Fairvalue-Calculator Fairvalue-Calculator
EN DE

Kalmar Oyj (KALMAR) Fair Value & Analysis

Industrials · FI · Market cap €2.8B

Price€40.02
Fair Value€43.08
Upside+7.6%
Quality95/100
Evidence: High Range €30.16 – €61.90

Analysis

Kalmar Oyj (KALMAR) currently trades at €40.02, while our model-based Fair Value estimate is €43.08 — implying the stock looks roughly 7.6% undervalued today. We read business quality at 95/100 (high quality), in the Industrials 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: high) — always confirm before acting.

About the company

Kalmar Oyj provides heavy material handling equipment and services for ports, terminals, distribution centres, manufacturing, and heavy logistics industries in the Americas, Europe, Asia, the Middle East, and Africa. It operates in two segments, Equipment and Services. The company offers reach stackers, forklift trucks, empty container handlers, terminal tractors, straddle carriers, and spreaders. It also provides spare parts; on-call and contract maintenance services; and data, analytics, and AI services. In addition, the company offers lifecycle services, including refurbishments, and fleet management and upgrades; equipment that can use hydrotreated vegetable oil (HVO); and robotics equipment. The company was incorporated in 2024 and is headquartered in Helsinki, Finland.

Open the full interactive analysis →

Similar stocks

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.