DNL (DNL) Fair Value & Analysis
Consumer Non-Cyclicals · PH · Market cap 35.8B PHP
Fair value as of: Jun 24, 2026
From 13 valuation models · updated 5 days ago
Share price +3.6% over the past month.
Price vs Fair Value (12 months)
12‑month range 3.28 PHP – 5.54 PHP · fair‑value band 3.53 PHP – 6.49 PHP · the 3.70 PHP price screens below the 5.04 PHP fair value. As of Jun 24, 2026.
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DNL (DNL) currently trades at 3.70 PHP, while our model-based Fair Value estimate is 5.04 PHP — implying the stock looks roughly 36.2% undervalued today. We read business quality at 92/100 (high quality), in the Consumer Non-Cyclicals sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: medium) — always confirm before acting.
The stock trades on a trailing P/E of 17.8. Fundamentals as of Jun 24, 2026
Revenue & earnings trend
FY2020 – FY2024 · reported fiscal years
DNL reported revenue of 40.7B PHP in FY2024 versus 21.7B PHP in FY2020, a compound +17.0%/yr. Reported net income was 2.3B PHP in FY2024, compounding +3.9%/yr from FY2020.
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Similar stocks
6 more Food & Tobacco stocks, each showing price versus our Fair Value estimate (as of Jun 24, 2026).
| Stock | Price | Fair Value | vs Fair Value |
|---|---|---|---|
| FB FB | 49.00 PHP | 44.00 PHP | -10% |
| URC URC | 59.00 PHP | 45.32 PHP | -23% |
| MONDE MONDE | 6.99 PHP | 1.11 PHP | -84% |
| CNPF CNPF | 26.75 PHP | 56.29 PHP | +110% |
| RFM RFM | 5.49 PHP | 10.59 PHP | +93% |
| AXLM AXLM | 2.57 PHP | 2.33 PHP | -9% |
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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.