FRP Advisory Group (FRP) Fair Value & Analysis
Financial Services · GB · Market cap 272M GBX
Fair value as of: Jun 26, 2026
Analysis
FRP Advisory Group (FRP) currently trades at p1.13, while our model-based Fair Value estimate is p0.5400 — implying the stock looks roughly 52.0% overvalued today. We read business quality at 95/100 (high quality), in the Financial Services 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
FRP Advisory Group plc, together with its subsidiaries, provides business advisory services to companies, lenders, investors, individuals, and other stakeholders in the United Kingdom. The company's corporate finance advisory services include mergers and acquisitions (M&A), strategic advisory and valuations, capital raising, special situations M&A, partial exits, and financial due diligence; and debt advisory services comprise asset based lending, raising and refinancing debt, debt amendments and extensions, restructuring debt, and corporate and leveraged debt advisory. Its forensic services include forensic investigations, dispute, compliance and risk advisory, and forensic technology services; and restructuring advisory services comprise corporate financial advisory, formal insolvency appointments, informal restructuring advisory, personal insolvency, and general advice to various stakeholders. In addition, the company buys and sells side financial due diligence, as well as provid…
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Frequently asked questions
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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.