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Cirralto Limited (MOT) Fair Value & Analysis

Financial Services · AU · Market cap A$567M

PriceA$1.67
Fair ValueA$3.07
Upside+84.4%
Quality95/100
Evidence: High Range A$2.30 – A$3.84

Fair value as of: Jun 26, 2026

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Analysis

Cirralto Limited (MOT) currently trades at A$1.67, while our model-based Fair Value estimate is A$3.07 — implying the stock looks roughly 84.4% 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

Cirralto Limited engages in data migration business in Australia. The company is involved in the conversion, migration, and management of server based legacy data and systems to the cloud. It offers 2SQL, a service on premise access software migration solution. Cirralto Limited is based in Fitzroy, Australia.

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Frequently asked questions

Is Cirralto Limited (MOT) undervalued?
As of Jun 26, 2026, our model estimates a fair value of A$3.07 versus a price of A$1.67 — about +84% (undervalued). Model-based estimate, not financial advice.
What is the fair value of MOT?
Our 21-model fair value for Cirralto Limited is A$3.07 (as of Jun 26, 2026), built from audited fundamentals. The current price is A$1.67.
What is the quality score of MOT?
Cirralto 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.