Kennametal Inc (KMT) Fair Value & Analysis
Industrials · US · Market cap $2.5B
Analysis
Kennametal Inc (KMT) currently trades at $35.10, while our model-based Fair Value estimate is $15.58 — implying the stock looks roughly 55.6% overvalued today. We read business quality at 93/100 (high quality), in the Industrials 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).
About the company
Kennametal Inc. engages in development and application of tungsten carbides, ceramics, and hard materials and solutions worldwide. It operates through two segments, Metal Cutting and Infrastructure. The Metal Cutting segment offers milling, hole making, turning, threading, and toolmaking systems used in the manufacture of airframes, aero engines, trucks and automobiles, ships, and various types of industrial equipment under the Kennametal, WIDIA, WIDIA Hanita, and WIDIA GTD brands through its direct sales force, a network of independent and national distributors, integrated supplier channels, and digitally. Its Infrastructure segment produces engineered tungsten carbide and ceramic components, earth-cutting tools, and metallurgical powders, such as compacts, nozzles, frac seats, and custom components used in oil and gas and petrochemical industries; rod blanks and abrasive water jet nozzles for general industries; earth cutting tools and systems used in underground mining, trenching…
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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.