Fairvalue-Calculator Fairvalue-Calculator
EN DE

NRCP (NRCP) Fair Value & Analysis

Financials · PH · Market cap 1.3B PHP

Price1.10 PHP
Fair Value2.20 PHP
Upside+100.0%
Quality92/100
Evidence: Medium Range 1.65 PHP – 2.75 PHP

Fair value as of: Jun 25, 2026

Analysis

NRCP (NRCP) currently trades at 1.10 PHP, while our model-based Fair Value estimate is 2.20 PHP — implying the stock looks roughly 100.0% undervalued today. We read business quality at 92/100 (high quality), in the Financials 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.

Open the full interactive analysis →

Similar stocks

American Financial Group (AFGE) -8% Federal Home Loan Mortgage Corporation (FMCKK) +100% Affiliated Managers Group (MGR) +89% LTG (LTG) +100% Mirae Asset Venture Investment Co (100790) -68% Hercules Capital, Inc (HCXY) +100%

Frequently asked questions

Is NRCP (NRCP) undervalued?
As of Jun 25, 2026, our model estimates a fair value of 2.20 PHP versus a price of 1.10 PHP — about +100% (undervalued). Model-based estimate, not financial advice.
What is the fair value of NRCP?
Our 21-model fair value for NRCP is 2.20 PHP (as of Jun 25, 2026), built from audited fundamentals. The current price is 1.10 PHP.
What is the quality score of NRCP?
NRCP 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.