1080 (1080) Fair Value & Analysis
Energy · Market cap HK$248M
Fair value as of: Jul 2, 2026
From 3 valuation models · updated today
Price vs Fair Value (12 months)
12‑month range HK$0.0360 – HK$0.1510 · the HK$0.0640 price screens above the HK$0.0294 fair value. As of Jul 2, 2026.
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1080 (1080) currently trades at HK$0.0640, while our model-based Fair Value estimate is HK$0.0294 — implying the stock looks roughly 54.0% overvalued today. We read business quality at 53/100 (solid quality), in the Energy sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: low).
Over the trailing twelve months, 1080 generated revenue of HK$903M at a net margin of -2.1%. Revenue grew 123.4% year over year. It earns a return on equity of -4.6%. Net debt stands at HK$216M. Fundamentals as of Jul 2, 2026
Key figures & financial health
More key figures
Figures from reported company fundamentals (EODHD) · as of Jul 2, 2026. TTM = trailing twelve months.
Revenue & earnings trend
FY2021 – FY2025 · reported fiscal years
1080 reported revenue of HK$903M in FY2025 versus HK$1.5B in FY2021, a compound −12.3%/yr. Reported net income was −HK$18.9M in FY2025.
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6 more Oil & Gas Equipment & Services stocks, each showing price versus our Fair Value estimate (as of Jul 2, 2026).
| Stock | Price | Fair Value | vs Fair Value |
|---|---|---|---|
| SLB N.V SLBN | 993.35 MXN | 30.75 MXN | -97% |
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| Tenaris S.A T1SS34 | R$162.40 | R$108.11 | -33% |
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Frequently asked questions
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.