Chapter 51
Institutional Holdings from SEC 13F Bulk Data
NotebookPython 3 (ipykernel)20 cells
Institutional Holdings from SEC 13F Bulk Data
Chapter 4: Fundamental and Alternative Data
Docker image: ml4t
Section Reference: See Section 4.1 for SEC filing concepts
Purpose
Work with the SEC's quarterly 13F-HR bulk data set as a whole: ~80 MB per quarter gives ~3M holdings across ~7K institutional managers. Form 13F requires investment managers with >$100M in qualifying securities to disclose their equity positions, so the bulk set is the cleanest cross- section of "smart money" positioning publicly available.
This notebook consumes the canonical bulk artifact produced by
data/equities/positioning/13f_download.py --mode bulk. The downloader
parses the SEC zip once and writes a normalized parquet; we load it here
and do the aggregation, ranking, and graph construction.
Important: 13F reports equity holdings only, not total AUM. Use "reported 13F value" (not "AUM") when referring to these figures.
Learning Objectives
After completing this notebook, you will be able to:
- Load the full-universe 13F holdings artifact for a quarter
- Rank managers by reported 13F equity value and extract their holdings
- Identify "crowded" stocks by manager breadth
- Build bipartite manager → stock edges plus stock-stock co-ownership edges for downstream graph work (Ch22/23)
- Reason about the trade-off between the bulk quarterly set and per-CIK live monitoring
Cross-References
- Upstream:
data/equities/positioning/13f_download.py --mode bulk(SEC quarterly data sets) - Parallel:
data/equities/positioning/13f_download.py(default per-cik mode - curated list, multi-quarter history, used by Ch22/23) - Downstream: Ch8
institutional_flow_features.py; Ch22 NB 07; Ch23 knowledge-graph notebooks
Data source: https://www.sec.gov/data-research/sec-markets-data/form-13f-data-sets
In [1]python · cell 2
python
"""Institutional Holdings from SEC 13F Bulk Data - analyze top managers and ownership networks."""
import plotly.express as px
import polars as pl
from data import load_13f_bulk_holdings
from utils.paths import get_output_dir
from utils.style import COLORS # importing utils.style activates the ml4t Plotly templateIn [2]python · cell 3
python
# Production defaults - Papermill injects overrides for CI
QUARTER = "2024Q3" # SEC filing-window label (Sep 1 – Nov 30 2024 in this case)
TOP_N = 500 # Top managers to analyze by reported 13F value
MIN_SHARED_MANAGERS = 5 # Co-ownership edge threshold
CO_OWNERSHIP_UNIVERSE = 500 # Cap stocks used in the O(n²) co-ownership passIn [3]python · cell 4
python
OUTPUT_DIR = get_output_dir(4, "sec_13f")
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)1. Load SEC Bulk 13F Holdings
The downloader produces a single parquet per quarter under
$ML4T_DATA_PATH/equities/positioning/13f/bulk/<YYYYQN>/. If the
artifact is missing the loader raises DataNotFoundError with the
exact command to run:
bash
python data/equities/positioning/13f_download.py --mode bulk --quarters 2024Q3The canonical schema matches the per-cik mode used by Ch22/23 so the same downstream code works on either source:
| Column | Meaning |
|---|---|
cik | Zero-padded filer CIK |
accession_no | SEC accession number (uniquely IDs the filing) |
issuer / cusip | Security identity |
value_thousands | Reported dollar value (raw 13F XML field) |
shares | Reported shares held |
filing_date | Date the filing was submitted to EDGAR |
company_name | Manager name as reported on the cover page |
In [4]python · cell 6
python
holdings = load_13f_bulk_holdings(QUARTER)
print(f"Loaded {len(holdings):,} holdings from {QUARTER}")
print(f"Managers (distinct CIK): {holdings['cik'].n_unique():,}")
print(f"Issuers (distinct CUSIP): {holdings['cusip'].n_unique():,}")
print(f"Filing window: {holdings['filing_date'].min()} → {holdings['filing_date'].max()}")Output
Loaded 2,863,210 holdings from 2024Q3 Managers (distinct CIK): 7,355 Issuers (distinct CUSIP): 34,446 Filing window: 2024-09-03 → 2024-11-29
2. Identify Top Managers by Reported 13F Value
A manager may appear more than once in a filing window - the bulk zip captures both originals and amendments, and occasionally spans two report periods (holdings reported for Q2 filed in September; for Q3 filed in November). We keep the latest accession per manager per quarter so each filer is counted once.
