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Tailyn Technologies, Inc (5353) Fair Value & Analysis

Technology · TW · Market cap 2.4B TWD

Price31.55 TWD
Fair Value27.32 TWD
Upside-13.4%
Quality95/100
Evidence: High Range 20.49 TWD – 34.15 TWD

Fair value as of: Jun 24, 2026

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Analysis

Tailyn Technologies, Inc (5353) currently trades at 31.55 TWD, while our model-based Fair Value estimate is 27.32 TWD — implying the stock looks roughly 13.4% overvalued today. We read business quality at 95/100 (high quality), in the Technology sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: high).

About the company

Tailyn Technologies, Inc. provides industrial communication products for communication and applications sectors in Taiwan. The company provides software and hardware design services. The company was founded in 1969 and is based in Taoyuan City, Taiwan.

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Frequently asked questions

Is Tailyn Technologies, Inc (5353) undervalued?
As of Jun 24, 2026, our model estimates a fair value of 27.32 TWD versus a price of 31.55 TWD — about −13% (overvalued). Model-based estimate, not financial advice.
What is the fair value of 5353?
Our 21-model fair value for Tailyn Technologies, Inc is 27.32 TWD (as of Jun 24, 2026), built from audited fundamentals. The current price is 31.55 TWD.
What is the quality score of 5353?
Tailyn Technologies, Inc 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.