Knowles Corporation (KN) Fair Value & Analysis
Technology · US · Market cap $3.2B
Fair value as of: Jun 24, 2026
Analysis
Knowles Corporation (KN) currently trades at $40.09, while our model-based Fair Value estimate is $9.48 — implying the stock looks roughly 76.4% overvalued today. We read business quality at 95/100 (high quality), in the Technology 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
Knowles Corporation offers capacitors, radio frequency (RF) and microwave filters, balanced armature speakers, and medtech microphones in Asia, the United States, Europe, rest of Americas, and internationally. It operates in two segments, Precision Devices (PD) and Medtech & Specialty Audio (MSA). The PD segment focuses on the custom design and delivery of high performance capacitor products and RF solutions primarily serving the defense, industrial, medtech, and electrification/energy markets. The MSA segment designs and manufactures balanced armature speakers and microphones for the hearing health; and specialty audio applications that serve the medtech and industrial markets. The company manufactures and sells film, electrolytic, and mica capacitors that are used in medtech, defense, and industrial electrification applications. It serves the defense, medtech, electric vehicle, industrial, and communications markets through original equipment manufacturers, their contract manufact…
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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.