Note: This is reported 13F equity value, not total AUM. Managers may hold significant non-13F assets (bonds, private equity, etc.).
In [5]python · cell 8
python
# Latest filing per manager in this window (amendments have later accessions).
latest_accessions = (
holdings.select(["cik", "company_name", "accession_no", "filing_date"])
.unique(subset=["cik", "accession_no"])
.sort("accession_no", descending=True)
.group_by("cik")
.first()
)
# Aggregate reported value by filing
filings_aggregated = holdings.group_by("accession_no").agg(
[
pl.col("value_thousands").sum().alias("reported_13f_value"),
pl.col("issuer").n_unique().alias("num_positions"),
]
)
manager_values = filings_aggregated.join(latest_accessions, on="accession_no", how="inner").sort(
"reported_13f_value", descending=True
)
top_managers = manager_values.head(TOP_N)
total_13f_value = top_managers["reported_13f_value"].sum()
print(f"Combined reported 13F value across top {TOP_N} managers: ${total_13f_value / 1e12:.1f}T")
top_managers.head(20).select(
pl.col("company_name"),
(pl.col("reported_13f_value") / 1e9).round(1).alias("value_b_usd"),
pl.col("num_positions"),
)Output
Combined reported 13F value across top 500 managers: $51.1T
shape: (20, 3) ┌─────────────────────────────────┬─────────────┬───────────────┐ │ company_name ┆ value_b_usd ┆ num_positions │ │ --- ┆ --- ┆ --- │ │ str ┆ f64 ┆ u32 │ ╞═════════════════════════════════╪═════════════╪═══════════════╡ │ VANGUARD GROUP INC ┆ 5584.5 ┆ 4207 │ │ BlackRock, Inc. ┆ 4763.7 ┆ 4520 │ │ Members Trust Co ┆ 3084.0 ┆ 89 │ │ STATE STREET CORP ┆ 2457.6 ┆ 4028 │ │ FMR LLC ┆ 1643.4 ┆ 4039 │ │ … ┆ … ┆ … │ │ CHARLES SCHWAB INVESTMENT MANA… ┆ 524.0 ┆ 3385 │ │ Capital International Investor… ┆ 521.0 ┆ 417 │ │ CITADEL ADVISORS LLC ┆ 518.3 ┆ 4554 │ │ Invesco Ltd. ┆ 518.0 ┆ 3492 │ │ Royal Bank of Canada ┆ 489.8 ┆ 4664 │ └─────────────────────────────────┴─────────────┴───────────────┘
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}
shape: (20, 3)
| company_name | value_b_usd | num_positions |
|---|---|---|
| str | f64 | u32 |
| "VANGUARD GROUP INC" | 5584.5 | 4207 |
| "BlackRock, Inc." | 4763.7 | 4520 |
| "Members Trust Co" | 3084.0 | 89 |
| "STATE STREET CORP" | 2457.6 | 4028 |
| "FMR LLC" | 1643.4 | 4039 |
| … | … | … |
| "CHARLES SCHWAB INVESTMENT MANA… | 524.0 | 3385 |
| "Capital International Investor… | 521.0 | 417 |
| "CITADEL ADVISORS LLC" | 518.3 | 4554 |
| "Invesco Ltd." | 518.0 | 3492 |
| "Royal Bank of Canada" | 489.8 | 4664 |
3. Extract Holdings for Top Managers
Filter the holdings panel down to the top managers' latest filings, then carry the reported 13F value forward so downstream aggregations can weight by manager size if needed.
