Bharatiya Global Infomedia Limited (BGLOBAL) Fair Value & Analysis
Technology · IN · Market cap ₹52.4M
Fair value as of: Jun 29, 2026
Analysis
Bharatiya Global Infomedia Limited (BGLOBAL) currently trades at ₹3.30, while our model-based Fair Value estimate is ₹2.05 — implying the stock looks roughly 37.9% overvalued today. We read business quality at 96/100 (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: medium).
About the company
Bharatiya Global Infomedia Limited engages in the information technology and IT based product businesses in India. It operates through I.T. and Media & Entertainment segments. It offers traffic engineering solutions, including parking management, parking guidance, toll management, automatic number plate recognition, under vehicle surveillance, ticket dispensing, and automatic vehicle counter-cum-classifier systems; and facility management solutions, such as smart campus, visitor management, canteen management, asset tracking, laptop tracking, file tracking, and lighting management systems, as well as CCTV, access control, perimeter fencing, etc. The company also provides personnel management solutions comprising employee tracking, time-attendance, library automation and management, and student information systems; and customized solutions consisting of automated binning systems, intelligent converters, GSM based mast or I/O controllers, GSM logic controllers, boom barriers, loop det…
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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.