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Byline Bancorp, Inc.: filings

Every Byline Bancorp, Inc. annual and quarterly report in the SEC record with the published financial measures it tagged, 37 filings, each linked to its SEC index.

Filings with published measures

Each page shows what one filing reported, as tagged in that filing, with the periods it covered. Later filings can restate a value; the company overview shows the latest-filed value per period.

FormFiledFiscal periodMeasuresFactsSEC accession
10-Q2026-08-06fiscal Q2 202633930001193125-26-337960
10-Q2026-05-01fiscal Q1 202634730001193125-26-201603
10-K2026-02-27fiscal FY 202534920001193125-26-083194
10-Q2025-11-07fiscal Q3 202533930001193125-25-272446
10-Q2025-08-07fiscal Q2 202533930000950170-25-105126
10-Q2025-05-02fiscal Q1 202533710000950170-25-062758
10-K2025-02-28fiscal FY 202436980000950170-25-030118
10-Q2024-11-01fiscal Q3 202433930000950170-24-120042
10-Q2024-08-05fiscal Q2 202434970000950170-24-090900
10-Q2024-05-03fiscal Q1 202433710000950170-24-052982
10-K2024-03-04fiscal FY 2023381030000950170-24-024969
10-Q2023-11-03fiscal Q3 2023361030000950170-23-058881
10-Q2023-08-03fiscal Q2 2023361030000950170-23-038065
10-Q2023-05-05fiscal Q1 202336820000950170-23-018314
10-K2023-03-07fiscal FY 2022381030000950170-23-006310
10-Q2022-11-04fiscal Q3 2022361100000950170-22-022206
10-Q2022-08-04fiscal Q2 2022351050000950170-22-014865
10-Q2022-05-06fiscal Q1 202235790000950170-22-008133
10-K2022-03-07fiscal FY 2021391050000950170-22-003015
10-Q2021-11-05fiscal Q3 2021351080000950170-21-003241
10-Q2021-08-05fiscal Q2 2021351050000950170-21-000958
10-Q2021-05-07fiscal Q1 202135790001564590-21-025614
10-K2021-03-04fiscal FY 2020371560001564590-21-010980
10-Q2020-11-06fiscal Q3 2020341050001564590-20-052003
10-Q2020-08-10fiscal Q2 2020341030001564590-20-038946
10-Q2020-05-05fiscal Q1 202034770001564590-20-021236
10-K2020-03-12fiscal FY 2019361550001564590-20-010516
10-Q2019-11-08fiscal Q3 2019331050001564590-19-041900
10-Q2019-08-08fiscal Q2 2019321010001564590-19-030266
10-Q2019-05-09fiscal Q1 201932690001564590-19-017518
10-K2019-03-15fiscal FY 2018341500001564590-19-008059
10-Q2018-11-13fiscal Q3 201832930001564590-18-028994
10-Q2018-08-13fiscal Q2 201831890001564590-18-021356
10-Q2018-05-11fiscal Q1 201828610001564590-18-013106
10-K2018-03-30fiscal FY 2017321380001564590-18-007218
10-Q2017-11-14fiscal Q3 201728820001564590-17-023833
10-Q2017-08-14fiscal Q2 201728810001564590-17-017646

Inspect the source

Entity
Byline Bancorp, Inc. / CIK 0001702750
Captured
2026-09-21T17:24:36.889Z
SEC response SHA-256
b2b5d8e15695f4a1afa6e3edb525aa6bf2e0283f31f5d417f7d59e44da57b23f

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

Every published concept a filing tagged, with the periods it covered, as reported in that filing at capture time. Forms 10-K, 10-K/A, 10-Q, 10-Q/A, 20-F, 20-F/A, 40-F, 40-F/A. A filing page needs at least 8 published concepts. Later filings can restate these values; the company history pages show the latest-filed value per period.

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/0001702750.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"])))