In [6]python · cell 10
python
top_accessions = set(top_managers["accession_no"].to_list())
top_holdings = holdings.filter(pl.col("accession_no").is_in(top_accessions)).join(
top_managers.select(["accession_no", "company_name", "reported_13f_value"]),
on="accession_no",
how="left",
suffix="_mgr",
)
print(f"Holdings for top {TOP_N} managers: {len(top_holdings):,} rows")
top_holdings.head(5)Output
Holdings for top 500 managers: 1,241,728 rows
shape: (5, 10) ┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐ │ cik ┆ accession ┆ issuer ┆ cusip ┆ … ┆ filing_da ┆ company_n ┆ company_n ┆ reported │ │ --- ┆ _no ┆ --- ┆ --- ┆ ┆ te ┆ ame ┆ ame_mgr ┆ _13f_val │ │ str ┆ --- ┆ str ┆ str ┆ ┆ --- ┆ --- ┆ --- ┆ ue │ │ ┆ str ┆ ┆ ┆ ┆ date ┆ str ┆ str ┆ --- │ │ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ i64 │ ╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡ │ 000177727 ┆ 000177727 ┆ BURLINGTO ┆ 122017106 ┆ … ┆ 2024-11-2 ┆ Sanctuary ┆ Sanctuary ┆ 15784004 │ │ 1 ┆ 1-24-0000 ┆ N STORES ┆ ┆ ┆ 5 ┆ Advisors, ┆ Advisors, ┆ 619 │ │ ┆ 05 ┆ INC ┆ ┆ ┆ ┆ LLC ┆ LLC ┆ │ │ 000177727 ┆ 000177727 ┆ BUTTERFLY ┆ 124155102 ┆ … ┆ 2024-11-2 ┆ Sanctuary ┆ Sanctuary ┆ 15784004 │ │ 1 ┆ 1-24-0000 ┆ NETWORK ┆ ┆ ┆ 5 ┆ Advisors, ┆ Advisors, ┆ 619 │ │ ┆ 05 ┆ INC ┆ ┆ ┆ ┆ LLC ┆ LLC ┆ │ │ 000177727 ┆ 000177727 ┆ BWX TECHN ┆ 05605H100 ┆ … ┆ 2024-11-2 ┆ Sanctuary ┆ Sanctuary ┆ 15784004 │ │ 1 ┆ 1-24-0000 ┆ OLOGIES ┆ ┆ ┆ 5 ┆ Advisors, ┆ Advisors, ┆ 619 │ │ ┆ 05 ┆ INC ┆ ┆ ┆ ┆ LLC ┆ LLC ┆ │ │ 000177727 ┆ 000177727 ┆ BXP INC ┆ 101121101 ┆ … ┆ 2024-11-2 ┆ Sanctuary ┆ Sanctuary ┆ 15784004 │ │ 1 ┆ 1-24-0000 ┆ ┆ ┆ ┆ 5 ┆ Advisors, ┆ Advisors, ┆ 619 │ │ ┆ 05 ┆ ┆ ┆ ┆ ┆ LLC ┆ LLC ┆ │ │ 000177727 ┆ 000177727 ┆ C H ┆ 12541W209 ┆ … ┆ 2024-11-2 ┆ Sanctuary ┆ Sanctuary ┆ 15784004 │ │ 1 ┆ 1-24-0000 ┆ ROBINSON ┆ ┆ ┆ 5 ┆ Advisors, ┆ Advisors, ┆ 619 │ │ ┆ 05 ┆ WORLDWIDE ┆ ┆ ┆ ┆ LLC ┆ LLC ┆ │ │ ┆ ┆ INC ┆ ┆ ┆ ┆ ┆ ┆ │ └───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘
.dataframe > thead > tr,
.dataframe > tbody > tr {
text-align: right;
white-space: pre-wrap;
}
shape: (5, 10)
| cik | accession_no | issuer | cusip | value_thousands | shares | filing_date | company_name | company_name_mgr | reported_13f_value |
|---|---|---|---|---|---|---|---|---|---|
| str | str | str | str | i64 | i64 | date | str | str | i64 |
