Acorn Energy, Inc (ACFN) Fair Value & Analysis
Technology · US · Market cap $43.0M
Fair value as of: Jun 24, 2026
Analysis
Acorn Energy, Inc (ACFN) currently trades at $16.41, while our model-based Fair Value estimate is $12.27 — implying the stock looks roughly 25.2% overvalued today. We read business quality at 94/100 (high quality), in the Technology 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: high).
About the company
Acorn Energy, Inc., through its subsidiary, develops and markets wireless remote monitoring and control systems in the United States and internationally. It operates through two segments: Power Generation ("PG") and Cathodic Protection ("CP"). The company offers wireless remote monitoring and control systems and IoT applications for residential and commercial/industrial power generation equipment; and power generator monitors and AIRGuard products, which remotely monitors and controls industrial air compressors. It also provides OmniPro commercial monitor and the Omni residential monitor platforms designed to enhance connectivity, reliability and performance in remote monitoring systems; and remote monitoring and control products for cathodic protection systems on gas pipelines serving the gas utilities market and pipeline operators; and first RAD, a remote AC mitigation disconnect that mounts onto existing solid-state decouplers in the field and can remotely disconnect/connect thes…
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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.