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Thorney Technologies Ltd (TEK) Fair Value & Analysis

Financial Services · AU · Market cap A$33.0M

PriceA$0.0900
Fair ValueA$0.1039
Upside+15.4%
Quality95/100
Evidence: Medium Range A$0.0546 – A$0.1592

Fair value as of: Jun 26, 2026

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Analysis

Thorney Technologies Ltd (TEK) currently trades at A$0.0900, while our model-based Fair Value estimate is A$0.1039 — implying the stock looks roughly 15.4% 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: medium) — always confirm before acting.

About the company

Thorney Technologies Ltd invests in technology related companies. The company was formerly known as Australian Renewable Fuels Limited and changed its name to Thorney Technologies Ltd in December 2016. Thorney Technologies Ltd was founded in 2005 and is based in Melbourne, Australia.

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Frequently asked questions

Is Thorney Technologies Ltd (TEK) undervalued?
As of Jun 26, 2026, our model estimates a fair value of A$0.1039 versus a price of A$0.0900 — about +15% (undervalued). Model-based estimate, not financial advice.
What is the fair value of TEK?
Our 21-model fair value for Thorney Technologies Ltd is A$0.1039 (as of Jun 26, 2026), built from audited fundamentals. The current price is A$0.0900.
What is the quality score of TEK?
Thorney Technologies Ltd 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.