| "0001777271" | "0001777271-24-000005" | "BURLINGTON STORES INC" | "122017106" | 760068 | 2656 | 2024-11-25 | "Sanctuary Advisors, LLC" | "Sanctuary Advisors, LLC" | 15784004619 |
| "0001777271" | "0001777271-24-000005" | "BUTTERFLY NETWORK INC" | "124155102" | 33628 | 10379 | 2024-11-25 | "Sanctuary Advisors, LLC" | "Sanctuary Advisors, LLC" | 15784004619 |
| "0001777271" | "0001777271-24-000005" | "BWX TECHNOLOGIES INC" | "05605H100" | 11496232 | 86509 | 2024-11-25 | "Sanctuary Advisors, LLC" | "Sanctuary Advisors, LLC" | 15784004619 |
| "0001777271" | "0001777271-24-000005" | "BXP INC" | "101121101" | 1810706 | 22449 | 2024-11-25 | "Sanctuary Advisors, LLC" | "Sanctuary Advisors, LLC" | 15784004619 |
| "0001777271" | "0001777271-24-000005" | "C H ROBINSON WORLDWIDE INC" | "12541W209" | 795903 | 7396 | 2024-11-25 | "Sanctuary Advisors, LLC" | "Sanctuary Advisors, LLC" | 15784004619 |
4. Most Widely Held Stocks
Which issuers appear in the most top-manager portfolios? Breadth is a rough proxy for crowding - stocks held by many institutional managers can face coordinated selling pressure during risk-off episodes.
In [7]python · cell 12
python
widely_held = (
top_holdings.group_by(["issuer", "cusip"])
.agg(
[
pl.col("company_name").n_unique().alias("num_managers"),
pl.col("value_thousands").sum().alias("total_value"),
pl.col("shares").sum().alias("total_shares"),
]
)
.sort("num_managers", descending=True)
)
widely_held.head(20)Output
shape: (20, 5) ┌──────────────────────────────┬───────────┬──────────────┬───────────────┬──────────────┐ │ issuer ┆ cusip ┆ num_managers ┆ total_value ┆ total_shares │ │ --- ┆ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ str ┆ u32 ┆ i64 ┆ i64 │ ╞══════════════════════════════╪═══════════╪══════════════╪═══════════════╪══════════════╡ │ MICROSOFT CORP ┆ 594918104 ┆ 380 ┆ 1972663725062 ┆ 4558427680 │ │ APPLE INC ┆ 037833100 ┆ 344 ┆ 1926873362513 ┆ 8176667001 │ │ AMAZON COM INC ┆ 023135106 ┆ 327 ┆ 936821043405 ┆ 5022224661 │ │ ALPHABET INC ┆ 02079K305 ┆ 327 ┆ 577944185645 ┆ 3473775968 │ │ UNITEDHEALTH GROUP INC ┆ 91324P102 ┆ 325 ┆ 400698719645 ┆ 683997946 │ │ … ┆ … ┆ … ┆ … ┆ … │ │ THERMO FISHER SCIENTIFIC INC ┆ 883556102 ┆ 305 ┆ 164550903794 ┆ 266305331 │ │ JOHNSON & JOHNSON ┆ 478160104 ┆ 304 ┆ 232474193832 ┆ 1426957403 │ │ QUALCOMM INC ┆ 747525103 ┆ 304 ┆ 122005786019 ┆ 714999977 │ │ PEPSICO INC ┆ 713448108 ┆ 304 ┆ 144943733223 ┆ 845635009 │ │ NVIDIA CORPORATION ┆ 67066G104 ┆ 303 ┆ 1632438094281 ┆ 13340921680 │ └──────────────────────────────┴───────────┴──────────────┴───────────────┴──────────────┘
