ZipLime/earnings-calendar

US Earnings Calendar

When a company announced its results, to the second, what those results were, and which session could act on them.

454 611 earnings releases · 258 482 paired with reported figures · 4 382 182 corporate events · 2003-04-25 to 2026-09-04

The pipeline lives in recipe/ at the same revision as the data. See PIPELINE.md for the method.

This is not a calendar of future releases

Nothing here schedules an announcement. A row appears when EDGAR accepted the 8-K — that is, once the release has already happened — and the dataset is built for measuring reactions, not for staying out of the market beforehand.

The obvious extrapolation does not work well enough to lean on. Over 406 837 consecutive release intervals, "the previous release plus 91 days" has a median absolute error of 7 days and lands within a week only 54.7% of the time. The intervals themselves: median 91, p10 56, p25 79, p75 95, p90 112.

The time of day is the whole point

A release at 07:00 New York trades on the open. One at 16:30 trades overnight. Four hours of error swaps them, and with them the sign of any event study.

Session	Releases
after_close	227 500
pre_market	138 534
market_hours	87 978
non_session	599

80.7% land outside the session, which is what earnings releases do — and the fact that they do is the check that the timestamps are right.

Every timestamp is read from the filing's own SGML header, where ACCEPTANCE-DATETIME is unambiguous US Eastern. Not from EDGAR's bulk submissions.zip, whose acceptanceDateTime carries a Z and is Eastern local time for much of the corpus:

Filed	Share of bulk timestamps that are Eastern, not UTC
2004–2009	44%
2010–2015	62%
2016–2020	50%
2021–2026	6%

Nothing in the value distinguishes the two, so reading them costs one request per release — 445 271 of them. That is why this dataset took a day to build and why its times can be trusted.

first_tradeable_session: one date instead of three arguments

The session boundaries come from the NYSE calendar, not from 09:30 and 16:00. On the 50 half days in this period the exchange closes at 13:00, and on 25 days it held no session at all while EDGAR kept accepting filings — 303 releases arrived during the two days Sandy shut the floor in October 2012.

first_tradeable_session is the first session whose close falls after the filing was accepted:

Release	session	first_tradeable_session
Wed 07:00	pre_market	that same day
Wed 16:30	after_close	Thursday
Fri 14:19, day after Thanksgiving	after_close (13:00 close)	Monday
Mon 29 Oct 2012, Sandy	non_session	Wed 31 Oct

One column, and the holidays, the half days and the 16:30 releases stop being three separate arguments a consumer has to have.

Symbols, resolved as of the day

ticker is the symbol the company carried on the day it filed, from security-master's 29 511 dated spans. ticker_confidence says how it was resolved:

	Releases
as_of	366 458	a span covering that day: no assumption
carried_back	56 611	before EDGAR's symbol data starts in 2006
carried_forward	7 446	after the last day the symbol was seen
(none)	24 096	no symbol on record for that company

Resolving from today's map instead would put back exactly the look-ahead the rest of this family removes: a company that changed symbol in 2020 would carry the new one across its whole history, and the prices found under it are the post-change ones. Meta's releases up to the first quarter of 2022 say FB.

What is paired, and what is not

An 8-K's own reportDate is the day of the event, not the quarter being reported: Apple's release of 30 July 2026 is dated 30 July and reports the quarter that ended in June. So the period comes from the statement that follows — the 10-Q or 10-K filed after the release, for a period that had already closed.

88.4% of releases from 2011 to 2025 pair with a reported period. Including 2026 the figure is 87.2%, because this year's statements are not all filed yet; before 2011 the rate falls away entirely, because the figures come from XBRL and XBRL begins in 2009. The blended rate over all years is 56.9% and means nothing.

Field	Present in paired rows
net income	90.5%
revenue	86.5%
diluted EPS	73.2%
revenue year-on-year	77.8%
EPS year-on-year	64.4%
revenue_sue	56.5%
sue	45.3%

The median company files its statement 4 days after announcing.

sue, because consensus is not available and was never needed

Analyst consensus is licensed data. The drift literature does not use it: it uses standardised unexpected earnings — the surprise against the same period a year earlier, divided by the standard deviation of that surprise over the eight periods before it. Every input is already here.

