Fairvalue-Calculator Fairvalue-Calculator
EN DE

DFNN (DFNN) Fair Value & Analysis

Technology · PH · Market cap 962M PHP

Price0.7600 PHP
Fair Value1.15 PHP
Upside+51.3%
Quality92/100
Evidence: Low Range 0.8700 PHP – 1.44 PHP

Fair value as of: Jun 23, 2026

✦ Find undervalued quality stocks — 34,000+ analysed Find stocks →

Analysis

DFNN (DFNN) currently trades at 0.7600 PHP, while our model-based Fair Value estimate is 1.15 PHP — implying the stock looks roughly 51.3% undervalued today. We read business quality at 92/100 (high quality), in the Technology 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: low) — always confirm before acting.

Open the full interactive analysis →

Similar stocks

Wemade Co (112040) +230% KINX, Inc (093320) -29% AfreecaTV Co (067160) +474% Hancom Inc (030520) +44% Gabia Inc (079940) +11% NEOWIZ (095660) +184%

Frequently asked questions

Is DFNN (DFNN) undervalued?
As of Jun 23, 2026, our model estimates a fair value of 1.15 PHP versus a price of 0.7600 PHP — about +51% (undervalued). Model-based estimate, not financial advice.
What is the fair value of DFNN?
Our 21-model fair value for DFNN is 1.15 PHP (as of Jun 23, 2026), built from audited fundamentals. The current price is 0.7600 PHP.
What is the quality score of DFNN?
DFNN has a Quality Score of 92/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

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.