Luna Innovations Incorporated (LUNA) Fair Value & Analysis
Technology · US · Market cap $52.0M
Fair value as of: Jun 25, 2026
Analysis
Luna Innovations Incorporated (LUNA) currently trades at $1.30, while our model-based Fair Value estimate is $1.54 — implying the stock looks roughly 18.5% undervalued today. We read business quality at 89/100 (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: medium) — always confirm before acting.
About the company
Luna Innovations Incorporated provides fiber optic test, measurement, and control products worldwide. The company offers test and measurement equipment for fiber optic components and sub-assemblies; polarization control products, including components, modules, and instruments to measure, manage, and control polarization and group delay in fiber optic networks; tunable lasers; and single frequency lasers. It also provides ODiSI sensing solution, which provides distributed strain and temperature measurements; distributed temperature sensing system; hyperion sensing products; Terahertz Sensing Systems that provide precise single and multi-layer thickness, density, basis weight, and caliper thickness measurements; and distributed acoustic sensing products. The company markets its products to telecommunications companies, defense agencies, government system integrators, researchers, original equipment manufacturers, distributors, testing labs, and strategic partners directly, as well as …
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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.