Strip Tinning Holdings (STG) Fair Value & Analysis
Consumer Cyclical · GB · Market cap 3.3M GBX
Fair value as of: Jun 26, 2026
Analysis
Strip Tinning Holdings (STG) currently trades at p0.1750, while our model-based Fair Value estimate is p0.2400 — implying the stock looks roughly 37.1% undervalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical 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
Strip Tinning Holdings plc manufactures and supplies flexible electrical connectors for heating and antennae systems embedded within automotive glazing and to the connection of the cells within electric vehicle (EV) battery packs in the United Kingdom, Europe, and internationally. It operates through Glazing and Battery Technologies segments. The company provides windshield soldering connectors and heating materials, including FPC antenna connectors, flat foil connectors, solder busbars, adhesive busbars, preformed busbars, and heating wires; back-lite soldering connectors comprising solder bridge connectors, crimp contact connectors, and round wire connectors with capacitors and coils; and automotive flex products, such as lighting and heating products, smart glass products, and antennas. It also offers battery technologies, including heavy duty laminated busbars, integrated laminated busbar cell contact system, rigid carrier cell contact system, and sensing FPC; and roof glass PDL…
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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.