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Alpa Laboratories Limited (ALPA) Fair Value & Analysis

Healthcare · IN · Market cap ₹1.4B

Price₹68.50
Fair Value₹73.68
Upside+7.6%
Quality97/100
Evidence: High Range ₹48.63 – ₹92.10

Fair value as of: Jun 29, 2026

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Analysis

Alpa Laboratories Limited (ALPA) currently trades at ₹68.50, while our model-based Fair Value estimate is ₹73.68 — implying the stock looks roughly 7.6% undervalued today. We read business quality at 97/100 (high quality), in the Healthcare 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

Alpa Laboratories Limited engages in the manufacture and sale of drugs and chemicals in India. It provides formulations that address various therapeutic segments. The company was incorporated in 1988 and is based in Indore, India.

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Frequently asked questions

Is Alpa Laboratories Limited (ALPA) undervalued?
As of Jun 29, 2026, our model estimates a fair value of ₹73.68 versus a price of ₹68.50 — about +8% (undervalued). Model-based estimate, not financial advice.
What is the fair value of ALPA?
Our 21-model fair value for Alpa Laboratories Limited is ₹73.68 (as of Jun 29, 2026), built from audited fundamentals. The current price is ₹68.50.
What is the quality score of ALPA?
Alpa Laboratories Limited has a Quality Score of 97/100, 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.