Diginex Limited (DGNX) Fair Value & Analysis
Industrials · US · Market cap $26.2M
Fair value as of: Jun 24, 2026
Analysis
Diginex Limited (DGNX) currently trades at $0.8803, while our model-based Fair Value estimate is $0.4700 — implying the stock looks roughly 46.6% overvalued today. We read business quality at 91/100 (high quality), in the Industrials 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: low).
About the company
Diginex Limited, an investment holding company, engages in the provision of environmental, social, and governance (ESG) reporting solution services, advisory, and developing customization solutions in Hong Kong, the United Kingdom, and the United States. Its suite of products include diginexESG, a cloud based ESG platform that offers end to end reporting from topic discovery, data collection, and collaborative report publishing services; diginexLUMEN that allows companies to execute supply chain risk assessments, and monitoring; diginexAPPRISE, a multilingual application that collects standardized and actionable data related to working conditions directly from workers in supply chains; diginexCLIMATE, a carbon footprint calculator based on the GHG protocols; diginexADVISORY that provides clients strategy and advisory support for credible reporting; and diginexPARTNERS that develops white label versions of diginexESG and diginexLUMEN. The company has a strategic alliance with Sustain…
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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.