.dataframe > thead > tr,
.dataframe > tbody > tr {
text-align: right;
white-space: pre-wrap;
}
shape: (20, 5)
| issuer | cusip | num_managers | total_value | total_shares |
|---|---|---|---|---|
| str | str | u32 | i64 | i64 |
| "MICROSOFT CORP" | "594918104" | 380 | 1972663725062 | 4558427680 |
| "APPLE INC" | "037833100" | 344 | 1926873362513 | 8176667001 |
| "AMAZON COM INC" | "023135106" | 327 | 936821043405 | 5022224661 |
| "ALPHABET INC" | "02079K305" | 327 | 577944185645 | 3473775968 |
| "UNITEDHEALTH GROUP INC" | "91324P102" | 325 | 400698719645 | 683997946 |
| … | … | … | … | … |
| "THERMO FISHER SCIENTIFIC INC" | "883556102" | 305 | 164550903794 | 266305331 |
| "JOHNSON & JOHNSON" | "478160104" | 304 | 232474193832 | 1426957403 |
| "QUALCOMM INC" | "747525103" | 304 | 122005786019 | 714999977 |
| "PEPSICO INC" | "713448108" | 304 | 144943733223 | 845635009 |
| "NVIDIA CORPORATION" | "67066G104" | 303 | 1632438094281 | 13340921680 |
Largest aggregate positions (sum of reported value across the top managers):
In [8]python · cell 14
python
widely_held.sort("total_value", descending=True).head(20).select(
pl.col("issuer"),
(pl.col("total_value") / 1e9).round(1).alias("value_b_usd"),
pl.col("num_managers"),
)Output
shape: (20, 3) ┌────────────────────────────┬─────────────┬──────────────┐ │ issuer ┆ value_b_usd ┆ num_managers │ │ --- ┆ --- ┆ --- │ │ str ┆ f64 ┆ u32 │ ╞════════════════════════════╪═════════════╪══════════════╡ │ MICROSOFT CORP ┆ 1972.7 ┆ 380 │ │ APPLE INC ┆ 1926.9 ┆ 344 │ │ NVIDIA CORPORATION ┆ 1632.4 ┆ 303 │ │ SPDR S&P 500 ETF TR ┆ 1092.3 ┆ 220 │ │ AMAZON COM INC ┆ 936.8 ┆ 327 │ │ … ┆ … ┆ … │ │ VISA INC ┆ 315.1 ┆ 316 │ │ ISHARES TR ┆ 310.0 ┆ 132 │ │ BERKSHIRE HATHAWAY INC DEL ┆ 301.0 ┆ 266 │ │ INVESCO QQQ TR ┆ 295.4 ┆ 180 │ │ EXXON MOBIL CORP ┆ 294.0 ┆ 305 │ └────────────────────────────┴─────────────┴──────────────┘
.dataframe > thead > tr,
.dataframe > tbody > tr {
text-align: right;
white-space: pre-wrap;
}
shape: (20, 3)
