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Challenger Limited (CFIGF) Fair Value & Analysis

Financial Services · US · Market cap $3.8B

Price$5.60
Fair Value$2.53
Upside-54.8%
Quality95/100
Evidence: High Range $1.89 – $3.16

Fair value as of: Jun 25, 2026

Analysis

Challenger Limited (CFIGF) currently trades at $5.60, while our model-based Fair Value estimate is $2.53 — implying the stock looks roughly 54.8% 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

Challenger Limited is a publicly owned investment manager. The company also provides retirement services to its clients. It manages equity mutual funds. The firm invests into the public equity markets across the world. Challenger Limited was founded in 1985 and is based in Australia, Asia and United Kingdom.

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Frequently asked questions

Is Challenger Limited (CFIGF) undervalued?
As of Jun 25, 2026, our model estimates a fair value of $2.53 versus a price of $5.60 — about −55% (overvalued). Model-based estimate, not financial advice.
What is the fair value of CFIGF?
Our 21-model fair value for Challenger Limited is $2.53 (as of Jun 25, 2026), built from audited fundamentals. The current price is $5.60.
What is the quality score of CFIGF?
Challenger Limited has a Quality Score of 95/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

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.