Wolverine World Wide, Inc (WWW) Fair Value & Analysis
Consumer Cyclical · US · Market cap $1.4B
Analysis
Wolverine World Wide, Inc (WWW) currently trades at $17.54, while our model-based Fair Value estimate is $13.48 — implying the stock looks roughly 23.1% overvalued today. We read business quality at 94/100 (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: high).
About the company
Wolverine World Wide, Inc. designs, manufactures, sources, markets, licenses, and distributes footwear, apparel, and accessories in the United States, Europe, the Middle East, Africa, the Asia Pacific, Canada and Latin America. It operates through two segments: Active Group and Work Group. The company offers casual footwear and apparel; performance outdoor and athletic footwear and apparel; kids' footwear; industrial work boots and apparel; and uniform shoes and boots. It sources and markets a range of footwear and apparel styles, such as shoes, boots and sandals under the Bates, Cat, Chaco, Harley-Davidson, Hush Puppies, Hytest, Merrell, Saucony, Sperry, Keds, Sweaty Betty, and Wolverine brands; and licenses under the Stride Rite brand. The company markets Merrell and Wolverine branded apparel and accessories, as well as licenses its brands for use on non-footwear products, including the Hush Puppies apparel, eyewear, watches, socks, handbags, and plush toys; and Wolverine branded …
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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.