TDC

TeradataA

NYSE / Software & Services

At close

2026-07-18

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AI scenario view

RankAlpha Sentiment Codex

B+

Bull case

25%

Probability

Target price

$40.00

+31.1% vs current

Most likely

Base case

50%

Probability

Target price

$34.90

+14.4% vs current

B-

Bear case

25%

Probability

Target price

$25.50

-16.4% vs current

AI sentiment snapshot

Latest data as of 2026-07-19

AI commentary

Primary-source support is strong, but the forward setup remains a monitoring thesis. Recent headlines emphasize AI positioning and activist interest, while social, options, short-interest, and employee-sentiment metrics are unavailable in the packet. The August 4 earnings event is the next meaningful validation point; missing analyst-revision data limits confidence.

RankAlpha Sentiment Codex - 2026-07-19

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Evidence flagged

No evidence quality warning is currently attached to this memo.

Impact

standard

Confidence

AI events

2026-08-04eventQ2 2026 earnings checkpointMedium impact

Teradata plans to release Q2 2026 results after the market close on August 4, 2026. The key test is whether cloud ARR and recurring revenue can improve from the soft near-term outlook disclosed with Q1 results. [#PR-EARNINGS-2026-07-14] [#SEC-8K-2026-05-05]

2026-09-30catalystAutonomous Knowledge Platform commercializationHigh impact

The Q3 availability of Teradata's Autonomous Knowledge Platform could support a rerating if accompanied by customer adoption and faster cloud ARR conversion. Product launch messaging alone is unlikely to be sufficient.

2026-12-31catalystSoft near-term outlook and settlement-related cash taxesMedium impact

The prior Q1 update pointed to recurring revenue down 2% to flat year over year and total revenue down 4% to down 2% for Q2. Settlement-related cash taxes of approximately $57 million expected by year-end also limit the quality of headline cash-flow improvement. [#SEC-8K-2026-05-05]

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Recommendation

N/A

No formal recommendation provided.

Open AI Memo

As of 2026-07-19 • Updated nightlySource: Internal modelMethodology