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SL GREEN REALTY CORP: basic weighted-average shares

Basic weighted-average shares for SL GREEN REALTY CORP. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All SL GREEN REALTY CORP financial histories

What this measure means

Time-weighted shares used for basic earnings per share. This denominator differs from shares outstanding at a single reporting date.

Exact concept: us-gaap:WeightedAverageNumberOfSharesOutstandingBasic. Each value covers an annual-duration reporting interval, shown with both start and end dates. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2008-01-01 to 2025-12-31. The SEC response was captured on 2026-09-20.

Selected filing history

Basic weighted-average shares in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-01-012025-12-3170,443,000shares2026-02-1710-K · 0001628280-26-008669
2024-01-012024-12-3165,062,000shares2026-02-1710-K · 0001628280-26-008669
2023-01-012023-12-3163,809,000shares2026-02-1710-K · 0001628280-26-008669
2022-01-012022-12-3163,917,000shares2025-04-1710-K/A · 0001040971-25-000019
2021-01-012021-12-3165,740,000shares2024-02-2310-K · 0001040971-24-000010
2020-01-012020-12-3170,397,000shares2023-02-1710-K · 0001040971-23-000009
2019-01-012019-12-3177,057,000shares2022-02-1810-K · 0001040971-22-000014
2018-01-012018-12-3184,090,000shares2021-02-2610-K · 0001040971-21-000007
2017-01-012017-12-3198,571,000shares2020-02-2810-K · 0001040971-20-000006
2016-01-012016-12-31100,185,000shares2019-02-2710-K · 0001040971-19-000007
2015-01-012015-12-3199,345,000shares2018-02-2310-K · 0001040971-18-000006
2014-01-012014-12-3195,774,000shares2017-02-2110-K · 0001040971-17-000005
2013-01-012013-12-3192,269,000shares2016-02-2910-K · 0001040971-16-000016
2012-01-012012-12-3189,319,000shares2015-02-2410-K · 0001040971-15-000003
2011-01-012011-12-3183,762,000shares2014-02-2510-K · 0001040971-14-000005
2010-01-012010-12-3178,101,000shares2013-02-2710-K · 0001047469-13-001830
2009-01-012009-12-3169,735,000shares2012-02-2810-K · 0001047469-12-001784
2008-01-012008-12-3157,996,000shares2011-02-2810-K · 0001047469-11-001531

Related financial histories

Inspect the source

Entity
SL GREEN REALTY CORP / CIK 0001040971
Captured
2026-09-20T05:15:50.822Z
SEC response SHA-256
ee2ec35d8ec10eb7bb4a94ce3d53e0ec9fa9770e818dc8f66ce68a312c8b4ee0

Current SEC company facts · Download the original response snapshot (gzip) · Download the selected JSON

Latest-filed annual-report facts per unit and reporting period at capture time. Duration facts cover 300 to 400 days. This selection can include restatements and is not a point-in-time backtest dataset. Missing concepts are omitted, never zero-filled. Values retain original units and are not currency converted. Extended concepts require compatible unit shapes and at least three reporting ends with changing values within one unit. Constant or incompatible added histories are omitted.

Public company accounting reference, not market prices, returns, an investment recommendation, or ALPHAC performance. Validate a separately constructed return series with the validation API; accounting values are not returns.

Use this in research

A financial period ends before its results become public. Use the filing date as a minimum availability boundary, inspect amendments, and retain the original filing vintage when testing historical signals. This latest-filed selection can contain information unavailable at the time.

These pages do not supply prices, total-return histories, corporate-action adjustments or a tradable universe. Build those inputs separately before evaluating a strategy. A profitable backtest can still reflect selection bias or costs that were left out.

Research methodology · Execution and cost assumptions · Check backtest overfitting

Build with the open-source tools

Use these accounting records as inspectable inputs. When you have constructed a return series, the validation tools can help test its statistical evidence and preserve the result with its limitations.

Read the published dataset with Python
import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0001040971.json") as response:
    record = json.load(response)
print(record["fetched_at"])
print(record["policy"])
for concept in record["concepts"]:
    print(concept["tag"], next(iter(concept["observations"])))