13F Filing Deadlines and Reporting Calendar
SEC 13F quarterly filing deadlines, when data becomes available, how to track the filing cycle, and why timing matters for interpreting holdings data.
This guide is for educational purposes only. Not investment advice.
The Quarterly Filing Cycle
SEC Form 13F follows a strict quarterly calendar. Institutional investment managers with at least $100 million in qualifying assets under management must report their long equity holdings as of the last day of each calendar quarter. The filing is due 45 calendar days after the quarter ends.
2026 Filing Deadlines
| Quarter | Holdings Date | Filing Deadline | Data Availability |
|---|---|---|---|
| Q4 2025 | December 31, 2025 | February 14, 2026 | Late February 2026 |
| Q1 2026 | March 31, 2026 | May 15, 2026 | Late May 2026 |
| Q2 2026 | June 30, 2026 | August 14, 2026 | Late August 2026 |
| Q3 2026 | September 30, 2026 | November 14, 2026 | Late November 2026 |
Why Timing Matters
The 45-day gap between the holdings date and the filing deadline is the most important thing to understand about 13F data. When you see that a fund holds a stock as of March 31, that information does not become public until mid-May at the earliest. During those 45 days, the fund may have sold the position entirely, doubled it, or made other changes.
This delay means that 13F data is best used for understanding institutional trends, ownership patterns, and long-term strategic positions rather than for short-term trading decisions. Funds that are known to trade frequently (many hedge funds) may have substantially different portfolios by the time their 13F is filed.
Filing Patterns to Watch
Not all filers submit on the same schedule:
- Early filers: Some large funds file within days of the quarter end, especially those with fewer positions. Early filings can signal what the broader filing season will reveal.
- Deadline filers: Most institutional managers file in the final week before the deadline. This is when the bulk of new data appears on EDGAR.
- Late filers: Some managers file after the deadline. The SEC may grant extensions. Late filings can contain surprises that the market has not yet priced in.
- Confidential treatment: Managers can request that the SEC delay publication of specific positions. This is most common for large positions that are being actively accumulated. Confidential holdings eventually become public but may lag by quarters.
How to Track New Filings
PlainFundData updates its database as new filings appear on SEC EDGAR. You can track a specific fund by visiting its profile page to see its latest reported holdings. The SEC also provides an EDGAR full-text search index that can be queried for new 13F-HR filings. For bulk monitoring, the SEC EDGAR EFTS API provides programmatic access to new filing notifications.
Frequently Asked Questions
When are SEC 13F filings due?
13F filings are due 45 calendar days after the end of each calendar quarter. Q1 filings are due by May 15, Q2 by August 14, Q3 by November 14, and Q4 by February 14. If the deadline falls on a weekend or holiday, the due date is the next business day.
How long after the deadline does it take to see all filings?
Most large institutions file within the first two weeks of the deadline. However, some managers file late, request extensions, or obtain confidential treatment. The majority of filings for a given quarter are available on EDGAR within 2-3 weeks after the deadline, with stragglers continuing to appear for several months.
Do 13F filings show current holdings?
No. 13F filings show positions as of the last day of the calendar quarter, but are not filed until 45 days later. By the time you see the data, positions may have changed significantly. This delay is the most important limitation of 13F data. Major events (earnings surprises, market corrections) between the quarter end and the filing date can cause substantial portfolio changes that are not reflected in the filing.
Related Resources
Understanding the Data
The information presented throughout this guide is informed by publicly available SEC fund data published by U.S. Securities and Exchange Commission EDGAR filings. Our database aggregates and standardizes these records to make them more accessible and easier to interpret for general audiences. When we reference specific statistics or trends, they are drawn directly from these authoritative sources unless explicitly noted otherwise.
It is important to understand the limitations of any large-scale mutual fund dataset. Records may contain errors from the original data collection process, some fields may be incomplete for older entries, and classification systems may have changed over time. Our analysis accounts for these factors by clearly labeling data vintage, flagging records with missing critical fields, and noting when temporal comparisons span methodology changes in the source data.
For readers who want to conduct their own research, we recommend going directly to the source whenever possible. U.S. Securities and Exchange Commission EDGAR filings provides detailed documentation on collection methodology, sampling frames, and known data quality issues. Our goal is not to replace primary sources but to make them more approachable and to highlight patterns that may not be immediately obvious when browsing raw records.
How We Analyze Mutual fund Records
Our analytical approach involves several steps designed to surface meaningful insights from large datasets. First, we clean and standardize the raw data, handling variations in naming conventions, date formats, and categorical labels. Then we compute summary statistics, distributions, and comparative benchmarks across relevant dimensions such as geography, time period, and category type.
Key metrics we examine include net asset values, expense ratios, fund holdings, performance returns. These indicators provide a multi-dimensional view of each entity in our database, allowing users to understand not just individual records but how they compare to peers, regional averages, and national benchmarks. We believe this contextual approach is far more valuable than presenting raw numbers in isolation.
Understanding the Data
The information presented throughout this guide is informed by publicly available SEC fund data published by U.S. Securities and Exchange Commission EDGAR filings. Our database aggregates and standardizes these records to make them more accessible and easier to interpret for general audiences. When we reference specific statistics or trends, they are drawn directly from these authoritative sources unless explicitly noted otherwise.
It is important to understand the limitations of any large-scale mutual fund dataset. Records may contain errors from the original data collection process, some fields may be incomplete for older entries, and classification systems may have changed over time. Our analysis accounts for these factors by clearly labeling data vintage, flagging records with missing critical fields, and noting when temporal comparisons span methodology changes in the source data.
For readers who want to conduct their own research, we recommend going directly to the source whenever possible. U.S. Securities and Exchange Commission EDGAR filings provides detailed documentation on collection methodology, sampling frames, and known data quality issues. Our goal is not to replace primary sources but to make them more approachable and to highlight patterns that may not be immediately obvious when browsing raw records.
How We Analyze Mutual fund Records
Our analytical approach involves several steps designed to surface meaningful insights from large datasets. First, we clean and standardize the raw data, handling variations in naming conventions, date formats, and categorical labels. Then we compute summary statistics, distributions, and comparative benchmarks across relevant dimensions such as geography, time period, and category type.
Key metrics we examine include net asset values, expense ratios, fund holdings, performance returns. These indicators provide a multi-dimensional view of each entity in our database, allowing users to understand not just individual records but how they compare to peers, regional averages, and national benchmarks. We believe this contextual approach is far more valuable than presenting raw numbers in isolation.
