Ategrity Specialty Insurance Company (ASIC) Fair Value & Analysis
Financial Services · US · Market cap $920M
Fair value as of: Jun 25, 2026
Analysis
Ategrity Specialty Insurance Company (ASIC) currently trades at $21.58, while our model-based Fair Value estimate is $20.03 — implying the stock looks roughly 7.2% overvalued today. We read business quality at 87/100 (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: medium).
About the company
Ategrity Specialty Insurance Company Holdings, through its subsidiaries, provides excess and surplus lines insurance and reinsurance products to small and medium-sized businesses in the United States. The company offers property and casualty insurance solutions, including general liability, commercial property, management liability, miscellaneous PL, allied healthcare, and architects and engineers insurance products to the retail, real estate, hospitality, and construction sectors. It markets and distributes its products through brokerage and small business channels. The company was formerly known as Ategrity Specialty Holdings LLC and changed its name to Ategrity Specialty Insurance Company Holdings in June 2025. Ategrity Specialty Insurance Company Holdings was incorporated in 2017 and is based in New York, New York. Ategrity Specialty Insurance Company Holdings operates as a subsidiary of Zimmer Financial Services Group LLC.
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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.