PCS Edventures!, Inc (PCSV) Fair Value & Analysis
Consumer Defensive · US · Market cap $14.8M
Fair value as of: Jun 24, 2026
Analysis
PCS Edventures!, Inc (PCSV) currently trades at $1.60, while our model-based Fair Value estimate is $2.14 — implying the stock looks roughly 33.8% undervalued today. We read business quality at 95/100 (high quality), in the Consumer Defensive 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
PCS Edventures!, Inc. provides experiential, hands-on, and transitional kindergarten through 12th science, technology, engineering, and math (STEM) education products and curriculum in the United States. The company provides enrichment programs for the informal learning market, including Podcasting; Ready, Set, Drone!;Drone Designers; Rockin Robots; Claymation; Cubelets Bot Builder; Influencer Camp; The Science of Super Powers; Flight and Aerodynamics; and Build a Better World. It also offers discover series products for the makerspace environment and include engaging STEM activities, such as Discover Podcasting, Discover STEM Dynamic Duo, and Discover Digital Video Lab; and BrickLAB Products for the grade school market which uses its bricks and curriculum to engage students to explore, imagine and create within a STEM education framework. In addition, the company provides Discover Drones Indoor Coding Bundle, Discover Drones Indoor Racing Add-On, Discover Drones Outdoor Practice Ad…
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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.