Spectra Systems Corporation (SCTQ) Fair Value & Analysis
Technology · US · Market cap $103M
Fair value as of: Jun 25, 2026
Analysis
Spectra Systems Corporation (SCTQ) currently trades at $2.13, while our model-based Fair Value estimate is $5.00 — implying the stock looks roughly 134.7% undervalued today. We read business quality at 90/100 (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: high) — always confirm before acting.
About the company
Spectra Systems Corporation invents, develops, and sells integrated optical systems in the United States and internationally. It operates through four segments: Authentication Systems, Secure Transactions, Security Printing, and Banknote Cleaning. It offers integrated solutions, including a system of taggant materials and sensor equipment to authenticate banknotes and security documents; and banknote cleaning and disinfection systems. The company also provides optical materials for security and quality control, such as fluorescent and phosphorescent pigments and dyes, invisible pigments and dyes, and gasochromic response materials; customized materials and hardware solutions; and internal control systems for integrity of the lottery and sports betting games. In addition, it offers software and hardware systems comprising high-speed currency authentication sensors; and conventional and hybrid postage stamps, tax stamps, vouchers, coupons, certificates, and high-security documents. Th…
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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.