ACCESS Newswire Inc (ACCS) Fair Value & Analysis
Communication Services · US · Market cap $23.3M
Fair value as of: Jun 25, 2026
Analysis
ACCESS Newswire Inc (ACCS) currently trades at $6.62, while our model-based Fair Value estimate is $14.57 — implying the stock looks roughly 120.1% undervalued today. We read business quality at 95/100 (high quality), in the Communication Services 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
ACCESS Newswire Inc. provides solutions for public relations and investor relations in the United States and internationally. The company offers press release distribution and optimizer, media database and pitching, media monitoring, and media room; ACCESS Newswire, a news dissemination and media outreach solution; and Webcaster Platform, a cloud-based webcast, webinar, and virtual meeting platform that delivers live and on-demand streaming of events to audiences of various sizes, as well as allows customers to create, produce, and deliver events. It also offers professional conference and events software, a mobile app that offers one-on-one meetings, as well as manages schedules and lodging, performs event promotion and sponsorship, and prints attendee badges; and investor relations content network, a series of data feeds, which include news feeds, stock feeds, fundamentals, regulatory filings, corporate governance, and other components. In addition, the company provides whistleblo…
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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.