Fairvalue-Calculator Fairvalue-Calculator
EN DE

WRL (WRL) Fair Value & Analysis

Financials · PL · Market cap 4.9M PLN

Price0.6440 PLN
Fair Value0.1000 PLN
Upside-84.5%
Quality95/100
Evidence: High Range 0.0800 PLN – 0.1300 PLN

Fair value as of: Jun 26, 2026

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

Analysis

WRL (WRL) currently trades at 0.6440 PLN, while our model-based Fair Value estimate is 0.1000 PLN — implying the stock looks roughly 84.5% overvalued today. We read business quality at 95/100 (high quality), in the Financials 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: high).

Open the full interactive analysis →

Similar stocks

Federal Home Loan Mortgage Corporation (FMCKK) +100% LTG (LTG) +100% FDC (FDC) +99% Han Kook Capital.Co., Ltd (023760) +100% Sangsangin Co (038540) +5% BPC (BPC) -88%

Frequently asked questions

Is WRL (WRL) undervalued?
As of Jun 26, 2026, our model estimates a fair value of 0.1000 PLN versus a price of 0.6440 PLN — about −84% (overvalued). Model-based estimate, not financial advice.
What is the fair value of WRL?
Our 21-model fair value for WRL is 0.1000 PLN (as of Jun 26, 2026), built from audited fundamentals. The current price is 0.6440 PLN.
What is the quality score of WRL?
WRL has a Quality Score of 95/100, 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.