| issuer | value_b_usd | num_managers |
|---|---|---|
| str | f64 | u32 |
| "MICROSOFT CORP" | 1972.7 | 380 |
| "APPLE INC" | 1926.9 | 344 |
| "NVIDIA CORPORATION" | 1632.4 | 303 |
| "SPDR S&P 500 ETF TR" | 1092.3 | 220 |
| "AMAZON COM INC" | 936.8 | 327 |
| … | … | … |
| "VISA INC" | 315.1 | 316 |
| "ISHARES TR" | 310.0 | 132 |
| "BERKSHIRE HATHAWAY INC DEL" | 301.0 | 266 |
| "INVESCO QQQ TR" | 295.4 | 180 |
| "EXXON MOBIL CORP" | 294.0 | 305 |
In [9]python · cell 15
python
fig = px.bar(
# Cast the manager count to a signed int so Plotly renders a continuous
# color axis (a uint column is treated as categorical → off-palette hues).
widely_held.head(15).with_columns(pl.col("num_managers").cast(pl.Int64)).to_pandas(),
x="issuer",
y="total_value",
color="num_managers",
title="Institutional ownership concentrates in a handful of megacaps",
labels={
"total_value": "Aggregate reported 13F value (USD)",
"issuer": "Company",
"num_managers": "Managers holding",
},
color_continuous_scale=[COLORS["silver_muted"], COLORS["blue"]],
)
# The rotated ticks are the company names, so the axis title is redundant;
# extra bottom/right margin keeps the longest label from clipping.
fig.update_layout(xaxis_tickangle=45, xaxis_title=None, margin=dict(b=150, r=120))
fig.show()Output
5. Build Ownership Edges for Knowledge Graphs
Two edge sets feed Chapter 23 graph models:
- Manager → Stock (bipartite): weights are position value
- Stock ↔ Stock (co-ownership): shared-manager intersection, useful for propagating signals across peers
In [10]python · cell 17
python
manager_stock_edges = (
top_holdings.select(["company_name", "issuer", "cusip", "value_thousands"])
.group_by(["company_name", "issuer", "cusip"])
.agg(pl.col("value_thousands").sum().alias("position_value"))
)
print(f"Manager-Stock edges: {len(manager_stock_edges):,}")
manager_stock_edges.head(10)Output
Manager-Stock edges: 639,124
shape: (10, 4) ┌─────────────────────────────────┬──────────────────────────────┬───────────┬────────────────┐ │ company_name ┆ issuer ┆ cusip ┆ position_value │ │ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ str ┆ str ┆ i64 │ ╞═════════════════════════════════╪══════════════════════════════╪═══════════╪════════════════╡ │ HUNTINGTON NATIONAL BANK ┆ DELPHI CORPORATION ┆ 247126105 ┆ 0 │ │ UBS Group AG ┆ BERKSHIRE HILLS BANCORP INC ┆ 084680107 ┆ 1559812 │ │ OSAIC HOLDINGS, INC. ┆ DBX ETF TR ┆ 233051820 ┆ 49311 │ │ GEODE CAPITAL MANAGEMENT, LLC ┆ SJW GROUP ┆ 784305104 ┆ 42006100 │ │ EP Wealth Advisors, LLC ┆ GOLDMAN SACHS GROUP INC ┆ 38141G104 ┆ 2821580 │ │ STRS OHIO ┆ METLIFE INC ┆ 59156R108 ┆ 26975084 │ │ AMERICAN CENTURY COMPANIES INC ┆ HIGHPEAK ENERGY INC ┆ 43114Q105 ┆ 8914541 │ │ AMERICAN CENTURY COMPANIES INC ┆ UNITED RENTALS INC ┆ 911363109 ┆ 64163236 │ │ PNC Financial Services Group, … ┆ CALAMOS CONV & HIGH INCOME F ┆ 12811P108 ┆ 95870 │ │ STRS OHIO ┆ CENCORA INC ┆ 03073E105 ┆ 14470618 │ └─────────────────────────────────┴──────────────────────────────┴───────────┴────────────────┘
.dataframe > thead > tr,
.dataframe > tbody > tr {
text-align: right;
white-space: pre-wrap;
}
shape: (10, 4)
| company_name | issuer | cusip | position_value |
|---|---|---|---|
| str | str | str | i64 |
| "HUNTINGTON NATIONAL BANK" | "DELPHI CORPORATION" | "247126105" | 0 |
| "UBS Group AG" | "BERKSHIRE HILLS BANCORP INC" | "084680107" | 1559812 |
| "OSAIC HOLDINGS, INC." | "DBX ETF TR" | "233051820" | 49311 |
| "GEODE CAPITAL MANAGEMENT, LLC" | "SJW GROUP" | "784305104" | 42006100 |
| "EP Wealth Advisors, LLC" | "GOLDMAN SACHS GROUP INC" | "38141G104" | 2821580 |
| "STRS OHIO" | "METLIFE INC" | "59156R108" | 26975084 |
| "AMERICAN CENTURY COMPANIES INC" | "HIGHPEAK ENERGY INC" | "43114Q105" | 8914541 |
| "AMERICAN CENTURY COMPANIES INC" | "UNITED RENTALS INC" | "911363109" | 64163236 |
| "PNC Financial Services Group, … | "CALAMOS CONV & HIGH INCOME F" | "12811P108" | 95870 |
| "STRS OHIO" | "CENCORA INC" | "03073E105" | 14470618 |
In [11]python · cell 18
python
def build_co_ownership_edges(
holdings_panel: pl.DataFrame,
min_shared: int = 3,
universe_cap: int = 500,
) -> pl.DataFrame:
"""Build stock-stock edges based on shared institutional owners.
