Inno Instrument Inc (215790) Fair Value & Analysis
Technology · KR · Market cap 60.4B KRW
Fair value as of: Jun 24, 2026
Analysis
Inno Instrument Inc (215790) currently trades at 1,140 KRW, while our model-based Fair Value estimate is 1,527 KRW — implying the stock looks roughly 34.0% undervalued today. We read business quality at 95/100 (high quality), in the Technology 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: low) — always confirm before acting.
About the company
Inno Instrument Inc. engages in the manufacture and sale of communication equipment worldwide. The company offers fusion splicers; fiber cleavers; test and measurement products, such as optical time domain reflectometers, optical fiber testers, fiber microscopes, and power meters; and 5G and RF solutions, such as 5G spectrum analyzers, 5G distributed antenna systems (DAS), and cable antenna analyzers. It also provides tools and accessories, including thermal fiber strippers, ribbonizers, fiber holders, flat cable strippers, IWS packages, splice-on connectors, OTDR cleaning kits, and splicer tool kits. In addition, the company offers IoT security solutions comprising IoT smart locks, FTTx solutions, and support services. Further, it provides VIEW Pro Manager, a set of cloud-based tools that organizes and manages fusion splicing projects and assets. The company was formerly known as Rbk Group Inc. and changed its name to Inno Instrument Inc. in July 2023. Inno Instrument Inc. was foun…
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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.