Skip to content

LEARNING TREE INTERNATIONAL, INC.: filings

Every LEARNING TREE INTERNATIONAL, INC. annual and quarterly report in the SEC record with the published financial measures it tagged, 30 filings, each linked to its SEC index.

Filing record ends 2018-12-21

The latest filing in this captured record is a 10-K filed 2018-12-21. No later filing is in the SEC companyfacts record captured on 2026-09-22. LEARNING TREE INTERNATIONAL, INC. may have stopped filing, merged, or changed its reporting entity; nothing on this page describes its current status. Values are as reported at the time.

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-K2018-12-21fiscal FY 201840820001437749-18-022476
10-Q2018-08-07fiscal Q3 2018371000001437749-18-014737
10-Q2018-05-08fiscal Q2 201836960001437749-18-009034
10-Q2018-02-08fiscal Q1 201837770001437749-18-002067
10-K2017-12-15fiscal FY 201739800001437749-17-020789
10-Q2017-08-08fiscal Q3 2017371000001437749-17-014095
10-Q2017-05-11fiscal Q2 201736980001437749-17-008775
10-Q2017-02-09fiscal Q1 201736750001437749-17-002033
10-K2017-01-13fiscal FY 201638780001437749-17-000670
10-Q2016-08-15fiscal Q3 201636990001437749-16-037486
10-Q2016-05-10fiscal Q2 201636990001437749-16-031430
10-Q2016-02-09fiscal Q1 201635730001437749-16-025027
10-K2015-12-15fiscal FY 2015401780001437749-15-022309
10-Q2015-08-11fiscal Q3 2015371010001437749-15-015519
10-Q2015-05-14fiscal Q2 2015371010001437749-15-010160
10-Q2015-02-10fiscal Q1 201539810001437749-15-002262
10-K2014-12-18fiscal FY 2014421820001437749-14-022308
10-Q2014-08-07fiscal Q3 2014401130001437749-14-014587
10-Q2014-05-08fiscal Q2 2014401130001437749-14-008425
10-Q2014-02-13fiscal Q1 201440880001437749-14-002076
10-K2013-12-12fiscal FY 2013431840001437749-13-015924
10-Q2013-08-06fiscal Q3 2013421270001437749-13-009984
10-Q2013-05-09fiscal Q2 2013411170001437749-13-005624
10-Q2013-02-07fiscal Q1 201340830001193125-13-043544
10-K2012-12-18fiscal FY 2012442160001193125-12-506687
10-Q2012-08-07fiscal Q3 2012411190001193125-12-340976
10-Q2012-05-08fiscal Q2 2012411160001193125-12-218462
10-Q2012-02-07fiscal Q1 201240820001193125-12-043751
10-K2011-12-13fiscal FY 2011401080001193125-11-339516
10-Q2011-08-10fiscal Q3 2011401120001193125-11-218159

Inspect the source

Entity
LEARNING TREE INTERNATIONAL, INC. / CIK 0001002037
Captured
SEC response SHA-256
e5099904de2fb4215f74a951528e578dc6566033876b1d4baa5bb8be4c45b909

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/0001002037.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"])))