Caps the universe at `universe_cap` stocks (by number of unique
managers) to avoid an O(n²) blow-up on the full cross-section.
"""
stock_managers = (
holdings_panel.group_by(["issuer", "cusip"])
.agg(pl.col("company_name").alias("managers"))
.with_columns(pl.col("managers").list.len().alias("n_managers"))
.sort("n_managers", descending=True)
.head(universe_cap)
)
stocks = stock_managers["issuer"].to_list()
cusips = stock_managers["cusip"].to_list()
manager_sets = [set(m) for m in stock_managers["managers"].to_list()]
edges = []
for i in range(len(stocks)):
for j in range(i + 1, len(stocks)):
shared = manager_sets[i] & manager_sets[j]
if len(shared) >= min_shared:
edges.append(
{
"stock_a": stocks[i],
"cusip_a": cusips[i],
"stock_b": stocks[j],
"cusip_b": cusips[j],
"shared_managers": len(shared),
}
)
return pl.DataFrame(
edges,
schema={
"stock_a": pl.Utf8,
"cusip_a": pl.Utf8,
"stock_b": pl.Utf8,
"cusip_b": pl.Utf8,
"shared_managers": pl.Int64,
},
)
co_ownership = build_co_ownership_edges(
top_holdings, min_shared=MIN_SHARED_MANAGERS, universe_cap=CO_OWNERSHIP_UNIVERSE
)
print(f"Co-ownership edges (≥{MIN_SHARED_MANAGERS} shared managers): {len(co_ownership):,}")
co_ownership.sort("shared_managers", descending=True).head(10)Output
Co-ownership edges (≥5 shared managers): 124,750
shape: (10, 5) ┌────────────────┬───────────┬────────────────────────┬───────────┬─────────────────┐ │ stock_a ┆ cusip_a ┆ stock_b ┆ cusip_b ┆ shared_managers │ │ --- ┆ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ str ┆ str ┆ str ┆ i64 │ ╞════════════════╪═══════════╪════════════════════════╪═══════════╪═════════════════╡ │ MICROSOFT CORP ┆ 594918104 ┆ APPLE INC ┆ 037833100 ┆ 338 │ │ MICROSOFT CORP ┆ 594918104 ┆ UNITEDHEALTH GROUP INC ┆ 91324P102 ┆ 322 │ │ MICROSOFT CORP ┆ 594918104 ┆ ALPHABET INC ┆ 02079K305 ┆ 320 │ │ MICROSOFT CORP ┆ 594918104 ┆ AMAZON COM INC ┆ 023135106 ┆ 319 │ │ MICROSOFT CORP ┆ 594918104 ┆ NETFLIX INC ┆ 64110L106 ┆ 316 │ │ MICROSOFT CORP ┆ 594918104 ┆ VISA INC ┆ 92826C839 ┆ 313 │ │ MICROSOFT CORP ┆ 594918104 ┆ BROADCOM INC ┆ 11135F101 ┆ 312 │ │ MICROSOFT CORP ┆ 594918104 ┆ META PLATFORMS INC ┆ 30303M102 ┆ 308 │ │ MICROSOFT CORP ┆ 594918104 ┆ ELI LILLY & CO ┆ 532457108 ┆ 308 │ │ MICROSOFT CORP ┆ 594918104 ┆ ADOBE INC ┆ 00724F101 ┆ 308 │ └────────────────┴───────────┴────────────────────────┴───────────┴─────────────────┘
.dataframe > thead > tr,
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shape: (10, 5)
| stock_a | cusip_a | stock_b | cusip_b | shared_managers |
|---|---|---|---|---|
| str | str | str | str | i64 |
| "MICROSOFT CORP" | "594918104" | "APPLE INC" | "037833100" | 338 |
| "MICROSOFT CORP" | "594918104" | "UNITEDHEALTH GROUP INC" | "91324P102" | 322 |
| "MICROSOFT CORP" | "594918104" | "ALPHABET INC" | "02079K305" | 320 |
