PHINIA Inc (PHIN) Fair Value & Analysis
Consumer Cyclical · US · Market cap $3.0B
Analysis
PHINIA Inc (PHIN) currently trades at $80.52, while our model-based Fair Value estimate is $80.47 — implying the stock looks roughly 0.1% overvalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical 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
PHINIA Inc. engages in the development, design, and manufacture of integrated components and systems. The company operates through Fuel Systems and Aftermarket segments. The Fuel Systems segment provides advanced fuel injection systems, including pumps, injectors, fuel rail assemblies, and engine control modules; fuel delivery modules; canisters; sensors; and electronic control modules. This segment also offers complete systems comprising software and calibration services that reduce emissions and enhance fuel economy for traditional and hybrid applications. The Aftermarket segment engages in the provision of maintenance, test equipment, and vehicle diagnostics solutions; original equipment service solutions and remanufactured products; and sale of sale of starters and alternators to original equipment manufacturers. Its products are used for commercial vehicles and industrial applications, such as medium-duty and heavy-duty trucks, buses and other off-highway construction, marine, …
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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.