Science Applications International Corporation (SAIC) Fair Value & Analysis
Technology · US · Market cap $4.8B
Analysis
Science Applications International Corporation (SAIC) currently trades at $105.16, while our model-based Fair Value estimate is $128.01 — implying the stock looks roughly 21.7% undervalued today. We read business quality at 97/100 (high quality), in the Technology 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
Science Applications International Corporation provides technical, engineering, and mission and enterprise information technology (IT) services in the United States. It operates through two segments, Defense and Intelligence; and Civilian. The company offers IT modernization services for defense, intelligence, and civilian agencies; digital engineering services; artificial intelligence (AI) solutions; mission systems support and advisory; training and simulation; and ground vehicle support services for the nation's armed forces. It also provides services for the design, development, integration, deployment, management and operations, sustainability, and security of IT infrastructure; mission IT solutions comprising CJADC2, data and AI, digital transformation, and quantum technologies; enterprise IT solutions consisting of service management, cloud, cybersecurity, and digital workplace; engineering services, including system integration and delivery services; and professional service…
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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.