MonotaRO Co (MONOY) Fair Value & Analysis
Consumer Cyclical · US · Market cap $5.5B
Analysis
MonotaRO Co (MONOY) currently trades at $10.79, while our model-based Fair Value estimate is $8.82 — implying the stock looks roughly 18.3% 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: high).
About the company
MonotaRO Co., Ltd., together with its subsidiaries, operates an online MRO products store for factories in Japan and internationally. It offers safety protective equipment, work clothes, and safety shoes; logistics, storage, and packing supplies; tapes; safety, disaster prevention, and crime prevention products; safety signs; office supplies; office furniture/lighting/cleaning supplies; cutting tools and abrasives; measurement and surveying equipment; work tools/electric and pneumatic tools; sprays, oils, greases, and paints; adhesives and repair materials; welding supplies; and plumbing, plumbing members, pumps, pneumatics, hydraulic equipment, and hoses. It also provides mechanical parts; control equipment; soldering/static countermeasure products; building hardware and materials; painting and interior supplies; air conditioning and electrical equipment; electrical materials; screws, bolts, nails, and materials; automotive, truck, motorcycle, and bicycle supplies; scientific resea…
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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.