Geospace Technologies Corporation (GEOS) Fair Value & Analysis
Energy · US · Market cap $93.4M
Fair value as of: Jun 25, 2026
Analysis
Geospace Technologies Corporation (GEOS) currently trades at $6.70, while our model-based Fair Value estimate is $4.71 — implying the stock looks roughly 29.7% overvalued today. We read business quality at 95/100 (high quality), in the Energy 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: low).
About the company
Geospace Technologies Corporation designs and manufactures instruments and equipment used in the oil and gas industry to acquire seismic data in order to locate, characterize, and monitor hydrocarbon producing reservoirs. The company operates through three segments: Smart Water, Energy Solutions and Intelligent Industrial. The Energy Solutions segment offers wireless seismic data acquisition systems and reservoir characterization products and services, as well as traditional seismic exploration products, such as geophones, hydrophones, leader wires, connectors, cables, marine streamer retrieval and steering devices, and other seismic products. The Smart Water segment engages in the water management industry. This business segment contains the Hydroconn, a smart water connectivity offerings, and Aquana products. The Intelligent Industrial segment designs and sells products used for border and perimeter security surveillance, cross-border tunneling detection, and other products target…
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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.