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OPT-Sciences Corporation (OPST) Fair Value & Analysis

Industrials · US · Market cap $17.6M

Price$22.75
Fair Value$20.57
Upside-9.6%
Quality89/100
Evidence: Medium Range $15.08 – $24.67

Fair value as of: Jun 24, 2026

Analysis

OPT-Sciences Corporation (OPST) currently trades at $22.75, while our model-based Fair Value estimate is $20.57 — implying the stock looks roughly 9.6% overvalued today. We read business quality at 89/100 (high quality), in the Industrials 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: medium).

About the company

OPT-Sciences Corporation provides anti-glare solutions to the aerospace industry. It offers anti-glare optical coatings and panels for LCDs; LCD glass heaters; and EMI shielded glasses. The company was founded in 1950 and is headquartered in Cinnaminson, New Jersey.

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Frequently asked questions

Is OPT-Sciences Corporation (OPST) undervalued?
As of Jun 24, 2026, our model estimates a fair value of $20.57 versus a price of $22.75 — about −10% (overvalued). Model-based estimate, not financial advice.
What is the fair value of OPST?
Our 21-model fair value for OPT-Sciences Corporation is $20.57 (as of Jun 24, 2026), built from audited fundamentals. The current price is $22.75.
What is the quality score of OPST?
OPT-Sciences Corporation has a Quality Score of 89/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

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.