Mphasis Limited (MPHASIS) Fair Value & Analysis
Technology · IN · Market cap ₹445B
Fair value as of: Jun 24, 2026
Analysis
Mphasis Limited (MPHASIS) currently trades at ₹2,245, while our model-based Fair Value estimate is ₹1,952 — implying the stock looks roughly 13.0% overvalued today. We read business quality at 97/100 (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
Mphasis Limited, together with its subsidiaries, operates as an information technology solutions provider that specializes in cloud and cognitive services in the United States, India, Europe, the Middle East, Africa, and internationally. It operates through Banking and Financial Services; Logistics and Transportation; Technology Media and Telecom; Insurance; and Others segments. The company offers application, blockchain, business process, cognitive, cyber security, DevOps, AI, digital, enterprise automation, experience design, infrastructure, modernization, data, IT operations, product engineering, salesforce consulting, and cloud; governance, risk, and compliance; and platform and protocols services under the Mphasis XaaP. It serves the banking and capital markets comprising asset management, corporate and investment banking, retail banking, wealth management and brokerage, and mortgage industries; and insurance, payment, financial supply chain, hospitality, travel and transportat…
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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.