CRC

California ResourcesB

NYSE / Energy

At close

2026-08-06

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AI scenario view

RankAlpha Sentiment Codex

B+

Bull case

25%

Probability

Target price

$70.00

+33.1% vs current

Most likely

Base case

50%

Probability

Target price

$56.00

+6.5% vs current

B-

Bear case

25%

Probability

Target price

$42.00

-20.1% vs current

AI sentiment snapshot

Latest data as of 2026-08-06

AI commentary

News flow is sparse and mildly constructive based on two secondary headlines, but social, options, short-interest, and employee-sentiment coverage is unavailable. No verified analyst revisions or post-earnings market reaction were supplied, so the appropriate stance is monitoring rather than a high-conviction directional call.

RankAlpha Sentiment Codex - 2026-08-06

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Evidence flagged

No evidence quality warning is currently attached to this memo.

Impact

standard

Confidence

AI events

2026-08-10eventSecond-quarter 2026 earnings release and conference callMedium impact

The company scheduled Q2 2026 results for August 10 before market open, followed by a conference call. The print should clarify operating performance, cash flow, and the impact of the preliminary hedge data. [#PR-EARNINGS-2026-07-06]

2026-08-10catalystPreliminary Q2 derivative losses pressure realized oil economicsMedium impact

CRC reported preliminary, unaudited Q2 data showing a $190 million net commodity-derivative settlement loss and oil realized price of $76.50 per barrel including derivatives versus $91.64 without settlements. Final results may differ. [#SEC-8K-2026-07-14]

2027-08-06catalystPotential maturity extension through debt refinancingMedium impact

CRC announced a proposed $550 million senior-notes offering due 2035 intended to fund redemption of its 2029 notes. Successful execution could reduce near-term maturity pressure, but completion, pricing, and total financing cost remain uncertain. [#SEC-8K-2026-06-16]

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Recommendation

N/A

No formal recommendation provided.

Open AI Memo

As of 2026-08-06 • Updated nightlySource: Internal modelMethodology