LiqTech International, Inc (LIQT) Fair Value & Analysis
Industrials · US · Market cap $29.7M
Fair value as of: Jun 24, 2026
Analysis
LiqTech International, Inc (LIQT) currently trades at $0.8843, while our model-based Fair Value estimate is $1.42 — implying the stock looks roughly 60.6% 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: low) — always confirm before acting.
About the company
LiqTech International, Inc., together with its subsidiaries, engages in the design, development, production, marketing, and sale of automated filtering solutions and systems in the Americas, the Asia-Pacific, Europe, the Middle East, and Africa. It operates through three segments: Systems and Aftermarket; Filters and Membranes; and Components. The company offers silicon carbide ceramic filtration technologies for liquid and gas purification, including hybrid technology membranes and aqua solutions; and diesel particulate filters for exhaust emission control solutions under the LiqTech brand name. It also provides liquid filtration systems and solutions for pool and spa water, produced water, marine water treatment for dual fuel engine vessels, and industrial applications. In addition, the company is involved in plastics manufacturing comprising machining, welding, bending, and solvent cementing. It sells its products through direct sales, systems integrators, distributors, agents, a…
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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.