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Appulse Corporation (APL) Fair Value & Analysis

Financial Services · CA · Market cap A$17.3M

PriceA$0.1900
Fair ValueA$0.3800
Upside+100.0%
Quality95/100
Evidence: Medium Range A$0.1880 – A$0.5720

Fair value as of: Jun 26, 2026

Analysis

Appulse Corporation (APL) currently trades at A$0.1900, while our model-based Fair Value estimate is A$0.3800 — implying the stock looks roughly 100.0% 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: medium) — always confirm before acting.

About the company

Appulse Corporation does not have significant operations. Previously, it was engaged in selling new and refurbished centrifuge machines and related parts; renting centrifuge equipment; and providing maintenance, consulting, and design advice to industries. Appulse Corporation is based in Calgary, Canada.

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Frequently asked questions

Is Appulse Corporation (APL) undervalued?
As of Jun 26, 2026, our model estimates a fair value of A$0.3800 versus a price of A$0.1900 — about +100% (undervalued). Model-based estimate, not financial advice.
What is the fair value of APL?
Our 21-model fair value for Appulse Corporation is A$0.3800 (as of Jun 26, 2026), built from audited fundamentals. The current price is A$0.1900.
What is the quality score of APL?
Appulse Corporation 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.