It is dimensionless, which raw growth is not. A utility growing 3% and a biotech growing 300% cannot be ranked against each other on revenue_yoy_change; a cross-sectional sort on growth sorts volatility as much as surprise. sue and revenue_sue can be ranked. Only periods whose statements had already been filed when the release was made enter the window, so the measure is knowable at its own knowledge date. The distribution: median 0.12, p10 −1.8, p90 2.4, and a 1.8% tail beyond ±10 for companies whose past surprises were unusually small.

eps_quality, because a few EPS values are totals

net_income / eps_diluted is the share count a row implies. Across the rows carrying both, p1 is 2.07 million and the median 56.7 million — and 197 rows imply fewer than fifty thousand shares. Halliburton's fourth quarter of 2023 implies 903. Those are totals that were tagged into a per-share field upstream.

Where the row's own columns contradict its EPS, eps_diluted and eps_yoy_change are blanked and eps_quality is implausible. The announcement and its timestamp survive — they are correct, and they are what the dataset is for. unverified (6 940 rows) means net income was missing and the check could not run.

Everything else the scan saw

Isolating earnings means reading the item codes of every 8-K, so the rest of the taxonomy came free: 4 382 182 dated corporate events across 46 item codes, each with a ticker and a knowledge date of its own.

Item	Meaning	Count
9.01	financial statements and exhibits	1 353 927
8.01	other event	468 250
2.02	results of operations	430 429
7.01	Regulation FD disclosure	337 199
5.02	director or officer changed	315 589
1.01	material agreement entered	310 448
2.03	direct financial obligation created	110 396
5.07	shareholder vote results	90 115

Bare 7 and 5 are the pre-2004 numbering, kept as filed.

knowledge_date is exact for the 23.1% of rows whose headers were read for the calendar; every other event is dated to 23:59:59 Eastern on its filing day, which is late by up to a day and never early. knowledge_estimated says which is which. (63 065 events carry an exact timestamp earlier than their filing date: EDGAR stamps anything accepted after 17:30 with the next business day.)

Configs

Config	Rows	What one row is
earnings	258 482	a release paired with the period and figures it announced
announcements	454 611	a company's earnings release, timed to the second
events	4 382 182	one 8-K item: a dated corporate event of any kind
pit	258 482	one point-in-time earnings knowledge event, plus a Delta table

All four configs carry knowledge_date, so all four mount, not only pit: in announcements and earnings it is the acceptance instant itself, in events it is exact where a header was read and the end of the filing day otherwise. A config whose knowledge column is named anything else is refused by ziplime rather than indexed on an event date.

entity_id in pit is the issuer CIK, the same key as every sibling dataset, so an earnings date joins directly to company-fundamentals, insider-trading and the rest. pit also carries form and accession_number, so an 8-K/A amendment is distinguishable from a first announcement and every row joins back to announcements.

import polars as pl

pit = pl.read_parquet("data/pit/*.parquet")
# Everything a strategy could have known at this instant
known = pit.filter(pl.col("knowledge_date") <= "2025-02-01T00:00:00Z")
# Large positive surprises, ranked comparably across industries
surprises = known.filter(pl.col("sue") > 2).select(
    "ticker", "first_tradeable_session", "sue", "eps_diluted"
)

Known gaps

• No analyst estimates, so no surprise against consensus. sue measures the surprise against the company's own history instead, which is what the drift literature does.
• Releases before 2009 have no figures. The announcement, its time and its session are there; the numbers are not, because XBRL had not started.
• A release is not always about the latest quarter. The pairing requires the period to have closed and the statement to follow within 150 days; releases that satisfy neither are in announcements and not in earnings.
• Six releases have an unreadable header and one filing is missing from EDGAR. They are absent from announcements rather than dated by guess.
• 5.3% of releases have no symbol and a further 12.5% resolve to one carried back from a later observation, almost all of them before 2006.
• Co-registrants file jointly. Six thousand releases are filed by a parent and its subsidiaries in one document; each company gets its own row, and the event id is the company and the filing together.

Provenance and updates

Sources: SEC EDGAR bulk submissions (item codes, dates) and each filing's own SGML header (acceptance time). Figures from ZipLime/company-fundamentals, symbols from ZipLime/security-master, sessions from the XNYS calendar. US Government works, public domain. Rebuilt weekly on Hugging Face Jobs.