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Digital Turbine, Inc: investing cash flow

Digital Turbine investing cash flow: -$30.6 million for the year ending 2026-03-31, down 11.4% on the year. 16 values since 2011, each linked to its SEC filing.

All Digital Turbine, Inc financial histories

At a glance

Digital Turbine reported investing cash flow of -$30.6 million for the year ending 2026-03-31, down 11.4% from -$27.5 million a year earlier. Across the last five reported years it ranged from -$172.0 million (2022-03-31) to -$27.5 million (2025-03-31).

Computed from the values in the table below, in USD; each links to the filing it came from.

What this measure means

Net cash from investing activities, including asset purchases, disposals and investment transactions. This differs from capital expenditure payments alone.

Exact concept: us-gaap:NetCashProvidedByUsedInInvestingActivities. 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 2010-04-01 to 2026-03-31. The SEC response was captured on 2026-09-19.

Selected filing history

Investing cash flow in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-04-012026-03-31-30,619,000USD2026-05-2610-K · 0001628280-26-038115 SEC
2024-04-012025-03-31-27,477,000USD2026-05-2610-K · 0001628280-26-038115 SEC
2023-04-012024-03-31-43,848,000USD2026-05-2610-K · 0001628280-26-038115 SEC
2022-04-012023-03-31-35,065,000USD2025-06-1610-K · 0001628280-25-031628 SEC
2021-04-012022-03-31-172,002,000USD2024-05-2810-K · 0001628280-24-025455 SEC
2020-04-012021-03-31-37,808,000USD2023-05-2510-K · 0001628280-23-019814 SEC
2019-04-012020-03-31-46,717,000USD2022-06-0610-K · 0001628280-22-016294 SEC
2018-04-012019-03-31-2,314,000USD2021-06-1010-K · 0001628280-21-012023 SEC
2017-04-012018-03-31-2,134,000USD2020-06-0210-K · 0001628280-20-008824 SEC
2016-04-012017-03-31-596,000USD2019-06-0310-K · 0001628280-19-007512 SEC
2015-04-012016-03-31-674,000USD2018-06-1210-K · 0001628280-18-007821 SEC
2014-04-012015-03-31-878,000USD2017-06-1410-K · 0001628280-17-006453 SEC
2013-04-012014-03-31-981,000USD2016-06-1410-K · 0001628280-16-017110 SEC
2012-04-012013-03-31-3,369,000USD2014-06-3010-K · 0001193125-14-256053 SEC
2011-04-012012-03-31-17,000USD2013-07-0210-K/A · 0001193125-13-281259 SEC
2010-04-012011-03-31-1,635,000USD2012-06-2910-K · 0001144204-12-037402 SEC

Related Digital Turbine, Inc histories

All 64 Digital Turbine histories

Inspect the source

Entity
Digital Turbine, Inc / CIK 0000317788
Captured
SEC response SHA-256
6a6dd5088bcdde2a6d0eb6e4fd1c567a8fef1e1510ff5cb16ddc5aab49b088b2

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.

The MCP server is free and MIT-licensed: npx -y canli-mcp. If these records help your research, a star on GitHub helps other researchers find them.

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

with urlopen("https://canlicapital.com/company-data/0000317788.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"])))