Albany International Corp (AIN) Fair Value & Analysis
Consumer Cyclical · US · Market cap $2.0B
Analysis
Albany International Corp (AIN) currently trades at $70.37, while our model-based Fair Value estimate is $19.60 — implying the stock looks roughly 72.1% 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
Albany International Corp., together with its subsidiaries, engages in the machine clothing and engineered composites businesses in the United States, Switzerland, France, Brazil, China, Mexico, Germany, and internationally. The company operates through two segments: Machine Clothing (MC) and Albany Engineered Composites (AEC). The MC segment designs, manufactures, and markets paper machine clothing for use in the manufacturing of papers, paperboard, tissues, towels, pulps, building products, tannery, and textiles, as well as nonwovens, fiber cement, and several other industrial applications. This segment offers paper machine clothing forming, pressing, and drying fabrics, as well as processing belts; and engineered fabrics. Its AEC segment 3D-woven and injected composite components for aircraft engines composite airframe and engine components for military and commercial aircraft. The company sells its products to commercial and defense markets, space-launch vehicles and the emergin…
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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.