TXN

Texas InstrumentsD

Nasdaq / Semiconductors & Semiconductor Equipment

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AI scenario view

RankAlpha Sentiment AI

B+

Bull case

25%

Probability

Target price

$260.00

Analysis reference price unavailable

Analysis 2026-01-28 · RankAlpha Sentiment AI

Most likely

Base case

55%

Probability

Target price

$198.00

Analysis reference price unavailable

Analysis 2026-01-28 · RankAlpha Sentiment AI

B-

Bear case

20%

Probability

Target price

$150.00

Analysis reference price unavailable

Analysis 2026-01-28 · RankAlpha Sentiment AI

The current persistence contract does not provide an exact AI reference price. RankAlpha therefore does not calculate scenario return from the live quote. How scenarios are presented

AI sentiment snapshot

Latest data as of 2026-01-28

AI commentary

Sentiment is neutral-to-cautious: the anchor (207.5) sits modestly above the analyst median, implying limited near-term upside while analyst targets remain widely dispersed [#SERP-4][#SERP-1]. Near-term conviction will hinge on upcoming earnings/guidance and capital-return signals, supporting a Hold stance with moderate confidence [#SERP-3].

RankAlpha Sentiment AI - 2026-01-28

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Evidence flagged

No evidence quality warning is currently attached to this memo.

Impact

standard

Confidence

AI events

2026-04-28eventNext quarterly earnings release (estimated)Medium impact

Upcoming quarterly earnings and guidance can materially move the stock; date estimated conservatively when not disclosed [#SERP-4].

2026-04-28catalystAnalyst revisions and target updatesHigh impact

Near-term analyst target changes (upgrades/downgrades) have driven recent price action and can cause renewed volatility [#SERP-9], [#SERP-2].

2027-01-28catalystCyclical demand recovery in analog/embedded marketsHigh impact

If industrial and automotive end-demand recover, TI's content gains and operating leverage could drive above-consensus upside [#SERP-1].

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Recommendation

N/A

No formal recommendation provided.

Open AI Memo

As of 2026-01-28 • Updated nightlySource: Internal modelMethodology