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Administradora de Fondos de Pensiones Provida S.A (PROVIDA) Fair Value & Analysis

Financial Services · CL · Market cap 1.7T CLP

Price5,001 CLP
Fair Value5,090 CLP
Upside+1.8%
Quality95/100
Evidence: High Range 3,818 CLP – 6,363 CLP

Fair value as of: Jun 25, 2026

Analysis

Administradora de Fondos de Pensiones Provida S.A (PROVIDA) currently trades at 5,001 CLP, while our model-based Fair Value estimate is 5,090 CLP — implying the stock looks roughly 1.8% undervalued today. We read business quality at 95/100 (high quality), in the Financial 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

Administradora de Fondos de Pensiones Provida S.A. manages equity and fixed income pension funds. The firm was founded on June 1983.

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Frequently asked questions

Is Administradora de Fondos de Pensiones Provida S.A (PROVIDA) undervalued?
As of Jun 25, 2026, our model estimates a fair value of 5,090 CLP versus a price of 5,001 CLP — about +2% (undervalued). Model-based estimate, not financial advice.
What is the fair value of PROVIDA?
Our 21-model fair value for Administradora de Fondos de Pensiones Provida S.A is 5,090 CLP (as of Jun 25, 2026), built from audited fundamentals. The current price is 5,001 CLP.
What is the quality score of PROVIDA?
Administradora de Fondos de Pensiones Provida S.A 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.