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NaaS Technology Inc.: profit or loss including noncontrolling interests

Profit or loss including noncontrolling interests for NaaS Technology Inc. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All NaaS Technology Inc. financial histories

What this measure means

Net income or loss including the portion attributable to noncontrolling interests. It can differ from the net income attributable to the parent that per-share figures use.

Exact concept: us-gaap:ProfitLoss. 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 2015-01-01 to 2021-12-31. The SEC response was captured on 2026-09-22.

This selected history ends more than two years before capture. Do not treat its final value as a current balance or current annual result. More recent filings may use another accounting tag; inspect the filings before drawing conclusions about the company.

Coverage by original unit

These are separate reported series. A newer period in one unit does not update another unit’s history or establish a currency conversion.

Selected filing history

Profit or loss including noncontrolling interests in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2021-01-012021-12-31-258,184,000CNY2022-05-1320-F · 0001193125-22-150252
2020-01-012020-12-31-141,444,000CNY2022-05-1320-F · 0001193125-22-150252
2019-01-012019-12-31144,560,000CNY2022-05-1320-F · 0001193125-22-150252
2018-01-012018-12-31142,436,000CNY2021-04-1920-F · 0001193125-21-121322
2017-01-012017-12-31-53,600,000CNY2020-04-1720-F · 0001193125-20-109840
2016-01-012016-12-3150,843,000CNY2019-04-1920-F · 0001193125-19-111694
2015-01-012015-12-31-31,741,000CNY2018-04-1920-F · 0001144204-18-021371
2021-01-012021-12-31-40,515,000USD2022-05-1320-F · 0001193125-22-150252
2020-01-012020-12-31-21,677,000USD2021-04-1920-F · 0001193125-21-121322
2019-01-012019-12-3120,765,000USD2020-04-1720-F · 0001193125-20-109840
2018-01-012018-12-3120,716,000USD2019-04-1920-F · 0001193125-19-111694
2017-01-012017-12-31-8,238,000USD2018-04-1920-F · 0001144204-18-021371

Related NaaS Technology Inc. histories

Inspect the source

Entity
NaaS Technology Inc. / CIK 0001712178
Captured
SEC response SHA-256
5e98a95b58c274345ccc43f0d68d6debd8bd7a8d95c67140d10519175f50c198

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/0001712178.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"])))