Johnson Outdoors Inc (JOUT) Fair Value & Analysis
Consumer Cyclical · US · Market cap $487M
Fair value as of: Jun 24, 2026
Analysis
Johnson Outdoors Inc (JOUT) currently trades at $45.58, while our model-based Fair Value estimate is $69.48 — implying the stock looks roughly 52.4% undervalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical 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: medium) — always confirm before acting.
About the company
Johnson Outdoors Inc. designs, manufactures, and markets seasonal and outdoor recreation products for fishing worldwide. It operates through Fishing; Camping & Watercraft Recreation; and Diving segments. The Fishing segment offers electric motors for quiet trolling or primary propulsion, marine battery chargers, and shallow water anchors; sonar and GPS equipment for fish finding, navigation and marine cartography; and downriggers for controlled-depth fishing. This segment sells its products under the Minn Kota, Humminbird, and Cannon brands through outdoor specialty retailers, retail store chains, internet dealers, original equipment manufacturers, and distributors. The Camping & Watercraft Recreation segment provides portable outdoor cooking systems, single burner and two burner stoves, and accessories under the Jetboil brand to camping and backpacking specialty stores, sporting goods stores, internet retailers, and direct customer through website and distributors; and kayaks, cano…
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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.