Hudson Technologies, Inc (HDSN) Fair Value & Analysis
Basic Materials · US · Market cap $243M
Fair value as of: Jun 24, 2026
Analysis
Hudson Technologies, Inc (HDSN) currently trades at $5.76, while our model-based Fair Value estimate is $8.32 — implying the stock looks roughly 44.4% undervalued today. We read business quality at 95/100 (high quality), in the Basic Materials 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
Hudson Technologies, Inc., through its subsidiary, Hudson Technologies Company, provides solutions to recurring problems within the refrigeration industry in the United States. The company offers refrigerant and industrial gas; refrigerant management services comprising primarily of reclamation of refrigerants, laboratory testing through its laboratory, and banking storage services; and RefrigerantSide Services comprising system decontamination to remove moisture, oils, and other contaminants intended to restore systems to designed capacity. It also provides diagnostic services for use in the prediction of potential problems in air conditioning, process cooling, and refrigeration systems; Chiller Chemistry, which integrates several fluid tests of an operating system and the corresponding laboratory results into an engineering report; Fluid Chemistry for use in identification of systems that require further examination; and SmartEnergy OPS, a system for measuring, modifying, and impr…
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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.