CDTi Advanced Materials, Inc (CDTI) Fair Value & Analysis
Consumer Cyclical · US · Market cap $3.2M
Fair value as of: Jun 24, 2026
Analysis
CDTi Advanced Materials, Inc (CDTI) currently trades at $0.6800, while our model-based Fair Value estimate is $0.5100 — implying the stock looks roughly 25.0% overvalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical 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: medium).
About the company
CDTi Advanced Materials, Inc. provides technology and solutions for the emission catalyst, industrial catalyst, and electrocatalyst markets in the United States and Europe. The company's solutions include direct air capture coating solution; emission systems, consisting of oxidation catalysts that reduces emissions of harmful pollutants, such as carbon monoxide, hydrocarbons, and volatile organic compounds and diesel particulate filters for the for reductions of diesel engine exhaust particulate/ soot; fuel borne catalysts, including PatFluid, a universal aftermarket replacement for diesel particulate filter (DPF) additives and Platinum Plus, a diesel fuel additive that improves the operation of diesel engines with DPFs, reduces emissions, lowers the risk of DPF blockage or damage, and saves money on maintenance costs; and electrocatalyst materials, an oxide materials platform to minimize the usage of platinum group metals, reducing the cost of producing green hydrogen. It serves co…
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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.