Stereotaxis, Inc (STXS) Fair Value & Analysis
Healthcare · US · Market cap $183M
Fair value as of: Jun 24, 2026
Analysis
Stereotaxis, Inc (STXS) currently trades at $1.75, while our model-based Fair Value estimate is $0.8600 — implying the stock looks roughly 50.9% overvalued today. We read business quality at 95/100 (high quality), in the Healthcare 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
Stereotaxis, Inc. designs, manufactures, and markets robotic systems, instruments, and information systems for the interventional laboratory in the United States and internationally. The company offers robotic magnetic navigation (RMN) systems, such as Genesis RMN, which enable physicians to complete complex interventional procedures by providing image-guided delivery of catheters through the blood vessels and chambers of the heart to treatment sites; and GenesisX RMN System, which is designed to enhance the accessibility of Robotic Magnetic Navigation by reducing the lengthy construction cycle necessary to install prior generation RMN systems. It also offers Odyssey, a Synchrony and SynX Solution offer integrated, real-time information solution to manage, control, record and share procedures across networks; and x-ray systems. In addition, the company offers disposables and other accessories, including MAGiC catheter, a robotically navigated magnetic ablation catheter designed to p…
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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.