| "MICROSOFT CORP" | "594918104" | "AMAZON COM INC" | "023135106" | 319 |
| "MICROSOFT CORP" | "594918104" | "NETFLIX INC" | "64110L106" | 316 |
| "MICROSOFT CORP" | "594918104" | "VISA INC" | "92826C839" | 313 |
| "MICROSOFT CORP" | "594918104" | "BROADCOM INC" | "11135F101" | 312 |
| "MICROSOFT CORP" | "594918104" | "META PLATFORMS INC" | "30303M102" | 308 |
| "MICROSOFT CORP" | "594918104" | "ELI LILLY & CO" | "532457108" | 308 |
| "MICROSOFT CORP" | "594918104" | "ADOBE INC" | "00724F101" | 308 |
6. Bulk Data vs. Live Filing Monitoring
The bulk data set is assembled retroactively after each filing window closes (a Q3 file covers Sep 1 – Nov 30 filings and is published in early December). For trading applications where timeliness matters, readers instead monitor individual filings as they land on EDGAR.
13F Filing Timeline
- Quarter end: Sep 30 (Q3 reports)
- Filing deadline: 45 days later → Nov 14
- Individual filings: Public immediately on EDGAR
- Bulk window: Sep 1 – Nov 30 (captures late/amended filings)
- Bulk file published: After the window closes (~early December)
Why This Matters
If you are trading on "what are top managers accumulating?", the bulk file is already stale by publication. Professional systems poll EDGAR and parse filings within minutes - this is standard practice, not front-running, since the information is public the instant it's submitted.
Two Ingestion Paths, One Canonical Schema
The data/equities/positioning/13f_download.py script supports both:
| Mode | Source | Scope | Use case |
|---|---|---|---|
--mode per-cik (default) | SEC JSON submissions API + per-filing XML | Curated institution list × N recent quarters | Ch22/23 - named-manager history |
--mode bulk | SEC quarterly zip | All ~7K filers × one quarter | This notebook - full cross-section |
Both modes write the same canonical schema, so downstream code does not need to know which ingestion path produced the data.
7. Key Takeaways
-
SEC bulk data is the canonical source for historical 13F analysis: one ~80 MB quarterly file gives full-universe holdings from ~7K managers, no rate limits, no vendor fees.
-
For live trading, monitor EDGAR directly: bulk files are published after the filing window closes. The per-cik mode of the canonical downloader is the starting point for live ingestion.
-
Institutional ownership is highly concentrated: the top 500 managers collectively report tens of trillions in 13F equity value. Remember this is reported 13F value, not AUM.
-
Crowded trades are identifiable: stocks held by hundreds of managers may face coordinated selling pressure during stress.
-
Canonical schema keeps the pipeline simple: the same downstream code consumes per-cik and bulk artifacts. New ingestion paths only need to land canonical columns in the right location.