list | the title of each code and the office that reviews it | 444 |
| Census Bureau 1987 SIC → 1997 NAICS concordance | the NAICS bridge, and the 1987 hierarchy's own titles | 1 854 |

## Coverage and honest gaps

* **97.2% of live tickers get an answer.** The remainder are dormant filers
  EDGAR has never classified and whose forms say nothing either.
* **Nothing before 2009.** XBRL headers begin in 2009 Q2. A company's code in
  1998 is in the SGML header of its filings and is not read here.
* **`date_basis` tells you what the dates are worth.** `dated` (15 565 rows)
  means a filing stated the code inside that window. `undated` (12 765) means
  a code is known for the filer but nothing dates it to that window, so the
  latest one is applied across it. `unknown` (2 490) means no code at all.
* **Two fifths of EDGAR's codes are roll-ups.** `2000` means all of food,
  `3570` all of computer and office equipment. `sic_level` says which you
  have: 284 of the 444 are real four-digit industries, 118 are three-digit
  groups, 42 are two-digit major groups.
* **The NAICS bridge is weak and labelled as such.** Only 32.7% of codes
  resolve to exactly one 1997 NAICS industry and 71.8% to one NAICS sector.
  `naics_1997` and `naics_sector` are filled only where the answer is unique;
  `naics_candidates` says how many there were. The full crosswalk is in
  `naics_map`.
* **8731 mixes contract research with research-stage biotech.** Charles River
  and Incyte share a code, and nothing in a filing separates them.
* **51 symbols are current for two filers, 28 of them in different sectors.**
  Genuine ticker transfers and reuse — Barnes Group handed `B` to Barrick.
  Each row is right for its own filer; deciding who owns a symbol today is
  [security-master](https://huggingface.co/datasets/ZipLime/security-master)'s
  job.
* **The classification is the SEC's opinion, not the company's.** Assigned
  codes go stale; the SEC does not re-review them on a schedule.

## Provenance and updates

Sources: SEC EDGAR (filing headers via the sibling dataset, and the
submissions archive), the SEC's SIC code list, and the US Census Bureau's
SIC-to-NAICS concordance — all US Government works in the public domain. The
sector taxonomy is ZipLime's own work under Apache-2.0. See [NOTICE](NOTICE).

Rebuilt weekly, Monday 09:40 UTC, by a Hugging Face Job
([`jobs/run.py`](jobs/run.py)) that lints, tests, rebuilds, verifies and only
then publishes.