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CHNR

China Natural ResourcesC
Nasdaq / Materials
Last Price
At close
2026-07-22
View Chart
Current thesis
The post-earnings bull read is that FY2025 losses narrowed materially, derivative liabilities dropped, and CHNR still shows asset backing far above its tiny enterprise narrative; if the HooRii transaction or any mine-related monetization becomes definitive, the stock could re-rate sharply from a low base. No reliable post-print analyst target or estimate-revision data was found, so this remains a thin-evidence setup rather than a confirmed upward revision cycle.
Posture
Mixed
Lead driver
Momentum
What changed
Momentum remains the lead driver in the composite.
What can break
Cash and cash equivalents were only US$68 thousand at December 31, 2025, leaving very limited liquidity cushion.
Momentum
72
Value
43
Sentiment
54
Setup hits (3d)
0 · Net Neutral
AI TargetsBase $4.10 · Bull $5.80 · Bear $3.00
Data freshness
Prices
As of 2026-07-22
Fundamentals
As of 2026-07-22 • Vendor: Data Vendor v1
Scores
As of 2026-07-22 • Model: HYBRID_IC_RP
AI Memo
As of 2026-05-16 • Model: RankAlpha Sentiment Codex
Investment thesis
As of 2026-07-22
Supporting evidence
What
Grade C · Mixed
Confidence Medium · Net Neutral
Why
Momentum72 · Δ7d -
Value43 · Δ7d +0.1
Sentiment54 · Δ7d -
So what
Balanced signals (Net Neutral). Wait for confirmation before sizing up.
Lead driver: Momentum · See technicals
Momentum
72
26% active weight
Current posture
7d trendTrend unavailable
Trend unavailable
Δ7d
-
Δ21d
-
Value
43
39% active weight
Current posture
7d trendFlat
Δ7d
+0.1
Δ21d
+0.1
Sentiment
54
34% active weight
Current posture
7d trendTrend unavailable
Trend unavailable
Δ7d
-
Δ21d
-
Why this grade

Composite grade C. Momentum 71.8 / Value 43.4 / Sentiment 54.2

Fundamentals (TTM)
As of 2026-07-22
Market Cap
$5.14M
Beta
0.14
Shares Out
1.26M
P/E (TTM) · Derived
-30.0
P/S (TTM)
-
P/FCF (TTM) · Derived
-6.40
Rev YoY
-
EPS YoY
+0.6%
Gross Margin
-63.8%
Op Margin
-
Net Debt
-$68K
Current Ratio
0.12
As of 2026-07-22 • Updated nightlySource: Internal modelMethodology